Time Saving Calibration of near-infrared Spectroscopy NIRS | nir Chemistry LINK
Near-Infrared Spectroscopy (NIRS)
“Artificial Intelligence-based Prediction of In Vitro Dissolution Profile of Immediate Release Tablets with Near-infrared and Raman Spectroscopy” LINK
“Combination of NIR spectroscopy and algorithms for rapid differentiation between one-year and two-year stored rice” LINK
“Gaussian Process Regression for Quantitative Degree of Polymerization Analysis of Oil-paper Insulation by EPO-NIRS: A Solution to Field Moisture” LINK
“Biosensors : NIR Luminescent Oxygen-Sensing Nanoparticles for Continuous Glucose and Lactate Monitoring” | LINK
“Application of Portable Near‐Infrared (NIR) Spectroscopy for Rapid Detection and Quantification of Adulterants in Baobab Fruit Pulp Powder” LINK
“Mesane çıkış obstrüksiyonu bulunan hastalarda non-invaziv yöntem olan? near-ınfrared spectroscopy?(NIRS) uygulaması” LINK
“Quality Evaluation of Fair-Trade Cocoa Beans from Different Origins Using Portable Near-Infrared Spectroscopy (NIRS)” | LINK
“Near-infrared Spectroscopy as Process Analytical Technology in Continuous Solid Dosage Form Manufacturing” LINK
“Non-invasive measurement of shear force in chicken meat using near infrared spectroscopy supported by neural network analysis” LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
“Hydrogen Bonding from Perspective of Overtones and Combination Modes: Near‐Infrared Spectroscopic Study” LINK
Hyperspectral Imaging (HSI)
“Chemical Imaging-Is an Image Always Worth a Thousand Spectra?” LINK
“Raw Beef Patty Analysis Using Near-Infrared Hyperspectral Imaging: Identification of Four Patty Categories” LINK
“Hyperspectral image-based measurement of total flavonoid content of leaf-use Ginkgo biloba L.” LINK
Chemometrics and Machine Learning
“Remote Sensing : Lithium-Bearing Pegmatite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Dahongliutan Area, NW China” LINK
“Applied Sciences : Robust Full-Screen Wavelength Calibration Algorithm” LINK
“Authentication/discrimination, identification and quantification of cinnamon adulterants using NIR spectroscopy and different chemometric tools: A tutorial to deal with …” LINK
Equipment for Spectroscopy
“Effect of spectrum measurement position on detection of Klason lignin content of snow pears by a portable NIR spectrometer” LINK
“Present and Future of Miniaturized NIR Spectrometers Combined with Challenging Data Management Strategies” | LINK
“1D-Inception-Resnet for NIR quantitative analysis and its transferability between different spectrometers” | LINK
Environment NIR-Spectroscopy Application
“Sensors : Feasibility of Skin Water Content Imaging Using CMOS Sensors” | LINK
Agriculture NIR-Spectroscopy Usage
“Plants : Soybean Seed Sugars: A Role in the Mechanism of Resistance to Charcoal Rot and Potential Use as Biomarkers in Selection” LINK
“Polymers : Investigating the Synthesis and Characteristics of UV-Cured Bio-Based Epoxy Vegetable Oil-Lignin Composites Mediated by Structure-Directing Agents” LINK
Horticulture NIR-Spectroscopy Applications
“Is this pear sweeter than this apple? A universal SSC model for fruits with similar physicochemical properties” | LINK
Food & Feed Industry NIR Usage
“Foods : Comprehensive Evaluation of Quality Characteristics of Four Oil-Tea Camellia Species with Red Flowers and Large Fruit” | LINK
Pharma Industry NIR Usage
“Bakir oksit/çinko oksit heteroeklem yapilarin elektriksel ve optiksel özelliklerinin incelenmesi” LINK
Laboratory and NIR-Spectroscopy
“Application of Hyper-Spectral Imaging Technique for Colorimetric Analysis of Paintings” LINK
Other
“Detailed optical analysis of Dy3+ and Pr3+ co-doped alumino-borate glasses for visible lighting applications” LINK
“Characterization of Biodegraded Ignitable Liquids by Headspace-Ion Mobility Spectrometry” | 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
Spectroscopy and Chemometrics News Weekly 7, 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)
"Nirs Technology (Near Infrared Reflectance Spectroscopy) for Detecting Soil Fertility Case Study in Aceh Province" LINK
"Fourier transform and near infrared dataset of dialdehyde celluloses
used to determine the degree of oxidation with chemometric analysis" LINK
"NIRS and Aquaphotomics Trace Robusta-to-Arabica Ratio in Liquid Coffee Blends" LINK
"Development of a generic NIRS calibration pipeline using deep learning
and model ensembling: application to some reference datasets" LINK
"Foods : Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with
Non-Destructive Methods and Classification Models: Bioelectrical
Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time
Domain Reflectometry (TDR)" LINK
"Rapid and accurate determination of prohibited components in pesticides based on near infrared spectroscopy" LINK
"NIR spectroscopic methods for monitoring blend potency in a feed frame -
calibration transfer between offline and inline using a continuum
regression filter" LINK
"A FT-NIR Process Analytical Technology Approach for Milk Renneting Control" LINK
"Construction and Verification of a Mathematical Model for Near-Infrared
Spectroscopy Analysis of Gel Consistency in Southern Indica Rice" LINK
"Research on Construction of Visible-Near Infrared Spectroscopy Analysis
Model for Soluble Solid Content in Different Colors of Jujube" LINK
"Quantitative Detection of Agaricus Bisporus Freshness Based on VIS-NIR Spectroscopy" LINK
"Foods : Rapid Determination of β-Glucan Content of Hulled and Naked
Oats Using near Infrared Spectroscopy Combined with Chemometrics" LINK
"A ready-to-use portable VIS-NIR spectroscopy device to assess superior EVOO quality" | LINK
"Challenges, Opportunities and Recent Advances in Near Infrared
Spectroscopy Applications for Monitoring Blend Uniformity in the
Continuous Manufacturing of Solid ..." LINK
"The use of near-infrared reflectance spectroscopy (NIRS) to predict dairy fibre feeds in vitro digestibility" LINK
"Near-Infrared Reflectance Spectroscopy (NIRS) detection to
differentiate morning and afternoon milk based on nutrient contents and
fatty acid profiles" LINK
"Application of Various Algorithms for Spectral Variable Selection in NIRS Modeling of Red Ginseng Extraction" LINK
"Near infrared spectroscopy to predict plaque progression in plaque-free artery regions" LINK
"The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation" LINK
"Quantitative Detection of Agaricus Bisporus Freshness Based on VIS-NIR Spectroscopy" LINK
"Application of SG-MSC-MC-UVE-PLS Algorithm in Whole Blood Hemoglobin
Concentration Detection Based on Near Infrared Spectroscopy" LINK
"Near infrared reflectance spectroscopy as a tool to predict non-starch
polysaccharide composition and starch digestibility profiles in common
monogastric ..." LINK
"Development of SOP for NIRS spectral measurement on fresh grounded yam tubers and cassava roots" LINK
"Development of calibration models within a closed feed frame to determine drug concentration using near infrared spectroscopy" LINK
"Amazonian cacao-clone nibs discrimination using NIR spectroscopy
coupled to naïve Bayes classifier and a new waveband selection approach"
LINK
"Foods : A FT-NIR Process Analytical Technology Approach for Milk Renneting Control" LINK
"Study on Soil Salinity Estimation Method of “Moisture Resistance” Using Visible-Near Infrared Spectroscopy in Coastal Region" LINK
"Can Grassland Chemical Quality Be Quantified Using Transform Near-Infrared Spectroscopy?" LINK
"High Near-Infrared Reflective Zn 1-x A x WO 4 Pigments with Various
Hues Facilely Fabricated by Tuning Doped Transition Metal Ions (A= Co,
Mn, and Fe)" LINK
"Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with
Non-Destructive Methods and Classification Models: Bioelectrical
Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time
Domain Reflectometry (TDR)" LINK
"The Relationship Between Genetic Variations and NIRs Differences of Eucalyptus Pellita Provenances" LINK
"Prediction of quality of total mixed ration for dairy cows by near infrared reflectance spectroscopy and empirical equations" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Agricultural products quality determination by means of near infrared spectroscopy" LINK
"A Near-Infrared TDLAS Online Detection Device for Dissolved Gas in Transformer Oil" LINK
"Near-Infrared Spectroscopy Detection of Cotton/Polyester Content Based on Dropout Deep Belief Network" LINK
"Study on Near-Infrared Spectrum Acquisition Method of Non-Uniform Solid Particles" LINK
"Scanning interferometric near-infrared spectroscopy" LINK
"A broadband near‐infrared Sc1‐x(PO3)3:xCr3+ phosphor with enhanced thermal stability and quantum yield by Yb3+ codoping" LINK
"Minerals : Geometallurgical Characterisation with Portable FTIR: Application to Sediment-Hosted Cu-Co Ores" LINK
Hyperspectral Imaging (HSI)
"Developing deep learning based regression approaches for prediction of
firmness and pH in Kyoho grape using Vis/NIR hyperspectral imaging" LINK
"Robustness and accuracy evaluation of moisture prediction model for black tea withering process using hyperspectral imaging" LINK
Spectral Imaging
"Remote Sensing : Improved Method to Detect the Tailings Ponds from
Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer
Learning" LINK
Chemometrics and Machine Learning
"Nir Spectral Techniques and Chemometrics Applied to Food Processing" | LINK
"Consistent Value Creation from Bioprocess Data with Customized Algorithms: Opportunities Beyond Multivariate Analysis" LINK
"Effect of variable selection algorithms on model performance for
predicting moisture content in biological materials using spectral data"
LINK
"Chemometrics: An Excavator in Temperature-Dependent Near-Infrared Spectroscopy" LINK
"Development of Calibration-Free/Minimal Calibration Wavelength
Selection for Iterative Optimization Technology Algorithms toward
Process Analytical Technology Application" LINK
Equipment for Spectroscopy
"The utility of a near-infrared spectrometer to predict the maturity of green peas (Pisum sativum)" LINK
"Effect of lanthanum content on the thermophysical properties and
near-infrared reflection properties of lanthanum-cerium oxides" LINK
Process Control and NIR Sensors
"Emerging non-destructive imaging techniques for fruit damage detection: Image processing and analysis" LINK
Environment NIR-Spectroscopy Application
"Fusion of visible nearinfrared and midinfrared data for modelling key soilforming processes in loess soils" LINK
Agriculture NIR-Spectroscopy Usage
"Preharvest phenotypic prediction of grain quality and yield of durum wheat using multispectral imaging" LINK
"Remote Sensing : A Random Forest Algorithm for Retrieving Canopy
Chlorophyll Content of Wheat and Soybean Trained with PROSAIL
Simulations Using Adjusted Average Leaf Angle" LINK
"Agriculture : Application of Fourier Transform Infrared Spectroscopy
and Multivariate Analysis Methods for the Non-Destructive Evaluation of
Phenolics Compounds in Moringa Powder" LINK
Horticulture NIR-Spectroscopy Applications
"Nondestructive detection of total soluble solids in grapes using VMDRC and hyperspectral imaging" LINK
Forestry and Wood Industry NIR Usage
"Influence of growth parameters on wood density of Acacia auriculiformis" | LINK
Food & Feed Industry NIR Usage
"Data fusion of near-infrared diffuse reflectance spectra and
transmittance spectra for the accurate determination of rice flour
constituents" LINK
Medicinal Spectroscopy
"Carbon Dots with Intrinsic Bioactivities for Photothermal Optical
Coherence Tomography, tumorspecific Therapy and Postoperative Wound
Management" LINK
Other
"Potential denitrification activity response to long-term nitrogen fertilization-A global meta-analysis" LINK
"Annealing induced phase transformation from amorphous to
polycrystalline SnSe2 thin film photo detector with enhanced
light-matter interaction" LINK
"MoS2/PVA Hybrid Hydrogel with Excellent LightResponsive Antibacterial
Activity and Enhanced Mechanical Properties for Wound Dressing" LINK
"Influence of Substrate Temperature on Structural and Optical Properties of Co-Evaporated Cu2SnS3/ITO Thin Films" | 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)
"Nirs Technology (Near Infrared Reflectance Spectroscopy) for Detecting Soil Fertility Case Study in Aceh Province" LINK
"Fourier transform and near infrared dataset of dialdehyde celluloses
used to determine the degree of oxidation with chemometric analysis" LINK
"NIRS and Aquaphotomics Trace Robusta-to-Arabica Ratio in Liquid Coffee Blends" LINK
"Development of a generic NIRS calibration pipeline using deep learning
and model ensembling: application to some reference datasets" LINK
"Foods : Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with
Non-Destructive Methods and Classification Models: Bioelectrical
Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time
Domain Reflectometry (TDR)" LINK
"Rapid and accurate determination of prohibited components in pesticides based on near infrared spectroscopy" LINK
"NIR spectroscopic methods for monitoring blend potency in a feed frame -
calibration transfer between offline and inline using a continuum
regression filter" LINK
"A FT-NIR Process Analytical Technology Approach for Milk Renneting Control" LINK
"Construction and Verification of a Mathematical Model for Near-Infrared
Spectroscopy Analysis of Gel Consistency in Southern Indica Rice" LINK
"Research on Construction of Visible-Near Infrared Spectroscopy Analysis
Model for Soluble Solid Content in Different Colors of Jujube" LINK
"Quantitative Detection of Agaricus Bisporus Freshness Based on VIS-NIR Spectroscopy" LINK
"Foods : Rapid Determination of β-Glucan Content of Hulled and Naked
Oats Using near Infrared Spectroscopy Combined with Chemometrics" LINK
"A ready-to-use portable VIS-NIR spectroscopy device to assess superior EVOO quality" | LINK
"Challenges, Opportunities and Recent Advances in Near Infrared
Spectroscopy Applications for Monitoring Blend Uniformity in the
Continuous Manufacturing of Solid ..." LINK
"The use of near-infrared reflectance spectroscopy (NIRS) to predict dairy fibre feeds in vitro digestibility" LINK
"Near-Infrared Reflectance Spectroscopy (NIRS) detection to
differentiate morning and afternoon milk based on nutrient contents and
fatty acid profiles" LINK
"Application of Various Algorithms for Spectral Variable Selection in NIRS Modeling of Red Ginseng Extraction" LINK
"Near infrared spectroscopy to predict plaque progression in plaque-free artery regions" LINK
"The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation" LINK
"Quantitative Detection of Agaricus Bisporus Freshness Based on VIS-NIR Spectroscopy" LINK
"Application of SG-MSC-MC-UVE-PLS Algorithm in Whole Blood Hemoglobin
Concentration Detection Based on Near Infrared Spectroscopy" LINK
"Near infrared reflectance spectroscopy as a tool to predict non-starch
polysaccharide composition and starch digestibility profiles in common
monogastric ..." LINK
"Development of SOP for NIRS spectral measurement on fresh grounded yam tubers and cassava roots" LINK
"Development of calibration models within a closed feed frame to determine drug concentration using near infrared spectroscopy" LINK
"Amazonian cacao-clone nibs discrimination using NIR spectroscopy
coupled to naïve Bayes classifier and a new waveband selection approach"
LINK
"Foods : A FT-NIR Process Analytical Technology Approach for Milk Renneting Control" LINK
"Study on Soil Salinity Estimation Method of “Moisture Resistance” Using Visible-Near Infrared Spectroscopy in Coastal Region" LINK
"Can Grassland Chemical Quality Be Quantified Using Transform Near-Infrared Spectroscopy?" LINK
"High Near-Infrared Reflective Zn 1-x A x WO 4 Pigments with Various
Hues Facilely Fabricated by Tuning Doped Transition Metal Ions (A= Co,
Mn, and Fe)" LINK
"Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with
Non-Destructive Methods and Classification Models: Bioelectrical
Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time
Domain Reflectometry (TDR)" LINK
"The Relationship Between Genetic Variations and NIRs Differences of Eucalyptus Pellita Provenances" LINK
"Prediction of quality of total mixed ration for dairy cows by near infrared reflectance spectroscopy and empirical equations" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Agricultural products quality determination by means of near infrared spectroscopy" LINK
"A Near-Infrared TDLAS Online Detection Device for Dissolved Gas in Transformer Oil" LINK
"Near-Infrared Spectroscopy Detection of Cotton/Polyester Content Based on Dropout Deep Belief Network" LINK
"Study on Near-Infrared Spectrum Acquisition Method of Non-Uniform Solid Particles" LINK
"Scanning interferometric near-infrared spectroscopy" LINK
"A broadband near‐infrared Sc1‐x(PO3)3:xCr3+ phosphor with enhanced thermal stability and quantum yield by Yb3+ codoping" LINK
"Minerals : Geometallurgical Characterisation with Portable FTIR: Application to Sediment-Hosted Cu-Co Ores" LINK
Hyperspectral Imaging (HSI)
"Developing deep learning based regression approaches for prediction of
firmness and pH in Kyoho grape using Vis/NIR hyperspectral imaging" LINK
"Robustness and accuracy evaluation of moisture prediction model for black tea withering process using hyperspectral imaging" LINK
Spectral Imaging
"Remote Sensing : Improved Method to Detect the Tailings Ponds from
Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer
Learning" LINK
Chemometrics and Machine Learning
"Nir Spectral Techniques and Chemometrics Applied to Food Processing" | LINK
"Consistent Value Creation from Bioprocess Data with Customized Algorithms: Opportunities Beyond Multivariate Analysis" LINK
"Effect of variable selection algorithms on model performance for
predicting moisture content in biological materials using spectral data"
LINK
"Chemometrics: An Excavator in Temperature-Dependent Near-Infrared Spectroscopy" LINK
"Development of Calibration-Free/Minimal Calibration Wavelength
Selection for Iterative Optimization Technology Algorithms toward
Process Analytical Technology Application" LINK
Equipment for Spectroscopy
"The utility of a near-infrared spectrometer to predict the maturity of green peas (Pisum sativum)" LINK
"Effect of lanthanum content on the thermophysical properties and
near-infrared reflection properties of lanthanum-cerium oxides" LINK
Process Control and NIR Sensors
"Emerging non-destructive imaging techniques for fruit damage detection: Image processing and analysis" LINK
Environment NIR-Spectroscopy Application
"Fusion of visible nearinfrared and midinfrared data for modelling key soilforming processes in loess soils" LINK
Agriculture NIR-Spectroscopy Usage
"Preharvest phenotypic prediction of grain quality and yield of durum wheat using multispectral imaging" LINK
"Remote Sensing : A Random Forest Algorithm for Retrieving Canopy
Chlorophyll Content of Wheat and Soybean Trained with PROSAIL
Simulations Using Adjusted Average Leaf Angle" LINK
"Agriculture : Application of Fourier Transform Infrared Spectroscopy
and Multivariate Analysis Methods for the Non-Destructive Evaluation of
Phenolics Compounds in Moringa Powder" LINK
Horticulture NIR-Spectroscopy Applications
"Nondestructive detection of total soluble solids in grapes using VMDRC and hyperspectral imaging" LINK
Forestry and Wood Industry NIR Usage
"Influence of growth parameters on wood density of Acacia auriculiformis" | LINK
Food & Feed Industry NIR Usage
"Data fusion of near-infrared diffuse reflectance spectra and
transmittance spectra for the accurate determination of rice flour
constituents" LINK
Medicinal Spectroscopy
"Carbon Dots with Intrinsic Bioactivities for Photothermal Optical
Coherence Tomography, tumorspecific Therapy and Postoperative Wound
Management" LINK
Other
"Potential denitrification activity response to long-term nitrogen fertilization-A global meta-analysis" LINK
"Annealing induced phase transformation from amorphous to
polycrystalline SnSe2 thin film photo detector with enhanced
light-matter interaction" LINK
"MoS2/PVA Hybrid Hydrogel with Excellent LightResponsive Antibacterial
Activity and Enhanced Mechanical Properties for Wound Dressing" LINK
"Influence of Substrate Temperature on Structural and Optical Properties of Co-Evaporated Cu2SnS3/ITO Thin Films" | 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)
"Nirs Technology (Near Infrared Reflectance Spectroscopy) for Detecting Soil Fertility Case Study in Aceh Province" LINK
"Fourier transform and near infrared dataset of dialdehyde celluloses
used to determine the degree of oxidation with chemometric analysis" LINK
"NIRS and Aquaphotomics Trace Robusta-to-Arabica Ratio in Liquid Coffee Blends" LINK
"Development of a generic NIRS calibration pipeline using deep learning
and model ensembling: application to some reference datasets" LINK
"Foods : Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with
Non-Destructive Methods and Classification Models: Bioelectrical
Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time
Domain Reflectometry (TDR)" LINK
"Rapid and accurate determination of prohibited components in pesticides based on near infrared spectroscopy" LINK
"NIR spectroscopic methods for monitoring blend potency in a feed frame -
calibration transfer between offline and inline using a continuum
regression filter" LINK
"A FT-NIR Process Analytical Technology Approach for Milk Renneting Control" LINK
"Construction and Verification of a Mathematical Model for Near-Infrared
Spectroscopy Analysis of Gel Consistency in Southern Indica Rice" LINK
"Research on Construction of Visible-Near Infrared Spectroscopy Analysis
Model for Soluble Solid Content in Different Colors of Jujube" LINK
"Quantitative Detection of Agaricus Bisporus Freshness Based on VIS-NIR Spectroscopy" LINK
"Foods : Rapid Determination of β-Glucan Content of Hulled and Naked
Oats Using near Infrared Spectroscopy Combined with Chemometrics" LINK
"A ready-to-use portable VIS-NIR spectroscopy device to assess superior EVOO quality" | LINK
"Challenges, Opportunities and Recent Advances in Near Infrared
Spectroscopy Applications for Monitoring Blend Uniformity in the
Continuous Manufacturing of Solid ..." LINK
"The use of near-infrared reflectance spectroscopy (NIRS) to predict dairy fibre feeds in vitro digestibility" LINK
"Near-Infrared Reflectance Spectroscopy (NIRS) detection to
differentiate morning and afternoon milk based on nutrient contents and
fatty acid profiles" LINK
"Application of Various Algorithms for Spectral Variable Selection in NIRS Modeling of Red Ginseng Extraction" LINK
"Near infrared spectroscopy to predict plaque progression in plaque-free artery regions" LINK
"The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation" LINK
"Quantitative Detection of Agaricus Bisporus Freshness Based on VIS-NIR Spectroscopy" LINK
"Application of SG-MSC-MC-UVE-PLS Algorithm in Whole Blood Hemoglobin
Concentration Detection Based on Near Infrared Spectroscopy" LINK
"Near infrared reflectance spectroscopy as a tool to predict non-starch
polysaccharide composition and starch digestibility profiles in common
monogastric ..." LINK
"Development of SOP for NIRS spectral measurement on fresh grounded yam tubers and cassava roots" LINK
"Development of calibration models within a closed feed frame to determine drug concentration using near infrared spectroscopy" LINK
"Amazonian cacao-clone nibs discrimination using NIR spectroscopy
coupled to naïve Bayes classifier and a new waveband selection approach"
LINK
"Foods : A FT-NIR Process Analytical Technology Approach for Milk Renneting Control" LINK
"Study on Soil Salinity Estimation Method of “Moisture Resistance” Using Visible-Near Infrared Spectroscopy in Coastal Region" LINK
"Can Grassland Chemical Quality Be Quantified Using Transform Near-Infrared Spectroscopy?" LINK
"High Near-Infrared Reflective Zn 1-x A x WO 4 Pigments with Various
Hues Facilely Fabricated by Tuning Doped Transition Metal Ions (A= Co,
Mn, and Fe)" LINK
"Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with
Non-Destructive Methods and Classification Models: Bioelectrical
Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time
Domain Reflectometry (TDR)" LINK
"The Relationship Between Genetic Variations and NIRs Differences of Eucalyptus Pellita Provenances" LINK
"Prediction of quality of total mixed ration for dairy cows by near infrared reflectance spectroscopy and empirical equations" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Agricultural products quality determination by means of near infrared spectroscopy" LINK
"A Near-Infrared TDLAS Online Detection Device for Dissolved Gas in Transformer Oil" LINK
"Near-Infrared Spectroscopy Detection of Cotton/Polyester Content Based on Dropout Deep Belief Network" LINK
"Study on Near-Infrared Spectrum Acquisition Method of Non-Uniform Solid Particles" LINK
"Scanning interferometric near-infrared spectroscopy" LINK
"A broadband near‐infrared Sc1‐x(PO3)3:xCr3+ phosphor with enhanced thermal stability and quantum yield by Yb3+ codoping" LINK
"Minerals : Geometallurgical Characterisation with Portable FTIR: Application to Sediment-Hosted Cu-Co Ores" LINK
Hyperspectral Imaging (HSI)
"Developing deep learning based regression approaches for prediction of
firmness and pH in Kyoho grape using Vis/NIR hyperspectral imaging" LINK
"Robustness and accuracy evaluation of moisture prediction model for black tea withering process using hyperspectral imaging" LINK
Spectral Imaging
"Remote Sensing : Improved Method to Detect the Tailings Ponds from
Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer
Learning" LINK
Chemometrics and Machine Learning
"Nir Spectral Techniques and Chemometrics Applied to Food Processing" | LINK
"Consistent Value Creation from Bioprocess Data with Customized Algorithms: Opportunities Beyond Multivariate Analysis" LINK
"Effect of variable selection algorithms on model performance for
predicting moisture content in biological materials using spectral data"
LINK
"Chemometrics: An Excavator in Temperature-Dependent Near-Infrared Spectroscopy" LINK
"Development of Calibration-Free/Minimal Calibration Wavelength
Selection for Iterative Optimization Technology Algorithms toward
Process Analytical Technology Application" LINK
Equipment for Spectroscopy
"The utility of a near-infrared spectrometer to predict the maturity of green peas (Pisum sativum)" LINK
"Effect of lanthanum content on the thermophysical properties and
near-infrared reflection properties of lanthanum-cerium oxides" LINK
Process Control and NIR Sensors
"Emerging non-destructive imaging techniques for fruit damage detection: Image processing and analysis" LINK
Environment NIR-Spectroscopy Application
"Fusion of visible nearinfrared and midinfrared data for modelling key soilforming processes in loess soils" LINK
Agriculture NIR-Spectroscopy Usage
"Preharvest phenotypic prediction of grain quality and yield of durum wheat using multispectral imaging" LINK
"Remote Sensing : A Random Forest Algorithm for Retrieving Canopy
Chlorophyll Content of Wheat and Soybean Trained with PROSAIL
Simulations Using Adjusted Average Leaf Angle" LINK
"Agriculture : Application of Fourier Transform Infrared Spectroscopy
and Multivariate Analysis Methods for the Non-Destructive Evaluation of
Phenolics Compounds in Moringa Powder" LINK
Horticulture NIR-Spectroscopy Applications
"Nondestructive detection of total soluble solids in grapes using VMDRC and hyperspectral imaging" LINK
Forestry and Wood Industry NIR Usage
"Influence of growth parameters on wood density of Acacia auriculiformis" | LINK
Food & Feed Industry NIR Usage
"Data fusion of near-infrared diffuse reflectance spectra and
transmittance spectra for the accurate determination of rice flour
constituents" LINK
Medicinal Spectroscopy
"Carbon Dots with Intrinsic Bioactivities for Photothermal Optical
Coherence Tomography, tumorspecific Therapy and Postoperative Wound
Management" LINK
Other
"Potential denitrification activity response to long-term nitrogen fertilization-A global meta-analysis" LINK
"Annealing induced phase transformation from amorphous to
polycrystalline SnSe2 thin film photo detector with enhanced
light-matter interaction" LINK
"MoS2/PVA Hybrid Hydrogel with Excellent LightResponsive Antibacterial
Activity and Enhanced Mechanical Properties for Wound Dressing" LINK
"Influence of Substrate Temperature on Structural and Optical Properties of Co-Evaporated Cu2SnS3/ITO Thin Films" | 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)
"Development and Submission of Near Infrared Analytical Procedures Guidance for Industry" FDA LINK
"Identification prediction moisture content of Thai coconut sugar (Cocos nucifera L.) using FT-NIR spectroscopy" LINK
"Blood identification of NIR spectroscopy based on BP neural network combined with particle swarm optimization" LINK
"A preliminary study on the utilisation of near infrared spectroscopy to predict age and in vivo human metabolism" LINK
"Near-infrared guidance finalized for small molecule testing, with biologics to come" RAPS LINK
"Wavelength Selection Method for Near Infrared Spectroscopy Based on Iteratively Retains Informative Variables and Successive Projections Algorithm" LINK
"Near-infrared spectroscopy calibration strategies to predict multiple nutritional parameters of pasture species from different" LINK
" Comparison of metabolites and variety authentication of Amomum tsao-ko and Amomum paratsao-ko using GC-MS and NIR spectroscopy" LINK
"Hyperfine-Resolved Near-Infrared Spectra of H(2)(17)O" LINK
"Geographical Differentiation of Hom Mali Rice Cultivated in Different Regions of Thailand Using FTIR-ATR and NIR Spectroscopy" LINK
"Seasonal Snowpack Classification Based on Physical Properties Using Near-Infrared Proximal Hyperspectral Data" | LINK
"NIR-based sensing system for non-Invasive detection of Hemoglobin for point-of-care applications" LINK
"A promising inorganic YFeO3 pigments with high near-infrared reflectance and infrared emission" LINK
"Classification of Softwoods using Wood Extract Information and Near Infrared Spectroscopy." LINK
"Rapid determination and origin identification of total polysaccharides contents in Schisandra chinensis by near-infrared spectroscopy" LINK
"Near-Infrared Spectroscopy Technology in Food" | LINK
"Postharvest ripeness assessment of 'Hass' avocado based on development of a new ripening index and Vis-NIR spectroscopy" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Utility of near‐infrared spectroscopy to detect the extent of lipid core plaque leading to periprocedural myocardial infarction" LINK
"Scaling up Sagebrush Chemistry with Near-Infrared Spectroscopy and Uas-Acquired Hyperspectral Imagery" LINK
Hyperspectral Imaging (HSI)
"Spatially Resolved Spectroscopic Characterization of Nanostructured Films by Hyperspectral Dark-Field Microscopy" LINK
"Visual attention-driven framework to incorporate spatial-spectral features for hyperspectral anomaly detection" LINK
Spectral Imaging
"Spectral Super-Resolution of Multispectral Images Using Spatial-Spectral Residual Attention Network" LINK
Chemometrics and Machine Learning
"Feasibility of a chromameter and chemometric techniques to discriminate pure and mixed organic and conventional red pepper powders: A pilot study" LINK
"Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage" LINK
"Applied Sciences : A Novel Principal Component Analysis Integrating Long Short-Term Memory Network and Its Application in Productivity Prediction of Cutter Suction Dredgers" LINK
"Plants : Morpho-Physiological Classification of Italian Tomato Cultivars (Solanum lycopersicum L.) According to Drought Tolerance during Vegetative and Reproductive Growth" LINK
"Automatic food and beverage authentication and adulteration detection by classification hybrid fusion" LINK
"Remote Sensing : Improving Accuracy of Herbage Yield Predictions in Perennial Ryegrass with UAV-Based Structural and Spectral Data Fusion and Machine Learning" LINK
"Estimating Fat Components of Potato Chips Using Visible and Near-Infrared Spectroscopy and a Compositional Calibration Model" LINK
"Wavelengths selection based on genetic algorithm (GA) and successive projections algorithms (SPA) combine with PLS regression for determination the soluble ..." LINK
"Comparison of PLS and SVM models for soil organic matter and particle size using vis-NIR spectral libraries" LINK
"Verified the rapid evaluation of the edible safety of wild porcini mushrooms, using deep learning and PLS‐DA" LINK
Optics for Spectroscopy
"Scientists Teach AI Cameras to See Depth in Photos Better" AI Camera Depth LINK
Facts
"Deep learning accelerates super-resolution microscopy by up to ten times" | DeepLearning microscopy LINK
"Statistical Learning to Operationalize a Domain Agnostic Data Quality Scoring. (arXiv:2108.08905v1 [cs.LG])" LINK
Research on Spectroscopy
"Foods : Instrumentation for Routine Analysis of Acrylamide in French Fries: Assessing Limitations for Adoption" LINK
Process Control and NIR Sensors
"NIR spectroscopy for monitoring of the critical manufacturing steps and quality attributes of paliperidone prolonged release tablets" LINK
Environment NIR-Spectroscopy Application
"Spatial Differentiation Analysis of Water Quality in Dianchi Lake Based on GF-5 NDVI Characteristic Optimization" LINK
"Remote Sensing : Using Sentinel-2 for Simplifying Soil Sampling and Mapping: Two Case Studies in Umbria, Italy" LINK
"Sensors : Ground-Based Hyperspectral Remote Sensing for Estimating Water Stress in Tomato Growth in Sandy Loam and Silty Loam Soils" LINK
"Potential of high-spectral resolution for field phenotyping in plant breeding: Application to maize under water stress" LINK
Agriculture NIR-Spectroscopy Usage
"Study the Genetic Diversity in Protein, Zinc and Iron in Germplasm Pools of Desi Type Chickpeas as Implicated in Quality Breeding" LINK
"Additives and soy detection in powder rice beverage by vibrational spectroscopy as an alternative method for quality and safety control" LINK
"Remote Sensing : Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel" LINK
"Fodder biomass, nutritive value, and grain yield of dual‐purpose Pearl Millet, Sorghum and Maize cultivars across different agro‐ecologies in Burkina Faso" LINK
"Agronomy : Effects of the Foliar Application of Potassium Fertilizer on the Grain Protein and Dough Quality of Wheat" LINK
"Remote Sensing : Sentinel-2 and Landsat-8 Multi-Temporal Series to Estimate Topsoil Properties on Croplands" LINK
"Sensors : Assessment of Grain Harvest Moisture Content Using Machine Learning on Smartphone Images for Optimal Harvest Timing" LINK
"Using UAV image data to monitor the effects of different nitrogen application rates on tea quality" LINK
"A single calibration of near-infrared spectroscopy to determine the quality of forage for multiple species" LINK
"Foods : Temporal Sensory Perceptions of Sugar-Reduced 3D Printed Chocolates" LINK
"Foods : Rapid Nondestructive Simultaneous Detection for Physicochemical Properties of Different Types of Sheep Meat Cut Using Portable Vis/NIR Reflectance Spectroscopy System" LINK
"Foods : Real-Time Gauging of the Gelling Maturity of Duck Eggs Pickled in Strong Alkaline Solutions" LINK
Chemical Industry NIR Usage
"Polymers : Drug Amorphous Solid Dispersions Based on Poly(vinyl Alcohol): Evaluating the Effect of Poly(propylene Succinate) as Plasticizer" LINK
Pharma Industry NIR Usage
" Effects of acetazolamide and furosemide on ventilation and cerebral blood volume in normocapnic and hypercapnic COPD patients" LINK
Other
"Advances, challenges and perspectives of quantum chemical approaches in molecular spectroscopy of the condensed phase" LINK
"Calidad composicional y sensorial de la carne bovina y su determinación mediante infrarrojo cercano" LINK
"Bioinspired StimuliResponsive Hydrogel with Reversible Switching and Fluorescence Behavior Served as LightControlled Soft Actuators" LINK
"Neural Efficiency in Athletes: A Systematic Review" LINK
"Tailored Chiral Copper Selenide Nanochannels for Ultrasensitive Enantioselective Recognition and Detection" LINK
"In vivo diffuse reflectance spectroscopic analysis of fatty liver with inflammation in mice" LINK
"Métodos de análise da composição química e valor nutricional de alimentos para ruminantes" LINK
"Dissociation between exercise intensity thresholds: mechanistic insights from supine exercise" LINK
NIR Calibration-Model Services
Spectroscopy and Chemometrics/Machine-Learning News Weekly 41, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensor 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)
"Development and Submission of Near Infrared Analytical Procedures Guidance for Industry" FDA LINK
"Identification prediction moisture content of Thai coconut sugar (Cocos nucifera L.) using FT-NIR spectroscopy" LINK
"Blood identification of NIR spectroscopy based on BP neural network combined with particle swarm optimization" LINK
"A preliminary study on the utilisation of near infrared spectroscopy to predict age and in vivo human metabolism" LINK
"Near-infrared guidance finalized for small molecule testing, with biologics to come" RAPS LINK
"Wavelength Selection Method for Near Infrared Spectroscopy Based on Iteratively Retains Informative Variables and Successive Projections Algorithm" LINK
"Near-infrared spectroscopy calibration strategies to predict multiple nutritional parameters of pasture species from different" LINK
" Comparison of metabolites and variety authentication of Amomum tsao-ko and Amomum paratsao-ko using GC-MS and NIR spectroscopy" LINK
"Hyperfine-Resolved Near-Infrared Spectra of H(2)(17)O" LINK
"Geographical Differentiation of Hom Mali Rice Cultivated in Different Regions of Thailand Using FTIR-ATR and NIR Spectroscopy" LINK
"Seasonal Snowpack Classification Based on Physical Properties Using Near-Infrared Proximal Hyperspectral Data" | LINK
"NIR-based sensing system for non-Invasive detection of Hemoglobin for point-of-care applications" LINK
"A promising inorganic YFeO3 pigments with high near-infrared reflectance and infrared emission" LINK
"Classification of Softwoods using Wood Extract Information and Near Infrared Spectroscopy." LINK
"Rapid determination and origin identification of total polysaccharides contents in Schisandra chinensis by near-infrared spectroscopy" LINK
"Near-Infrared Spectroscopy Technology in Food" | LINK
"Postharvest ripeness assessment of 'Hass' avocado based on development of a new ripening index and Vis-NIR spectroscopy" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Utility of near‐infrared spectroscopy to detect the extent of lipid core plaque leading to periprocedural myocardial infarction" LINK
"Scaling up Sagebrush Chemistry with Near-Infrared Spectroscopy and Uas-Acquired Hyperspectral Imagery" LINK
Hyperspectral Imaging (HSI)
"Spatially Resolved Spectroscopic Characterization of Nanostructured Films by Hyperspectral Dark-Field Microscopy" LINK
"Visual attention-driven framework to incorporate spatial-spectral features for hyperspectral anomaly detection" LINK
Spectral Imaging
"Spectral Super-Resolution of Multispectral Images Using Spatial-Spectral Residual Attention Network" LINK
Chemometrics and Machine Learning
"Feasibility of a chromameter and chemometric techniques to discriminate pure and mixed organic and conventional red pepper powders: A pilot study" LINK
"Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage" LINK
"Applied Sciences : A Novel Principal Component Analysis Integrating Long Short-Term Memory Network and Its Application in Productivity Prediction of Cutter Suction Dredgers" LINK
"Plants : Morpho-Physiological Classification of Italian Tomato Cultivars (Solanum lycopersicum L.) According to Drought Tolerance during Vegetative and Reproductive Growth" LINK
"Automatic food and beverage authentication and adulteration detection by classification hybrid fusion" LINK
"Remote Sensing : Improving Accuracy of Herbage Yield Predictions in Perennial Ryegrass with UAV-Based Structural and Spectral Data Fusion and Machine Learning" LINK
"Estimating Fat Components of Potato Chips Using Visible and Near-Infrared Spectroscopy and a Compositional Calibration Model" LINK
"Wavelengths selection based on genetic algorithm (GA) and successive projections algorithms (SPA) combine with PLS regression for determination the soluble ..." LINK
"Comparison of PLS and SVM models for soil organic matter and particle size using vis-NIR spectral libraries" LINK
"Verified the rapid evaluation of the edible safety of wild porcini mushrooms, using deep learning and PLS‐DA" LINK
Optics for Spectroscopy
"Scientists Teach AI Cameras to See Depth in Photos Better" AI Camera Depth LINK
Facts
"Deep learning accelerates super-resolution microscopy by up to ten times" | DeepLearning microscopy LINK
"Statistical Learning to Operationalize a Domain Agnostic Data Quality Scoring. (arXiv:2108.08905v1 [cs.LG])" LINK
Research on Spectroscopy
"Foods : Instrumentation for Routine Analysis of Acrylamide in French Fries: Assessing Limitations for Adoption" LINK
Process Control and NIR Sensors
"NIR spectroscopy for monitoring of the critical manufacturing steps and quality attributes of paliperidone prolonged release tablets" LINK
Environment NIR-Spectroscopy Application
"Spatial Differentiation Analysis of Water Quality in Dianchi Lake Based on GF-5 NDVI Characteristic Optimization" LINK
"Remote Sensing : Using Sentinel-2 for Simplifying Soil Sampling and Mapping: Two Case Studies in Umbria, Italy" LINK
"Sensors : Ground-Based Hyperspectral Remote Sensing for Estimating Water Stress in Tomato Growth in Sandy Loam and Silty Loam Soils" LINK
"Potential of high-spectral resolution for field phenotyping in plant breeding: Application to maize under water stress" LINK
Agriculture NIR-Spectroscopy Usage
"Study the Genetic Diversity in Protein, Zinc and Iron in Germplasm Pools of Desi Type Chickpeas as Implicated in Quality Breeding" LINK
"Additives and soy detection in powder rice beverage by vibrational spectroscopy as an alternative method for quality and safety control" LINK
"Remote Sensing : Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel" LINK
"Fodder biomass, nutritive value, and grain yield of dual‐purpose Pearl Millet, Sorghum and Maize cultivars across different agro‐ecologies in Burkina Faso" LINK
"Agronomy : Effects of the Foliar Application of Potassium Fertilizer on the Grain Protein and Dough Quality of Wheat" LINK
"Remote Sensing : Sentinel-2 and Landsat-8 Multi-Temporal Series to Estimate Topsoil Properties on Croplands" LINK
"Sensors : Assessment of Grain Harvest Moisture Content Using Machine Learning on Smartphone Images for Optimal Harvest Timing" LINK
"Using UAV image data to monitor the effects of different nitrogen application rates on tea quality" LINK
"A single calibration of near-infrared spectroscopy to determine the quality of forage for multiple species" LINK
"Foods : Temporal Sensory Perceptions of Sugar-Reduced 3D Printed Chocolates" LINK
"Foods : Rapid Nondestructive Simultaneous Detection for Physicochemical Properties of Different Types of Sheep Meat Cut Using Portable Vis/NIR Reflectance Spectroscopy System" LINK
"Foods : Real-Time Gauging of the Gelling Maturity of Duck Eggs Pickled in Strong Alkaline Solutions" LINK
Chemical Industry NIR Usage
"Polymers : Drug Amorphous Solid Dispersions Based on Poly(vinyl Alcohol): Evaluating the Effect of Poly(propylene Succinate) as Plasticizer" LINK
Pharma Industry NIR Usage
" Effects of acetazolamide and furosemide on ventilation and cerebral blood volume in normocapnic and hypercapnic COPD patients" LINK
Other
"Advances, challenges and perspectives of quantum chemical approaches in molecular spectroscopy of the condensed phase" LINK
"Calidad composicional y sensorial de la carne bovina y su determinación mediante infrarrojo cercano" LINK
"Bioinspired StimuliResponsive Hydrogel with Reversible Switching and Fluorescence Behavior Served as LightControlled Soft Actuators" LINK
"Neural Efficiency in Athletes: A Systematic Review" LINK
"Tailored Chiral Copper Selenide Nanochannels for Ultrasensitive Enantioselective Recognition and Detection" LINK
"In vivo diffuse reflectance spectroscopic analysis of fatty liver with inflammation in mice" LINK
"Métodos de análise da composição química e valor nutricional de alimentos para ruminantes" LINK
"Dissociation between exercise intensity thresholds: mechanistic insights from supine exercise" LINK
NIR Calibration-Model Services
Spectroscopy and Chemometrics/Machine-Learning News Weekly 41, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensor 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)
"Development and Submission of Near Infrared Analytical Procedures Guidance for Industry" FDA LINK
"Identification prediction moisture content of Thai coconut sugar (Cocos nucifera L.) using FT-NIR spectroscopy" LINK
"Blood identification of NIR spectroscopy based on BP neural network combined with particle swarm optimization" LINK
"A preliminary study on the utilisation of near infrared spectroscopy to predict age and in vivo human metabolism" LINK
"Near-infrared guidance finalized for small molecule testing, with biologics to come" RAPS LINK
"Wavelength Selection Method for Near Infrared Spectroscopy Based on Iteratively Retains Informative Variables and Successive Projections Algorithm" LINK
"Near-infrared spectroscopy calibration strategies to predict multiple nutritional parameters of pasture species from different" LINK
" Comparison of metabolites and variety authentication of Amomum tsao-ko and Amomum paratsao-ko using GC-MS and NIR spectroscopy" LINK
"Hyperfine-Resolved Near-Infrared Spectra of H(2)(17)O" LINK
"Geographical Differentiation of Hom Mali Rice Cultivated in Different Regions of Thailand Using FTIR-ATR and NIR Spectroscopy" LINK
"Seasonal Snowpack Classification Based on Physical Properties Using Near-Infrared Proximal Hyperspectral Data" | LINK
"NIR-based sensing system for non-Invasive detection of Hemoglobin for point-of-care applications" LINK
"A promising inorganic YFeO3 pigments with high near-infrared reflectance and infrared emission" LINK
"Classification of Softwoods using Wood Extract Information and Near Infrared Spectroscopy." LINK
"Rapid determination and origin identification of total polysaccharides contents in Schisandra chinensis by near-infrared spectroscopy" LINK
"Near-Infrared Spectroscopy Technology in Food" | LINK
"Postharvest ripeness assessment of 'Hass' avocado based on development of a new ripening index and Vis-NIR spectroscopy" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Utility of near‐infrared spectroscopy to detect the extent of lipid core plaque leading to periprocedural myocardial infarction" LINK
"Scaling up Sagebrush Chemistry with Near-Infrared Spectroscopy and Uas-Acquired Hyperspectral Imagery" LINK
Hyperspectral Imaging (HSI)
"Spatially Resolved Spectroscopic Characterization of Nanostructured Films by Hyperspectral Dark-Field Microscopy" LINK
"Visual attention-driven framework to incorporate spatial-spectral features for hyperspectral anomaly detection" LINK
Spectral Imaging
"Spectral Super-Resolution of Multispectral Images Using Spatial-Spectral Residual Attention Network" LINK
Chemometrics and Machine Learning
"Feasibility of a chromameter and chemometric techniques to discriminate pure and mixed organic and conventional red pepper powders: A pilot study" LINK
"Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage" LINK
"Applied Sciences : A Novel Principal Component Analysis Integrating Long Short-Term Memory Network and Its Application in Productivity Prediction of Cutter Suction Dredgers" LINK
"Plants : Morpho-Physiological Classification of Italian Tomato Cultivars (Solanum lycopersicum L.) According to Drought Tolerance during Vegetative and Reproductive Growth" LINK
"Automatic food and beverage authentication and adulteration detection by classification hybrid fusion" LINK
"Remote Sensing : Improving Accuracy of Herbage Yield Predictions in Perennial Ryegrass with UAV-Based Structural and Spectral Data Fusion and Machine Learning" LINK
"Estimating Fat Components of Potato Chips Using Visible and Near-Infrared Spectroscopy and a Compositional Calibration Model" LINK
"Wavelengths selection based on genetic algorithm (GA) and successive projections algorithms (SPA) combine with PLS regression for determination the soluble ..." LINK
"Comparison of PLS and SVM models for soil organic matter and particle size using vis-NIR spectral libraries" LINK
"Verified the rapid evaluation of the edible safety of wild porcini mushrooms, using deep learning and PLS‐DA" LINK
Optics for Spectroscopy
"Scientists Teach AI Cameras to See Depth in Photos Better" AI Camera Depth LINK
Facts
"Deep learning accelerates super-resolution microscopy by up to ten times" | DeepLearning microscopy LINK
"Statistical Learning to Operationalize a Domain Agnostic Data Quality Scoring. (arXiv:2108.08905v1 [cs.LG])" LINK
Research on Spectroscopy
"Foods : Instrumentation for Routine Analysis of Acrylamide in French Fries: Assessing Limitations for Adoption" LINK
Process Control and NIR Sensors
"NIR spectroscopy for monitoring of the critical manufacturing steps and quality attributes of paliperidone prolonged release tablets" LINK
Environment NIR-Spectroscopy Application
"Spatial Differentiation Analysis of Water Quality in Dianchi Lake Based on GF-5 NDVI Characteristic Optimization" LINK
"Remote Sensing : Using Sentinel-2 for Simplifying Soil Sampling and Mapping: Two Case Studies in Umbria, Italy" LINK
"Sensors : Ground-Based Hyperspectral Remote Sensing for Estimating Water Stress in Tomato Growth in Sandy Loam and Silty Loam Soils" LINK
"Potential of high-spectral resolution for field phenotyping in plant breeding: Application to maize under water stress" LINK
Agriculture NIR-Spectroscopy Usage
"Study the Genetic Diversity in Protein, Zinc and Iron in Germplasm Pools of Desi Type Chickpeas as Implicated in Quality Breeding" LINK
"Additives and soy detection in powder rice beverage by vibrational spectroscopy as an alternative method for quality and safety control" LINK
"Remote Sensing : Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel" LINK
"Fodder biomass, nutritive value, and grain yield of dual‐purpose Pearl Millet, Sorghum and Maize cultivars across different agro‐ecologies in Burkina Faso" LINK
"Agronomy : Effects of the Foliar Application of Potassium Fertilizer on the Grain Protein and Dough Quality of Wheat" LINK
"Remote Sensing : Sentinel-2 and Landsat-8 Multi-Temporal Series to Estimate Topsoil Properties on Croplands" LINK
"Sensors : Assessment of Grain Harvest Moisture Content Using Machine Learning on Smartphone Images for Optimal Harvest Timing" LINK
"Using UAV image data to monitor the effects of different nitrogen application rates on tea quality" LINK
"A single calibration of near-infrared spectroscopy to determine the quality of forage for multiple species" LINK
"Foods : Temporal Sensory Perceptions of Sugar-Reduced 3D Printed Chocolates" LINK
"Foods : Rapid Nondestructive Simultaneous Detection for Physicochemical Properties of Different Types of Sheep Meat Cut Using Portable Vis/NIR Reflectance Spectroscopy System" LINK
"Foods : Real-Time Gauging of the Gelling Maturity of Duck Eggs Pickled in Strong Alkaline Solutions" LINK
Chemical Industry NIR Usage
"Polymers : Drug Amorphous Solid Dispersions Based on Poly(vinyl Alcohol): Evaluating the Effect of Poly(propylene Succinate) as Plasticizer" LINK
Pharma Industry NIR Usage
" Effects of acetazolamide and furosemide on ventilation and cerebral blood volume in normocapnic and hypercapnic COPD patients" LINK
Other
"Advances, challenges and perspectives of quantum chemical approaches in molecular spectroscopy of the condensed phase" LINK
"Calidad composicional y sensorial de la carne bovina y su determinación mediante infrarrojo cercano" LINK
"Bioinspired StimuliResponsive Hydrogel with Reversible Switching and Fluorescence Behavior Served as LightControlled Soft Actuators" LINK
"Neural Efficiency in Athletes: A Systematic Review" LINK
"Tailored Chiral Copper Selenide Nanochannels for Ultrasensitive Enantioselective Recognition and Detection" LINK
"In vivo diffuse reflectance spectroscopic analysis of fatty liver with inflammation in mice" LINK
"Métodos de análise da composição química e valor nutricional de alimentos para ruminantes" LINK
"Dissociation between exercise intensity thresholds: mechanistic insights from supine exercise" LINK
Digitalization is advancing, also in NIR spectroscopy, which enables trainable miniature smart sensors e.g. for analyses in the food&feed, chemical and pharmaceutical sectors.
The calibration is the core of a NIR spectroscopy sensor, it enables the numerous applications and should therefore not be the weakest link in the measurement chain.
The development of calibrations that turn NIR spectrometers into smart sensors is done manually by experts (NIR specialist, chemometrician, data scientist) with so-called chemometrics software.
This is very time-consuming (time to market) and the result is person-dependent and thus suboptimal, because each expert has his own preferred way of proceeding.
In addition, the calibrations have to be maintained, as new data has been collected in the meantime, which can be used to extend and improve the calibrations.
This is where our automated service comes in, combining the knowledge and good practices of NIR spectroscopy and chemometrics collected in one software and using machine learning to generate optimal calibrations.
Based on this, we have developed a complete technology platform (Time to Market) that covers the entire process from sending NIR + Lab data, to NIR Calibration as a Service, from online purchase of calibrations, to NIR Predictor software that directly evaluates newly measured NIR data locally and generates result reports.
Besides the free desktop version with user interface, the NIR Predictor can also be integrated (OEM). This can be integrated in parallel as a complement to your current Predictor, allowing the user to choose how they want to calibrate.
And give them the advantage in NIR feasibility studies and NIR spectrometer evaluations to quickly provide the customer with a solid and accurate calibration that will make their NIR system deliver better results.
Advantages for your NIR users (internal or external)
no initial costs (no chemometrics software license required),
calculable operating costs (fixed amount instead of time and hourly rate) (calibration development, calibration maintenance)
easy to use (no chemometrics and software training),
quicker to use (no calibration development work) and
Our chargeable service is based on the calibration development and the annual calibration use.
Calibration development and calibration use can also be carried out separately (manufacturer / user).
For you as a spectrometer manufacturer, this means that you can deliver your system pre-calibrated for certain applications without incurring software license costs. And without your application specialists having to provide additional calibration services.
The unique advantages of our calibration service together with the free NIR Predictor are:
no software license costs (chemometrics software, predictor software, OEM integration)
no chemometrics know-how necessary
no time needed to develop optimal NIR calibrations.
About CalibrationModel.com : Time and knowledge intensive creation and optimization of chemometric evaluation methods for spectrometers as a service to enable more accurate analysis and measurement results.
Die Digitalisierung schreitet voran, so auch in der NIR-Spektroskopie, die trainierbare miniatur Smart-Sensors ermöglicht z.B. für Analysen im Bereich Food&Feed, Chemie und Pharma.
Die Kalibration ist das Kernstück eines NIR-Spektroskopie Sensors, sie ermöglicht die zahlreichen Applikationen und sollte darum nicht das schwächste Glied in der Messkette sein.
Das Entwickeln von Kalibrationen die NIR-Spektrometer zu Smart-Sensoren macht, wird bis an hin von Experten (NIR-Spezialist, Chemometriker, Data Scientist) manuell gemacht mit sogenannter Chemometrie Software.
Das ist sehr zeitintensiv (Time to Market) und das Ergebnis ist personenabhängig und somit suboptimal, denn jeder Experte hat seine eigene bevorzugte Weise wie er vorgeht.
Dazu kommt, dass die Kalibrationen gewartet werden müssen, da in der Zwischenzeit neue Daten gesammelt wurden, die zur Erweiterung und Verbesserung der Kalibrationen genutzt werden kann.
Hier setzt unser automatisierter Service an, der das Wissen und Good-Practices der NIR-Spektroskopie und Chemometrie gesammelt in einer Software vereint und mittels Machine-Learning optimale Kalibrationen erzeugt.
Wir haben darauf aufbauend eine komplette Technologie-Plattform entwickelt (Time to Market), die den ganzen Ablauf vom Senden der NIR + Lab Daten, zu NIR-Kalibration as a Service, vom Online-Kauf der Kalibrationen, bis hin zur NIR-Predictor Software die neu gemessene NIR Daten direkt lokal auswertet und Ergebnis Reports erstellt.
Nebst der freien Desktop Variante mit User Interface kann der NIR-Predictor auch integriert werden (OEM). Das kann parallel als Ergänzung zu ihrem jetzigen Predictor integriert werden und so dem Anwender die Wahl ermöglichen, wie er Kalibrieren möchte.
Und ihnen so den Vorteil verschaffen, bei NIR Feasibility Studies und NIR-Spektrometer Evaluationen, dem Kunden rasch eine solide und genaue Kalibration bereitzustellen, die ihr NIR System bessere Ergebnisse liefern lässt.
Vorteile für ihre NIR-Anwender (intern oder extern)
keine Initial-Kosten (keine Chemometrie Software Lizenz nötig),
kalkulierbare Betriebs Kosten (fix Betrag statt nach Aufwand und Stundensatz) (Kalibrationsentwicklung, Kalibrations-Pflege)
einfach Anwendbar (keine Chemometrie- und Software-Trainings),
schneller Einsatzbereit (keine Kalibrations-Entwicklungs Arbeit) und
Unsere kostenpflichtige Serviceleistung beruht auf der Kalibrationsentwicklung und der jährlichen Kalibrationsnutzung.
Dabei kann die Kalibrationsentwicklung und Kalibrationsnutzung auch getrennt voneinander (Hersteller / User) erfolgen.
Für Sie als Spektrometer Hersteller kommt so die Möglichkeit hinzu, dass Sie für bestimmte Applikationen ihr System Vorkalibriert ausliefern können, ohne dass Software-Lizenz-Kosten fällig werden. Und ohne dass ihre Applikations-Spezialisten zusätzliche Kalibrationsleistung erbringen müssen.
Die einzigartigen Vorteile unseres Calibrations-Service zusammen mit dem free NIR-Predictor sind:
keine Software Lizenz Kosten (Chemometrie Software, Predictor Software, OEM integration)
kein Chemometrie Know-How nötig
kein Zeitaufwand nötig um optimale NIR-Kalibrationen zu entwickeln.
Bei Interesse zur Nutzung/Evaluation des Services :
Über CalibrationModel.com : Zeit- und Wissens-intensive Erstellung und Optimierung von chemometrischen Auswertemethoden für Spektrometer als Service, um präzisere Analysen- und Messergebnisse zu ermöglichen.
La digitalizzazione sta progredendo, anche nella spettroscopia NIR, che consente l'uso di sensori intelligenti in miniatura addestrabili, ad esempio per analisi nei settori alimentare e dei mangimi, chimico e farmaceutico.
La calibrazione è il cuore di un sensore di spettroscopia NIR, consente le numerose applicazioni e non dovrebbe quindi essere l'anello più debole della catena di misura.
Lo sviluppo delle calibrazioni che trasformano gli spettrometri NIR in sensori intelligenti viene effettuato manualmente da esperti (specialista NIR, chemiometrista, scienziato dei dati) con il cosiddetto software di chemiometria.
Ciò richiede molto tempo (time to market) e il risultato dipende dalla persona ed è quindi subottimale, perché ogni esperto ha il suo modo di procedere preferito.
Inoltre, le calibrazioni devono essere mantenute, poiché nel frattempo sono stati raccolti nuovi dati che possono essere utilizzati per ampliare e migliorare le calibrazioni.
Qui entra in gioco il nostro servizio automatizzato, che combina le conoscenze e le buone pratiche della spettroscopia NIR e della chemiometria in un unico software e genera calibrazioni ottimali mediante l'apprendimento automatico.
Su questa base, abbiamo sviluppato una piattaforma tecnologica completa (Time to Market), che copre l'intero processo dall'invio dei dati NIR + Lab, alla calibrazione NIR come servizio, dall'acquisto online delle calibrazioni, al software NIR Predictor, che valuta direttamente i dati NIR appena misurati a livello locale e genera rapporti sui risultati.
Oltre alla versione desktop gratuita con interfaccia utente, il NIR Predictor può essere integrato (OEM). Questo può essere integrato in parallelo come complemento al vostro Predictor attuale, permettendo all'utente di scegliere come vuole calibrare.
Questo vi offre il vantaggio negli studi di fattibilità NIR e nelle valutazioni degli spettrometri NIR per fornire rapidamente al cliente una calibrazione solida e accurata che farà sì che il vostro sistema NIR fornisca risultati migliori.
Vantaggi per i vostri utenti NIR (interni o esterni)
nessun costo iniziale (non è necessaria la licenza del software di chemiometria),
costi operativi calcolabili (importo fisso anziché tariffa oraria) (sviluppo della taratura, manutenzione della taratura)
facile da usare (nessuna chemiometria e formazione software),
più veloce da usare (nessun lavoro di sviluppo di calibrazione) e
Il nostro servizio a pagamento si basa sullo sviluppo della taratura e sull'utilizzo annuale della taratura.
Lo sviluppo della taratura e l'uso della taratura possono essere effettuati anche separatamente (produttore/utente).
Per voi, in qualità di produttori di spettrometri, ciò significa che potete fornire il vostro sistema pre-calibrato per determinate applicazioni senza incorrere in costi di licenza del software. E senza che i vostri specialisti delle applicazioni debbano fornire ulteriori servizi di taratura.
I vantaggi unici del nostro servizio di calibrazione insieme al predittore NIR Predictor gratuito sono:
nessun costo di licenza software (software di chemiometria, software di previsione, integrazione OEM)
non è necessario alcun know-how in chemiometria
non c'è bisogno di tempo per sviluppare calibrazioni NIR ottimali.
Informazioni su CalibrationModel.com : Creazione e ottimizzazione dei metodi di valutazione chemiometrica per gli spettrometri come servizio per consentire analisi e risultati di misura più precisi.
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
With the NIR-Predictor software,
you can use your NIRS calibration files locally and offline.
That means you can predict as much NIR data as you want,
at full speed without waiting at no extra cost (it's NOT a cloud prediction where you pay per result).
The NIR-Predictor shows which samples should be sent to the laboratory for reference analysis in order to complete the data for the next calibration.
This minimizes the laboratory effort and further costs.
This is based on the fact that sample spectra that are foreign to the calibration are marked as outliers in the prediction report generated by the NIR-Predictor. This way, these samples can be analyzed in the laboratory and used to enhance the NIR + Lab data.
Mit der NIR-Predictor-Software können Sie Ihre NIRS-Kalibrierungsdateien lokal und offline verwenden.
Das heißt, Sie können so viele NIR-Daten vorhersagen, wie Sie wollen,
bei voller Geschwindigkeit, ohne zu warten und ohne zusätzliche Kosten (es handelt sich NICHT um eine Cloud Vorhersage, bei der Sie pro Ergebnis bezahlen).
Der NIR-Predictor zeigt auf, welche Samples zur Referenz Analytik ins Labor sollten, um die Daten für die nächste Kalibrierung zu vervollständigen.
Das minimiert den Labor Aufwand und weitere Kosten.
Dies basiert darauf, dass der Kalibration fremde Sample Spektren als Outlier markiert werden im Predcition Report das der NIR-Predictor erzeugt. So können diese Sample im Labor genau analysiert werden und zur Erweiterung der NIR + Lab Daten herangezogen werden.
Quantitative Analyse von Konzentrationen und Inhaltsstoffen mittels NIR-Spektroskopie (NIR Messung ist schnell, harmlos, zerstörungsfrei und miniaturisiert)
Chemometrische Kalibrierung und Analyse für die NIR-Spektroskopie leicht gemacht
NIR-Kalibrierungsoptimierung und Vorhersagemodelle anwenden
Einsatz von NIR-Spektrometern mit Kalibrierkurven/Gleichungen
Bleiben Sie auf dem Laufenden mit der NIR-Spektroskopie - Forschung, Wissenschaft und Ausrüstung :
mit unserem kostenlosen
NIRS and Chemometrics News Weekly und täglichen Nachrichten auf Twitter @CalibModel
With the NIR-Predictor software,
you can use your NIRS calibration files locally and offline.
That means you can predict as much NIR data as you want,
at full speed without waiting at no extra cost (it's NOT a cloud prediction where you pay per result).
The NIR-Predictor shows which samples should be sent to the laboratory for reference analysis in order to complete the data for the next calibration.
This minimizes the laboratory effort and further costs.
This is based on the fact that sample spectra that are foreign to the calibration are marked as outliers in the prediction report generated by the NIR-Predictor. This way, these samples can be analyzed in the laboratory and used to enhance the NIR + Lab data.