“Foods : Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy” LINK
” The influence of water of crystallization in NIR-based MDMA∙ HCl detection” LINK
“Study on the secondary structure and hydration effect of human serum albumin under acidic pH and ethanol perturbation with IR/NIR spectroscopy” | LINK
“Application of the Combination Method Based on RF and LE in Near Infrared Spectral Modeling” LINK
“A chemometric method for the viability analysis of spinach seeds by near infrared spectroscopy with variable selection using successive projections algorithm” LINK
“Application of Near-Infrared Spectroscopy to characterize volatile phenols and sensory profile of aged wine spirits” | et al.pdf LINK
“A novel fast method for identifying the origin of Maojian using NIR spectroscopy with deep learning algorithms” | LINK
“Highly efficient and thermally stable broadband NIR phosphors by rationally bridging Cr 3+-Yb 3+ in LiScGe 2 O 6 for optical bioimaging” | LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
“Quantitative and convenient protocol for analysis of surfacemodified silica nanoparticles using 29SiNMR and nearinfrared diffuse reflection spectroscopy” LINK
Hyperspectral Imaging (HSI)
“Prediction of TVB-N content in beef with packaging films using visible-near infrared hyperspectral imaging” LINK
“Non-destructive Detection of Fatty Acid Content of Camellia Seed Based on Hyperspectral” LINK
Chemometrics and Machine Learning
“Overview of Cocaine Identification by Vibrational Spectroscopy and Chemometrics” LINK
“Remote Sensing : VIS-NIR-SWIR Hyperspectroscopy Combined with Data Mining and Machine Learning for Classification of Predicted Chemometrics of Green Lettuce” | LINK
Optics for Spectroscopy
“Highly Sensitive TinLead Perovskite Photodetectors with Over 450 Days Stability Enabled by Synergistic Engineering for Pulse Oximetry System” LINK
Research on Spectroscopy
“Methodologies based on ASCA to elucidate the influence of a subprocess: vinification as a case of study” LINK
Environment NIR-Spectroscopy Application
“Satellite imagery dataset of manure application on pasture fields” LINK
“Remote Sensing : Exploring the Best-Matching Plant Traits and Environmental Factors for Vegetation Indices in Estimates of Global Gross Primary Productivity” LINK
“Remote Sensing : Multispectral Characteristics of Glacier Surface Facies (Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard) through Investigations of Pixel and Object-Based Mapping Using Variable Processing Routines” LINK
“Remote Sensing : Estimation of the Key Water Quality Parameters in the Surface Water, Middle of Northeast China, Based on Gaussian Process Regression” LINK
Agriculture NIR-Spectroscopy Usage
“Recent advances in the ultrasound-assisted osmotic dehydration of agricultural products: A review” LINK
“Miniaturized NearInfrared Spectroscopy The Ultimate Analytical Tool in Food and Agriculture” LINK
“Remote Sensing : UAV-Based Estimation of Grain Yield for Plant Breeding: Applied Strategies for Optimizing the Use of Sensors, Vegetation Indices, Growth Stages, and Machine Learning Algorithms” | LINK
“Agriculture : Ripeness Evaluation of Achacha Fruit Using Hyperspectral Image Data” LINK
“A review of the opportunities for spectral‐based technologies in post‐harvest testing of pulse grains” LINK
Food & Feed Industry NIR Usage
“Nutritional labelling of food products purchased from online retail outlets: screening of compliance with European Union tolerance limits by near infrared spectroscopy” LINK
“Sensors : Non-Destructive Detection of Abnormal Chicken Eggs by Using an Optimized Spectral Analysis System” | LINK
Chemical Industry NIR Usage
“Polymers : A Tissue Paper/Hydrogel Composite Light-Responsive Biomimetic Actuator Fabricated by In Situ Polymerization” | LINK
Petro Industry NIR Usage
“Prospects for the utilization of the prairie cordgrass spartina pectinata for bioenergy production in Moldova” LINK
Medicinal Spectroscopy
“Noninvasive Monitoring of Glucose Using Near-Infrared Reflection Spectroscopy of Skin—Constraints and Effective Novel Strategy in Multivariate Calibration” LINK
Other
“Applied Sciences : Verification of Convolutional Neural Network Cephalometric Landmark Identification” | LINK
“Multi-Dimensional Self Attention based Approach for Remaining Useful Life Estimation. (arXiv:2212.05772v1 [cs.LG])” LINK
"Why People and AI Make Good Business Partners" | human AI
relationships AI as a Service ( AIaaS ) LabManager NIRS MachineLearning
LINK
"A novel aquaphotomics based approach for understanding salvianolic acid A conversion reaction with near infrared spectroscopy" LINK
"ex type determination in papaya seeds and leaves using near infrared
spectroscopy combined with multivariate techniques and machine learnin" LINK
"DETERMINATION OF QUALITY AND RIPENING STAGES OF 'PACOVAN'BANANAS USING VIS-NIR SPECTROSCOPY AND MACHINE LEARNING" LINK
"Rapid authentication and composition determination of cellulose films by UV-VIS-NIR spectroscopy" LINK
"Interoceptive Attentiveness Induces Significantly More PFC Activation
during a Synchronized Linguistic Task Compared to a Motor Task as
Revealed by Functional Near-Infrared Spectroscopy" | LINK
"Near-infrared spectroscopy and machine learning-based technique to predict quality-related parameters in instant tea" | LINK
"Sensors : LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI" LINK
"Using metaheuristic algorithms to improve the estimation of acidity in Fuji apples using NIR spectroscopy" LINK
"Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks" LINK
"Multimodal diffuse optical system integrating DSCA-NIRS and cSFDI for measuring tissue metabolism" LINK
extruded granules extruder NIR AEE audible acoustic emission granule drying process PAT LINK
"Fast Noniterative Data Analysis Method for Frequency-Domain Near-Infrared Spectroscopy with the Microscopic Beer-Lambert Law" LINK
"Vis-NIR Hyperspectral Dimensionality Reduction for Nondestructive Identification of China Northeast Rice" | LINK
"FT-NIR Spectroscopy for the Non-Invasive Study of Binders and
Multi-Layered Structures in Ancient Paintings: Artworks of the Lombard
Renaissance as Case Studies" LINK
"In Vivo Measurement Strategy for Near-Infrared Noninvasive Glucose Detection and Human Body Verification" LINK
"A Standard-Free Calibration Transfer Strategy for a Discrimination Model of Apple Origins Based on Near-Infrared Spectroscopy" LINK
"Comparative study on the real-time monitoring of a fluid bed drying
process of extruded granules using near-infrared spectroscopy and
audible acoustic emission" LINK
"Fast detection of cotton content in silk/cotton textiles by handheld
near-infrared spectroscopy: a performance comparison of four different
instruments" LINK
"Evaluation of optical properties of tofu samples produced with
different coagulation temperatures and times using near-infrared
transmission spectroscopy" LINK
"Near-Infrared Spectroscopy and Mode Cloning (NIR-MC) for In-Situ Analysis of Crude Protein in Bamboo" LINK
"Near-infrared spectroscopy to estimate the chemical element concentration in soils and sediments in a rural catchment" LINK
"Ensemble classification and regression techniques combined with
portable near infrared spectroscopy for facile and rapid detection of
water adulteration in bovine ..." LINK
"Characterization of crude oils with a portable NIR spectrometer" CrudeOil NIRspectrometer LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"A Device for Measuring Apple Sweetness Using Near Infrared Spectroscopy" LINK
"Nearinfrared fluorophores based on heptamethine cyanine dyes: from
their synthesis and photophysical properties to recent optical sensing
and bioimaging applications" LINK
"Use of Attenuated Total Reflection Fourier Transform Infrared
Spectroscopy and Principal Component Analysis for the Assessment of
Engine Oils" | LINK
"Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview" LINK
"Analyzing the Water Confined in Hydrogel Using Near-Infrared Spectroscopy" LINK
"Near-infrared spectra of aqueous glucose solutions" LINK
"Determination of storage period of harvested plums by nearinfrared spectroscopy and quality attributes" LINK
Hyperspectral Imaging (HSI)
"Rapid Detection of Different Types of Soil Nitrogen Using Near-Infrared Hyperspectral Imaging" LINK
"Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview" LINK
"Estimating soil moisture content under grassland with hyperspectral
data using radiative transfer modelling and machine learning" LINK
"Improving rice nitrogen stress diagnosis by denoising strips in hyperspectral images via deep learning" LINK
"Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach" LINK
Chemometrics and Machine Learning
"Remote Sensing : Early Detection of Dendroctonus valens Infestation
with Machine Learning Algorithms Based on Hyperspectral Reflectance" LINK
"Applied microwave power estimation of black carrot powders using spectroscopy combined with chemometrics" LINK
DataScientist Job: Expectation vs. Reality [infographic] BigData
DataScience Analytics AI MachineLearning ArtificialIntelligence Data
DataAnalytics Python SQL Statistics DataViz Careers Jobs
FeatureEngineering DataPrep DataCleaning LINK
"Agronomy : Detection of Adulterations in Fruit Juices Using Machine Learning Methods over FT-IR Spectroscopic Data" LINK
"Reflectance Based Models for Non-Destructive Prediction of Lycopene Content in Tomato Fruits" | LINK
"The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation" LINK
"Machine Learning Strategies for the Retrieval of Leaf-Chlorophyll
Dynamics: Model Choice, Sequential Versus Retraining Learning, and
Hyperspectral Predictors" | LINK
"In-line near-infrared analysis of milk coupled with machine learning
methods for the daily prediction of blood metabolic profile in dairy
cattle" LINK
"Near-infrared spectroscopy with chemometrics for identification and
quantification of adulteration in high-quality stingless bee honey" LINK
"Rapid identification and quantification of intramuscular fat
adulteration in lamb meat with VIS-NIR spectroscopy and chemometrics
methods" LINK
Optics for Spectroscopy
"Spectrum Reconstruction with Filter-Free Photodetectors Based on Graded-Band-Gap Perovskite Quantum Dot Heterojunctions" LINK
Facts
"Sensors : Dietary Patterns Associated with Diabetes in an Older
Population from Southern Italy Using an Unsupervised Learning Approach" |
LINK
Research on Spectroscopy
"A Study of C= O... HO and OH... OH (Dimer, Trimer, and Oligomer)
Hydrogen Bonding in a Poly (4-vinylphenol) 30%/Poly (methyl
methacrylate) 70% Blend and its ..." LINK
"Deeper insights into the photoluminescence properties and (photo)
chemical reactivity of cadmium red (CdS1− xSex) paints in renowned
twentieth century ..." | LINK
Equipment for Spectroscopy
"Green Textile Materials for Surface Enhanced Raman Spectroscopy
Identification of Pesticides Using a Raman Handheld Spectrometer for
In-Field Detection" LINK
"Characterization of Crude Oils with a Portable Nir Spectrometer" LINK
"Discrimination of the Red Jujube Varieties Using a Portable NIR Spectrometer and Fuzzy Improved Linear Discriminant Analysis" LINK
"Rapid authentication of the geographical origin of milk using portable
near‐infrared spectrometer and fuzzy uncorrelated discriminant
transformation" LINK
Environment NIR-Spectroscopy Application
"Remote Sensing : Estimating Forest Soil Properties for Humus Assessment—Is Vis-NIR the Way to Go?" LINK
"Sensors : Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils" LINK
"Evaluation of Vis-Nir Pretreatments Combined with Pls Regression for
Estimation SOC, Cec and Clay in Silty Soils from Eastern Croatia" LINK
"Comparing Two Different Development Methods of External Parameter
Orthogonalization for Estimating Organic Carbon from Field-Moist Intact
Soils by Reflectance ..." LINK
Agriculture NIR-Spectroscopy Usage
"Site-specific seeding for maize production using management zone maps delineated with multi-sensors data fusion scheme" LINK
"Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain
Spectroscopy and Grey Wolf Optimizer-Support Vector Machine" | LINK
"A LUCASbased midinfrared soil spectral library: Its usefulness for soil survey and precision agriculture" LINK
"Identification of Microplastics in Biosolids Using Ftir and Vis-Nir Spectroscopy Enhanced by Chemometric Methods" LINK
"Agriculture : Feature Wavelength Selection Based on the Combination of
Image and Spectrum for Aflatoxin B1 Concentration Classification in
Single Maize Kernels" LINK
Food & Feed Industry NIR Usage
"Agronomy : Analysis of Physico-Chemical and Organoleptic Fruit Parameters Relevant for Tomato Quality" LINK
Chemical Industry NIR Usage
"Polymers : Microscopic and Structural Studies of an Antimicrobial
Polymer Film Modified with a Natural Filler Based on Triterpenoids" LINK
Laboratory and NIR-Spectroscopy
"Laboratory Hyperspectral Image Acquisition System Setup and Validation" LINK
Other
"A sensor combination based automatic sorting system for waste washing machine parts" LINK
.
NIR Calibration-Model Services
NIR User? Get better results faster | Food Science QC Lab Laboratory Manager chemist LabWork Chemie analytik LINK
Spectroscopy and Chemometrics News Weekly 19, 2022 | NIRS NIR
Spectroscopy MachineLearning Spectrometer Spectrometric Analytical
Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software
IoT Sensors QA QC Testing Quality LINK
"Why People and AI Make Good Business Partners" | human AI
relationships AI as a Service ( AIaaS ) LabManager NIRS MachineLearning
LINK
"A novel aquaphotomics based approach for understanding salvianolic acid A conversion reaction with near infrared spectroscopy" LINK
"ex type determination in papaya seeds and leaves using near infrared
spectroscopy combined with multivariate techniques and machine learnin" LINK
"DETERMINATION OF QUALITY AND RIPENING STAGES OF 'PACOVAN'BANANAS USING VIS-NIR SPECTROSCOPY AND MACHINE LEARNING" LINK
"Rapid authentication and composition determination of cellulose films by UV-VIS-NIR spectroscopy" LINK
"Interoceptive Attentiveness Induces Significantly More PFC Activation
during a Synchronized Linguistic Task Compared to a Motor Task as
Revealed by Functional Near-Infrared Spectroscopy" | LINK
"Near-infrared spectroscopy and machine learning-based technique to predict quality-related parameters in instant tea" | LINK
"Sensors : LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI" LINK
"Using metaheuristic algorithms to improve the estimation of acidity in Fuji apples using NIR spectroscopy" LINK
"Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks" LINK
"Multimodal diffuse optical system integrating DSCA-NIRS and cSFDI for measuring tissue metabolism" LINK
extruded granules extruder NIR AEE audible acoustic emission granule drying process PAT LINK
"Fast Noniterative Data Analysis Method for Frequency-Domain Near-Infrared Spectroscopy with the Microscopic Beer-Lambert Law" LINK
"Vis-NIR Hyperspectral Dimensionality Reduction for Nondestructive Identification of China Northeast Rice" | LINK
"FT-NIR Spectroscopy for the Non-Invasive Study of Binders and
Multi-Layered Structures in Ancient Paintings: Artworks of the Lombard
Renaissance as Case Studies" LINK
"In Vivo Measurement Strategy for Near-Infrared Noninvasive Glucose Detection and Human Body Verification" LINK
"A Standard-Free Calibration Transfer Strategy for a Discrimination Model of Apple Origins Based on Near-Infrared Spectroscopy" LINK
"Comparative study on the real-time monitoring of a fluid bed drying
process of extruded granules using near-infrared spectroscopy and
audible acoustic emission" LINK
"Fast detection of cotton content in silk/cotton textiles by handheld
near-infrared spectroscopy: a performance comparison of four different
instruments" LINK
"Evaluation of optical properties of tofu samples produced with
different coagulation temperatures and times using near-infrared
transmission spectroscopy" LINK
"Near-Infrared Spectroscopy and Mode Cloning (NIR-MC) for In-Situ Analysis of Crude Protein in Bamboo" LINK
"Near-infrared spectroscopy to estimate the chemical element concentration in soils and sediments in a rural catchment" LINK
"Ensemble classification and regression techniques combined with
portable near infrared spectroscopy for facile and rapid detection of
water adulteration in bovine ..." LINK
"Characterization of crude oils with a portable NIR spectrometer" CrudeOil NIRspectrometer LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"A Device for Measuring Apple Sweetness Using Near Infrared Spectroscopy" LINK
"Nearinfrared fluorophores based on heptamethine cyanine dyes: from
their synthesis and photophysical properties to recent optical sensing
and bioimaging applications" LINK
"Use of Attenuated Total Reflection Fourier Transform Infrared
Spectroscopy and Principal Component Analysis for the Assessment of
Engine Oils" | LINK
"Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview" LINK
"Analyzing the Water Confined in Hydrogel Using Near-Infrared Spectroscopy" LINK
"Near-infrared spectra of aqueous glucose solutions" LINK
"Determination of storage period of harvested plums by nearinfrared spectroscopy and quality attributes" LINK
Hyperspectral Imaging (HSI)
"Rapid Detection of Different Types of Soil Nitrogen Using Near-Infrared Hyperspectral Imaging" LINK
"Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview" LINK
"Estimating soil moisture content under grassland with hyperspectral
data using radiative transfer modelling and machine learning" LINK
"Improving rice nitrogen stress diagnosis by denoising strips in hyperspectral images via deep learning" LINK
"Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach" LINK
Chemometrics and Machine Learning
"Remote Sensing : Early Detection of Dendroctonus valens Infestation
with Machine Learning Algorithms Based on Hyperspectral Reflectance" LINK
"Applied microwave power estimation of black carrot powders using spectroscopy combined with chemometrics" LINK
DataScientist Job: Expectation vs. Reality [infographic] BigData
DataScience Analytics AI MachineLearning ArtificialIntelligence Data
DataAnalytics Python SQL Statistics DataViz Careers Jobs
FeatureEngineering DataPrep DataCleaning LINK
"Agronomy : Detection of Adulterations in Fruit Juices Using Machine Learning Methods over FT-IR Spectroscopic Data" LINK
"Reflectance Based Models for Non-Destructive Prediction of Lycopene Content in Tomato Fruits" | LINK
"The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation" LINK
"Machine Learning Strategies for the Retrieval of Leaf-Chlorophyll
Dynamics: Model Choice, Sequential Versus Retraining Learning, and
Hyperspectral Predictors" | LINK
"In-line near-infrared analysis of milk coupled with machine learning
methods for the daily prediction of blood metabolic profile in dairy
cattle" LINK
"Near-infrared spectroscopy with chemometrics for identification and
quantification of adulteration in high-quality stingless bee honey" LINK
"Rapid identification and quantification of intramuscular fat
adulteration in lamb meat with VIS-NIR spectroscopy and chemometrics
methods" LINK
Optics for Spectroscopy
"Spectrum Reconstruction with Filter-Free Photodetectors Based on Graded-Band-Gap Perovskite Quantum Dot Heterojunctions" LINK
Facts
"Sensors : Dietary Patterns Associated with Diabetes in an Older
Population from Southern Italy Using an Unsupervised Learning Approach" |
LINK
Research on Spectroscopy
"A Study of C= O... HO and OH... OH (Dimer, Trimer, and Oligomer)
Hydrogen Bonding in a Poly (4-vinylphenol) 30%/Poly (methyl
methacrylate) 70% Blend and its ..." LINK
"Deeper insights into the photoluminescence properties and (photo)
chemical reactivity of cadmium red (CdS1− xSex) paints in renowned
twentieth century ..." | LINK
Equipment for Spectroscopy
"Green Textile Materials for Surface Enhanced Raman Spectroscopy
Identification of Pesticides Using a Raman Handheld Spectrometer for
In-Field Detection" LINK
"Characterization of Crude Oils with a Portable Nir Spectrometer" LINK
"Discrimination of the Red Jujube Varieties Using a Portable NIR Spectrometer and Fuzzy Improved Linear Discriminant Analysis" LINK
"Rapid authentication of the geographical origin of milk using portable
near‐infrared spectrometer and fuzzy uncorrelated discriminant
transformation" LINK
Environment NIR-Spectroscopy Application
"Remote Sensing : Estimating Forest Soil Properties for Humus Assessment—Is Vis-NIR the Way to Go?" LINK
"Sensors : Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils" LINK
"Evaluation of Vis-Nir Pretreatments Combined with Pls Regression for
Estimation SOC, Cec and Clay in Silty Soils from Eastern Croatia" LINK
"Comparing Two Different Development Methods of External Parameter
Orthogonalization for Estimating Organic Carbon from Field-Moist Intact
Soils by Reflectance ..." LINK
Agriculture NIR-Spectroscopy Usage
"Site-specific seeding for maize production using management zone maps delineated with multi-sensors data fusion scheme" LINK
"Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain
Spectroscopy and Grey Wolf Optimizer-Support Vector Machine" | LINK
"A LUCASbased midinfrared soil spectral library: Its usefulness for soil survey and precision agriculture" LINK
"Identification of Microplastics in Biosolids Using Ftir and Vis-Nir Spectroscopy Enhanced by Chemometric Methods" LINK
"Agriculture : Feature Wavelength Selection Based on the Combination of
Image and Spectrum for Aflatoxin B1 Concentration Classification in
Single Maize Kernels" LINK
Food & Feed Industry NIR Usage
"Agronomy : Analysis of Physico-Chemical and Organoleptic Fruit Parameters Relevant for Tomato Quality" LINK
Chemical Industry NIR Usage
"Polymers : Microscopic and Structural Studies of an Antimicrobial
Polymer Film Modified with a Natural Filler Based on Triterpenoids" LINK
Laboratory and NIR-Spectroscopy
"Laboratory Hyperspectral Image Acquisition System Setup and Validation" LINK
Other
"A sensor combination based automatic sorting system for waste washing machine parts" LINK
.
NIR Calibration-Model Services
NIR User? Get better results faster | Food Science QC Lab Laboratory Manager chemist LabWork Chemie analytik LINK
Spectroscopy and Chemometrics News Weekly 19, 2022 | NIRS NIR
Spectroscopy MachineLearning Spectrometer Spectrometric Analytical
Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software
IoT Sensors QA QC Testing Quality LINK
"Why People and AI Make Good Business Partners" | human AI
relationships AI as a Service ( AIaaS ) LabManager NIRS MachineLearning
LINK
"A novel aquaphotomics based approach for understanding salvianolic acid A conversion reaction with near infrared spectroscopy" LINK
"ex type determination in papaya seeds and leaves using near infrared
spectroscopy combined with multivariate techniques and machine learnin" LINK
"DETERMINATION OF QUALITY AND RIPENING STAGES OF 'PACOVAN'BANANAS USING VIS-NIR SPECTROSCOPY AND MACHINE LEARNING" LINK
"Rapid authentication and composition determination of cellulose films by UV-VIS-NIR spectroscopy" LINK
"Interoceptive Attentiveness Induces Significantly More PFC Activation
during a Synchronized Linguistic Task Compared to a Motor Task as
Revealed by Functional Near-Infrared Spectroscopy" | LINK
"Near-infrared spectroscopy and machine learning-based technique to predict quality-related parameters in instant tea" | LINK
"Sensors : LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI" LINK
"Using metaheuristic algorithms to improve the estimation of acidity in Fuji apples using NIR spectroscopy" LINK
"Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks" LINK
"Multimodal diffuse optical system integrating DSCA-NIRS and cSFDI for measuring tissue metabolism" LINK
extruded granules extruder NIR AEE audible acoustic emission granule drying process PAT LINK
"Fast Noniterative Data Analysis Method for Frequency-Domain Near-Infrared Spectroscopy with the Microscopic Beer-Lambert Law" LINK
"Vis-NIR Hyperspectral Dimensionality Reduction for Nondestructive Identification of China Northeast Rice" | LINK
"FT-NIR Spectroscopy for the Non-Invasive Study of Binders and
Multi-Layered Structures in Ancient Paintings: Artworks of the Lombard
Renaissance as Case Studies" LINK
"In Vivo Measurement Strategy for Near-Infrared Noninvasive Glucose Detection and Human Body Verification" LINK
"A Standard-Free Calibration Transfer Strategy for a Discrimination Model of Apple Origins Based on Near-Infrared Spectroscopy" LINK
"Comparative study on the real-time monitoring of a fluid bed drying
process of extruded granules using near-infrared spectroscopy and
audible acoustic emission" LINK
"Fast detection of cotton content in silk/cotton textiles by handheld
near-infrared spectroscopy: a performance comparison of four different
instruments" LINK
"Evaluation of optical properties of tofu samples produced with
different coagulation temperatures and times using near-infrared
transmission spectroscopy" LINK
"Near-Infrared Spectroscopy and Mode Cloning (NIR-MC) for In-Situ Analysis of Crude Protein in Bamboo" LINK
"Near-infrared spectroscopy to estimate the chemical element concentration in soils and sediments in a rural catchment" LINK
"Ensemble classification and regression techniques combined with
portable near infrared spectroscopy for facile and rapid detection of
water adulteration in bovine ..." LINK
"Characterization of crude oils with a portable NIR spectrometer" CrudeOil NIRspectrometer LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"A Device for Measuring Apple Sweetness Using Near Infrared Spectroscopy" LINK
"Nearinfrared fluorophores based on heptamethine cyanine dyes: from
their synthesis and photophysical properties to recent optical sensing
and bioimaging applications" LINK
"Use of Attenuated Total Reflection Fourier Transform Infrared
Spectroscopy and Principal Component Analysis for the Assessment of
Engine Oils" | LINK
"Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview" LINK
"Analyzing the Water Confined in Hydrogel Using Near-Infrared Spectroscopy" LINK
"Near-infrared spectra of aqueous glucose solutions" LINK
"Determination of storage period of harvested plums by nearinfrared spectroscopy and quality attributes" LINK
Hyperspectral Imaging (HSI)
"Rapid Detection of Different Types of Soil Nitrogen Using Near-Infrared Hyperspectral Imaging" LINK
"Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview" LINK
"Estimating soil moisture content under grassland with hyperspectral
data using radiative transfer modelling and machine learning" LINK
"Improving rice nitrogen stress diagnosis by denoising strips in hyperspectral images via deep learning" LINK
"Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach" LINK
Chemometrics and Machine Learning
"Remote Sensing : Early Detection of Dendroctonus valens Infestation
with Machine Learning Algorithms Based on Hyperspectral Reflectance" LINK
"Applied microwave power estimation of black carrot powders using spectroscopy combined with chemometrics" LINK
DataScientist Job: Expectation vs. Reality [infographic] BigData
DataScience Analytics AI MachineLearning ArtificialIntelligence Data
DataAnalytics Python SQL Statistics DataViz Careers Jobs
FeatureEngineering DataPrep DataCleaning LINK
"Agronomy : Detection of Adulterations in Fruit Juices Using Machine Learning Methods over FT-IR Spectroscopic Data" LINK
"Reflectance Based Models for Non-Destructive Prediction of Lycopene Content in Tomato Fruits" | LINK
"The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation" LINK
"Machine Learning Strategies for the Retrieval of Leaf-Chlorophyll
Dynamics: Model Choice, Sequential Versus Retraining Learning, and
Hyperspectral Predictors" | LINK
"In-line near-infrared analysis of milk coupled with machine learning
methods for the daily prediction of blood metabolic profile in dairy
cattle" LINK
"Near-infrared spectroscopy with chemometrics for identification and
quantification of adulteration in high-quality stingless bee honey" LINK
"Rapid identification and quantification of intramuscular fat
adulteration in lamb meat with VIS-NIR spectroscopy and chemometrics
methods" LINK
Optics for Spectroscopy
"Spectrum Reconstruction with Filter-Free Photodetectors Based on Graded-Band-Gap Perovskite Quantum Dot Heterojunctions" LINK
Facts
"Sensors : Dietary Patterns Associated with Diabetes in an Older
Population from Southern Italy Using an Unsupervised Learning Approach" |
LINK
Research on Spectroscopy
"A Study of C= O... HO and OH... OH (Dimer, Trimer, and Oligomer)
Hydrogen Bonding in a Poly (4-vinylphenol) 30%/Poly (methyl
methacrylate) 70% Blend and its ..." LINK
"Deeper insights into the photoluminescence properties and (photo)
chemical reactivity of cadmium red (CdS1− xSex) paints in renowned
twentieth century ..." | LINK
Equipment for Spectroscopy
"Green Textile Materials for Surface Enhanced Raman Spectroscopy
Identification of Pesticides Using a Raman Handheld Spectrometer for
In-Field Detection" LINK
"Characterization of Crude Oils with a Portable Nir Spectrometer" LINK
"Discrimination of the Red Jujube Varieties Using a Portable NIR Spectrometer and Fuzzy Improved Linear Discriminant Analysis" LINK
"Rapid authentication of the geographical origin of milk using portable
near‐infrared spectrometer and fuzzy uncorrelated discriminant
transformation" LINK
Environment NIR-Spectroscopy Application
"Remote Sensing : Estimating Forest Soil Properties for Humus Assessment—Is Vis-NIR the Way to Go?" LINK
"Sensors : Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils" LINK
"Evaluation of Vis-Nir Pretreatments Combined with Pls Regression for
Estimation SOC, Cec and Clay in Silty Soils from Eastern Croatia" LINK
"Comparing Two Different Development Methods of External Parameter
Orthogonalization for Estimating Organic Carbon from Field-Moist Intact
Soils by Reflectance ..." LINK
Agriculture NIR-Spectroscopy Usage
"Site-specific seeding for maize production using management zone maps delineated with multi-sensors data fusion scheme" LINK
"Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain
Spectroscopy and Grey Wolf Optimizer-Support Vector Machine" | LINK
"A LUCASbased midinfrared soil spectral library: Its usefulness for soil survey and precision agriculture" LINK
"Identification of Microplastics in Biosolids Using Ftir and Vis-Nir Spectroscopy Enhanced by Chemometric Methods" LINK
"Agriculture : Feature Wavelength Selection Based on the Combination of
Image and Spectrum for Aflatoxin B1 Concentration Classification in
Single Maize Kernels" LINK
Food & Feed Industry NIR Usage
"Agronomy : Analysis of Physico-Chemical and Organoleptic Fruit Parameters Relevant for Tomato Quality" LINK
Chemical Industry NIR Usage
"Polymers : Microscopic and Structural Studies of an Antimicrobial
Polymer Film Modified with a Natural Filler Based on Triterpenoids" LINK
Laboratory and NIR-Spectroscopy
"Laboratory Hyperspectral Image Acquisition System Setup and Validation" LINK
Other
"A sensor combination based automatic sorting system for waste washing machine parts" LINK
"Towards a NIR Spectroscopy ensemble learning technique competing with
the standard ASTM-CFR: An optimal boosting and bagging extreme learning
machine ..." LINK
"Near-infrared spectroscopy and HPLC combined with chemometrics for
comprehensive evaluation of six organic acids in Ginkgo biloba leaf
extract" | LINK
"Application of near infrared spectroscopy and real time release testing
combined with statistical process control charts for on-line quality
control of industrial ..." LINK
"Agronomy : Quality Assessment of Red Wine Grapes through NIR Spectroscopy" LINK
"An authenticity method for determining hybrid rice varieties using fusion of LIBS and NIRS" LINK
"Agriculture : A Standard-Free Calibration Transfer Strategy for a
Discrimination Model of Apple Origins Based on Near-Infrared
Spectroscopy" LINK
"Quantitative Analysis of Agricultural Compost Indicator Factors Based on Different Nir Feature Variable Selection Methods" LINK
"Near-infrared spectroscopy for the inline classification and
characterization of fruit juices for a product-customized flash
pasteurization" LINK
"Foods : Determination of Cultivation Regions and Quality Parameters of
Poria cocos by Near-Infrared Spectroscopy and Chemometrics" LINK
"Prediction of formaldehyde and residual methanol concentration in formalin using near infrared spectroscopy" LINK
"Feasibility of Near-Infrared Spectroscopy for Rapid Detection of
Available Nitrogen in Vermiculite Substrates in Desert Facility
Agriculture" LINK
"Near-infrared spectroscopy and HPLC combined with chemometrics for
comprehensive evaluation of six organic acids in Ginkgo biloba leaf
extract" LINK
"A Review of Visible and Near-Infrared (Vis-NIR) Spectroscopy Application in Plant Stress Detection" LINK
"Plants : Comparative Determination of Phenolic Compounds in Arabidopsis
thaliana Leaf Powder under Distinct Stress Conditions Using
Fourier-Transform Infrared (FT-IR) and Near-Infrared (FT-NIR)
Spectroscopy" LINK
"A feasibility study on improving the non-invasive detection accuracy of
bottled Shuanghuanglian oral liquid using near infrared spectroscopy" LINK
"Application of portable visible and near-infrared spectroscopy for
rapid detection of cooking loss rate in pork: Comparing spectra from
frozen and thawed pork" LINK
"Development of a calibration model for near infrared spectroscopy using a convolutional neural network" LINK
"Feasibility of near infrared spectroscopy to classify lamb hamburgers
according to the presence and percentage of cherry as a natural
ingredient" LINK
"Gaming behavior and brain activation using functional near-infrared
spectroscopy, Iowa gambling task, and machine learning techniques" | LINK
"A non-motorized spectro-goniometric system to measure the
bi-directional reflectance spectra of particulate surfaces in the
visible and near-infrared" LINK
"Sand fractions micromorphometry detected by VisNIRMIR and its impact on water retention" LINK
"Sensors : Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Infrared Organic Photodetectors Employing Ultralow Bandgap Polymer and Non-Fullerene Acceptors for Biometric Monitoring" | LINK
"Infrared Organic Photodetectors Employing Ultralow Bandgap Polymer and NonFullerene Acceptors for Biometric Monitoring" LINK
"Polymers : Infrared Linear Dichroism for the Analysis of Molecular Orientation in Polymers and in Polymer Composites" LINK
"Nearinfrared chemiluminescent carbon nanogels for oncology imaging and therapy" LINK
"Applied Sciences : Identification of Biochemical Differences in White and Brown Adipocytes Using FTIR Spectroscopy" LINK
Raman Spectroscopy
"Surface-Enhanced Raman Scattering Spectroscopy Combined With Chemical
Imaging Analysis for Detecting Apple Valsa Canker at an Early Stage" | LINK
"Recent advances in background-free Raman scattering for bioanalysis" | LINK
Hyperspectral Imaging (HSI)
"Sensors : Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview" LINK
"Remote Sensing : Classification of Tree Species in Different Seasons and Regions Based on Leaf Hyperspectral Images" LINK
"Visible and Near-Infrared Hyperspectral Imaging (HSI) can reliably
quantify CD3 and CD45 positive inflammatory cells in myocarditis: Pilot
study on formalin-fixed ..." LINK
Chemometrics and Machine Learning
"Molecules : Application of Transmission Raman Spectroscopy in
Combination with Partial Least-Squares (PLS) for the Fast Quantification
of Paracetamol" LINK
"Digital Assessment and Classification of Wine Faults Using a Low-Cost
Electronic Nose, Near-Infrared Spectroscopy and Machine Learning
Modelling" LINK
"Interpersonal Neural Synchronization Predicting Learning Outcomes From Teaching-Learning Interaction: A Meta-Analysis" | LINK
"Non-destructive near infrared spectroscopy externally validated using
large number sets for creation of robust calibration models enabling
prediction of apple firmness" LINK
"Sensors : Behavioural Classification of Cattle Using Neck-Mounted Accelerometer-Equipped Collars" LINK
Optics for Spectroscopy
"Physically Detachable and Operationally Stable Cs2SnI6 Photodetector
Arrays Integrated with LEDs for Broadband Flexible Optical System" LINK
Facts
"A fast multi-source information fusion strategy based on deep learning for species identification of boletes" LINK
Research on Spectroscopy
"Reactivity between late first-row transition metal halides and the
ligand bis (2-pyridylmethyl) disulfide: vibrantly-colored compounds with
variable molecular ..." LINK
Equipment for Spectroscopy
"Foods : Discrimination of the Red Jujube Varieties Using a Portable NIR
Spectrometer and Fuzzy Improved Linear Discriminant Analysis" LINK
Environment NIR-Spectroscopy Application
"Remote Sensing : Comparing Sentinel-2 and WorldView-3 Imagery for Coastal Bottom Habitat Mapping in Atlantic Canada" LINK
"Soil water repellency prediction in high‐organic agricultural soils
from Greenland: Comparing vis-NIRS to pedotransfer functions" LINK
"Remote Sensing : Simulation of Soil Organic Carbon Content Based on
Laboratory Spectrum in the Three-Rivers Source Region of China" LINK
"Remote Sensing : Unmanned Aerial Vehicle (UAV)-Based Remote Sensing for Early-Stage Detection of Ganoderma" LINK
"Soil water repellency prediction in highorganic agricultural soils from
Greenland: Comparing visNIRS to pedotransfer functions" LINK
Agriculture NIR-Spectroscopy Usage
"Assessing the nutritional quality of stored grain legume fodders:
Correlations among farmers' perceptions, sheep preferences, leaf-stem
ratios and laboratory ..." LINK
"Use of spectroscopic sensors in meat and livestock industries" LINK
"IJMS : Fourier-Transform Infra-Red Microspectroscopy Can Accurately Diagnose Colitis and Assess Severity of Inflammation" LINK
"Agronomy : Combining Variable Selection and Multiple Linear Regression
for Soil Organic Matter and Total Nitrogen Estimation by DRIFT-MIR
Spectroscopy" LINK
"Development of a Low-Cost Method for Quantifying Microplastics in Soils and Compost Using Near-Infrared Spectroscopy" LINK
"Applied Sciences : Optimization of Soybean Protein Extraction Using
By-Products from NaCl Electrolysis as an Application of the Industrial
Symbiosis Concept" LINK
Horticulture NIR-Spectroscopy Applications
"Nondestructive prediction of total soluble solids in strawberry using near infrared spectroscopy" LINK
Food & Feed Industry NIR Usage
"Effects of Irrigation Strategy and Plastic Film Mulching on Soil N2O Emissions and Fruit Yields of Greenhouse Tomato" LINK
"Microfluidic Synthesis of Block Copolymer Micelles: Application as Drug
Nanocarriers and as Photothermal Transductors When Loading Pd
Nanosheets" LINK
"Towards a NIR Spectroscopy ensemble learning technique competing with
the standard ASTM-CFR: An optimal boosting and bagging extreme learning
machine ..." LINK
"Near-infrared spectroscopy and HPLC combined with chemometrics for
comprehensive evaluation of six organic acids in Ginkgo biloba leaf
extract" | LINK
"Application of near infrared spectroscopy and real time release testing
combined with statistical process control charts for on-line quality
control of industrial ..." LINK
"Agronomy : Quality Assessment of Red Wine Grapes through NIR Spectroscopy" LINK
"An authenticity method for determining hybrid rice varieties using fusion of LIBS and NIRS" LINK
"Agriculture : A Standard-Free Calibration Transfer Strategy for a
Discrimination Model of Apple Origins Based on Near-Infrared
Spectroscopy" LINK
"Quantitative Analysis of Agricultural Compost Indicator Factors Based on Different Nir Feature Variable Selection Methods" LINK
"Near-infrared spectroscopy for the inline classification and
characterization of fruit juices for a product-customized flash
pasteurization" LINK
"Foods : Determination of Cultivation Regions and Quality Parameters of
Poria cocos by Near-Infrared Spectroscopy and Chemometrics" LINK
"Prediction of formaldehyde and residual methanol concentration in formalin using near infrared spectroscopy" LINK
"Feasibility of Near-Infrared Spectroscopy for Rapid Detection of
Available Nitrogen in Vermiculite Substrates in Desert Facility
Agriculture" LINK
"Near-infrared spectroscopy and HPLC combined with chemometrics for
comprehensive evaluation of six organic acids in Ginkgo biloba leaf
extract" LINK
"A Review of Visible and Near-Infrared (Vis-NIR) Spectroscopy Application in Plant Stress Detection" LINK
"Plants : Comparative Determination of Phenolic Compounds in Arabidopsis
thaliana Leaf Powder under Distinct Stress Conditions Using
Fourier-Transform Infrared (FT-IR) and Near-Infrared (FT-NIR)
Spectroscopy" LINK
"A feasibility study on improving the non-invasive detection accuracy of
bottled Shuanghuanglian oral liquid using near infrared spectroscopy" LINK
"Application of portable visible and near-infrared spectroscopy for
rapid detection of cooking loss rate in pork: Comparing spectra from
frozen and thawed pork" LINK
"Development of a calibration model for near infrared spectroscopy using a convolutional neural network" LINK
"Feasibility of near infrared spectroscopy to classify lamb hamburgers
according to the presence and percentage of cherry as a natural
ingredient" LINK
"Gaming behavior and brain activation using functional near-infrared
spectroscopy, Iowa gambling task, and machine learning techniques" | LINK
"A non-motorized spectro-goniometric system to measure the
bi-directional reflectance spectra of particulate surfaces in the
visible and near-infrared" LINK
"Sand fractions micromorphometry detected by VisNIRMIR and its impact on water retention" LINK
"Sensors : Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Infrared Organic Photodetectors Employing Ultralow Bandgap Polymer and Non-Fullerene Acceptors for Biometric Monitoring" | LINK
"Infrared Organic Photodetectors Employing Ultralow Bandgap Polymer and NonFullerene Acceptors for Biometric Monitoring" LINK
"Polymers : Infrared Linear Dichroism for the Analysis of Molecular Orientation in Polymers and in Polymer Composites" LINK
"Nearinfrared chemiluminescent carbon nanogels for oncology imaging and therapy" LINK
"Applied Sciences : Identification of Biochemical Differences in White and Brown Adipocytes Using FTIR Spectroscopy" LINK
Raman Spectroscopy
"Surface-Enhanced Raman Scattering Spectroscopy Combined With Chemical
Imaging Analysis for Detecting Apple Valsa Canker at an Early Stage" | LINK
"Recent advances in background-free Raman scattering for bioanalysis" | LINK
Hyperspectral Imaging (HSI)
"Sensors : Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview" LINK
"Remote Sensing : Classification of Tree Species in Different Seasons and Regions Based on Leaf Hyperspectral Images" LINK
"Visible and Near-Infrared Hyperspectral Imaging (HSI) can reliably
quantify CD3 and CD45 positive inflammatory cells in myocarditis: Pilot
study on formalin-fixed ..." LINK
Chemometrics and Machine Learning
"Molecules : Application of Transmission Raman Spectroscopy in
Combination with Partial Least-Squares (PLS) for the Fast Quantification
of Paracetamol" LINK
"Digital Assessment and Classification of Wine Faults Using a Low-Cost
Electronic Nose, Near-Infrared Spectroscopy and Machine Learning
Modelling" LINK
"Interpersonal Neural Synchronization Predicting Learning Outcomes From Teaching-Learning Interaction: A Meta-Analysis" | LINK
"Non-destructive near infrared spectroscopy externally validated using
large number sets for creation of robust calibration models enabling
prediction of apple firmness" LINK
"Sensors : Behavioural Classification of Cattle Using Neck-Mounted Accelerometer-Equipped Collars" LINK
Optics for Spectroscopy
"Physically Detachable and Operationally Stable Cs2SnI6 Photodetector
Arrays Integrated with LEDs for Broadband Flexible Optical System" LINK
Facts
"A fast multi-source information fusion strategy based on deep learning for species identification of boletes" LINK
Research on Spectroscopy
"Reactivity between late first-row transition metal halides and the
ligand bis (2-pyridylmethyl) disulfide: vibrantly-colored compounds with
variable molecular ..." LINK
Equipment for Spectroscopy
"Foods : Discrimination of the Red Jujube Varieties Using a Portable NIR
Spectrometer and Fuzzy Improved Linear Discriminant Analysis" LINK
Environment NIR-Spectroscopy Application
"Remote Sensing : Comparing Sentinel-2 and WorldView-3 Imagery for Coastal Bottom Habitat Mapping in Atlantic Canada" LINK
"Soil water repellency prediction in high‐organic agricultural soils
from Greenland: Comparing vis-NIRS to pedotransfer functions" LINK
"Remote Sensing : Simulation of Soil Organic Carbon Content Based on
Laboratory Spectrum in the Three-Rivers Source Region of China" LINK
"Remote Sensing : Unmanned Aerial Vehicle (UAV)-Based Remote Sensing for Early-Stage Detection of Ganoderma" LINK
"Soil water repellency prediction in highorganic agricultural soils from
Greenland: Comparing visNIRS to pedotransfer functions" LINK
Agriculture NIR-Spectroscopy Usage
"Assessing the nutritional quality of stored grain legume fodders:
Correlations among farmers' perceptions, sheep preferences, leaf-stem
ratios and laboratory ..." LINK
"Use of spectroscopic sensors in meat and livestock industries" LINK
"IJMS : Fourier-Transform Infra-Red Microspectroscopy Can Accurately Diagnose Colitis and Assess Severity of Inflammation" LINK
"Agronomy : Combining Variable Selection and Multiple Linear Regression
for Soil Organic Matter and Total Nitrogen Estimation by DRIFT-MIR
Spectroscopy" LINK
"Development of a Low-Cost Method for Quantifying Microplastics in Soils and Compost Using Near-Infrared Spectroscopy" LINK
"Applied Sciences : Optimization of Soybean Protein Extraction Using
By-Products from NaCl Electrolysis as an Application of the Industrial
Symbiosis Concept" LINK
Horticulture NIR-Spectroscopy Applications
"Nondestructive prediction of total soluble solids in strawberry using near infrared spectroscopy" LINK
Food & Feed Industry NIR Usage
"Effects of Irrigation Strategy and Plastic Film Mulching on Soil N2O Emissions and Fruit Yields of Greenhouse Tomato" LINK
"Microfluidic Synthesis of Block Copolymer Micelles: Application as Drug
Nanocarriers and as Photothermal Transductors When Loading Pd
Nanosheets" LINK
"Towards a NIR Spectroscopy ensemble learning technique competing with
the standard ASTM-CFR: An optimal boosting and bagging extreme learning
machine ..." LINK
"Near-infrared spectroscopy and HPLC combined with chemometrics for
comprehensive evaluation of six organic acids in Ginkgo biloba leaf
extract" | LINK
"Application of near infrared spectroscopy and real time release testing
combined with statistical process control charts for on-line quality
control of industrial ..." LINK
"Agronomy : Quality Assessment of Red Wine Grapes through NIR Spectroscopy" LINK
"An authenticity method for determining hybrid rice varieties using fusion of LIBS and NIRS" LINK
"Agriculture : A Standard-Free Calibration Transfer Strategy for a
Discrimination Model of Apple Origins Based on Near-Infrared
Spectroscopy" LINK
"Quantitative Analysis of Agricultural Compost Indicator Factors Based on Different Nir Feature Variable Selection Methods" LINK
"Near-infrared spectroscopy for the inline classification and
characterization of fruit juices for a product-customized flash
pasteurization" LINK
"Foods : Determination of Cultivation Regions and Quality Parameters of
Poria cocos by Near-Infrared Spectroscopy and Chemometrics" LINK
"Prediction of formaldehyde and residual methanol concentration in formalin using near infrared spectroscopy" LINK
"Feasibility of Near-Infrared Spectroscopy for Rapid Detection of
Available Nitrogen in Vermiculite Substrates in Desert Facility
Agriculture" LINK
"Near-infrared spectroscopy and HPLC combined with chemometrics for
comprehensive evaluation of six organic acids in Ginkgo biloba leaf
extract" LINK
"A Review of Visible and Near-Infrared (Vis-NIR) Spectroscopy Application in Plant Stress Detection" LINK
"Plants : Comparative Determination of Phenolic Compounds in Arabidopsis
thaliana Leaf Powder under Distinct Stress Conditions Using
Fourier-Transform Infrared (FT-IR) and Near-Infrared (FT-NIR)
Spectroscopy" LINK
"A feasibility study on improving the non-invasive detection accuracy of
bottled Shuanghuanglian oral liquid using near infrared spectroscopy" LINK
"Application of portable visible and near-infrared spectroscopy for
rapid detection of cooking loss rate in pork: Comparing spectra from
frozen and thawed pork" LINK
"Development of a calibration model for near infrared spectroscopy using a convolutional neural network" LINK
"Feasibility of near infrared spectroscopy to classify lamb hamburgers
according to the presence and percentage of cherry as a natural
ingredient" LINK
"Gaming behavior and brain activation using functional near-infrared
spectroscopy, Iowa gambling task, and machine learning techniques" | LINK
"A non-motorized spectro-goniometric system to measure the
bi-directional reflectance spectra of particulate surfaces in the
visible and near-infrared" LINK
"Sand fractions micromorphometry detected by VisNIRMIR and its impact on water retention" LINK
"Sensors : Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Infrared Organic Photodetectors Employing Ultralow Bandgap Polymer and Non-Fullerene Acceptors for Biometric Monitoring" | LINK
"Infrared Organic Photodetectors Employing Ultralow Bandgap Polymer and NonFullerene Acceptors for Biometric Monitoring" LINK
"Polymers : Infrared Linear Dichroism for the Analysis of Molecular Orientation in Polymers and in Polymer Composites" LINK
"Nearinfrared chemiluminescent carbon nanogels for oncology imaging and therapy" LINK
"Applied Sciences : Identification of Biochemical Differences in White and Brown Adipocytes Using FTIR Spectroscopy" LINK
Raman Spectroscopy
"Surface-Enhanced Raman Scattering Spectroscopy Combined With Chemical
Imaging Analysis for Detecting Apple Valsa Canker at an Early Stage" | LINK
"Recent advances in background-free Raman scattering for bioanalysis" | LINK
Hyperspectral Imaging (HSI)
"Sensors : Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview" LINK
"Remote Sensing : Classification of Tree Species in Different Seasons and Regions Based on Leaf Hyperspectral Images" LINK
"Visible and Near-Infrared Hyperspectral Imaging (HSI) can reliably
quantify CD3 and CD45 positive inflammatory cells in myocarditis: Pilot
study on formalin-fixed ..." LINK
Chemometrics and Machine Learning
"Molecules : Application of Transmission Raman Spectroscopy in
Combination with Partial Least-Squares (PLS) for the Fast Quantification
of Paracetamol" LINK
"Digital Assessment and Classification of Wine Faults Using a Low-Cost
Electronic Nose, Near-Infrared Spectroscopy and Machine Learning
Modelling" LINK
"Interpersonal Neural Synchronization Predicting Learning Outcomes From Teaching-Learning Interaction: A Meta-Analysis" | LINK
"Non-destructive near infrared spectroscopy externally validated using
large number sets for creation of robust calibration models enabling
prediction of apple firmness" LINK
"Sensors : Behavioural Classification of Cattle Using Neck-Mounted Accelerometer-Equipped Collars" LINK
Optics for Spectroscopy
"Physically Detachable and Operationally Stable Cs2SnI6 Photodetector
Arrays Integrated with LEDs for Broadband Flexible Optical System" LINK
Facts
"A fast multi-source information fusion strategy based on deep learning for species identification of boletes" LINK
Research on Spectroscopy
"Reactivity between late first-row transition metal halides and the
ligand bis (2-pyridylmethyl) disulfide: vibrantly-colored compounds with
variable molecular ..." LINK
Equipment for Spectroscopy
"Foods : Discrimination of the Red Jujube Varieties Using a Portable NIR
Spectrometer and Fuzzy Improved Linear Discriminant Analysis" LINK
Environment NIR-Spectroscopy Application
"Remote Sensing : Comparing Sentinel-2 and WorldView-3 Imagery for Coastal Bottom Habitat Mapping in Atlantic Canada" LINK
"Soil water repellency prediction in high‐organic agricultural soils
from Greenland: Comparing vis-NIRS to pedotransfer functions" LINK
"Remote Sensing : Simulation of Soil Organic Carbon Content Based on
Laboratory Spectrum in the Three-Rivers Source Region of China" LINK
"Remote Sensing : Unmanned Aerial Vehicle (UAV)-Based Remote Sensing for Early-Stage Detection of Ganoderma" LINK
"Soil water repellency prediction in highorganic agricultural soils from
Greenland: Comparing visNIRS to pedotransfer functions" LINK
Agriculture NIR-Spectroscopy Usage
"Assessing the nutritional quality of stored grain legume fodders:
Correlations among farmers' perceptions, sheep preferences, leaf-stem
ratios and laboratory ..." LINK
"Use of spectroscopic sensors in meat and livestock industries" LINK
"IJMS : Fourier-Transform Infra-Red Microspectroscopy Can Accurately Diagnose Colitis and Assess Severity of Inflammation" LINK
"Agronomy : Combining Variable Selection and Multiple Linear Regression
for Soil Organic Matter and Total Nitrogen Estimation by DRIFT-MIR
Spectroscopy" LINK
"Development of a Low-Cost Method for Quantifying Microplastics in Soils and Compost Using Near-Infrared Spectroscopy" LINK
"Applied Sciences : Optimization of Soybean Protein Extraction Using
By-Products from NaCl Electrolysis as an Application of the Industrial
Symbiosis Concept" LINK
Horticulture NIR-Spectroscopy Applications
"Nondestructive prediction of total soluble solids in strawberry using near infrared spectroscopy" LINK
Food & Feed Industry NIR Usage
"Effects of Irrigation Strategy and Plastic Film Mulching on Soil N2O Emissions and Fruit Yields of Greenhouse Tomato" LINK
"Microfluidic Synthesis of Block Copolymer Micelles: Application as Drug
Nanocarriers and as Photothermal Transductors When Loading Pd
Nanosheets" LINK
This week's NIR news Weekly is sponsored by Your-Company-Name-Here - NIR-spectrometers. Check out their product page ... link
Get the Spectroscopy and Chemometrics News Weekly in real time on Twitter @ CalibModel and follow us.
Near-Infrared Spectroscopy (NIRS)
"Use of Near-Infrared Spectroscopy combined with chemometrics for authentication and traceability of intact lemon fruits" LINK
"Application of NIR Spectroscopy and Multivariate Analysis for
Non-destructive Evaluation of Apple Moisture Content during Ultrasonic
Drying" LINK
"FT-NIR spectroscopy analysis for monitoring the microbial production of 2-phenylethanol using crude glycerol as carbon source" LINK
"Animals : Assessment of the Effectiveness of a Portable NIRS Instrument
in Controlling the Mixer Wagon Tuning and Ration Management" LINK
"Scarcity Mindset Neuro Network Decoding With Reward: A Tree-Based Model and Functional Near-Infrared Spectroscopy Study" LINK
"Effectiveness of near-infrared spectroscopy as a non-invasive tool to
discriminate spectral profiles of in vitro cultured oocytes from goats" LINK
"In-vivo quantification of lactate using Near Infrared reflectance spectroscopy" LINK
"Brain and renal oxygenation measured by NIRS related to patent ductus
arteriosus in preterm infants: a prospective observational study" | LINK
"Rapid Quantitative Determination of Chemical Oxygen Demand in Different
Water Systems Based on Near-Infrared Spectroscopy Combined with Binary
Grey Wolf ..." LINK
"Classification of fNIRS data with LDA and SVM: a proof-of-concept for application in infant studies" LINK
"Chemosensors : Detection of Monilia Contamination in Plum and Plum Juice with NIR Spectroscopy and Electronic Tongue" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Radiometric calibration accuracy and stability of GOES-16 ABI Infrared radiance" | LINK
"Monte Carlo Characterization of Short-Wave Infrared Optical Wavelengths for Biosensing Applications" LINK
Hyperspectral Imaging (HSI)
"Agriculture : Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm" LINK
"Agronomy : Non-invasive Monitoring of Berry Ripening Using On-the-Go Hyperspectral Imaging in the Vineyard" LINK
"Prediction of Aqueous Glucose Concentration Using Hyperspectral Imaging" LINK
Spectral Imaging
"Agriculture : A Handheld Grassland Vegetation Monitoring System Based on Multispectral Imaging" LINK
Chemometrics and Machine Learning
"Multivariate analysis of food fraud: A review of NIR based instruments in tandem with chemometrics" LINK
"Recent Chemometric Opportunities in Criminalistics" LINK
"Non-Invasive Methods for Predicting the Quality of Processed
Horticultural Food Products, with Emphasis on Dried Powders, Juices and
Oils: A Review" LINK
"Hyperspectral inversion of nitrogen content in phyllostachys pubescens based on partial least squares regression model" LINK
"Applied Sciences : Identification of a Suitable Machine Learning Model
for Detection of Asymptomatic Ganoderma boninense Infection in Oil Palm
Seedlings Using Hyperspectral Data" LINK
Research on Spectroscopy
"A variable selection method based on mutual information and variance inflation factor" LINK
Environment NIR-Spectroscopy Application
"Remote Sensing : Reducing Scaling Effect on Downscaled Land Surface Temperature Maps in Heterogenous Urban Environments" LINK
Agriculture NIR-Spectroscopy Usage
"Biogenic and physicogenic aggregates: formation pathways, assessment techniques, and influence on soil properties" LINK
"Research on the transmission performance of multi-layer simulated mural surface by imaging spectrum" LINK
"Estimation of genetic parameters for carcass grading traits, image
analysis traits, and monounsaturated fatty acids in Japanese Black
cattle from Hyogo Prefecture" LINK
Pharma Industry NIR Usage
"CONDITION OF RENAL OXYGENATION IN PRETERM INFANTS WITH HEMODINAMICALLY SIGNIFICANT PATENT DUCTUS ARTERIOSUS" LINK
"Extent of lipid core plaque in patients with Achilles tendon xanthoma
undergoing percutaneous coronary intervention for coronary artery
disease" LINK
Other
"Textural and compositional effects of ilmenite on the spectra of high-titanium lunar basalts" LINK
.
NIR Calibration-Model Services
Spectroscopy and Chemometrics News Weekly 4, 2022 | NIRS NIR
Spectroscopy MachineLearning Spectrometer Spectrometric Analytical
Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software
IoT Sensors QA QC Testing Quality LINK
This week's NIR news Weekly is sponsored by Your-Company-Name-Here - NIR-spectrometers. Check out their product page ... link
Get the Spectroscopy and Chemometrics News Weekly in real time on Twitter @ CalibModel and follow us.
Near-Infrared Spectroscopy (NIRS)
"Use of Near-Infrared Spectroscopy combined with chemometrics for authentication and traceability of intact lemon fruits" LINK
"Application of NIR Spectroscopy and Multivariate Analysis for
Non-destructive Evaluation of Apple Moisture Content during Ultrasonic
Drying" LINK
"FT-NIR spectroscopy analysis for monitoring the microbial production of 2-phenylethanol using crude glycerol as carbon source" LINK
"Animals : Assessment of the Effectiveness of a Portable NIRS Instrument
in Controlling the Mixer Wagon Tuning and Ration Management" LINK
"Scarcity Mindset Neuro Network Decoding With Reward: A Tree-Based Model and Functional Near-Infrared Spectroscopy Study" LINK
"Effectiveness of near-infrared spectroscopy as a non-invasive tool to
discriminate spectral profiles of in vitro cultured oocytes from goats" LINK
"In-vivo quantification of lactate using Near Infrared reflectance spectroscopy" LINK
"Brain and renal oxygenation measured by NIRS related to patent ductus
arteriosus in preterm infants: a prospective observational study" | LINK
"Rapid Quantitative Determination of Chemical Oxygen Demand in Different
Water Systems Based on Near-Infrared Spectroscopy Combined with Binary
Grey Wolf ..." LINK
"Classification of fNIRS data with LDA and SVM: a proof-of-concept for application in infant studies" LINK
"Chemosensors : Detection of Monilia Contamination in Plum and Plum Juice with NIR Spectroscopy and Electronic Tongue" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Radiometric calibration accuracy and stability of GOES-16 ABI Infrared radiance" | LINK
"Monte Carlo Characterization of Short-Wave Infrared Optical Wavelengths for Biosensing Applications" LINK
Hyperspectral Imaging (HSI)
"Agriculture : Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm" LINK
"Agronomy : Non-invasive Monitoring of Berry Ripening Using On-the-Go Hyperspectral Imaging in the Vineyard" LINK
"Prediction of Aqueous Glucose Concentration Using Hyperspectral Imaging" LINK
Spectral Imaging
"Agriculture : A Handheld Grassland Vegetation Monitoring System Based on Multispectral Imaging" LINK
Chemometrics and Machine Learning
"Multivariate analysis of food fraud: A review of NIR based instruments in tandem with chemometrics" LINK
"Recent Chemometric Opportunities in Criminalistics" LINK
"Non-Invasive Methods for Predicting the Quality of Processed
Horticultural Food Products, with Emphasis on Dried Powders, Juices and
Oils: A Review" LINK
"Hyperspectral inversion of nitrogen content in phyllostachys pubescens based on partial least squares regression model" LINK
"Applied Sciences : Identification of a Suitable Machine Learning Model
for Detection of Asymptomatic Ganoderma boninense Infection in Oil Palm
Seedlings Using Hyperspectral Data" LINK
Research on Spectroscopy
"A variable selection method based on mutual information and variance inflation factor" LINK
Environment NIR-Spectroscopy Application
"Remote Sensing : Reducing Scaling Effect on Downscaled Land Surface Temperature Maps in Heterogenous Urban Environments" LINK
Agriculture NIR-Spectroscopy Usage
"Biogenic and physicogenic aggregates: formation pathways, assessment techniques, and influence on soil properties" LINK
"Research on the transmission performance of multi-layer simulated mural surface by imaging spectrum" LINK
"Estimation of genetic parameters for carcass grading traits, image
analysis traits, and monounsaturated fatty acids in Japanese Black
cattle from Hyogo Prefecture" LINK
Pharma Industry NIR Usage
"CONDITION OF RENAL OXYGENATION IN PRETERM INFANTS WITH HEMODINAMICALLY SIGNIFICANT PATENT DUCTUS ARTERIOSUS" LINK
"Extent of lipid core plaque in patients with Achilles tendon xanthoma
undergoing percutaneous coronary intervention for coronary artery
disease" LINK
Other
"Textural and compositional effects of ilmenite on the spectra of high-titanium lunar basalts" LINK
.
NIR Calibration-Model Services
Spectroscopy and Chemometrics News Weekly 4, 2022 | NIRS NIR
Spectroscopy MachineLearning Spectrometer Spectrometric Analytical
Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software
IoT Sensors QA QC Testing Quality LINK
This week's NIR news Weekly is sponsored by Your-Company-Name-Here - NIR-spectrometers. Check out their product page ... link
Get the Spectroscopy and Chemometrics News Weekly in real time on Twitter @ CalibModel and follow us.
Near-Infrared Spectroscopy (NIRS)
"Use of Near-Infrared Spectroscopy combined with chemometrics for authentication and traceability of intact lemon fruits" LINK
"Application of NIR Spectroscopy and Multivariate Analysis for
Non-destructive Evaluation of Apple Moisture Content during Ultrasonic
Drying" LINK
"FT-NIR spectroscopy analysis for monitoring the microbial production of 2-phenylethanol using crude glycerol as carbon source" LINK
"Animals : Assessment of the Effectiveness of a Portable NIRS Instrument
in Controlling the Mixer Wagon Tuning and Ration Management" LINK
"Scarcity Mindset Neuro Network Decoding With Reward: A Tree-Based Model and Functional Near-Infrared Spectroscopy Study" LINK
"Effectiveness of near-infrared spectroscopy as a non-invasive tool to
discriminate spectral profiles of in vitro cultured oocytes from goats" LINK
"In-vivo quantification of lactate using Near Infrared reflectance spectroscopy" LINK
"Brain and renal oxygenation measured by NIRS related to patent ductus
arteriosus in preterm infants: a prospective observational study" | LINK
"Rapid Quantitative Determination of Chemical Oxygen Demand in Different
Water Systems Based on Near-Infrared Spectroscopy Combined with Binary
Grey Wolf ..." LINK
"Classification of fNIRS data with LDA and SVM: a proof-of-concept for application in infant studies" LINK
"Chemosensors : Detection of Monilia Contamination in Plum and Plum Juice with NIR Spectroscopy and Electronic Tongue" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Radiometric calibration accuracy and stability of GOES-16 ABI Infrared radiance" | LINK
"Monte Carlo Characterization of Short-Wave Infrared Optical Wavelengths for Biosensing Applications" LINK
Hyperspectral Imaging (HSI)
"Agriculture : Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm" LINK
"Agronomy : Non-invasive Monitoring of Berry Ripening Using On-the-Go Hyperspectral Imaging in the Vineyard" LINK
"Prediction of Aqueous Glucose Concentration Using Hyperspectral Imaging" LINK
Spectral Imaging
"Agriculture : A Handheld Grassland Vegetation Monitoring System Based on Multispectral Imaging" LINK
Chemometrics and Machine Learning
"Multivariate analysis of food fraud: A review of NIR based instruments in tandem with chemometrics" LINK
"Recent Chemometric Opportunities in Criminalistics" LINK
"Non-Invasive Methods for Predicting the Quality of Processed
Horticultural Food Products, with Emphasis on Dried Powders, Juices and
Oils: A Review" LINK
"Hyperspectral inversion of nitrogen content in phyllostachys pubescens based on partial least squares regression model" LINK
"Applied Sciences : Identification of a Suitable Machine Learning Model
for Detection of Asymptomatic Ganoderma boninense Infection in Oil Palm
Seedlings Using Hyperspectral Data" LINK
Research on Spectroscopy
"A variable selection method based on mutual information and variance inflation factor" LINK
Environment NIR-Spectroscopy Application
"Remote Sensing : Reducing Scaling Effect on Downscaled Land Surface Temperature Maps in Heterogenous Urban Environments" LINK
Agriculture NIR-Spectroscopy Usage
"Biogenic and physicogenic aggregates: formation pathways, assessment techniques, and influence on soil properties" LINK
"Research on the transmission performance of multi-layer simulated mural surface by imaging spectrum" LINK
"Estimation of genetic parameters for carcass grading traits, image
analysis traits, and monounsaturated fatty acids in Japanese Black
cattle from Hyogo Prefecture" LINK
Pharma Industry NIR Usage
"CONDITION OF RENAL OXYGENATION IN PRETERM INFANTS WITH HEMODINAMICALLY SIGNIFICANT PATENT DUCTUS ARTERIOSUS" LINK
"Extent of lipid core plaque in patients with Achilles tendon xanthoma
undergoing percutaneous coronary intervention for coronary artery
disease" LINK
Other
"Textural and compositional effects of ilmenite on the spectra of high-titanium lunar basalts" LINK
How many other analytics you do in the Lab could be done faster and cheaper with NIR?
Is this possible and precise enough?
Try, we have the solution for you!
You have the NIR, scan the samples, you have the lab values and the spectra, we calibrate for you!
To see if the application is possible and how precise it can be due to knowledge based intensive model optimizations.
We do the NIR feasibility study with data for you. Fixed prices
NIR has huge application potentials and it's a Green analytical method, that's fast and easy to use.
And has today the possibility to scale out with inexpensive mobile NIR spectrometers.
Bring the Lab to the sample. To avoid sample transport and get immediate results for decision at the place or in the process.
Just try the NIR application, use the NIR daily, collect data in parallel, we develop, optimize and maintain the calibration models for you.
What is possible today with NIR? The number of different Applications exploded in the last 2-3 years!
See NIR research papers news daily on @CalibModel or the 7-day summaries "NIR News Weekly" here.
Haben Sie ein NIR-Spektrometer in Ihrem Labor?
Wie viele andere Analysen, die Sie im Labor durchführen, könnten mit NIR schneller und billiger durchgeführt werden?
Ist dies möglich und präzise genug?
Versuchen Sie es, wir haben die Lösung für Sie!
Sie haben das NIR, scannen sie Proben, Sie haben die Laborwerte und die Spektren, wir kalibrieren für Sie!
Um zu sehen, ob die Anwendung möglich ist und wie präzise sie aufgrund von wissensbasierten intensiven Modelloptimierungen sein kann.
Wir führen die NIR-Machbarkeitsstudie mit Daten für Sie durch. Fixpreise
NIR hat ein riesiges Anwendungspotential und ist eine grüne Analysemethode, die schnell und einfach anzuwenden ist.
Und hat heute die Möglichkeit, mit kostengünstigen mobilen NIR-Spektrometern zu skalieren.
Bringen Sie das Labor zu der Probe. So vermeiden Sie den Probentransport und erhalten sofortige Ergebnisse zur Entscheidung am Ort oder im Prozess.
Probieren Sie einfach die NIR-Anwendung aus, nutzen Sie das NIR täglich, sammeln Sie parallel dazu Daten, wir entwickeln, optimieren und warten die Kalibriermodelle für Sie.
Was ist heute mit NIR möglich? Die Zahl der verschiedenen Anwendungen ist in den letzten 2-3 Jahren explodiert!
Sehen Sie hier die täglichen NIR-Forschungsberichte über @CalibModel oder die 7-Tage-Zusammenfassungen "NIR News Weekly".
Avete uno spettrometro NIR nel vostro laboratorio?
Quante altre analisi si possono fare in laboratorio in modo più veloce ed economico con il NIR?
È possibile e sufficientemente preciso?
Provate, abbiamo la soluzione per voi!
Avete il NIR, scansionate i campioni, avete i valori di laboratorio e gli spettri, noi calibriamo per voi!
Per vedere se l'applicazione è possibile e quanto precisa può essere grazie all'ottimizzazione intensiva del modello basata sulla conoscenza.
Facciamo lo studio di fattibilità NIR con i dati per voi. Prezzi fissi
Il NIR ha enormi potenzialità applicative ed è un metodo analitico Green, veloce e facile da usare.
E ha oggi la possibilità di scalare con gli economici spettrometri mobili NIR.
Portate il laboratorio al campione. Per evitare il trasporto del campione e ottenere risultati immediati per la decisione sul posto o nel processo.
Basta provare l'applicazione NIR, usare il NIR quotidianamente, raccogliere dati in parallelo, noi sviluppiamo, ottimizziamo e manteniamo i modelli di calibrazione per voi.
Cosa è possibile oggi con il NIR? Il numero di diverse Applicazioni è esploso negli ultimi 2-3 anni!
Vedi le notizie dei giornali di ricerca NIR su @CalibModel o i riassunti di 7 giorni "NIR News Weekly" qui.
________________________________________________
Análise NIR no laboratório e laboratórios - também conhecidos como laboratórios NIR e testes NIR
Tem um espectrómetro NIR no seu laboratório?
Quantas outras análises que faz no Laboratório poderiam ser feitas mais rapidamente e mais baratas com o NIR? Será isto possível e suficientemente preciso?
Tente, nós temos a solução para si!
Tem o NIR, digitaliza as amostras, tem os valores de laboratório e os espectros, nós calibramos para si!
Para ver se a aplicação é possível e quão precisa pode ser devido a optimizações de modelos intensivas baseadas no conhecimento.
Fazemos o estudo de viabilidade do NIR com dados para si. Preços fixos
NIR tem um enorme potencial de aplicação e é um método analítico Verde, que é rápido e fácil de usar.
E tem hoje a possibilidade de ser escalado com espectrómetros NIR móveis de baixo custo.
Traga o Laboratório para a amostra. Para evitar o transporte de amostras e obter resultados imediatos para decisão no local ou no processo.
Basta experimentar a aplicação NIR, utilizar o NIR diariamente, recolher dados em paralelo, nós desenvolvemos, optimizamos e mantemos os modelos de calibração para si.
O que é possível hoje com o NIR? O número de diferentes Aplicações explodiu nos últimos 2-3 anos!
Ver os jornais de investigação NIR diariamente sobre @CalibModel ou os resumos de 7 dias "NIR News Weekly" aqui.
________________________________________________
El análisis NIR en el laboratorio y los laboratorios - también conocidos como laboratorios NIR y pruebas NIR
Tiene un espectrómetro NIR en su laboratorio?
Cuántos análisis más haces en el laboratorio podrían hacerse más rápido y más barato con el NIR? Es esto posible y suficientemente preciso?
¡Inténtelo, tenemos la solución para usted!
Tienes el NIR, escaneas las muestras, tienes los valores del laboratorio y los espectros, ¡calibramos para ti!
Para ver si la aplicación es posible y cuán precisa puede ser gracias a las optimizaciones intensivas de modelos basadas en el conocimiento.
Hacemos el estudio de viabilidad del NIR con los datos para usted. Precios fijos
El NIR tiene un enorme potencial de aplicación y es un método analítico verde, que es rápido y fácil de usar.
Y tiene hoy la posibilidad de escalar con espectrómetros NIR móviles baratos.
Trae el laboratorio a la muestra. Para evitar el transporte de la muestra y obtener resultados inmediatos para la decisión en el lugar o en el proceso.
Pruebe la aplicación NIR, utilice el NIR diariamente, recoja los datos en paralelo, nosotros desarrollamos, optimizamos y mantenemos los modelos de calibración para usted.
Qué es posible hoy en día con el NIR? ¡El número de aplicaciones diferentes explotó en los últimos 2-3 años!
Vea las noticias diarias de los trabajos de investigación del NIR en @CalibModel o los resúmenes de 7 días "NIR News Weekly" aquí.
Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us.
Near-Infrared Spectroscopy (NIRS)
"Non-Destructive Determination of Quality Traits of Cashew Apples (Anacardium Occidentale, L.) Using a Portable near Infrared Spectrophotometer" LINK
"Non-destructive classification and prediction of aflatoxin-B1 concentration in maize kernels using Vis–NIR (400–1000 nm) hyperspectral imaging" LINK
"Determination of glucose content with a concentration within the physiological range by FT-NIR spectroscopy in a trans-reflectance mode" LINK
"Evaluating taste-related attributes of black tea by micro-NIRS" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Rapid authentication of Pseudostellaria heterophylla (Taizishen) from different regions by nearinfrared spectroscopy combined with chemometric methods" LINK
"Agronomy, Vol. 10, Pages 828: Estimating Sensory Properties with Near-Infrared Spectroscopy: A Tool for Quality Control and Breeding of Calçots (Allium cepa L.)" LINK
"Spectral observation of agarwood by infrared spectroscopy: The differences of infected and normal Aquilaria microcarpa" LINK
"Quantitative near infrared spectroscopic analysis of Tricholoma matsutake based on information extraction using the elastic net" LINK
"Visible-near infrared spectroscopy for detection of blood in sheep faeces" LINK
" … dans le proche infrarouge et techniques de chimiométrie Detection of addition of barley to coffee using near infrared spectroscopy and chemometric techniques" LINK
"Forests, Vol. 11, Pages 644: A Comparison of the Loading Direction for Bending Strength with Different Wood Measurement Surfaces Using Near-Infrared Spectroscopy" LINK
"Rapid assessment of soil condition in Kenya through development of near infrared spectral indicatators" LINK
Chemometrics and Machine Learning
"Determination of apple varieties by near infrared reflectance spectroscopy coupled with improved possibilistic Gath–Geva clustering algorithm" LINK
"Two-Dimensional Correlation Spectroscopy: The Power of Power Spectra" LINK
"Simple and fast spectrophotometric method based on chemometrics for the measurement of multicomponent adsorption kinetics" LINK
"Real time detection of amphetamine in oral fluids by MicroNIR/Chemometrics." LINK
"In‐vitro digestion of the bioactives originating from the Lamiaceae family herbal teas: A kinetic and PLS modeling study" LINK
"Models for predicting the within-tree and regional variation of tracheid length and width for plantation loblolly pine" LINK
Research on Spectroscopy
"Study on rapid quality analysis method of Shengxuebao Mixture" LINK
"MD dating: molecular decay (MD) in pinewood as a dating method" LINK
Altersbestimmung von Holz mittels FTIR-Spektroskopie: Durch die Zusammenarbeit von Holz-, Materialwissenschaftler*innen und Statistikern konnte nach über 70 Jahren eine dritte Datierungsmethode neben der Jahrringanalyse und der Radiokarbonmethode im… LINK
Equipment for Spectroscopy
"Quality assessment of instant green tea using portable NIR spectrometer." LINK
Process Control and NIR Sensors
"From powder to tablets: Investigation of residence time distributions in a continuous manufacturing process train as basis for continuous process verification" LINK
"Non-destructive, non-invasive, in-line real-time phase-based reflectance for quality monitoring of fruit" LINK
Agriculture NIR-Spectroscopy Usage
"Estimating soil organic carbon density in Northern China's agro-pastoral ecotone using vis-NIR spectroscopy" LINK
"Retrieval of aboveground crop nitrogen content with a hybrid machine learning method" LINK
"Prediction of Soil Oxalate Phosphorus using Visible and Near-Infrared Spectroscopy in Natural and Cultivated System Soils of Madagascar" LINK
"Sensors, Vol. 20, Pages 3208: Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants" LINK
"The application of R language in the selection of characteristic bands for the prediction of protein content in milk powder by Near Infrared Spectroscopy" LINK
"Onsite nutritional diagnosis of tea plants using micro near-infrared spectrometer coupled with chemometrics" LINK
Horticulture NIR-Spectroscopy Applications
"Improving the accuracy of near-infrared (NIR) spectroscopy method to predict the oil content of oil palm fresh fruits" LINK
Laboratory and NIR-Spectroscopy
"Non-destructive determination of apple quality parameters of variety'red jonaprince'using near infrared spectroscopy." LINK
"Laboratory Methods for Evaluating Forage Quality" LINK
Other
"Automatic Walnut Sorting System Based on Adaptive Fuzzy Control" LINK
Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us.
Near-Infrared Spectroscopy (NIRS)
"Non-Destructive Determination of Quality Traits of Cashew Apples (Anacardium Occidentale, L.) Using a Portable near Infrared Spectrophotometer" LINK
"Non-destructive classification and prediction of aflatoxin-B1 concentration in maize kernels using Vis–NIR (400–1000 nm) hyperspectral imaging" LINK
"Determination of glucose content with a concentration within the physiological range by FT-NIR spectroscopy in a trans-reflectance mode" LINK
"Evaluating taste-related attributes of black tea by micro-NIRS" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Rapid authentication of Pseudostellaria heterophylla (Taizishen) from different regions by nearinfrared spectroscopy combined with chemometric methods" LINK
"Agronomy, Vol. 10, Pages 828: Estimating Sensory Properties with Near-Infrared Spectroscopy: A Tool for Quality Control and Breeding of Calçots (Allium cepa L.)" LINK
"Spectral observation of agarwood by infrared spectroscopy: The differences of infected and normal Aquilaria microcarpa" LINK
"Quantitative near infrared spectroscopic analysis of Tricholoma matsutake based on information extraction using the elastic net" LINK
"Visible-near infrared spectroscopy for detection of blood in sheep faeces" LINK
" … dans le proche infrarouge et techniques de chimiométrie Detection of addition of barley to coffee using near infrared spectroscopy and chemometric techniques" LINK
"Forests, Vol. 11, Pages 644: A Comparison of the Loading Direction for Bending Strength with Different Wood Measurement Surfaces Using Near-Infrared Spectroscopy" LINK
"Rapid assessment of soil condition in Kenya through development of near infrared spectral indicatators" LINK
Chemometrics and Machine Learning
"Determination of apple varieties by near infrared reflectance spectroscopy coupled with improved possibilistic Gath–Geva clustering algorithm" LINK
"Two-Dimensional Correlation Spectroscopy: The Power of Power Spectra" LINK
"Simple and fast spectrophotometric method based on chemometrics for the measurement of multicomponent adsorption kinetics" LINK
"Real time detection of amphetamine in oral fluids by MicroNIR/Chemometrics." LINK
"In‐vitro digestion of the bioactives originating from the Lamiaceae family herbal teas: A kinetic and PLS modeling study" LINK
"Models for predicting the within-tree and regional variation of tracheid length and width for plantation loblolly pine" LINK
Research on Spectroscopy
"Study on rapid quality analysis method of Shengxuebao Mixture" LINK
"MD dating: molecular decay (MD) in pinewood as a dating method" LINK
Altersbestimmung von Holz mittels FTIR-Spektroskopie: Durch die Zusammenarbeit von Holz-, Materialwissenschaftler*innen und Statistikern konnte nach über 70 Jahren eine dritte Datierungsmethode neben der Jahrringanalyse und der Radiokarbonmethode im… LINK
Equipment for Spectroscopy
"Quality assessment of instant green tea using portable NIR spectrometer." LINK
Process Control and NIR Sensors
"From powder to tablets: Investigation of residence time distributions in a continuous manufacturing process train as basis for continuous process verification" LINK
"Non-destructive, non-invasive, in-line real-time phase-based reflectance for quality monitoring of fruit" LINK
Agriculture NIR-Spectroscopy Usage
"Estimating soil organic carbon density in Northern China's agro-pastoral ecotone using vis-NIR spectroscopy" LINK
"Retrieval of aboveground crop nitrogen content with a hybrid machine learning method" LINK
"Prediction of Soil Oxalate Phosphorus using Visible and Near-Infrared Spectroscopy in Natural and Cultivated System Soils of Madagascar" LINK
"Sensors, Vol. 20, Pages 3208: Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants" LINK
"The application of R language in the selection of characteristic bands for the prediction of protein content in milk powder by Near Infrared Spectroscopy" LINK
"Onsite nutritional diagnosis of tea plants using micro near-infrared spectrometer coupled with chemometrics" LINK
Horticulture NIR-Spectroscopy Applications
"Improving the accuracy of near-infrared (NIR) spectroscopy method to predict the oil content of oil palm fresh fruits" LINK
Laboratory and NIR-Spectroscopy
"Non-destructive determination of apple quality parameters of variety'red jonaprince'using near infrared spectroscopy." LINK
"Laboratory Methods for Evaluating Forage Quality" LINK
Other
"Automatic Walnut Sorting System Based on Adaptive Fuzzy Control" LINK
Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us.
Near-Infrared Spectroscopy (NIRS)
"Non-Destructive Determination of Quality Traits of Cashew Apples (Anacardium Occidentale, L.) Using a Portable near Infrared Spectrophotometer" LINK
"Non-destructive classification and prediction of aflatoxin-B1 concentration in maize kernels using Vis–NIR (400–1000 nm) hyperspectral imaging" LINK
"Determination of glucose content with a concentration within the physiological range by FT-NIR spectroscopy in a trans-reflectance mode" LINK
"Evaluating taste-related attributes of black tea by micro-NIRS" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Rapid authentication of Pseudostellaria heterophylla (Taizishen) from different regions by nearinfrared spectroscopy combined with chemometric methods" LINK
"Agronomy, Vol. 10, Pages 828: Estimating Sensory Properties with Near-Infrared Spectroscopy: A Tool for Quality Control and Breeding of Calçots (Allium cepa L.)" LINK
"Spectral observation of agarwood by infrared spectroscopy: The differences of infected and normal Aquilaria microcarpa" LINK
"Quantitative near infrared spectroscopic analysis of Tricholoma matsutake based on information extraction using the elastic net" LINK
"Visible-near infrared spectroscopy for detection of blood in sheep faeces" LINK
" … dans le proche infrarouge et techniques de chimiométrie Detection of addition of barley to coffee using near infrared spectroscopy and chemometric techniques" LINK
"Forests, Vol. 11, Pages 644: A Comparison of the Loading Direction for Bending Strength with Different Wood Measurement Surfaces Using Near-Infrared Spectroscopy" LINK
"Rapid assessment of soil condition in Kenya through development of near infrared spectral indicatators" LINK
Chemometrics and Machine Learning
"Determination of apple varieties by near infrared reflectance spectroscopy coupled with improved possibilistic Gath–Geva clustering algorithm" LINK
"Two-Dimensional Correlation Spectroscopy: The Power of Power Spectra" LINK
"Simple and fast spectrophotometric method based on chemometrics for the measurement of multicomponent adsorption kinetics" LINK
"Real time detection of amphetamine in oral fluids by MicroNIR/Chemometrics." LINK
"In‐vitro digestion of the bioactives originating from the Lamiaceae family herbal teas: A kinetic and PLS modeling study" LINK
"Models for predicting the within-tree and regional variation of tracheid length and width for plantation loblolly pine" LINK
Research on Spectroscopy
"Study on rapid quality analysis method of Shengxuebao Mixture" LINK
"MD dating: molecular decay (MD) in pinewood as a dating method" LINK
Altersbestimmung von Holz mittels FTIR-Spektroskopie: Durch die Zusammenarbeit von Holz-, Materialwissenschaftler*innen und Statistikern konnte nach über 70 Jahren eine dritte Datierungsmethode neben der Jahrringanalyse und der Radiokarbonmethode im… LINK
Equipment for Spectroscopy
"Quality assessment of instant green tea using portable NIR spectrometer." LINK
Process Control and NIR Sensors
"From powder to tablets: Investigation of residence time distributions in a continuous manufacturing process train as basis for continuous process verification" LINK
"Non-destructive, non-invasive, in-line real-time phase-based reflectance for quality monitoring of fruit" LINK
Agriculture NIR-Spectroscopy Usage
"Estimating soil organic carbon density in Northern China's agro-pastoral ecotone using vis-NIR spectroscopy" LINK
"Retrieval of aboveground crop nitrogen content with a hybrid machine learning method" LINK
"Prediction of Soil Oxalate Phosphorus using Visible and Near-Infrared Spectroscopy in Natural and Cultivated System Soils of Madagascar" LINK
"Sensors, Vol. 20, Pages 3208: Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants" LINK
"The application of R language in the selection of characteristic bands for the prediction of protein content in milk powder by Near Infrared Spectroscopy" LINK
"Onsite nutritional diagnosis of tea plants using micro near-infrared spectrometer coupled with chemometrics" LINK
Horticulture NIR-Spectroscopy Applications
"Improving the accuracy of near-infrared (NIR) spectroscopy method to predict the oil content of oil palm fresh fruits" LINK
Laboratory and NIR-Spectroscopy
"Non-destructive determination of apple quality parameters of variety'red jonaprince'using near infrared spectroscopy." LINK
"Laboratory Methods for Evaluating Forage Quality" LINK
Other
"Automatic Walnut Sorting System Based on Adaptive Fuzzy Control" LINK
This week's NIR news Weekly is sponsored by Your-Company-Name-Here - NIR-spectrometers. Check out their product page ... link
Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us.
Near-Infrared Spectroscopy (NIRS)
"Non-invasive method to identify the type of green tea inside teabag using NIR spectroscopy, support vector machines and Bayesian optimization" LINK
"Online milk composition analysis with an on-farm near-infrared sensor" LINK
"Anonymous fecal sampling and NIRS studies of diet quality: Problem or opportunity?" LINK
"Organic and Symbiotic Fertilization of Tomato Plants Monitored by Litterbag-NIRS and Foliar-NIRS Rapid Spectroscopic Methods Running title: Litterbag-NIRS and Foliar-NIRS model in symbiotic tomato" LINK
"Determination of crude protein and metabolized energy with near infrared reflectance spectroscopy (NIRS) in ruminant mixed feeds" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Near Infrared Spectroscopy as an efficient tool for the Qualitative and Quantitative Determination of Sugar Adulteration in Milk" | LINK
"NEAR INFRARED SPECTROSCOPY AS A NEW FIRE SEVERITY METRIC" by Bushfire and Natural Hazards CRC LINK
"Near-infrared spectroscopy for the concurrent quality prediction and status monitoring of gasoline blending" LINK
"Application of Selective Near Infrared Spectroscopy for Qualitative and Quantitative Prediction of Water Adulteration in Milk" LINK
"Predicting Macronutrient of Baby Food using Near-infrared Spectroscopy and Deep Learning Approach" LINK
"Detection of heat treatment of honey with near infrared spectroscopy" LINK
"Use of near infrared spectroscopy in cotton seeds physiological quality evaluation" LINK
"Detection of Haemonchus contortus nematode eggs in sheep faeces using near and mid-infrared spectroscopy" LINK
"Feasibility of using near-infrared measurements to detect changes in water quality" LINK
Hyperspectral Imaging (HSI)
"Hyperspectral waveband selection algorithm based on weighted maximum relevance minimum redundancy and its stability analysis" LINK
Chemometrics and Machine Learning
"Comparative quantification of chlorophyll and polyphenol levels in grapevine leaves sampled from different geographical locations" LINK
"Screening method for determination of C18:1 trans fatty acids positional isomers in chocolate by 1H NMR and chemometrics" LINK
"Combination of efficient signal pre-processing and optimal band combination algorithm to predict soil organic matter through visible and near-infrared spectra" LINK
"A chemometric approach to the evaluation of the ageing ability of red wines" LINK
"Determination of Loline Alkaloids and Mycelial Biomass in Endophyte-Infected Schedonorus Pratensis by Near-Infrared Spectroscopy and Chemometrics" LINK
"A Feasible Approach to Detect Pesticides in Food Samples Using THz-FDS and Chemometrics" LINK
"Prediction of Soluble Solids Content in Green Plum by Using a Sparse Autoencoder" LINK
Process Control and NIR Sensors
"Real-time and field monitoring of the key parameters in industrial trough composting process using a handheld near infrared spectrometer" LINK
Environment NIR-Spectroscopy Application
"Detection and analysis of soil water content based on experimental reflectance spectrum data" LINK
" International Soil and Water Conservation Research" | LINK
Agriculture NIR-Spectroscopy Usage
"Detecting Low Concentrations of Nitrogen-Based Adulterants in Whey Protein Powder Using Benchtop and Handheld NIR Spectrometers and the Feasibility of Scanning through Plastic Bag." LINK
"Assessment of the genetic diversity of sweetpotato germplasm collections for protein content" LINK
"Near-infrared spectroscopy and imaging in protein research" LINK
"Foods, Vol. 9, Pages 710: Application of ATR-FT-MIR for Tracing the Geographical Origin of Honey Produced in the Maltese Islands" LINK
"Agriculture, Vol. 10, Pages 193: Content of Polyphenolic Compounds and Antioxidant Potential of Some Bulgarian Red Grape Varieties and Red Wines, Determined by HPLC, UV, and NIR Spectroscopy" LINK
"Agronomy, Vol. 10, Pages 787: Assessing Soil Key Fertility Attributes Using a Portable X-Ray Fluorescence: A Simple Method to Overcome Matrix Effect" LINK
Food & Feed Industry NIR Usage
"Non‑destructive testing technology for raw eggs freshness: a review" LINK
"Quantification of multiple adulterants in beef protein powder by FT-NIR" LINK
Beverage and Drink Industry NIR Usage
"Beer Aroma and Quality Traits Assessment Using Artificial Intelligence" LINK
Other
"Tetrahedral Mn4+ as chromophore in sillenite-type compounds" LINK
NIR Calibration-Model Services
Spectroscopy and Chemometrics News Weekly 28, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK
This week's NIR news Weekly is sponsored by Your-Company-Name-Here - NIR-spectrometers. Check out their product page ... link
Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us.
Near-Infrared Spectroscopy (NIRS)
"Non-invasive method to identify the type of green tea inside teabag using NIR spectroscopy, support vector machines and Bayesian optimization" LINK
"Online milk composition analysis with an on-farm near-infrared sensor" LINK
"Anonymous fecal sampling and NIRS studies of diet quality: Problem or opportunity?" LINK
"Organic and Symbiotic Fertilization of Tomato Plants Monitored by Litterbag-NIRS and Foliar-NIRS Rapid Spectroscopic Methods Running title: Litterbag-NIRS and Foliar-NIRS model in symbiotic tomato" LINK
"Determination of crude protein and metabolized energy with near infrared reflectance spectroscopy (NIRS) in ruminant mixed feeds" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Near Infrared Spectroscopy as an efficient tool for the Qualitative and Quantitative Determination of Sugar Adulteration in Milk" | LINK
"NEAR INFRARED SPECTROSCOPY AS A NEW FIRE SEVERITY METRIC" by Bushfire and Natural Hazards CRC LINK
"Near-infrared spectroscopy for the concurrent quality prediction and status monitoring of gasoline blending" LINK
"Application of Selective Near Infrared Spectroscopy for Qualitative and Quantitative Prediction of Water Adulteration in Milk" LINK
"Predicting Macronutrient of Baby Food using Near-infrared Spectroscopy and Deep Learning Approach" LINK
"Detection of heat treatment of honey with near infrared spectroscopy" LINK
"Use of near infrared spectroscopy in cotton seeds physiological quality evaluation" LINK
"Detection of Haemonchus contortus nematode eggs in sheep faeces using near and mid-infrared spectroscopy" LINK
"Feasibility of using near-infrared measurements to detect changes in water quality" LINK
Hyperspectral Imaging (HSI)
"Hyperspectral waveband selection algorithm based on weighted maximum relevance minimum redundancy and its stability analysis" LINK
Chemometrics and Machine Learning
"Comparative quantification of chlorophyll and polyphenol levels in grapevine leaves sampled from different geographical locations" LINK
"Screening method for determination of C18:1 trans fatty acids positional isomers in chocolate by 1H NMR and chemometrics" LINK
"Combination of efficient signal pre-processing and optimal band combination algorithm to predict soil organic matter through visible and near-infrared spectra" LINK
"A chemometric approach to the evaluation of the ageing ability of red wines" LINK
"Determination of Loline Alkaloids and Mycelial Biomass in Endophyte-Infected Schedonorus Pratensis by Near-Infrared Spectroscopy and Chemometrics" LINK
"A Feasible Approach to Detect Pesticides in Food Samples Using THz-FDS and Chemometrics" LINK
"Prediction of Soluble Solids Content in Green Plum by Using a Sparse Autoencoder" LINK
Process Control and NIR Sensors
"Real-time and field monitoring of the key parameters in industrial trough composting process using a handheld near infrared spectrometer" LINK
Environment NIR-Spectroscopy Application
"Detection and analysis of soil water content based on experimental reflectance spectrum data" LINK
" International Soil and Water Conservation Research" | LINK
Agriculture NIR-Spectroscopy Usage
"Detecting Low Concentrations of Nitrogen-Based Adulterants in Whey Protein Powder Using Benchtop and Handheld NIR Spectrometers and the Feasibility of Scanning through Plastic Bag." LINK
"Assessment of the genetic diversity of sweetpotato germplasm collections for protein content" LINK
"Near-infrared spectroscopy and imaging in protein research" LINK
"Foods, Vol. 9, Pages 710: Application of ATR-FT-MIR for Tracing the Geographical Origin of Honey Produced in the Maltese Islands" LINK
"Agriculture, Vol. 10, Pages 193: Content of Polyphenolic Compounds and Antioxidant Potential of Some Bulgarian Red Grape Varieties and Red Wines, Determined by HPLC, UV, and NIR Spectroscopy" LINK
"Agronomy, Vol. 10, Pages 787: Assessing Soil Key Fertility Attributes Using a Portable X-Ray Fluorescence: A Simple Method to Overcome Matrix Effect" LINK
Food & Feed Industry NIR Usage
"Non‑destructive testing technology for raw eggs freshness: a review" LINK
"Quantification of multiple adulterants in beef protein powder by FT-NIR" LINK
Beverage and Drink Industry NIR Usage
"Beer Aroma and Quality Traits Assessment Using Artificial Intelligence" LINK
Other
"Tetrahedral Mn4+ as chromophore in sillenite-type compounds" LINK
NIR Calibration-Model Services
Spectroscopy and Chemometrics News Weekly 28, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK
This week's NIR news Weekly is sponsored by Your-Company-Name-Here - NIR-spectrometers. Check out their product page ... link
Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us.
Near-Infrared Spectroscopy (NIRS)
"Non-invasive method to identify the type of green tea inside teabag using NIR spectroscopy, support vector machines and Bayesian optimization" LINK
"Online milk composition analysis with an on-farm near-infrared sensor" LINK
"Anonymous fecal sampling and NIRS studies of diet quality: Problem or opportunity?" LINK
"Organic and Symbiotic Fertilization of Tomato Plants Monitored by Litterbag-NIRS and Foliar-NIRS Rapid Spectroscopic Methods Running title: Litterbag-NIRS and Foliar-NIRS model in symbiotic tomato" LINK
"Determination of crude protein and metabolized energy with near infrared reflectance spectroscopy (NIRS) in ruminant mixed feeds" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Near Infrared Spectroscopy as an efficient tool for the Qualitative and Quantitative Determination of Sugar Adulteration in Milk" | LINK
"NEAR INFRARED SPECTROSCOPY AS A NEW FIRE SEVERITY METRIC" by Bushfire and Natural Hazards CRC LINK
"Near-infrared spectroscopy for the concurrent quality prediction and status monitoring of gasoline blending" LINK
"Application of Selective Near Infrared Spectroscopy for Qualitative and Quantitative Prediction of Water Adulteration in Milk" LINK
"Predicting Macronutrient of Baby Food using Near-infrared Spectroscopy and Deep Learning Approach" LINK
"Detection of heat treatment of honey with near infrared spectroscopy" LINK
"Use of near infrared spectroscopy in cotton seeds physiological quality evaluation" LINK
"Detection of Haemonchus contortus nematode eggs in sheep faeces using near and mid-infrared spectroscopy" LINK
"Feasibility of using near-infrared measurements to detect changes in water quality" LINK
Hyperspectral Imaging (HSI)
"Hyperspectral waveband selection algorithm based on weighted maximum relevance minimum redundancy and its stability analysis" LINK
Chemometrics and Machine Learning
"Comparative quantification of chlorophyll and polyphenol levels in grapevine leaves sampled from different geographical locations" LINK
"Screening method for determination of C18:1 trans fatty acids positional isomers in chocolate by 1H NMR and chemometrics" LINK
"Combination of efficient signal pre-processing and optimal band combination algorithm to predict soil organic matter through visible and near-infrared spectra" LINK
"A chemometric approach to the evaluation of the ageing ability of red wines" LINK
"Determination of Loline Alkaloids and Mycelial Biomass in Endophyte-Infected Schedonorus Pratensis by Near-Infrared Spectroscopy and Chemometrics" LINK
"A Feasible Approach to Detect Pesticides in Food Samples Using THz-FDS and Chemometrics" LINK
"Prediction of Soluble Solids Content in Green Plum by Using a Sparse Autoencoder" LINK
Process Control and NIR Sensors
"Real-time and field monitoring of the key parameters in industrial trough composting process using a handheld near infrared spectrometer" LINK
Environment NIR-Spectroscopy Application
"Detection and analysis of soil water content based on experimental reflectance spectrum data" LINK
" International Soil and Water Conservation Research" | LINK
Agriculture NIR-Spectroscopy Usage
"Detecting Low Concentrations of Nitrogen-Based Adulterants in Whey Protein Powder Using Benchtop and Handheld NIR Spectrometers and the Feasibility of Scanning through Plastic Bag." LINK
"Assessment of the genetic diversity of sweetpotato germplasm collections for protein content" LINK
"Near-infrared spectroscopy and imaging in protein research" LINK
"Foods, Vol. 9, Pages 710: Application of ATR-FT-MIR for Tracing the Geographical Origin of Honey Produced in the Maltese Islands" LINK
"Agriculture, Vol. 10, Pages 193: Content of Polyphenolic Compounds and Antioxidant Potential of Some Bulgarian Red Grape Varieties and Red Wines, Determined by HPLC, UV, and NIR Spectroscopy" LINK
"Agronomy, Vol. 10, Pages 787: Assessing Soil Key Fertility Attributes Using a Portable X-Ray Fluorescence: A Simple Method to Overcome Matrix Effect" LINK
Food & Feed Industry NIR Usage
"Non‑destructive testing technology for raw eggs freshness: a review" LINK
"Quantification of multiple adulterants in beef protein powder by FT-NIR" LINK
Beverage and Drink Industry NIR Usage
"Beer Aroma and Quality Traits Assessment Using Artificial Intelligence" LINK
Other
"Tetrahedral Mn4+ as chromophore in sillenite-type compounds" LINK
Knowledge-Based Variable Selection and Model Selection for near infrared spectroscopy NIRS LINK
Stop wasting too much time for NIRS Chemometrics Method development | foodanalyticaltechnologies analytic qualitycontrol foodindustry beverageindustry materialsensing LINK
Spectroscopy and Chemometrics News Weekly 7, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK
"Determination of Glucose by NIR Spectroscopy Under Magnetic Field" LINK
"Sensors, Vol. 20, Pages 230: The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods when using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods" LINK
"Quantum mechanical modeling of NIR spectra of thymol" LINK
"Using a handheld near-infrared spectroscopy (NIRS) scanner to predict meat quality" LINK
"NIR spectroscopy in simulation–a new way for augmenting near-infrared phytoanalysis" LINK
"Using visible-near-infrared spectroscopy to classify lichens at a Neotropical Dry Forest" LINK
"Near infrared spectroscopy as a rapid method for detecting paprika powder adulteration with corn flour" LINK
"Application of deep learning and near infrared spectroscopy in cereal analysis" LINK
"Using near infrared spectroscopy to determine the scots pine place of growth" LINK
"Chagas disease vectors identification using visible and near-infrared spectroscopy" LINK
"Ensemble of extreme learning machines for multivariate calibration of near-infrared spectroscopy" LINK
"Quantification of Silymarin in Silybum marianum with near-infrared spectroscopy: a comparison of benchtop vs. handheld devices" LINK
"N-way partial least squares combined with new self-construction strategy—A promising approach of using near infrared spectral data for quantitative determination of …" LINK
"Identification of rice flour types with near-infrared spectroscopy associated with PLS-DA and SVM methods" LINK
" Multivariate Classification of Prunus Dulcis Varieties using Leaves of Nursery Plants and Near Infrared Spectroscopy" LINK
Hyperspectral
"Frost damage to maize in northeast India: assessment and estimated loss of yield by hyperspectral proximal remote sensing" LINK
"Identification of authenticity, quality and origin of saffron using hyperspectral imaging and multivariate spectral analysis" LINK
Chemometrics
"Early detection of chilling injury in green bell peppers by hyperspectral imaging and chemometrics" LINK
"Evaluating photosynthetic pigment contents of maize using UVE-PLS based on continuous wavelet transform" LINK
"Near-infrared spectroscopy coupled with chemometrics algorithms for the quantitative determination of the germinability of Clostridium perfringens in four different …" LINK
"Analysis of residual moisture in a freeze-dried sample drug using a multivariate fitting regression model" LINK
"Spectroscopy based novel spectral indices, PCA-and PLSR-coupled machine learning models for salinity stress phenotyping of rice" LINK
"Standardisation of near infrared hyperspectral imaging for quantification and classification of DON contaminated wheat samples" LINK
"Vibrational spectroscopy and chemometric data analysis: the principle components of rapid quality control of herbal medicines" LINK
"A Model for Yellow Tea Polyphenols Content Estimation Based on Multi-Feature Fusion" LINK
Process Control
"Review of near-infrared spectroscopy as a process analytical technology for real-time product monitoring in dairy processing" LINK
Environment
"POTENTIAL OF SENSOR-BASED SORTING IN ENHANCED LANDFILL MINING" LINK
"Characterization of the salt marsh soils and visible-near-infrared spectroscopy along a chronosequence of Spartina alterniflora invasion in a coastal wetland of …" LINK
Agriculture
"Remote Sensing, Vol. 12, Pages 126: Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy" LINK
"Novel implementation of laser ablation tomography as an alternative technique to assess grain quality and internal insect development in stored products" LINK
"Comparative Study of Two Different Strategies for Determination of Soluble Solids Content of Apples From Multiple Geographical Regions by Using FT-NIR Spectroscopy" LINK
"Laboratory Raman and VNIR spectroscopic studies of jarosite and other secondary mineral mixtures relevant to Mars" LINK
Other
"Combining analytical tools to identify adulteration: some practical examples" LINK
"... questioned whether the growth and sustainability of AI technology will lead to the need for two copyright systems — one to address human creation and one to address machine creation." LINK
CalibrationModel.com
Knowledge-Based Variable Selection and Model Selection for near infrared spectroscopy NIRS LINK
Stop wasting too much time for NIRS Chemometrics Method development | foodanalyticaltechnologies analytic qualitycontrol foodindustry beverageindustry materialsensing LINK
Spectroscopy and Chemometrics News Weekly 7, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK
"Determination of Glucose by NIR Spectroscopy Under Magnetic Field" LINK
"Sensors, Vol. 20, Pages 230: The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods when using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods" LINK
"Quantum mechanical modeling of NIR spectra of thymol" LINK
"Using a handheld near-infrared spectroscopy (NIRS) scanner to predict meat quality" LINK
"NIR spectroscopy in simulation–a new way for augmenting near-infrared phytoanalysis" LINK
"Using visible-near-infrared spectroscopy to classify lichens at a Neotropical Dry Forest" LINK
"Near infrared spectroscopy as a rapid method for detecting paprika powder adulteration with corn flour" LINK
"Application of deep learning and near infrared spectroscopy in cereal analysis" LINK
"Using near infrared spectroscopy to determine the scots pine place of growth" LINK
"Chagas disease vectors identification using visible and near-infrared spectroscopy" LINK
"Ensemble of extreme learning machines for multivariate calibration of near-infrared spectroscopy" LINK
"Quantification of Silymarin in Silybum marianum with near-infrared spectroscopy: a comparison of benchtop vs. handheld devices" LINK
"N-way partial least squares combined with new self-construction strategy—A promising approach of using near infrared spectral data for quantitative determination of …" LINK
"Identification of rice flour types with near-infrared spectroscopy associated with PLS-DA and SVM methods" LINK
" Multivariate Classification of Prunus Dulcis Varieties using Leaves of Nursery Plants and Near Infrared Spectroscopy" LINK
Hyperspectral
"Frost damage to maize in northeast India: assessment and estimated loss of yield by hyperspectral proximal remote sensing" LINK
"Identification of authenticity, quality and origin of saffron using hyperspectral imaging and multivariate spectral analysis" LINK
Chemometrics
"Early detection of chilling injury in green bell peppers by hyperspectral imaging and chemometrics" LINK
"Evaluating photosynthetic pigment contents of maize using UVE-PLS based on continuous wavelet transform" LINK
"Near-infrared spectroscopy coupled with chemometrics algorithms for the quantitative determination of the germinability of Clostridium perfringens in four different …" LINK
"Analysis of residual moisture in a freeze-dried sample drug using a multivariate fitting regression model" LINK
"Spectroscopy based novel spectral indices, PCA-and PLSR-coupled machine learning models for salinity stress phenotyping of rice" LINK
"Standardisation of near infrared hyperspectral imaging for quantification and classification of DON contaminated wheat samples" LINK
"Vibrational spectroscopy and chemometric data analysis: the principle components of rapid quality control of herbal medicines" LINK
"A Model for Yellow Tea Polyphenols Content Estimation Based on Multi-Feature Fusion" LINK
Process Control
"Review of near-infrared spectroscopy as a process analytical technology for real-time product monitoring in dairy processing" LINK
Environment
"POTENTIAL OF SENSOR-BASED SORTING IN ENHANCED LANDFILL MINING" LINK
"Characterization of the salt marsh soils and visible-near-infrared spectroscopy along a chronosequence of Spartina alterniflora invasion in a coastal wetland of …" LINK
Agriculture
"Remote Sensing, Vol. 12, Pages 126: Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy" LINK
"Novel implementation of laser ablation tomography as an alternative technique to assess grain quality and internal insect development in stored products" LINK
"Comparative Study of Two Different Strategies for Determination of Soluble Solids Content of Apples From Multiple Geographical Regions by Using FT-NIR Spectroscopy" LINK
"Laboratory Raman and VNIR spectroscopic studies of jarosite and other secondary mineral mixtures relevant to Mars" LINK
Other
"Combining analytical tools to identify adulteration: some practical examples" LINK
"... questioned whether the growth and sustainability of AI technology will lead to the need for two copyright systems — one to address human creation and one to address machine creation." LINK
CalibrationModel.com
Knowledge-Based Variable Selection and Model Selection for near infrared spectroscopy NIRS LINK
Stop wasting too much time for NIRS Chemometrics Method development | foodanalyticaltechnologies analytic qualitycontrol foodindustry beverageindustry materialsensing LINK
Spectroscopy and Chemometrics News Weekly 7, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK
"Determination of Glucose by NIR Spectroscopy Under Magnetic Field" LINK
"Sensors, Vol. 20, Pages 230: The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods when using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods" LINK
"Quantum mechanical modeling of NIR spectra of thymol" LINK
"Using a handheld near-infrared spectroscopy (NIRS) scanner to predict meat quality" LINK
"NIR spectroscopy in simulation–a new way for augmenting near-infrared phytoanalysis" LINK
"Using visible-near-infrared spectroscopy to classify lichens at a Neotropical Dry Forest" LINK
"Near infrared spectroscopy as a rapid method for detecting paprika powder adulteration with corn flour" LINK
"Application of deep learning and near infrared spectroscopy in cereal analysis" LINK
"Using near infrared spectroscopy to determine the scots pine place of growth" LINK
"Chagas disease vectors identification using visible and near-infrared spectroscopy" LINK
"Ensemble of extreme learning machines for multivariate calibration of near-infrared spectroscopy" LINK
"Quantification of Silymarin in Silybum marianum with near-infrared spectroscopy: a comparison of benchtop vs. handheld devices" LINK
"N-way partial least squares combined with new self-construction strategy—A promising approach of using near infrared spectral data for quantitative determination of …" LINK
"Identification of rice flour types with near-infrared spectroscopy associated with PLS-DA and SVM methods" LINK
" Multivariate Classification of Prunus Dulcis Varieties using Leaves of Nursery Plants and Near Infrared Spectroscopy" LINK
Hyperspectral
"Frost damage to maize in northeast India: assessment and estimated loss of yield by hyperspectral proximal remote sensing" LINK
"Identification of authenticity, quality and origin of saffron using hyperspectral imaging and multivariate spectral analysis" LINK
Chemometrics
"Early detection of chilling injury in green bell peppers by hyperspectral imaging and chemometrics" LINK
"Evaluating photosynthetic pigment contents of maize using UVE-PLS based on continuous wavelet transform" LINK
"Near-infrared spectroscopy coupled with chemometrics algorithms for the quantitative determination of the germinability of Clostridium perfringens in four different …" LINK
"Analysis of residual moisture in a freeze-dried sample drug using a multivariate fitting regression model" LINK
"Spectroscopy based novel spectral indices, PCA-and PLSR-coupled machine learning models for salinity stress phenotyping of rice" LINK
"Standardisation of near infrared hyperspectral imaging for quantification and classification of DON contaminated wheat samples" LINK
"Vibrational spectroscopy and chemometric data analysis: the principle components of rapid quality control of herbal medicines" LINK
"A Model for Yellow Tea Polyphenols Content Estimation Based on Multi-Feature Fusion" LINK
Process Control
"Review of near-infrared spectroscopy as a process analytical technology for real-time product monitoring in dairy processing" LINK
Environment
"POTENTIAL OF SENSOR-BASED SORTING IN ENHANCED LANDFILL MINING" LINK
"Characterization of the salt marsh soils and visible-near-infrared spectroscopy along a chronosequence of Spartina alterniflora invasion in a coastal wetland of …" LINK
Agriculture
"Remote Sensing, Vol. 12, Pages 126: Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy" LINK
"Novel implementation of laser ablation tomography as an alternative technique to assess grain quality and internal insect development in stored products" LINK
"Comparative Study of Two Different Strategies for Determination of Soluble Solids Content of Apples From Multiple Geographical Regions by Using FT-NIR Spectroscopy" LINK
"Laboratory Raman and VNIR spectroscopic studies of jarosite and other secondary mineral mixtures relevant to Mars" LINK
Other
"Combining analytical tools to identify adulteration: some practical examples" LINK
"... questioned whether the growth and sustainability of AI technology will lead to the need for two copyright systems — one to address human creation and one to address machine creation." LINK
"Reduction of repeatability error for Analysis of variance-Simultaneous Component Analysis (REP-ASCA): Application to NIR spectroscopy on coffee sample" LINK
"Permafrost soil complexity evaluated by laboratory imaging Vis–NIR spectroscopy" LINK
"Total nitrogen in rice paddy field independently predicted from soil carbon using Near Infrared Reflectance Spectroscopy (NIRS)" LINK
" Visible-near Infrared (VIS-NIR) Spectroscopy as a Rapid Measurement Tool to Assess the Effect of Tillage on Oil Contaminated Sites" LINK
"Detection of aflatoxin B1 on corn kernel surfaces using visible-near infrared spectra" LINK
"The model updating based on near infrared spectroscopy for the sex identification of silkworm pupae from different varieties by a semi-supervised learning with pre-labeling method" LINK
"Quantification of Carbon Nanotube Doses in Adherent Cell Culture Assays Using UV-VIS-NIR Spectroscopy." LINK
"A new application of NIR spectroscopy to describe and predict purees quality from the non-destructive apple measurements." LINK
"Analysis of temperature influence on physical properties of aqueous extracts of winter savoury (Satureja montana L.) with UV-VIS and NIR spectroscopy" LINK
"Mixed Fuzzy Maximum Entropy Clustering Analysis of FT-NIR Spectra of Tea" LINK
"Determination of Chinese Honey Adulterated with Syrups by Near Infrared Spectroscopy Combined with Chemometrics" LINK
"Surface Analysis of Various Oxide Materials by using NIR Spectroscopy—Is Silica Surface Really Hydrophilic?—" LINK
"Grape Seeds: Chromatographic Profile of Fatty Acids and Phenolic Compounds and Qualitative Analysis by FTIR-ATR Spectroscopy" Foods LINK
"Continuous statistical modelling in characterisation of complex hydrocolloid mixtures using near infrared spectroscopy" LINK
"Probing Sucrose Contents in Everyday Drinks Using Miniaturized Near-Infrared Spectroscopy Scanners" LINK
" RAPID DETECTION OF FORMALIN IN MILK BY FOURIER-TRANSFORM NEAR-INFRARED SPECTROSCOPY" LINK
"Determination of sodium alginate in algae by near-infrared spectroscopy" LINK
"Near-infrared spectroscopy and hidden graphics applied in printing security documents in the offset technique" LINK
"Differentiation between normal and white striped turkey breasts by visible/near infrared spectroscopy and multivariate data analysis" LINK
"Rapid Determination of Holocellulose and Lignin in Wood by Near Infrared Spectroscopy and Kernel Extreme Learning Machine" LINK
Hyperspectral Imaging
"Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery" LINK
Chemometrics / Machine Learning
"Data Preprocessing and Homogeneity: The Influence on Robustness and Modeling by PLS Via NIR of Fish Burgers" LINK
"Identification of Rice Varieties and Transgenic Characteristics Based on Near-Infrared Diffuse Reflectance Spectroscopy and Chemometrics" LINK
"Rapid detection model of Bacillus subtilis in solidstate fermentation of rapeseed meal" LINK
" PREDICTION OF SOIL PROPERTIES WITH SPECTRORADIOMETRIC MEASUREMENTS" LINK
Optics
"The Jacopo Tintoretto “Wedding Feast at Cana”: A non-invasive and multi-technique analytical approach for studying painting materials" LINK
Environment
"The effect of soil moisture on the accuracy of the spectroscopy method in estimating the amount of soil organic matter" LINK
Agriculture
"Determination of The Effect of Technological Procedures Applied in Feed Factories on Mixed Feed Nutrition and Forming Quality Critical Points" LINK
" HARVEST TIMING DETERMINATION IN GRASS SEED CROPS BY PORTABLE NIR SPECTROSCOPY" LINK
"Spectroscopic diagnosis of zinc contaminated soils based on competitive adaptive reweighted sampling algorithm and an improved support vector machine" LINK
" High-protein rice in high-yielding background, cv. Naveen" LINK
Food & Feed
"Development and implementation of novel sensory evaluation procedures of consumer acceptability towards chocolate based on emotions and biometric responses" LINK
CalibrationModel.com
Spectroscopy and Chemometrics News Weekly 5, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK
"Reduction of repeatability error for Analysis of variance-Simultaneous Component Analysis (REP-ASCA): Application to NIR spectroscopy on coffee sample" LINK
"Permafrost soil complexity evaluated by laboratory imaging Vis–NIR spectroscopy" LINK
"Total nitrogen in rice paddy field independently predicted from soil carbon using Near Infrared Reflectance Spectroscopy (NIRS)" LINK
" Visible-near Infrared (VIS-NIR) Spectroscopy as a Rapid Measurement Tool to Assess the Effect of Tillage on Oil Contaminated Sites" LINK
"Detection of aflatoxin B1 on corn kernel surfaces using visible-near infrared spectra" LINK
"The model updating based on near infrared spectroscopy for the sex identification of silkworm pupae from different varieties by a semi-supervised learning with pre-labeling method" LINK
"Quantification of Carbon Nanotube Doses in Adherent Cell Culture Assays Using UV-VIS-NIR Spectroscopy." LINK
"A new application of NIR spectroscopy to describe and predict purees quality from the non-destructive apple measurements." LINK
"Analysis of temperature influence on physical properties of aqueous extracts of winter savoury (Satureja montana L.) with UV-VIS and NIR spectroscopy" LINK
"Mixed Fuzzy Maximum Entropy Clustering Analysis of FT-NIR Spectra of Tea" LINK
"Determination of Chinese Honey Adulterated with Syrups by Near Infrared Spectroscopy Combined with Chemometrics" LINK
"Surface Analysis of Various Oxide Materials by using NIR Spectroscopy—Is Silica Surface Really Hydrophilic?—" LINK
"Grape Seeds: Chromatographic Profile of Fatty Acids and Phenolic Compounds and Qualitative Analysis by FTIR-ATR Spectroscopy" Foods LINK
"Continuous statistical modelling in characterisation of complex hydrocolloid mixtures using near infrared spectroscopy" LINK
"Probing Sucrose Contents in Everyday Drinks Using Miniaturized Near-Infrared Spectroscopy Scanners" LINK
" RAPID DETECTION OF FORMALIN IN MILK BY FOURIER-TRANSFORM NEAR-INFRARED SPECTROSCOPY" LINK
"Determination of sodium alginate in algae by near-infrared spectroscopy" LINK
"Near-infrared spectroscopy and hidden graphics applied in printing security documents in the offset technique" LINK
"Differentiation between normal and white striped turkey breasts by visible/near infrared spectroscopy and multivariate data analysis" LINK
"Rapid Determination of Holocellulose and Lignin in Wood by Near Infrared Spectroscopy and Kernel Extreme Learning Machine" LINK
Hyperspectral Imaging
"Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery" LINK
Chemometrics / Machine Learning
"Data Preprocessing and Homogeneity: The Influence on Robustness and Modeling by PLS Via NIR of Fish Burgers" LINK
"Identification of Rice Varieties and Transgenic Characteristics Based on Near-Infrared Diffuse Reflectance Spectroscopy and Chemometrics" LINK
"Rapid detection model of Bacillus subtilis in solidstate fermentation of rapeseed meal" LINK
" PREDICTION OF SOIL PROPERTIES WITH SPECTRORADIOMETRIC MEASUREMENTS" LINK
Optics
"The Jacopo Tintoretto “Wedding Feast at Cana”: A non-invasive and multi-technique analytical approach for studying painting materials" LINK
Environment
"The effect of soil moisture on the accuracy of the spectroscopy method in estimating the amount of soil organic matter" LINK
Agriculture
"Determination of The Effect of Technological Procedures Applied in Feed Factories on Mixed Feed Nutrition and Forming Quality Critical Points" LINK
" HARVEST TIMING DETERMINATION IN GRASS SEED CROPS BY PORTABLE NIR SPECTROSCOPY" LINK
"Spectroscopic diagnosis of zinc contaminated soils based on competitive adaptive reweighted sampling algorithm and an improved support vector machine" LINK
" High-protein rice in high-yielding background, cv. Naveen" LINK
Food & Feed
"Development and implementation of novel sensory evaluation procedures of consumer acceptability towards chocolate based on emotions and biometric responses" LINK
CalibrationModel.com
Spectroscopy and Chemometrics News Weekly 5, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK
"Reduction of repeatability error for Analysis of variance-Simultaneous Component Analysis (REP-ASCA): Application to NIR spectroscopy on coffee sample" LINK
"Permafrost soil complexity evaluated by laboratory imaging Vis–NIR spectroscopy" LINK
"Total nitrogen in rice paddy field independently predicted from soil carbon using Near Infrared Reflectance Spectroscopy (NIRS)" LINK
" Visible-near Infrared (VIS-NIR) Spectroscopy as a Rapid Measurement Tool to Assess the Effect of Tillage on Oil Contaminated Sites" LINK
"Detection of aflatoxin B1 on corn kernel surfaces using visible-near infrared spectra" LINK
"The model updating based on near infrared spectroscopy for the sex identification of silkworm pupae from different varieties by a semi-supervised learning with pre-labeling method" LINK
"Quantification of Carbon Nanotube Doses in Adherent Cell Culture Assays Using UV-VIS-NIR Spectroscopy." LINK
"A new application of NIR spectroscopy to describe and predict purees quality from the non-destructive apple measurements." LINK
"Analysis of temperature influence on physical properties of aqueous extracts of winter savoury (Satureja montana L.) with UV-VIS and NIR spectroscopy" LINK
"Mixed Fuzzy Maximum Entropy Clustering Analysis of FT-NIR Spectra of Tea" LINK
"Determination of Chinese Honey Adulterated with Syrups by Near Infrared Spectroscopy Combined with Chemometrics" LINK
"Surface Analysis of Various Oxide Materials by using NIR Spectroscopy—Is Silica Surface Really Hydrophilic?—" LINK
"Grape Seeds: Chromatographic Profile of Fatty Acids and Phenolic Compounds and Qualitative Analysis by FTIR-ATR Spectroscopy" Foods LINK
"Continuous statistical modelling in characterisation of complex hydrocolloid mixtures using near infrared spectroscopy" LINK
"Probing Sucrose Contents in Everyday Drinks Using Miniaturized Near-Infrared Spectroscopy Scanners" LINK
" RAPID DETECTION OF FORMALIN IN MILK BY FOURIER-TRANSFORM NEAR-INFRARED SPECTROSCOPY" LINK
"Determination of sodium alginate in algae by near-infrared spectroscopy" LINK
"Near-infrared spectroscopy and hidden graphics applied in printing security documents in the offset technique" LINK
"Differentiation between normal and white striped turkey breasts by visible/near infrared spectroscopy and multivariate data analysis" LINK
"Rapid Determination of Holocellulose and Lignin in Wood by Near Infrared Spectroscopy and Kernel Extreme Learning Machine" LINK
Hyperspectral Imaging
"Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery" LINK
Chemometrics / Machine Learning
"Data Preprocessing and Homogeneity: The Influence on Robustness and Modeling by PLS Via NIR of Fish Burgers" LINK
"Identification of Rice Varieties and Transgenic Characteristics Based on Near-Infrared Diffuse Reflectance Spectroscopy and Chemometrics" LINK
"Rapid detection model of Bacillus subtilis in solidstate fermentation of rapeseed meal" LINK
" PREDICTION OF SOIL PROPERTIES WITH SPECTRORADIOMETRIC MEASUREMENTS" LINK
Optics
"The Jacopo Tintoretto “Wedding Feast at Cana”: A non-invasive and multi-technique analytical approach for studying painting materials" LINK
Environment
"The effect of soil moisture on the accuracy of the spectroscopy method in estimating the amount of soil organic matter" LINK
Agriculture
"Determination of The Effect of Technological Procedures Applied in Feed Factories on Mixed Feed Nutrition and Forming Quality Critical Points" LINK
" HARVEST TIMING DETERMINATION IN GRASS SEED CROPS BY PORTABLE NIR SPECTROSCOPY" LINK
"Spectroscopic diagnosis of zinc contaminated soils based on competitive adaptive reweighted sampling algorithm and an improved support vector machine" LINK
" High-protein rice in high-yielding background, cv. Naveen" LINK
Food & Feed
"Development and implementation of novel sensory evaluation procedures of consumer acceptability towards chocolate based on emotions and biometric responses" LINK
It’s easy to use with NIR-Predictor,
just drag & drop your data for getting the prediction results.
It supports an automatic file format detection.
So you don’t need to specify the instrument type and settings! See the list of supported formats and NIR Vendors: NIR-Predictor supported Spectral Data File Formats
Use the included data to checkout how it feels:
Open the demo Spectra folder by using the Menu > Open Demo Spectra or press F8.
There are files with spectra from different Vendors.
Drag & drop a spectra file onto the NIR-Predictor window (or press Ctrl+O as for ’Open some files).
The spectra will be
loaded
pre-processed
predicted and
reported
Note:
All the steps are fully automatic.
All calibrations that are compatible with the spectra, will produce prediction results in one go.
To select specific calibrations choose the Application. Where the " " empty means use all the calibrations.
To define a Application read more in chapter “Applications”
Hint:
To get access to Statistics of Predictions and Reports use the Menu > Show more/less (Ctrl+M) or you can simply resize the window. Here you can also re-do the Analyze step manually with changed inputs (e.g. Result Ordering).
Creating your own Calibrations
How it works - step by step
You have measured your samples with you NIR-Instrument Software.
And got the Lab-values of these samples.
Note: If you combined these data already in your NIR software used,
and you can export it as a JCAMP-DX file then use
Menu > Create Request File .req ... (F2)
and read the “Help.html” and NIR-Predictor JCAMP.
Else proceed as below.
The NIR-Predictor provides tooling for that:
Menu > Create Properties File... (F6)
Select the folder with your NIR spectra measured for an application.
NIR-Predictor creates a customized Properties file template for that data to enter the Lab values.
Note: You don’t need to specify your instrument or vendor or an application. It’s all done automatically. And also the sample spectra are detected and grouped automatically!
Use your favorite editor or spreadsheet program to enter and copy&paste
the Lab-references Values into the columns “Prop1”, “Prop2” etc. and save the file.
A final check of your entered data is done by NIR-Predictor,
to make sure your data ist complete and all is fine.
Menu > Create Calibration Request... (F7)
Select the folder with the filled file.
A CalibrationRequest.zip is created with the necessary data
if enougth diverse Lab values are entered.
Email the CalibrationRequest.zip file
to info@CalibrationModel.com to develop the calibrations.
When your calibrations are ready, you will receive an email with a link
to the CalibrationModel WebShop where
you can purchase and download the calibration files,
that work with our free NIR-Predictor software without internet access.
Note: Your sent NIR data is deleted after processing.
We do not collect your NIR data!
Note: Further details can be found under “Create Properties File” and “Create Calibration Request”.
Configure the Calibrations for prediction usage
Configuration:
in NIR-Predictor : Menu > Open Calibrations (F9)
an explorer window is opened where the calibrations are located
create a folder for your application, choose a name
copy the calibration file(s) (*.cm) into that folder
in NIR-Predictor : Menu > Search and load Applications (F4)
Usage:
in NIR-Predictor : open the Application drop down list, and select your application by name
if all is fine, the calibration file is valid and not expired, it shows : Calibration “1 valid calibation”
the NIR-Predictor is now ready to predict
to switch the application, goto 6.
Applications
The Application concept allows to group multiple Calibrations together for an Application. By selecting an Application before prediction, only the Calibrations belonging to the Application will be used for Prediction. In the Demo Data this is used to have multiple spectrometer as Application. This can be used easily as e.g. as Application “Meat Products” containing Fat and Moisture Calibration.
To create an Application, create a folder with the Application’s name inside the Calibrations folder, and move/copy all the Calibrations files to this Application folder. To remove a Calibration from the Application, remove the Calibration file from the Application folder.
After creating an new Application folder, press menu Search and load Applications (F4) to update the NIR-Predictor dialog where the Application can be selected via the dropdown list. You don’t need to close the NIR-Predictor.
After moving Calibration files around, press menu Search and load Calibrations (F5) to update the NIR-Predictor dialog.
The use-all case
In the NIR-Predictor dialog where the Application can be selected via the dropdown list, the empty "" name means that all (yes all) valid Calibrations will be used for prediction.
Note: The Prediction Report will contain only results from spectral compatible Calibrations with the given spectra. That allows to automatically handle the multi vendor NIR instrument usage.
Prediction Result Report
Histograms of Prediction Values per Property
Shows the distribution of the predicted results per calibration. The histogram range contains the range of the calibrated property and includes the predicted results.
The histogram bar (bin) color is defined as follow:
blue : all predictions inside calibration range.
red : all predictions outside calibration range.
orange : some overlaps with calibration range.
So not all spectra in a orange bin are outside calibration range.
Histograms
Note: Predicted values are always shown in Histogram table and Prediction Value List table, even if the spectrum does not fit into model (spectrum different to model, aka Residual Outlier) shown as Out = X.
Note: Old browsers like Microsoft Internet Explorer 11 don’t support the grafics for Histogram charts. Use an current browser like Firefox or Chrome or Edge.
Note: If your browser opens the report too slow, try to deactivate some browser plugins, because they can filter what you look at and some add-ons are really slow.
Spectra Plot Thumbnail on the Prediction Report
Visualizes the min,median,max spectrum of the spectra dropped as files on the NIR-Predictor. This gives a minimal and good spectral overview of the predicted property results.
Spectra Plot color legend: min,median,max spectrum by predicted property or if no calibration is available by spectral intensity.
The min,median,max is determined from the predicted properties or if not available from the intensity of the spectra.
Beside the histogram of the predicted properties, where the distribution can be seen, the spectra shown are the ones from min,median,max predicted property.
This gives a minimal and good spectral overview of the predicted property results.
The “Spectral Range” and number of datapoints is shown in the Prediction Report Header below the listed spectra files.
To zoom the spectra plot a little, zoom the report in the browser (hold ctrl + mouse wheel, or pinch on touch screen).
The spectra plots and histograms are stored with the report and can be archived.
Note
Note that the spectra are shown in the raw values that are loaded, they are not shown pre-processed as the calibration model uses them to make the prediction.
Note that the median property spectrum is the median from the predicted property pobulation and not the “median” of the calibration property range.
Note that in the multi calibration prediction case, the spectra are selected for each property based on the related predicted property values and so the spectra plots shows typical different spectra.
Spectra Plot
Outlier Detection
To safeguard the prediction results, outliers are automatically checked for each individual prediction. This is based on limits that are determined when creating the calibration with the base data. Thus, a strange spectral measurement can be detected and signaled as an outlier even without base data only by means of the calibration and the NIR predictor. A prediction result with outlier warning is to be distrusted. How the various outlier tests are interpreted and how to avoid them in practice is described here.
The spectrum is an outlier to the model, if the spectrum is not similar with the spectra and lab-values the model is built with.
This legend is shown on each NIR-Predictor prediction report below the results:
Outlier (Out) Symbol Description
“X” : spectrum does not fit into model (spectrum different to model)
“O” : spectrum is wide outside model center (spectrum similar to model but far away)
“=” : prediction is outside upper or lower range of model (property outside model range)
“-” : spectrum is incompatible to calibration
Note: A prediction result with outlier warning is to be distrusted.
There are 3 outlier cases (X, O, =) and the incompatible data case “-”.
The bad case is “X”
the medium case is “O”
and the soft case is “=”.
The technical names in literature correspond to:
“X” : Spectral Residual Outlier
“O” : Leverage Outlier
“=” : Property Range Outlier
These 3 outlier cases can appear in combinations, like “XO=” or “XO” or “O=” or “X=”. The more outlier marker are shown the more likely the spectrum is an Outlier.
The default setting in NIR-Predictor Menu > “Report with Simplified Outlier Symbols”
is ON, that will show only the worst case instead of all combinations to have a simplified minimal information.
if OFF, that will show the combinations (e.g. “XO=” or “XO” or “O=” or “X=”), which is more informative for analyzing problem cases.
Some hints to avoid these Outliers:
“X” : spectrum does not fit into model (spectrum different to model)
Check if the spectrum is noise only, or has no proper signal. That can happen when measured past the sample or measured into the air or at a different substance. If you have multiple NIR instruments of the same type, use spectra measured with different instruments for the calibration.
“O” : spectrum is wide outside model center (spectrum similar to model but far away) Sample temperature has an effect on NIR spectra shape, use spectra measured at different (typical use) temperatures (sample temperature, instrument temperature).
“=” : prediction is outside upper or lower range of model (property outside model range)
Use more spectra for the calibration in the Lab value region where your special interest is. If the predicted value is only a little bit out of the calibration range, it can be Ok. Add these spectra to the calibration spectra (with the Lab values), to extend the prediction range of the calibration.
“-” : spectrum is incompatible to calibration
The spectra (from the NIR instrument) has a different wavelength range or a different resolution than the spectra used for calibration. Check Instrument settings (wavelength range, resolution)
Result Ordering
To change the ordering, a drop-down-box is located below the Analyze button. If there is an analysis from the current session, and the Result Ordering is changed, the data is re-Analyzed and reported with the new Result Ordering setting. That allows to compare the different orderings. The Result Ordering is listed in the Prediction Report above the Prediction Value List and stored in the settings.
The order/sorting of the prediction results of the spectra can be defined:
GivenOrder (default) the given order of the spectra from file select dialog or drag&drop
*) sorted : ascending sort
Date_Name sorted by Date (if any) and then by Name
Name_Date sorted by Name and then by Date
Date_NamesWithNumbers sorted by Date (if any) and then by Name with number logic
NamesWithNumbers_Date sorted by Name with number logic (e.g. “ABC1” is before “ABC002” ) and then by Date
*) as above but sorted Rev : reverse sort = descending sort
Rev_Date_Name
Rev_Name_Date
Rev_Date_NamesWithNumbers
Rev_NamesWithNumbers_Date
E.g. with reverse sort by Rev_Date_Name, the newest spectra appear on top.
Print to PDF
Depending on how many calibrations are used the result table is getting broader. To print the report (e.g. to Adobe PDF, FreePDF or Microsoft XPS), sometimes the landscape format is shorter in number of pages or in portrait a scale of 80% fits nicely. Or try another internet browser (Mozilla Firefox, Google Chrome, Microsoft Edge, …) to print the report and set the browser as your default browser so it will be opened by default.
Archiving Reports
Each report is contained in one file only, including the grafics. To save storage space the report file folder can be compressed to a zip file (.zip, .7z).
Enter lab values to NIR spectra
Entering the laboratory reference values for NIR calibrations
We have developed specialized tools into NIR-Predictor to combine the NIR and Lab data is a sample-based safe manner.
The main target is to improve Data Quality during the step of combining of the Lab data and the NIR data, because to model a good reliable calibration the data that build the base needs to be of high quality.
It also simplifies to enter the lab values manually to the corresponding NIR data, because of automatically grouping repeated NIR measurements of the same sample, so the lab values can be entered sample based and not by spectrum.
It helps to avoid false reference data, because of the broken relation of NIR spectra and reference values, data entry on the wrong position in the table.
And Helps to detect errors of duplicated or multiple copies of spectra files, and checks for inconsistencies in Date-Time and Sample-Naming. It also checks for missing values.
That all increases the Data Quality for the next step of Calibration Development, and makes data entry a less time consuming and less risky work.
How it works
Menu > Create Properties File... (F6) select the folder with your NIR spectra measured for an application. NIR-Predictor creates a Properties file template for that data : PropertiesBySamples.csv.txt
Use your favorite editor or spreadsheet program to enter and copy&paste the Lab Values into the columns and save the file.
Menu > Create Calibration Request... (F7) select the folder with the filled file for a last check and a Calibration Request file is created with the needed data as a single zip file.
Email the Calibration Request file to info@CalibrationModel.com to develop the calibrations.
Ok that is it, the NIR-Predictor guides you through the steps needed. And if you need to know more details, the Chapter “Create Properties File” is for you.
Create Properties File
Note:
If you have (exported) JCAMP-DX files containing the Lab-Values, you don’t need to do this step.
You can send the JCAMP file with your Request (.req) file directly to the calibration service at info@CalibrationModel.com.
If your JCAMP-DX files does NOT contain Lab-Values, this is a way to go.
For calibrating the spectra to the lab-values you need to assign the lab-values to the spectra. The easiest way is to have a table where each spectrum (row) is linked to multiple lab-values (columns). This function Create Properties File build such a table for the selected spectra folder automatically!
This table is stored in the file PropertiesBySamples.csv.txt. This can be created for any spectra folder you like. The file extension is .csv.txt to make it easy to edit in a text editor and also in a spreadsheet (excel). The columns are standard TAB separated.
Prop1, Prop2, Prop3 are the place to enter the Lab Reference Concentrations properties corresponding to each spectrum. It can be extended to Prop4, Prop5, … etc. Of course you can enter real word names like “Fat (%)” instead of “Prop1”. It’s recommended to put the measurement unit beside the name.
Replicates is the number of replicated or repeated spectra of a sample that is grouped together in the Sample based property file. Sample name and the DateFirst / DateLast between the sample spectra are measured.
Date format is ISO-8601. Missing Dates are 0002-02-02T00:00:00.0000000.
If the file PropertiesBySamples.csv.txt already exist in the selected folder, the user will be notified (it will not be overwritten, because the file may contain user entered Lab-values). The Lab Reference Concentrations values are initialized to 0 (zero) and needed to be changed.
Note: 0 is not interpreted as missing value! If you have a 0 concentration value, put in 0 or 0.0 .
The entry of properties is as easy as possible, because it’s organized by Sample (and not by Spectra), so it’s like your Lab-Value Table that is sample based. The sample rows are sorted in a special way by Sample name. Sorting by Date or alphabetically by Sample can done easily in a spreadsheet program.
Note: when coping lab values to the samples make sure they correspond, so that there are no gaps and the sorting is the same.
The Spectra (rows) are initially sorted by name (and date) to have the replicates/repeats together. You can sort for your convenience in a spreadsheet program.
Enter the Lab Reference Concentrations to the spectra/sample.
Enter the Lab-Values in spreadsheet (e.g. Excel) or a text editor (e.g. Notepad++). If done, use the next menu Create Calibration Request.
Hints: Data handling:
The NIR-Predictor creates the PropertiesBySamples.csv.txt once, after that the user is responsible for its content. That means NIR-Predictor does not change this file anymore.
You can remove entire rows (spectra) in the property file. You don’t need to remove the spectra files. The NIR-Predictor is aware of the relation, the PropertiesBySamples.csv.txt defines what will be calibrated.
How to add more spectra files?
The additional spectra can be handled in a separate folder, create the property file and copy the spectra to the other folder and copy/merge the property files together in your editor or spreadsheet.
Or
Copy the spectra into the folder, rename the PropertiesBySamples.csv.txt to e.g. “PropertiesBySamples-Part1.csv.txt” and use Create Properties File to create a new PropertiesBySamples.csv.txt with all the spectra. You can copy/merge the content of the Properties files together in your editor or spreadsheet.
What happens with possible duplicate rows? It does no harm to the Calibration because we do an exact checking and data cleaning in the calibration process.
What happens to duplicate spectra names? The spectra names are not relevant for the calibration process. The spectra names are helpful to assign the lab-values to the corresponding spectrum entry. That’s why the table is initially sorted by name. The spectra names can be edited by the user.
Create Calibration Request
The menu function Create Calibration Request packs a created Properties file (see “Create Properties File”) and it’s linked spectra files in a compressed ZIP file for sending to the CalibrationModel.com Service.
Please note that the number of measured quantitative samples need to be at least 60 . That means you need at least 60 different spectra (not counting the replicate/repeated measurements).
It shows additional property information about the data you have entered, like - the property type (Quantitative) - it’s range (min - max) and - the number of unique values and - if the Lab-values are enough diverse to get calibrated.
First select the folder with the PropertiesBySamples.csv.txt and measured spectra files of samples you have Lab-values. The data is checked and you get notified what is missing or might be wrong. If something needs to be changed, edit the PropertiesBySamples.csv.txt and do Create Calibration Request again. Your last selected folder is remembered, so you can press return in the folder selection dialog.
Hint: The keyboard shortcuts for redoing it after you edited some entries is : F7 Return - that allows you to get the property information quickly.
Hint: If you open the PropertiesBySamples.csv.txt in a spreadsheet program, you can create Histogram plots of the entered Lab-values, to see in which range are to less samples measurements.
When all is fine
When all is fine the “CalibrationRequest.zip” file is created for that data.
The ZIP file contains:
your PropertiesBySamples.csv.txt
your personal REQuest file for your computer system, that looks like
e.g. “337dcdc06b2d6dfb0b5c4bba578642312edf2ae84d909281624d7e26283e8b07 WIN-GB0PB48GSK4.req”
the spectra data files
Note: If the CalibrationRequest.zip file is already created and you change the PropertiesBySamples.csv.txt make sure to delete the old CalibrationRequest.zip file first! In the dialog it states if it was successfully created or NOT because it already exist. So you are always on the safe side.
Note:CalibrationRequest.zip file name contains the property names to know what would be calibrated and at the end an identification number for referencing the file. E.g. “CalibrationRequest ‘Prop1’ - ‘Prop2’ h31T3wOH.zip”
Program Settings
The users program settings are stored in UserSettings.json
The program counters are stored in GlobalCounters.json
It’s easy to use with NIR-Predictor,
just drag & drop your data for getting the prediction results.
It supports an automatic file format detection.
So you don’t need to specify the instrument type and settings! See the list of supported formats and NIR Vendors: NIR-Predictor supported Spectral Data File Formats
Use the included data to checkout how it feels:
Open the demo Spectra folder by using the Menu > Open Demo Spectra or press F8.
There are files with spectra from different Vendors.
Drag & drop a spectra file onto the NIR-Predictor window (or press Ctrl+O as for ’Open some files).
The spectra will be
loaded
pre-processed
predicted and
reported
Note:
All the steps are fully automatic.
All calibrations that are compatible with the spectra, will produce prediction results in one go.
To select specific calibrations choose the Application. Where the " " empty means use all the calibrations.
To define a Application read more in chapter “Applications”
Hint:
To get access to Statistics of Predictions and Reports use the Menu > Show more/less (Ctrl+M) or you can simply resize the window. Here you can also re-do the Analyze step manually with changed inputs (e.g. Result Ordering).
Creating your own Calibrations
How it works - step by step
You have measured your samples with you NIR-Instrument Software.
And got the Lab-values of these samples.
Note: If you combined these data already in your NIR software used,
and you can export it as a JCAMP-DX file then use
Menu > Create Request File .req ... (F2)
and read the “Help.html” and NIR-Predictor JCAMP.
Else proceed as below.
The NIR-Predictor provides tooling for that:
Menu > Create Properties File... (F6)
Select the folder with your NIR spectra measured for an application.
NIR-Predictor creates a customized Properties file template for that data to enter the Lab values.
Note: You don’t need to specify your instrument or vendor or an application. It’s all done automatically. And also the sample spectra are detected and grouped automatically!
Use your favorite editor or spreadsheet program to enter and copy&paste
the Lab-references Values into the columns “Prop1”, “Prop2” etc. and save the file.
A final check of your entered data is done by NIR-Predictor,
to make sure your data ist complete and all is fine.
Menu > Create Calibration Request... (F7)
Select the folder with the filled file.
A CalibrationRequest.zip is created with the necessary data
if enougth diverse Lab values are entered.
Email the CalibrationRequest.zip file
to info@CalibrationModel.com to develop the calibrations.
When your calibrations are ready, you will receive an email with a link
to the CalibrationModel WebShop where
you can purchase and download the calibration files,
that work with our free NIR-Predictor software without internet access.
Note: Your sent NIR data is deleted after processing.
We do not collect your NIR data!
Note: Further details can be found under “Create Properties File” and “Create Calibration Request”.
Configure the Calibrations for prediction usage
Configuration:
in NIR-Predictor : Menu > Open Calibrations (F9)
an explorer window is opened where the calibrations are located
create a folder for your application, choose a name
copy the calibration file(s) (*.cm) into that folder
in NIR-Predictor : Menu > Search and load Applications (F4)
Usage:
in NIR-Predictor : open the Application drop down list, and select your application by name
if all is fine, the calibration file is valid and not expired, it shows : Calibration “1 valid calibation”
the NIR-Predictor is now ready to predict
to switch the application, goto 6.
Applications
The Application concept allows to group multiple Calibrations together for an Application. By selecting an Application before prediction, only the Calibrations belonging to the Application will be used for Prediction. In the Demo Data this is used to have multiple spectrometer as Application. This can be used easily as e.g. as Application “Meat Products” containing Fat and Moisture Calibration.
To create an Application, create a folder with the Application’s name inside the Calibrations folder, and move/copy all the Calibrations files to this Application folder. To remove a Calibration from the Application, remove the Calibration file from the Application folder.
After creating an new Application folder, press menu Search and load Applications (F4) to update the NIR-Predictor dialog where the Application can be selected via the dropdown list. You don’t need to close the NIR-Predictor.
After moving Calibration files around, press menu Search and load Calibrations (F5) to update the NIR-Predictor dialog.
The use-all case
In the NIR-Predictor dialog where the Application can be selected via the dropdown list, the empty "" name means that all (yes all) valid Calibrations will be used for prediction.
Note: The Prediction Report will contain only results from spectral compatible Calibrations with the given spectra. That allows to automatically handle the multi vendor NIR instrument usage.
Prediction Result Report
Histograms of Prediction Values per Property
Shows the distribution of the predicted results per calibration. The histogram range contains the range of the calibrated property and includes the predicted results.
The histogram bar (bin) color is defined as follow:
blue : all predictions inside calibration range.
red : all predictions outside calibration range.
orange : some overlaps with calibration range.
So not all spectra in a orange bin are outside calibration range.
Histograms
Note: Predicted values are always shown in Histogram table and Prediction Value List table, even if the spectrum does not fit into model (spectrum different to model, aka Residual Outlier) shown as Out = X.
Note: Old browsers like Microsoft Internet Explorer 11 don’t support the grafics for Histogram charts. Use an current browser like Firefox or Chrome or Edge.
Note: If your browser opens the report too slow, try to deactivate some browser plugins, because they can filter what you look at and some add-ons are really slow.
Spectra Plot Thumbnail on the Prediction Report
Visualizes the min,median,max spectrum of the spectra dropped as files on the NIR-Predictor. This gives a minimal and good spectral overview of the predicted property results.
Spectra Plot color legend: min,median,max spectrum by predicted property or if no calibration is available by spectral intensity.
The min,median,max is determined from the predicted properties or if not available from the intensity of the spectra.
Beside the histogram of the predicted properties, where the distribution can be seen, the spectra shown are the ones from min,median,max predicted property.
This gives a minimal and good spectral overview of the predicted property results.
The “Spectral Range” and number of datapoints is shown in the Prediction Report Header below the listed spectra files.
To zoom the spectra plot a little, zoom the report in the browser (hold ctrl + mouse wheel, or pinch on touch screen).
The spectra plots and histograms are stored with the report and can be archived.
Note
Note that the spectra are shown in the raw values that are loaded, they are not shown pre-processed as the calibration model uses them to make the prediction.
Note that the median property spectrum is the median from the predicted property pobulation and not the “median” of the calibration property range.
Note that in the multi calibration prediction case, the spectra are selected for each property based on the related predicted property values and so the spectra plots shows typical different spectra.
Spectra Plot
Outlier Detection
To safeguard the prediction results, outliers are automatically checked for each individual prediction. This is based on limits that are determined when creating the calibration with the base data. Thus, a strange spectral measurement can be detected and signaled as an outlier even without base data only by means of the calibration and the NIR predictor. A prediction result with outlier warning is to be distrusted. How the various outlier tests are interpreted and how to avoid them in practice is described here.
The spectrum is an outlier to the model, if the spectrum is not similar with the spectra and lab-values the model is built with.
This legend is shown on each NIR-Predictor prediction report below the results:
Outlier (Out) Symbol Description
“X” : spectrum does not fit into model (spectrum different to model)
“O” : spectrum is wide outside model center (spectrum similar to model but far away)
“=” : prediction is outside upper or lower range of model (property outside model range)
“-” : spectrum is incompatible to calibration
Note: A prediction result with outlier warning is to be distrusted.
There are 3 outlier cases (X, O, =) and the incompatible data case “-”.
The bad case is “X”
the medium case is “O”
and the soft case is “=”.
The technical names in literature correspond to:
“X” : Spectral Residual Outlier
“O” : Leverage Outlier
“=” : Property Range Outlier
These 3 outlier cases can appear in combinations, like “XO=” or “XO” or “O=” or “X=”. The more outlier marker are shown the more likely the spectrum is an Outlier.
The default setting in NIR-Predictor Menu > “Report with Simplified Outlier Symbols”
is ON, that will show only the worst case instead of all combinations to have a simplified minimal information.
if OFF, that will show the combinations (e.g. “XO=” or “XO” or “O=” or “X=”), which is more informative for analyzing problem cases.
Some hints to avoid these Outliers:
“X” : spectrum does not fit into model (spectrum different to model)
Check if the spectrum is noise only, or has no proper signal. That can happen when measured past the sample or measured into the air or at a different substance. If you have multiple NIR instruments of the same type, use spectra measured with different instruments for the calibration.
“O” : spectrum is wide outside model center (spectrum similar to model but far away) Sample temperature has an effect on NIR spectra shape, use spectra measured at different (typical use) temperatures (sample temperature, instrument temperature).
“=” : prediction is outside upper or lower range of model (property outside model range)
Use more spectra for the calibration in the Lab value region where your special interest is. If the predicted value is only a little bit out of the calibration range, it can be Ok. Add these spectra to the calibration spectra (with the Lab values), to extend the prediction range of the calibration.
“-” : spectrum is incompatible to calibration
The spectra (from the NIR instrument) has a different wavelength range or a different resolution than the spectra used for calibration. Check Instrument settings (wavelength range, resolution)
Result Ordering
To change the ordering, a drop-down-box is located below the Analyze button. If there is an analysis from the current session, and the Result Ordering is changed, the data is re-Analyzed and reported with the new Result Ordering setting. That allows to compare the different orderings. The Result Ordering is listed in the Prediction Report above the Prediction Value List and stored in the settings.
The order/sorting of the prediction results of the spectra can be defined:
GivenOrder (default) the given order of the spectra from file select dialog or drag&drop
*) sorted : ascending sort
Date_Name sorted by Date (if any) and then by Name
Name_Date sorted by Name and then by Date
Date_NamesWithNumbers sorted by Date (if any) and then by Name with number logic
NamesWithNumbers_Date sorted by Name with number logic (e.g. “ABC1” is before “ABC002” ) and then by Date
*) as above but sorted Rev : reverse sort = descending sort
Rev_Date_Name
Rev_Name_Date
Rev_Date_NamesWithNumbers
Rev_NamesWithNumbers_Date
E.g. with reverse sort by Rev_Date_Name, the newest spectra appear on top.
Print to PDF
Depending on how many calibrations are used the result table is getting broader. To print the report (e.g. to Adobe PDF, FreePDF or Microsoft XPS), sometimes the landscape format is shorter in number of pages or in portrait a scale of 80% fits nicely. Or try another internet browser (Mozilla Firefox, Google Chrome, Microsoft Edge, …) to print the report and set the browser as your default browser so it will be opened by default.
Archiving Reports
Each report is contained in one file only, including the grafics. To save storage space the report file folder can be compressed to a zip file (.zip, .7z).
Enter lab values to NIR spectra
Entering the laboratory reference values for NIR calibrations
We have developed specialized tools into NIR-Predictor to combine the NIR and Lab data is a sample-based safe manner.
The main target is to improve Data Quality during the step of combining of the Lab data and the NIR data, because to model a good reliable calibration the data that build the base needs to be of high quality.
It also simplifies to enter the lab values manually to the corresponding NIR data, because of automatically grouping repeated NIR measurements of the same sample, so the lab values can be entered sample based and not by spectrum.
It helps to avoid false reference data, because of the broken relation of NIR spectra and reference values, data entry on the wrong position in the table.
And Helps to detect errors of duplicated or multiple copies of spectra files, and checks for inconsistencies in Date-Time and Sample-Naming. It also checks for missing values.
That all increases the Data Quality for the next step of Calibration Development, and makes data entry a less time consuming and less risky work.
How it works
Menu > Create Properties File... (F6) select the folder with your NIR spectra measured for an application. NIR-Predictor creates a Properties file template for that data : PropertiesBySamples.csv.txt
Use your favorite editor or spreadsheet program to enter and copy&paste the Lab Values into the columns and save the file.
Menu > Create Calibration Request... (F7) select the folder with the filled file for a last check and a Calibration Request file is created with the needed data as a single zip file.
Email the Calibration Request file to info@CalibrationModel.com to develop the calibrations.
Ok that is it, the NIR-Predictor guides you through the steps needed. And if you need to know more details, the Chapter “Create Properties File” is for you.
Create Properties File
Note:
If you have (exported) JCAMP-DX files containing the Lab-Values, you don’t need to do this step.
You can send the JCAMP file with your Request (.req) file directly to the calibration service at info@CalibrationModel.com.
If your JCAMP-DX files does NOT contain Lab-Values, this is a way to go.
For calibrating the spectra to the lab-values you need to assign the lab-values to the spectra. The easiest way is to have a table where each spectrum (row) is linked to multiple lab-values (columns). This function Create Properties File build such a table for the selected spectra folder automatically!
This table is stored in the file PropertiesBySamples.csv.txt. This can be created for any spectra folder you like. The file extension is .csv.txt to make it easy to edit in a text editor and also in a spreadsheet (excel). The columns are standard TAB separated.
Prop1, Prop2, Prop3 are the place to enter the Lab Reference Concentrations properties corresponding to each spectrum. It can be extended to Prop4, Prop5, … etc. Of course you can enter real word names like “Fat (%)” instead of “Prop1”. It’s recommended to put the measurement unit beside the name.
Replicates is the number of replicated or repeated spectra of a sample that is grouped together in the Sample based property file. Sample name and the DateFirst / DateLast between the sample spectra are measured.
Date format is ISO-8601. Missing Dates are 0002-02-02T00:00:00.0000000.
If the file PropertiesBySamples.csv.txt already exist in the selected folder, the user will be notified (it will not be overwritten, because the file may contain user entered Lab-values). The Lab Reference Concentrations values are initialized to 0 (zero) and needed to be changed.
Note: 0 is not interpreted as missing value! If you have a 0 concentration value, put in 0 or 0.0 .
The entry of properties is as easy as possible, because it’s organized by Sample (and not by Spectra), so it’s like your Lab-Value Table that is sample based. The sample rows are sorted in a special way by Sample name. Sorting by Date or alphabetically by Sample can done easily in a spreadsheet program.
Note: when coping lab values to the samples make sure they correspond, so that there are no gaps and the sorting is the same.
The Spectra (rows) are initially sorted by name (and date) to have the replicates/repeats together. You can sort for your convenience in a spreadsheet program.
Enter the Lab Reference Concentrations to the spectra/sample.
Enter the Lab-Values in spreadsheet (e.g. Excel) or a text editor (e.g. Notepad++). If done, use the next menu Create Calibration Request.
Hints: Data handling:
The NIR-Predictor creates the PropertiesBySamples.csv.txt once, after that the user is responsible for its content. That means NIR-Predictor does not change this file anymore.
You can remove entire rows (spectra) in the property file. You don’t need to remove the spectra files. The NIR-Predictor is aware of the relation, the PropertiesBySamples.csv.txt defines what will be calibrated.
How to add more spectra files?
The additional spectra can be handled in a separate folder, create the property file and copy the spectra to the other folder and copy/merge the property files together in your editor or spreadsheet.
Or
Copy the spectra into the folder, rename the PropertiesBySamples.csv.txt to e.g. “PropertiesBySamples-Part1.csv.txt” and use Create Properties File to create a new PropertiesBySamples.csv.txt with all the spectra. You can copy/merge the content of the Properties files together in your editor or spreadsheet.
What happens with possible duplicate rows? It does no harm to the Calibration because we do an exact checking and data cleaning in the calibration process.
What happens to duplicate spectra names? The spectra names are not relevant for the calibration process. The spectra names are helpful to assign the lab-values to the corresponding spectrum entry. That’s why the table is initially sorted by name. The spectra names can be edited by the user.
Create Calibration Request
The menu function Create Calibration Request packs a created Properties file (see “Create Properties File”) and it’s linked spectra files in a compressed ZIP file for sending to the CalibrationModel.com Service.
Please note that the number of measured quantitative samples need to be at least 60 . That means you need at least 60 different spectra (not counting the replicate/repeated measurements).
It shows additional property information about the data you have entered, like - the property type (Quantitative) - it’s range (min - max) and - the number of unique values and - if the Lab-values are enough diverse to get calibrated.
First select the folder with the PropertiesBySamples.csv.txt and measured spectra files of samples you have Lab-values. The data is checked and you get notified what is missing or might be wrong. If something needs to be changed, edit the PropertiesBySamples.csv.txt and do Create Calibration Request again. Your last selected folder is remembered, so you can press return in the folder selection dialog.
Hint: The keyboard shortcuts for redoing it after you edited some entries is : F7 Return - that allows you to get the property information quickly.
Hint: If you open the PropertiesBySamples.csv.txt in a spreadsheet program, you can create Histogram plots of the entered Lab-values, to see in which range are to less samples measurements.
When all is fine
When all is fine the “CalibrationRequest.zip” file is created for that data.
The ZIP file contains:
your PropertiesBySamples.csv.txt
your personal REQuest file for your computer system, that looks like
e.g. “337dcdc06b2d6dfb0b5c4bba578642312edf2ae84d909281624d7e26283e8b07 WIN-GB0PB48GSK4.req”
the spectra data files
Note: If the CalibrationRequest.zip file is already created and you change the PropertiesBySamples.csv.txt make sure to delete the old CalibrationRequest.zip file first! In the dialog it states if it was successfully created or NOT because it already exist. So you are always on the safe side.
Note:CalibrationRequest.zip file name contains the property names to know what would be calibrated and at the end an identification number for referencing the file. E.g. “CalibrationRequest ‘Prop1’ - ‘Prop2’ h31T3wOH.zip”
Program Settings
The users program settings are stored in UserSettings.json
The program counters are stored in GlobalCounters.json
It’s easy to use with NIR-Predictor,
just drag & drop your data for getting the prediction results.
It supports an automatic file format detection.
So you don’t need to specify the instrument type and settings! See the list of supported formats and NIR Vendors: NIR-Predictor supported Spectral Data File Formats
Use the included data to checkout how it feels:
Open the demo Spectra folder by using the Menu > Open Demo Spectra or press F8.
There are files with spectra from different Vendors.
Drag & drop a spectra file onto the NIR-Predictor window (or press Ctrl+O as for ’Open some files).
The spectra will be
loaded
pre-processed
predicted and
reported
Note:
All the steps are fully automatic.
All calibrations that are compatible with the spectra, will produce prediction results in one go.
To select specific calibrations choose the Application. Where the " " empty means use all the calibrations.
To define a Application read more in chapter “Applications”
Hint:
To get access to Statistics of Predictions and Reports use the Menu > Show more/less (Ctrl+M) or you can simply resize the window. Here you can also re-do the Analyze step manually with changed inputs (e.g. Result Ordering).
Creating your own Calibrations
How it works - step by step
You have measured your samples with you NIR-Instrument Software.
And got the Lab-values of these samples.
Note: If you combined these data already in your NIR software used,
and you can export it as a JCAMP-DX file then use
Menu > Create Request File .req ... (F2)
and read the “Help.html” and NIR-Predictor JCAMP.
Else proceed as below.
The NIR-Predictor provides tooling for that:
Menu > Create Properties File... (F6)
Select the folder with your NIR spectra measured for an application.
NIR-Predictor creates a customized Properties file template for that data to enter the Lab values.
Note: You don’t need to specify your instrument or vendor or an application. It’s all done automatically. And also the sample spectra are detected and grouped automatically!
Use your favorite editor or spreadsheet program to enter and copy&paste
the Lab-references Values into the columns “Prop1”, “Prop2” etc. and save the file.
A final check of your entered data is done by NIR-Predictor,
to make sure your data ist complete and all is fine.
Menu > Create Calibration Request... (F7)
Select the folder with the filled file.
A CalibrationRequest.zip is created with the necessary data
if enougth diverse Lab values are entered.
Email the CalibrationRequest.zip file
to info@CalibrationModel.com to develop the calibrations.
When your calibrations are ready, you will receive an email with a link
to the CalibrationModel WebShop where
you can purchase and download the calibration files,
that work with our free NIR-Predictor software without internet access.
Note: Your sent NIR data is deleted after processing.
We do not collect your NIR data!
Note: Further details can be found under “Create Properties File” and “Create Calibration Request”.
Configure the Calibrations for prediction usage
Configuration:
in NIR-Predictor : Menu > Open Calibrations (F9)
an explorer window is opened where the calibrations are located
create a folder for your application, choose a name
copy the calibration file(s) (*.cm) into that folder
in NIR-Predictor : Menu > Search and load Applications (F4)
Usage:
in NIR-Predictor : open the Application drop down list, and select your application by name
if all is fine, the calibration file is valid and not expired, it shows : Calibration “1 valid calibation”
the NIR-Predictor is now ready to predict
to switch the application, goto 6.
Applications
The Application concept allows to group multiple Calibrations together for an Application. By selecting an Application before prediction, only the Calibrations belonging to the Application will be used for Prediction. In the Demo Data this is used to have multiple spectrometer as Application. This can be used easily as e.g. as Application “Meat Products” containing Fat and Moisture Calibration.
To create an Application, create a folder with the Application’s name inside the Calibrations folder, and move/copy all the Calibrations files to this Application folder. To remove a Calibration from the Application, remove the Calibration file from the Application folder.
After creating an new Application folder, press menu Search and load Applications (F4) to update the NIR-Predictor dialog where the Application can be selected via the dropdown list. You don’t need to close the NIR-Predictor.
After moving Calibration files around, press menu Search and load Calibrations (F5) to update the NIR-Predictor dialog.
The use-all case
In the NIR-Predictor dialog where the Application can be selected via the dropdown list, the empty "" name means that all (yes all) valid Calibrations will be used for prediction.
Note: The Prediction Report will contain only results from spectral compatible Calibrations with the given spectra. That allows to automatically handle the multi vendor NIR instrument usage.
Prediction Result Report
Histograms of Prediction Values per Property
Shows the distribution of the predicted results per calibration. The histogram range contains the range of the calibrated property and includes the predicted results.
The histogram bar (bin) color is defined as follow:
blue : all predictions inside calibration range.
red : all predictions outside calibration range.
orange : some overlaps with calibration range.
So not all spectra in a orange bin are outside calibration range.
Histograms
Note: Predicted values are always shown in Histogram table and Prediction Value List table, even if the spectrum does not fit into model (spectrum different to model, aka Residual Outlier) shown as Out = X.
Note: Old browsers like Microsoft Internet Explorer 11 don’t support the grafics for Histogram charts. Use an current browser like Firefox or Chrome or Edge.
Note: If your browser opens the report too slow, try to deactivate some browser plugins, because they can filter what you look at and some add-ons are really slow.
Spectra Plot Thumbnail on the Prediction Report
Visualizes the min,median,max spectrum of the spectra dropped as files on the NIR-Predictor. This gives a minimal and good spectral overview of the predicted property results.
Spectra Plot color legend: min,median,max spectrum by predicted property or if no calibration is available by spectral intensity.
The min,median,max is determined from the predicted properties or if not available from the intensity of the spectra.
Beside the histogram of the predicted properties, where the distribution can be seen, the spectra shown are the ones from min,median,max predicted property.
This gives a minimal and good spectral overview of the predicted property results.
The “Spectral Range” and number of datapoints is shown in the Prediction Report Header below the listed spectra files.
To zoom the spectra plot a little, zoom the report in the browser (hold ctrl + mouse wheel, or pinch on touch screen).
The spectra plots and histograms are stored with the report and can be archived.
Note
Note that the spectra are shown in the raw values that are loaded, they are not shown pre-processed as the calibration model uses them to make the prediction.
Note that the median property spectrum is the median from the predicted property pobulation and not the “median” of the calibration property range.
Note that in the multi calibration prediction case, the spectra are selected for each property based on the related predicted property values and so the spectra plots shows typical different spectra.
Spectra Plot
Outlier Detection
To safeguard the prediction results, outliers are automatically checked for each individual prediction. This is based on limits that are determined when creating the calibration with the base data. Thus, a strange spectral measurement can be detected and signaled as an outlier even without base data only by means of the calibration and the NIR predictor. A prediction result with outlier warning is to be distrusted. How the various outlier tests are interpreted and how to avoid them in practice is described here.
The spectrum is an outlier to the model, if the spectrum is not similar with the spectra and lab-values the model is built with.
This legend is shown on each NIR-Predictor prediction report below the results:
Outlier (Out) Symbol Description
“X” : spectrum does not fit into model (spectrum different to model)
“O” : spectrum is wide outside model center (spectrum similar to model but far away)
“=” : prediction is outside upper or lower range of model (property outside model range)
“-” : spectrum is incompatible to calibration
Note: A prediction result with outlier warning is to be distrusted.
There are 3 outlier cases (X, O, =) and the incompatible data case “-”.
The bad case is “X”
the medium case is “O”
and the soft case is “=”.
The technical names in literature correspond to:
“X” : Spectral Residual Outlier
“O” : Leverage Outlier
“=” : Property Range Outlier
These 3 outlier cases can appear in combinations, like “XO=” or “XO” or “O=” or “X=”. The more outlier marker are shown the more likely the spectrum is an Outlier.
The default setting in NIR-Predictor Menu > “Report with Simplified Outlier Symbols”
is ON, that will show only the worst case instead of all combinations to have a simplified minimal information.
if OFF, that will show the combinations (e.g. “XO=” or “XO” or “O=” or “X=”), which is more informative for analyzing problem cases.
Some hints to avoid these Outliers:
“X” : spectrum does not fit into model (spectrum different to model)
Check if the spectrum is noise only, or has no proper signal. That can happen when measured past the sample or measured into the air or at a different substance. If you have multiple NIR instruments of the same type, use spectra measured with different instruments for the calibration.
“O” : spectrum is wide outside model center (spectrum similar to model but far away) Sample temperature has an effect on NIR spectra shape, use spectra measured at different (typical use) temperatures (sample temperature, instrument temperature).
“=” : prediction is outside upper or lower range of model (property outside model range)
Use more spectra for the calibration in the Lab value region where your special interest is. If the predicted value is only a little bit out of the calibration range, it can be Ok. Add these spectra to the calibration spectra (with the Lab values), to extend the prediction range of the calibration.
“-” : spectrum is incompatible to calibration
The spectra (from the NIR instrument) has a different wavelength range or a different resolution than the spectra used for calibration. Check Instrument settings (wavelength range, resolution)
Result Ordering
To change the ordering, a drop-down-box is located below the Analyze button. If there is an analysis from the current session, and the Result Ordering is changed, the data is re-Analyzed and reported with the new Result Ordering setting. That allows to compare the different orderings. The Result Ordering is listed in the Prediction Report above the Prediction Value List and stored in the settings.
The order/sorting of the prediction results of the spectra can be defined:
GivenOrder (default) the given order of the spectra from file select dialog or drag&drop
*) sorted : ascending sort
Date_Name sorted by Date (if any) and then by Name
Name_Date sorted by Name and then by Date
Date_NamesWithNumbers sorted by Date (if any) and then by Name with number logic
NamesWithNumbers_Date sorted by Name with number logic (e.g. “ABC1” is before “ABC002” ) and then by Date
*) as above but sorted Rev : reverse sort = descending sort
Rev_Date_Name
Rev_Name_Date
Rev_Date_NamesWithNumbers
Rev_NamesWithNumbers_Date
E.g. with reverse sort by Rev_Date_Name, the newest spectra appear on top.
Print to PDF
Depending on how many calibrations are used the result table is getting broader. To print the report (e.g. to Adobe PDF, FreePDF or Microsoft XPS), sometimes the landscape format is shorter in number of pages or in portrait a scale of 80% fits nicely. Or try another internet browser (Mozilla Firefox, Google Chrome, Microsoft Edge, …) to print the report and set the browser as your default browser so it will be opened by default.
Archiving Reports
Each report is contained in one file only, including the grafics. To save storage space the report file folder can be compressed to a zip file (.zip, .7z).
Enter lab values to NIR spectra
Entering the laboratory reference values for NIR calibrations
We have developed specialized tools into NIR-Predictor to combine the NIR and Lab data is a sample-based safe manner.
The main target is to improve Data Quality during the step of combining of the Lab data and the NIR data, because to model a good reliable calibration the data that build the base needs to be of high quality.
It also simplifies to enter the lab values manually to the corresponding NIR data, because of automatically grouping repeated NIR measurements of the same sample, so the lab values can be entered sample based and not by spectrum.
It helps to avoid false reference data, because of the broken relation of NIR spectra and reference values, data entry on the wrong position in the table.
And Helps to detect errors of duplicated or multiple copies of spectra files, and checks for inconsistencies in Date-Time and Sample-Naming. It also checks for missing values.
That all increases the Data Quality for the next step of Calibration Development, and makes data entry a less time consuming and less risky work.
How it works
Menu > Create Properties File... (F6) select the folder with your NIR spectra measured for an application. NIR-Predictor creates a Properties file template for that data : PropertiesBySamples.csv.txt
Use your favorite editor or spreadsheet program to enter and copy&paste the Lab Values into the columns and save the file.
Menu > Create Calibration Request... (F7) select the folder with the filled file for a last check and a Calibration Request file is created with the needed data as a single zip file.
Email the Calibration Request file to info@CalibrationModel.com to develop the calibrations.
Ok that is it, the NIR-Predictor guides you through the steps needed. And if you need to know more details, the Chapter “Create Properties File” is for you.
Create Properties File
Note:
If you have (exported) JCAMP-DX files containing the Lab-Values, you don’t need to do this step.
You can send the JCAMP file with your Request (.req) file directly to the calibration service at info@CalibrationModel.com.
If your JCAMP-DX files does NOT contain Lab-Values, this is a way to go.
For calibrating the spectra to the lab-values you need to assign the lab-values to the spectra. The easiest way is to have a table where each spectrum (row) is linked to multiple lab-values (columns). This function Create Properties File build such a table for the selected spectra folder automatically!
This table is stored in the file PropertiesBySamples.csv.txt. This can be created for any spectra folder you like. The file extension is .csv.txt to make it easy to edit in a text editor and also in a spreadsheet (excel). The columns are standard TAB separated.
Prop1, Prop2, Prop3 are the place to enter the Lab Reference Concentrations properties corresponding to each spectrum. It can be extended to Prop4, Prop5, … etc. Of course you can enter real word names like “Fat (%)” instead of “Prop1”. It’s recommended to put the measurement unit beside the name.
Replicates is the number of replicated or repeated spectra of a sample that is grouped together in the Sample based property file. Sample name and the DateFirst / DateLast between the sample spectra are measured.
Date format is ISO-8601. Missing Dates are 0002-02-02T00:00:00.0000000.
If the file PropertiesBySamples.csv.txt already exist in the selected folder, the user will be notified (it will not be overwritten, because the file may contain user entered Lab-values). The Lab Reference Concentrations values are initialized to 0 (zero) and needed to be changed.
Note: 0 is not interpreted as missing value! If you have a 0 concentration value, put in 0 or 0.0 .
The entry of properties is as easy as possible, because it’s organized by Sample (and not by Spectra), so it’s like your Lab-Value Table that is sample based. The sample rows are sorted in a special way by Sample name. Sorting by Date or alphabetically by Sample can done easily in a spreadsheet program.
Note: when coping lab values to the samples make sure they correspond, so that there are no gaps and the sorting is the same.
The Spectra (rows) are initially sorted by name (and date) to have the replicates/repeats together. You can sort for your convenience in a spreadsheet program.
Enter the Lab Reference Concentrations to the spectra/sample.
Enter the Lab-Values in spreadsheet (e.g. Excel) or a text editor (e.g. Notepad++). If done, use the next menu Create Calibration Request.
Hints: Data handling:
The NIR-Predictor creates the PropertiesBySamples.csv.txt once, after that the user is responsible for its content. That means NIR-Predictor does not change this file anymore.
You can remove entire rows (spectra) in the property file. You don’t need to remove the spectra files. The NIR-Predictor is aware of the relation, the PropertiesBySamples.csv.txt defines what will be calibrated.
How to add more spectra files?
The additional spectra can be handled in a separate folder, create the property file and copy the spectra to the other folder and copy/merge the property files together in your editor or spreadsheet.
Or
Copy the spectra into the folder, rename the PropertiesBySamples.csv.txt to e.g. “PropertiesBySamples-Part1.csv.txt” and use Create Properties File to create a new PropertiesBySamples.csv.txt with all the spectra. You can copy/merge the content of the Properties files together in your editor or spreadsheet.
What happens with possible duplicate rows? It does no harm to the Calibration because we do an exact checking and data cleaning in the calibration process.
What happens to duplicate spectra names? The spectra names are not relevant for the calibration process. The spectra names are helpful to assign the lab-values to the corresponding spectrum entry. That’s why the table is initially sorted by name. The spectra names can be edited by the user.
Create Calibration Request
The menu function Create Calibration Request packs a created Properties file (see “Create Properties File”) and it’s linked spectra files in a compressed ZIP file for sending to the CalibrationModel.com Service.
Please note that the number of measured quantitative samples need to be at least 60 . That means you need at least 60 different spectra (not counting the replicate/repeated measurements).
It shows additional property information about the data you have entered, like - the property type (Quantitative) - it’s range (min - max) and - the number of unique values and - if the Lab-values are enough diverse to get calibrated.
First select the folder with the PropertiesBySamples.csv.txt and measured spectra files of samples you have Lab-values. The data is checked and you get notified what is missing or might be wrong. If something needs to be changed, edit the PropertiesBySamples.csv.txt and do Create Calibration Request again. Your last selected folder is remembered, so you can press return in the folder selection dialog.
Hint: The keyboard shortcuts for redoing it after you edited some entries is : F7 Return - that allows you to get the property information quickly.
Hint: If you open the PropertiesBySamples.csv.txt in a spreadsheet program, you can create Histogram plots of the entered Lab-values, to see in which range are to less samples measurements.
When all is fine
When all is fine the “CalibrationRequest.zip” file is created for that data.
The ZIP file contains:
your PropertiesBySamples.csv.txt
your personal REQuest file for your computer system, that looks like
e.g. “337dcdc06b2d6dfb0b5c4bba578642312edf2ae84d909281624d7e26283e8b07 WIN-GB0PB48GSK4.req”
the spectra data files
Note: If the CalibrationRequest.zip file is already created and you change the PropertiesBySamples.csv.txt make sure to delete the old CalibrationRequest.zip file first! In the dialog it states if it was successfully created or NOT because it already exist. So you are always on the safe side.
Note:CalibrationRequest.zip file name contains the property names to know what would be calibrated and at the end an identification number for referencing the file. E.g. “CalibrationRequest ‘Prop1’ - ‘Prop2’ h31T3wOH.zip”
Program Settings
The users program settings are stored in UserSettings.json
The program counters are stored in GlobalCounters.json