"Aplikasi Near Infrared Spectroscopy (NIRS) untuk Memprediksi Kandungan Kimia Minyak Cengkeh (Syzigium aromaticum)" LINK
"Unsupervised analysis of NIRS spectra to assess complex plant traits: leaf senescence as a use case" | LINK
"Expanded visible-near-infrared temperature sensing properties in view
of ultra-broadband tunable luminescence in Mg3Y2Ge3O12: Ce3+, Cr3+
phosphors with ..." LINK
"Prediction of Soil Available Boron Content in Visible-Near-Infrared
Hyperspectral Based on Different Preprocessing Transformations and
Characteristic ..." LINK
"Rapidly detecting fennel origin of the near-infrared spectroscopy based on extreme learning machine" | LINK
"Role of Near-infrared Spectroscopy in the Diagnosis and Assessment of
Necrotizing Enterocolitis" LINK
"Near-infrared spectroscopy for rapid identification of pharmaceutical excipients" LINK
"Evaluation of transcutaneous near-infrared spectroscopy for early detection of cardiac arrest in an animal model" LINK
"Analysis of Perovskite Solar Cell Degradation over Time Using NIR Spectroscopy—A Novel Approach" LINK
"Rapid Discrimination of the Country Origin of Soybeans Based on FT-NIR Spectroscopy and Data Expansion" | LINK
"Weight interpretation of artificial neural network model for analysis
of rice (Oryza sativa L.) with near-infrared spectroscopy" LINK
"Origins Classification of Egg with Different Storage Durations Using
FT-NIR: A Characteristic Wavelength Selection Approach Based on
Information Entropy" LINK
"Measurement of nitrogen content in rice plant using near infrared spectroscopy combined with different PLS algorithms" LINK
"Rapid determination of acidity index of peanuts by near-infrared
spectroscopy technology: Comparing the performance of different
near-infrared spectral models" LINK
"Infrared spectroscopy (NIRS and ATR-FTIR) together with multivariate
classification for non-destructive differentiation between female
mosquitoes of Aedes aegypti recently infected with dengue vs. uninfected
females" LINK
"Spectral Encoder to Extract the Features of Near-Infrared Spectra for Multivariate Calibration" LINK
"Internal disorder evaluation of 'Namdokmai Sithong'mango by near infrared spectroscopy" | LINK
"... Combined Models Significantly Improve the Predictive
Performance of Soil Organic Carbon from North-West India Using
Visible-Near Infrared Spectroscopy" LINK
"Optimizing functional near-infrared spectroscopy (fNIRS) channels for
schizophrenic identification during a verbal fluency task using
metaheuristic algorithms" LINK
"Soluble Solids Content Binary Classification of Miyagawa Satsuma in Chongming Island Based on Near Infrared Spectroscopy" | LINK
"Grading detection of "Red Fuji" apple in Luochuan based on machine vision and near-infrared spectroscopy" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Grading detection of “Red Fuji” apple in Luochuan based on machine vision and near-infrared spectroscopy" LINK
"A near‐infrared spectroscopy method for the detection of texture
profile analysis of Litopeneo vannamei based on partial least squares
regression" LINK
Hyperspectral Imaging (HSI)
"Detection of infestation by striped stemborer (Chilo suppressalis) in rice based on hyperspectral imaging" LINK
"Comparison of Visible-Near Infrared and Fluorescence Hyperspectral
Imaging Techniques for Non-Destructive Detection of Lipid Oxidation
Degree in Frozen-Thawed ..." LINK
Spectral Imaging
"Foods : Application of Fourier Transform Infrared (FT-IR) Spectroscopy,
Multispectral Imaging (MSI) and Electronic Nose (E-Nose) for the Rapid
Evaluation of the Microbiological Quality of Gilthead Sea Bream Fillets"
LINK
Chemometrics and Machine Learning
"Dual Wavelength based Approach with Partial Least Square Regression for the Prediction of Glucose Concentration" LINK
"Superiority of Two-Dimensional Correlation Spectroscopy Combined with ResNet in Species Identification of Bolete" LINK
"Transfer of a calibration model for the prediction of lignin in pulpwood among four portable near infrared spectrometers" LINK
Environment NIR-Spectroscopy Application
"Remote Sensing : Identifying Coffee Agroforestry System Types Using Multitemporal Sentinel-2 Aata and Auxiliary Information" LINK
Agriculture NIR-Spectroscopy Usage
"Biosensors : A Mouse Holder for Awake Functional Imaging in
Unanesthetized Mice: Applications in 31P Spectroscopy,
Manganese-Enhanced Magnetic Resonance Imaging Studies, and Resting-State
Functional Magnetic Resonance Imaging" LINK
"Assessment of Soil Characteristics Using a Three-Band Agricultural Digital Camera" LINK
"Texture profile and short-NIR spectral vibrations relationship
evaluated through Comdim: The case study for animal and vegetable
proteins" LINK
"Agriculture : Research on Wet Clutch Switching Quality in the Shifting Stage of an Agricultural Tractor Transmission System" LINK
"Agriculture : Application of a Fractional Order Differential to the Hyperspectral Inversion of Soil Iron Oxide" LINK
"Structural aspects of hyperspectral imaging data: a case study on
microplastics analysis from the viewpoint of chemometrics" LINK
"Bilateral diffuse uveal melanocytic proliferation associated with endometrial carcinoma-multimodal imaging analysis" LINK
"Patterns of denitrifier communities assembly and co-occurrence network
regulate N2O emissions in soils with long-term contrasting tillage
histories" LINK
"Linking long-term soil phosphorus management to nitrogen cycling microbial communities" LINK
Food & Feed Industry NIR Usage
"Foods : Antioxidant and Cytoprotective Capacities of Various Wheat (Triticum aestivum L.) Cultivars in Korea" LINK
Other
"Effect of Codend Design and Postponed Bleeding on Hemoglobin in Cod
Fillets Caught by Bottom Trawl in the Barents Sea Demersal Fishery" LINK
"Tracing the world's timber: the status of scientific verification technologies for species and origin identification" | LINK
"Structural, Morphological and Optical Properties of Zinc Oxide Nanorods
prepared by ZnO seed layer Annealed at Different Oxidation Temperature"
LINK
"Synthesis, characterization, and DFT study of linear and non-linear
optical properties of some novel thieno [2, 3-b] thiophene azo dye
derivatives" LINK
.
NIR Calibration-Model Services
Increase Your Profit with optimized NIRS Accuracy Food Feed FoodSafety ag Lab QC QA PAT QbD Pharma LINK
Spectroscopy and Chemometrics News Weekly 38, 2022 | NIRS NIR
Spectroscopy MachineLearning Spectrometer Spectrometric Analytical
Chemistry Chemical Analysis Laboratories Laboratory Software AG Sensors
QA QC Testing Quality LINK
"Aplikasi Near Infrared Spectroscopy (NIRS) untuk Memprediksi Kandungan Kimia Minyak Cengkeh (Syzigium aromaticum)" LINK
"Unsupervised analysis of NIRS spectra to assess complex plant traits: leaf senescence as a use case" | LINK
"Expanded visible-near-infrared temperature sensing properties in view
of ultra-broadband tunable luminescence in Mg3Y2Ge3O12: Ce3+, Cr3+
phosphors with ..." LINK
"Prediction of Soil Available Boron Content in Visible-Near-Infrared
Hyperspectral Based on Different Preprocessing Transformations and
Characteristic ..." LINK
"Rapidly detecting fennel origin of the near-infrared spectroscopy based on extreme learning machine" | LINK
"Role of Near-infrared Spectroscopy in the Diagnosis and Assessment of
Necrotizing Enterocolitis" LINK
"Near-infrared spectroscopy for rapid identification of pharmaceutical excipients" LINK
"Evaluation of transcutaneous near-infrared spectroscopy for early detection of cardiac arrest in an animal model" LINK
"Analysis of Perovskite Solar Cell Degradation over Time Using NIR Spectroscopy—A Novel Approach" LINK
"Rapid Discrimination of the Country Origin of Soybeans Based on FT-NIR Spectroscopy and Data Expansion" | LINK
"Weight interpretation of artificial neural network model for analysis
of rice (Oryza sativa L.) with near-infrared spectroscopy" LINK
"Origins Classification of Egg with Different Storage Durations Using
FT-NIR: A Characteristic Wavelength Selection Approach Based on
Information Entropy" LINK
"Measurement of nitrogen content in rice plant using near infrared spectroscopy combined with different PLS algorithms" LINK
"Rapid determination of acidity index of peanuts by near-infrared
spectroscopy technology: Comparing the performance of different
near-infrared spectral models" LINK
"Infrared spectroscopy (NIRS and ATR-FTIR) together with multivariate
classification for non-destructive differentiation between female
mosquitoes of Aedes aegypti recently infected with dengue vs. uninfected
females" LINK
"Spectral Encoder to Extract the Features of Near-Infrared Spectra for Multivariate Calibration" LINK
"Internal disorder evaluation of 'Namdokmai Sithong'mango by near infrared spectroscopy" | LINK
"... Combined Models Significantly Improve the Predictive
Performance of Soil Organic Carbon from North-West India Using
Visible-Near Infrared Spectroscopy" LINK
"Optimizing functional near-infrared spectroscopy (fNIRS) channels for
schizophrenic identification during a verbal fluency task using
metaheuristic algorithms" LINK
"Soluble Solids Content Binary Classification of Miyagawa Satsuma in Chongming Island Based on Near Infrared Spectroscopy" | LINK
"Grading detection of "Red Fuji" apple in Luochuan based on machine vision and near-infrared spectroscopy" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Grading detection of “Red Fuji” apple in Luochuan based on machine vision and near-infrared spectroscopy" LINK
"A near‐infrared spectroscopy method for the detection of texture
profile analysis of Litopeneo vannamei based on partial least squares
regression" LINK
Hyperspectral Imaging (HSI)
"Detection of infestation by striped stemborer (Chilo suppressalis) in rice based on hyperspectral imaging" LINK
"Comparison of Visible-Near Infrared and Fluorescence Hyperspectral
Imaging Techniques for Non-Destructive Detection of Lipid Oxidation
Degree in Frozen-Thawed ..." LINK
Spectral Imaging
"Foods : Application of Fourier Transform Infrared (FT-IR) Spectroscopy,
Multispectral Imaging (MSI) and Electronic Nose (E-Nose) for the Rapid
Evaluation of the Microbiological Quality of Gilthead Sea Bream Fillets"
LINK
Chemometrics and Machine Learning
"Dual Wavelength based Approach with Partial Least Square Regression for the Prediction of Glucose Concentration" LINK
"Superiority of Two-Dimensional Correlation Spectroscopy Combined with ResNet in Species Identification of Bolete" LINK
"Transfer of a calibration model for the prediction of lignin in pulpwood among four portable near infrared spectrometers" LINK
Environment NIR-Spectroscopy Application
"Remote Sensing : Identifying Coffee Agroforestry System Types Using Multitemporal Sentinel-2 Aata and Auxiliary Information" LINK
Agriculture NIR-Spectroscopy Usage
"Biosensors : A Mouse Holder for Awake Functional Imaging in
Unanesthetized Mice: Applications in 31P Spectroscopy,
Manganese-Enhanced Magnetic Resonance Imaging Studies, and Resting-State
Functional Magnetic Resonance Imaging" LINK
"Assessment of Soil Characteristics Using a Three-Band Agricultural Digital Camera" LINK
"Texture profile and short-NIR spectral vibrations relationship
evaluated through Comdim: The case study for animal and vegetable
proteins" LINK
"Agriculture : Research on Wet Clutch Switching Quality in the Shifting Stage of an Agricultural Tractor Transmission System" LINK
"Agriculture : Application of a Fractional Order Differential to the Hyperspectral Inversion of Soil Iron Oxide" LINK
"Structural aspects of hyperspectral imaging data: a case study on
microplastics analysis from the viewpoint of chemometrics" LINK
"Bilateral diffuse uveal melanocytic proliferation associated with endometrial carcinoma-multimodal imaging analysis" LINK
"Patterns of denitrifier communities assembly and co-occurrence network
regulate N2O emissions in soils with long-term contrasting tillage
histories" LINK
"Linking long-term soil phosphorus management to nitrogen cycling microbial communities" LINK
Food & Feed Industry NIR Usage
"Foods : Antioxidant and Cytoprotective Capacities of Various Wheat (Triticum aestivum L.) Cultivars in Korea" LINK
Other
"Effect of Codend Design and Postponed Bleeding on Hemoglobin in Cod
Fillets Caught by Bottom Trawl in the Barents Sea Demersal Fishery" LINK
"Tracing the world's timber: the status of scientific verification technologies for species and origin identification" | LINK
"Structural, Morphological and Optical Properties of Zinc Oxide Nanorods
prepared by ZnO seed layer Annealed at Different Oxidation Temperature"
LINK
"Synthesis, characterization, and DFT study of linear and non-linear
optical properties of some novel thieno [2, 3-b] thiophene azo dye
derivatives" LINK
.
NIR Calibration-Model Services
Increase Your Profit with optimized NIRS Accuracy Food Feed FoodSafety ag Lab QC QA PAT QbD Pharma LINK
Spectroscopy and Chemometrics News Weekly 38, 2022 | NIRS NIR
Spectroscopy MachineLearning Spectrometer Spectrometric Analytical
Chemistry Chemical Analysis Laboratories Laboratory Software AG Sensors
QA QC Testing Quality LINK
"Aplikasi Near Infrared Spectroscopy (NIRS) untuk Memprediksi Kandungan Kimia Minyak Cengkeh (Syzigium aromaticum)" LINK
"Unsupervised analysis of NIRS spectra to assess complex plant traits: leaf senescence as a use case" | LINK
"Expanded visible-near-infrared temperature sensing properties in view
of ultra-broadband tunable luminescence in Mg3Y2Ge3O12: Ce3+, Cr3+
phosphors with ..." LINK
"Prediction of Soil Available Boron Content in Visible-Near-Infrared
Hyperspectral Based on Different Preprocessing Transformations and
Characteristic ..." LINK
"Rapidly detecting fennel origin of the near-infrared spectroscopy based on extreme learning machine" | LINK
"Role of Near-infrared Spectroscopy in the Diagnosis and Assessment of
Necrotizing Enterocolitis" LINK
"Near-infrared spectroscopy for rapid identification of pharmaceutical excipients" LINK
"Evaluation of transcutaneous near-infrared spectroscopy for early detection of cardiac arrest in an animal model" LINK
"Analysis of Perovskite Solar Cell Degradation over Time Using NIR Spectroscopy—A Novel Approach" LINK
"Rapid Discrimination of the Country Origin of Soybeans Based on FT-NIR Spectroscopy and Data Expansion" | LINK
"Weight interpretation of artificial neural network model for analysis
of rice (Oryza sativa L.) with near-infrared spectroscopy" LINK
"Origins Classification of Egg with Different Storage Durations Using
FT-NIR: A Characteristic Wavelength Selection Approach Based on
Information Entropy" LINK
"Measurement of nitrogen content in rice plant using near infrared spectroscopy combined with different PLS algorithms" LINK
"Rapid determination of acidity index of peanuts by near-infrared
spectroscopy technology: Comparing the performance of different
near-infrared spectral models" LINK
"Infrared spectroscopy (NIRS and ATR-FTIR) together with multivariate
classification for non-destructive differentiation between female
mosquitoes of Aedes aegypti recently infected with dengue vs. uninfected
females" LINK
"Spectral Encoder to Extract the Features of Near-Infrared Spectra for Multivariate Calibration" LINK
"Internal disorder evaluation of 'Namdokmai Sithong'mango by near infrared spectroscopy" | LINK
"... Combined Models Significantly Improve the Predictive
Performance of Soil Organic Carbon from North-West India Using
Visible-Near Infrared Spectroscopy" LINK
"Optimizing functional near-infrared spectroscopy (fNIRS) channels for
schizophrenic identification during a verbal fluency task using
metaheuristic algorithms" LINK
"Soluble Solids Content Binary Classification of Miyagawa Satsuma in Chongming Island Based on Near Infrared Spectroscopy" | LINK
"Grading detection of "Red Fuji" apple in Luochuan based on machine vision and near-infrared spectroscopy" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Grading detection of “Red Fuji” apple in Luochuan based on machine vision and near-infrared spectroscopy" LINK
"A near‐infrared spectroscopy method for the detection of texture
profile analysis of Litopeneo vannamei based on partial least squares
regression" LINK
Hyperspectral Imaging (HSI)
"Detection of infestation by striped stemborer (Chilo suppressalis) in rice based on hyperspectral imaging" LINK
"Comparison of Visible-Near Infrared and Fluorescence Hyperspectral
Imaging Techniques for Non-Destructive Detection of Lipid Oxidation
Degree in Frozen-Thawed ..." LINK
Spectral Imaging
"Foods : Application of Fourier Transform Infrared (FT-IR) Spectroscopy,
Multispectral Imaging (MSI) and Electronic Nose (E-Nose) for the Rapid
Evaluation of the Microbiological Quality of Gilthead Sea Bream Fillets"
LINK
Chemometrics and Machine Learning
"Dual Wavelength based Approach with Partial Least Square Regression for the Prediction of Glucose Concentration" LINK
"Superiority of Two-Dimensional Correlation Spectroscopy Combined with ResNet in Species Identification of Bolete" LINK
"Transfer of a calibration model for the prediction of lignin in pulpwood among four portable near infrared spectrometers" LINK
Environment NIR-Spectroscopy Application
"Remote Sensing : Identifying Coffee Agroforestry System Types Using Multitemporal Sentinel-2 Aata and Auxiliary Information" LINK
Agriculture NIR-Spectroscopy Usage
"Biosensors : A Mouse Holder for Awake Functional Imaging in
Unanesthetized Mice: Applications in 31P Spectroscopy,
Manganese-Enhanced Magnetic Resonance Imaging Studies, and Resting-State
Functional Magnetic Resonance Imaging" LINK
"Assessment of Soil Characteristics Using a Three-Band Agricultural Digital Camera" LINK
"Texture profile and short-NIR spectral vibrations relationship
evaluated through Comdim: The case study for animal and vegetable
proteins" LINK
"Agriculture : Research on Wet Clutch Switching Quality in the Shifting Stage of an Agricultural Tractor Transmission System" LINK
"Agriculture : Application of a Fractional Order Differential to the Hyperspectral Inversion of Soil Iron Oxide" LINK
"Structural aspects of hyperspectral imaging data: a case study on
microplastics analysis from the viewpoint of chemometrics" LINK
"Bilateral diffuse uveal melanocytic proliferation associated with endometrial carcinoma-multimodal imaging analysis" LINK
"Patterns of denitrifier communities assembly and co-occurrence network
regulate N2O emissions in soils with long-term contrasting tillage
histories" LINK
"Linking long-term soil phosphorus management to nitrogen cycling microbial communities" LINK
Food & Feed Industry NIR Usage
"Foods : Antioxidant and Cytoprotective Capacities of Various Wheat (Triticum aestivum L.) Cultivars in Korea" LINK
Other
"Effect of Codend Design and Postponed Bleeding on Hemoglobin in Cod
Fillets Caught by Bottom Trawl in the Barents Sea Demersal Fishery" LINK
"Tracing the world's timber: the status of scientific verification technologies for species and origin identification" | LINK
"Structural, Morphological and Optical Properties of Zinc Oxide Nanorods
prepared by ZnO seed layer Annealed at Different Oxidation Temperature"
LINK
"Synthesis, characterization, and DFT study of linear and non-linear
optical properties of some novel thieno [2, 3-b] thiophene azo dye
derivatives" LINK
Get the Spectroscopy and Chemometrics / Machine-Learning News Weekly in real time on Twitter Twitter @ CalibModel and follow us.
Spectroscopy and Chemometrics News Weekly 44, 2021 | 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
Near-Infrared Spectroscopy (NIRS)
"Study on Characteristic Wavelength Extraction Method for Near Infrared Spectroscopy Identification Based on Genetic Algorithm" LINK
"Uji Karakteristik Biochar dengan Pendekatan Near Infrared Spectroscopy (NIRS)" LINK
"Establishment of online quantitative model for moisture content determination of hydroxychloroquine sulfate particles by near infrared spectroscopy" LINK
"Nondestructive detection model of soluble solids content of an apple using visible/near-infrared spectroscopy combined with CARS and MPGA" LINK
"Detecting cadmium contamination in loessal soils using near-infrared spectroscopy in the Xiaoqinling gold area" LINK
"Egg Freshness Evaluation Using Transmission and Reflection of NIR Spectroscopy Coupled Multivariate Analysis" LINK
"Determination of Alcohol Content in Beers of Different Styles Based on Portable Near-Infrared Spectroscopy and Multivariate Calibration" | LINK
"Analysing the Water Spectral Pattern by Near-Infrared Spectroscopy and Chemometrics as a Dynamic Multidimensional Biomarker in Preservation: Rice Germ ..." LINK
"Remote Sensing : The Application of NIRS to Determine Animal Physiological Traits for Wildlife Management and Conservation" LINK
"Wavelength selection method for near-infrared spectroscopy based on standard-sample calibration transfer of mango and apple" LINK
"A portable NIR-system for mixture powdery food analysis using deep learning" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Compositional and sensory quality of beef and its determination by near infrared" LINK
"Butyrylcholinesterase responsive supramolecular prodrug with targeted nearinfrared cellular imaging property" LINK
Raman Spectroscopy
"Quantitative analysis of binary and ternary organo-mineral solid dispersions by Raman spectroscopy for robotic planetary exploration missions on Mars" | OpenAccess LINK
Hyperspectral Imaging (HSI)
"A shallow network for hyperspectral image classification using an autoencoder with convolutional neural network" | LINK
"Hyperspectral camera development on an unmanned aerial vehicle" LINK
"Direct reflectance transformation methodology for drone-based hyperspectral imaging" LINK
Spectral Imaging
"Remote Sensing : Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves" LINK
Chemometrics and Machine Learning
"Sensors : Adaboost-Based Machine Learning Improved the Modeling Robust and Estimation Accuracy of Pear Leaf Nitrogen Concentration by In-Field VIS-NIR Spectroscopy" LINK
"Discrimination of Manufacturers Origin of Oxytetracycline Using Terahertz Time-Domain Spectroscopy with Chemometric Methods" LINK
Spectroscopy
"Sensors : Non-Invasive Monitoring of Ethanol and Methanol Levels in Grape-Derived Pisco Distillate by Vibrational Spectroscopy" LINK
Equipment for Spectroscopy
"Improving the thermoelectric performances of polymer via synchronously realizing of chemical doping and side-chain cleavage" LINK
Environment NIR-Spectroscopy Application
"Determining physical and mechanical volcanic rock properties via reflectance spectroscopy" LINK
"Unauthorized landfills of solid household and industrial wastes detection in the Arctic and Subarctic territories using remote sensing technologies" LINK
"Evaluating the effects of distinct water saturation states on the light penetration depths of sand-textured soils" LINK
Agriculture NIR-Spectroscopy Usage
"Sorghum Grains Grading for Food, Feed, and Fuel Using NIR Spectroscopy" LINK
"Estimation of leaf area index at the late growth stage of crops using unmanned aerial vehicle hyperspectral images" LINK
"Analysis of Spectral Reflectance Characteristics Using Hyperspectral Sensor at Diverse Phenological Stages of Soybeans" LINK
Horticulture NIR-Spectroscopy Applications
"Data fusion for fruit quality authentication: combining non-destructive sensing techniques to predict quality parameters of citrus cultivars" | LINK
Food & Feed Industry NIR Usage
"Buckwheat Identification by Combined UV-VIS-NIR Spectroscopy and Multivariate Analysis" LINK
Other
"Effect of the annealing temperature on the growth of the silver nanoparticles synthesized by physical route" LINK
NIR Calibration-Model Services
Get the Spectroscopy and Chemometrics / Machine-Learning News Weekly in real time on Twitter Twitter @ CalibModel and follow us.
This week's NIR news Weekly is sponsored by Your-Company-Name-Here - NIR-spectrometers. Check out their product page ... link
Near-Infrared Spectroscopy (NIRS)
"Study on Characteristic Wavelength Extraction Method for Near Infrared Spectroscopy Identification Based on Genetic Algorithm" LINK
"Uji Karakteristik Biochar dengan Pendekatan Near Infrared Spectroscopy (NIRS)" LINK
"Establishment of online quantitative model for moisture content determination of hydroxychloroquine sulfate particles by near infrared spectroscopy" LINK
"Nondestructive detection model of soluble solids content of an apple using visible/near-infrared spectroscopy combined with CARS and MPGA" LINK
"Detecting cadmium contamination in loessal soils using near-infrared spectroscopy in the Xiaoqinling gold area" LINK
"Egg Freshness Evaluation Using Transmission and Reflection of NIR Spectroscopy Coupled Multivariate Analysis" LINK
"Determination of Alcohol Content in Beers of Different Styles Based on Portable Near-Infrared Spectroscopy and Multivariate Calibration" | LINK
"Analysing the Water Spectral Pattern by Near-Infrared Spectroscopy and Chemometrics as a Dynamic Multidimensional Biomarker in Preservation: Rice Germ ..." LINK
"Remote Sensing : The Application of NIRS to Determine Animal Physiological Traits for Wildlife Management and Conservation" LINK
"Wavelength selection method for near-infrared spectroscopy based on standard-sample calibration transfer of mango and apple" LINK
"A portable NIR-system for mixture powdery food analysis using deep learning" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Compositional and sensory quality of beef and its determination by near infrared" LINK
"Butyrylcholinesterase responsive supramolecular prodrug with targeted nearinfrared cellular imaging property" LINK
Raman Spectroscopy
"Quantitative analysis of binary and ternary organo-mineral solid dispersions by Raman spectroscopy for robotic planetary exploration missions on Mars" | OpenAccess LINK
Hyperspectral Imaging (HSI)
"A shallow network for hyperspectral image classification using an autoencoder with convolutional neural network" | LINK
"Hyperspectral camera development on an unmanned aerial vehicle" LINK
"Direct reflectance transformation methodology for drone-based hyperspectral imaging" LINK
Spectral Imaging
"Remote Sensing : Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves" LINK
Chemometrics and Machine Learning
"Sensors : Adaboost-Based Machine Learning Improved the Modeling Robust and Estimation Accuracy of Pear Leaf Nitrogen Concentration by In-Field VIS-NIR Spectroscopy" LINK
"Discrimination of Manufacturers Origin of Oxytetracycline Using Terahertz Time-Domain Spectroscopy with Chemometric Methods" LINK
Spectroscopy
"Sensors : Non-Invasive Monitoring of Ethanol and Methanol Levels in Grape-Derived Pisco Distillate by Vibrational Spectroscopy" LINK
Equipment for Spectroscopy
"Improving the thermoelectric performances of polymer via synchronously realizing of chemical doping and side-chain cleavage" LINK
Environment NIR-Spectroscopy Application
"Determining physical and mechanical volcanic rock properties via reflectance spectroscopy" LINK
"Unauthorized landfills of solid household and industrial wastes detection in the Arctic and Subarctic territories using remote sensing technologies" LINK
"Evaluating the effects of distinct water saturation states on the light penetration depths of sand-textured soils" LINK
Agriculture NIR-Spectroscopy Usage
"Sorghum Grains Grading for Food, Feed, and Fuel Using NIR Spectroscopy" LINK
"Estimation of leaf area index at the late growth stage of crops using unmanned aerial vehicle hyperspectral images" LINK
"Analysis of Spectral Reflectance Characteristics Using Hyperspectral Sensor at Diverse Phenological Stages of Soybeans" LINK
Horticulture NIR-Spectroscopy Applications
"Data fusion for fruit quality authentication: combining non-destructive sensing techniques to predict quality parameters of citrus cultivars" | LINK
Food & Feed Industry NIR Usage
"Buckwheat Identification by Combined UV-VIS-NIR Spectroscopy and Multivariate Analysis" LINK
Other
"Effect of the annealing temperature on the growth of the silver nanoparticles synthesized by physical route" LINK
NIR Calibration-Model Services
Get the Spectroscopy and Chemometrics / Machine-Learning News Weekly in real time on Twitter Twitter @ CalibModel and follow us.
This week's NIR news Weekly is sponsored by Your-Company-Name-Here - NIR-spectrometers. Check out their product page ... link
Near-Infrared Spectroscopy (NIRS)
"Study on Characteristic Wavelength Extraction Method for Near Infrared Spectroscopy Identification Based on Genetic Algorithm" LINK
"Uji Karakteristik Biochar dengan Pendekatan Near Infrared Spectroscopy (NIRS)" LINK
"Establishment of online quantitative model for moisture content determination of hydroxychloroquine sulfate particles by near infrared spectroscopy" LINK
"Nondestructive detection model of soluble solids content of an apple using visible/near-infrared spectroscopy combined with CARS and MPGA" LINK
"Detecting cadmium contamination in loessal soils using near-infrared spectroscopy in the Xiaoqinling gold area" LINK
"Egg Freshness Evaluation Using Transmission and Reflection of NIR Spectroscopy Coupled Multivariate Analysis" LINK
"Determination of Alcohol Content in Beers of Different Styles Based on Portable Near-Infrared Spectroscopy and Multivariate Calibration" | LINK
"Analysing the Water Spectral Pattern by Near-Infrared Spectroscopy and Chemometrics as a Dynamic Multidimensional Biomarker in Preservation: Rice Germ ..." LINK
"Remote Sensing : The Application of NIRS to Determine Animal Physiological Traits for Wildlife Management and Conservation" LINK
"Wavelength selection method for near-infrared spectroscopy based on standard-sample calibration transfer of mango and apple" LINK
"A portable NIR-system for mixture powdery food analysis using deep learning" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Compositional and sensory quality of beef and its determination by near infrared" LINK
"Butyrylcholinesterase responsive supramolecular prodrug with targeted nearinfrared cellular imaging property" LINK
Raman Spectroscopy
"Quantitative analysis of binary and ternary organo-mineral solid dispersions by Raman spectroscopy for robotic planetary exploration missions on Mars" | OpenAccess LINK
Hyperspectral Imaging (HSI)
"A shallow network for hyperspectral image classification using an autoencoder with convolutional neural network" | LINK
"Hyperspectral camera development on an unmanned aerial vehicle" LINK
"Direct reflectance transformation methodology for drone-based hyperspectral imaging" LINK
Spectral Imaging
"Remote Sensing : Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves" LINK
Chemometrics and Machine Learning
"Sensors : Adaboost-Based Machine Learning Improved the Modeling Robust and Estimation Accuracy of Pear Leaf Nitrogen Concentration by In-Field VIS-NIR Spectroscopy" LINK
"Discrimination of Manufacturers Origin of Oxytetracycline Using Terahertz Time-Domain Spectroscopy with Chemometric Methods" LINK
Spectroscopy
"Sensors : Non-Invasive Monitoring of Ethanol and Methanol Levels in Grape-Derived Pisco Distillate by Vibrational Spectroscopy" LINK
Equipment for Spectroscopy
"Improving the thermoelectric performances of polymer via synchronously realizing of chemical doping and side-chain cleavage" LINK
Environment NIR-Spectroscopy Application
"Determining physical and mechanical volcanic rock properties via reflectance spectroscopy" LINK
"Unauthorized landfills of solid household and industrial wastes detection in the Arctic and Subarctic territories using remote sensing technologies" LINK
"Evaluating the effects of distinct water saturation states on the light penetration depths of sand-textured soils" LINK
Agriculture NIR-Spectroscopy Usage
"Sorghum Grains Grading for Food, Feed, and Fuel Using NIR Spectroscopy" LINK
"Estimation of leaf area index at the late growth stage of crops using unmanned aerial vehicle hyperspectral images" LINK
"Analysis of Spectral Reflectance Characteristics Using Hyperspectral Sensor at Diverse Phenological Stages of Soybeans" LINK
Horticulture NIR-Spectroscopy Applications
"Data fusion for fruit quality authentication: combining non-destructive sensing techniques to predict quality parameters of citrus cultivars" | LINK
Food & Feed Industry NIR Usage
"Buckwheat Identification by Combined UV-VIS-NIR Spectroscopy and Multivariate Analysis" LINK
Other
"Effect of the annealing temperature on the growth of the silver nanoparticles synthesized by physical route" 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)
"Absolute calibration of the spectral responsivity of thermal detectors in the near-infrared (NIR) and mid-infrared (MIR) regions by using blackbody radiation" LINK
"Quantifying the minerals abundances on planetary surfaces using VIS-NIR spectroscopy, what uncertainties should we expect? General results and application to the ..." LINK
"Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy" LINK
"Fast at-line characterization of solid organic waste: Comparing analytical performance of different compact near infrared spectroscopic systems with different measurement configurations" LINK
"Developing and testing a new quantitative near infrared spectroscopy online tracking measuring system for soil detection during automatic dishwashing" LINK
"Near infrared spectroscopy (NIRS) as tool for classification into official commercial categories and shelf-life storage times of pre-sliced modified atmosphere packaged Iberian dry-cured loin" LINK
"Relating Near-Infrared Light Path-Length Modifications to the Water Content of Scattering Media in Near-Infrared Spectroscopy: Toward a New Bouguer-Beer-Lambert Law" LINK
"Non-invasive identification of dyed textiles by using VIS-NIR FORS and hyperspectral imaging techniques" LINK
"The potential of handheld near infrared spectroscopy to detect food adulteration: Results of a global, multi-instrument inter-laboratory study" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Hyperspectral and Infrared Image Collaborative Classification Based on Morphology Feature Extraction" LINK
"Generic models for rapid detection of vanillin and melamine adulterated in infant formulas from diverse brands based on near-infrared hyperspectral imaging" LINK
"Rapid determination of fructooligosaccharide in solar-dried banana syrup by using near-infrared spectroscopy" LINK
"Sampling Optimization for Blend Monitoring of a Low Dose Formulation in a Tablet Press Feed Frame Using Spatially Resolved Near-Infrared Spectroscopy" LINK
"Data Fusion of UV-Vis and FTIR Spectra Combined with Principal Component Analysis for Distinguishing of Andrographis paniculata Extracts Based on Cultivation Ages and Solvent Extraction" LINK
"Evaluation of swelling properties and drug release from mechanochemical pre-gelatinized glutinous rice starch matrix tablets by near infrared spectroscopy" LINK
" Research Note: Non-destructive Detection of Super Grade Chick Embryos or Hatchlings using Near-infrared Spectroscopy" LINK
"Defining Eucalyptus urophylla with its hybrid and the rules of genetic recombination using near infrared spectroscopy" LINK
"Sensitivity of Near-Infrared Permanent Laser Scanning Intensity for Retrieving Soil Moisture on a Coastal Beach: Calibration Procedure Using In Situ Data" LINK
"Authentication of Antibiotics Using Portable Near-Infrared Spectroscopy and Multivariate Data Analysis" LINK
"Qualitative identification of waste textiles based on near-infrared spectroscopy and the back propagation artificial neural network" LINK
"Sampling Optimization for Blend Monitoring of a Low Dose Formulation in a Tablet Press Feed Frame Using Spatially Resolved Near-Infrared Spectroscopy." LINK
"Development of a Novel Green Tea Quality Roadmap and the Complex Sensory-associated Characteristics exploration using Rapid Near-Infrared Spectroscopy ..." LINK
Hyperspectral Imaging (HSI)
"A Scalable Approach for the Efficient Segmentation of Hyperspectral Images" LINK
"Hyperspectral image classification via principal component analysis, 2D spatial convolution, and support vector machines" LINK
Chemometrics and Machine Learning
"Comparison of methods to predict feed intake and residual feed intake using behavioral and metabolite data in addition to classical performance variables"LINK
"Insights into chemometric algorithms for quality attributes and hazards detection in foodstuffs using Raman/surface enhanced Raman spectroscopy" LINK
"A conjunction of <em>sn-</em>2 fatty acids and overall fatty acid composition combined with chemometric techniques increase the effectiveness of lard detection in fish feed" LINK
Equipment for Spectroscopy
"Physicochemical Analysis and Adulteration Detection in Malaysia Stingless Bee Honey Using a Handheld NearInfrared Spectrometer" LINK
"A Near-Infrared “Matchbox Size” Spectrometer to Detect and Quantify Malaria Parasitemia" LINK
Process Control and NIR Sensors
"A Hybrid NIR-Soft Sensor Method for Real Time In-Process Control During Continuous Direct Compression Manufacturing Operations" LINK
"Aging of polypropylene probed by near infrared spectroscopy" LINK
Environment NIR-Spectroscopy Application
"Satellite image processing based on percolation for physicochemical analysis of soil cover of industrial waste facilities" LINK
Agriculture NIR-Spectroscopy Usage
"progress update towards an international standard for push-broom hyper-spectral imagers" IEE LINK
"Hyperspectral imaging for the determination of the main unsaturated fatty acid distribution in shelled almonds analysed in bulk" LINK
"Nutrient Prediction for Tef (Eragrostis tef) Plant and Grain with HyperspectralData and Partial Least Squares Regression: Replicating Methods and Results across Environments" LINK
"Rapid and real-time detection of black tea fermentation quality by using an inexpensive data fusion system" LINK
"The application of spectroscopy techniques for diagnosis of malaria parasites and arboviruses and surveillance of mosquito vectors: A systematic review and critical appraisal of evidence" LINK
"Near infrared spectroscopy of plantation forest soil nutrients in Sabah, Malaysia, and the potential for microsite assessment" LINK
Food & Feed Industry NIR Usage
"Simultaneous assessment of nitrogen and water status in winter wheat using hyperspectral and thermal sensors" LINK
"Detection of Meat Adulteration Using Spectroscopy-Based Sensors" Foods LINK
"Assessment of Bulk Composition of Heterogeneous Food Matrices Using Raman Spectroscopy" LINK
"Composition and properties of bovine milk: A case study from dairy farms in Northern Sweden; Part II. Effect of monthly variation" LINK
"Characterization of the Triacylglycerol Fraction of Italian and Extra-European Hemp Seed Oil" Foods LINK
"Nondestructive Determination of Kiwifruit SSC using Visible/Near-Infrared
Spectroscopy with Genetic Algorithm" LINK
"Hyperspectral assessment of leaf nitrogen accumulation for winter wheat using different regression modeling" | LINK
"Study of Spectral Response Characteristics of Oilseed Rape (Brassica napus) to Particulate Matters Based on Hyper-Spectral Technique" LINK
Other
"Analysing spectroscopy data using two-step group penalized partial least squares regression" LINK
"Influence of type of video contents and display resolution on physiological and psychological evaluation" LINK
"PVC based flexible nanocomposites with the incorporation of Polyaniline and Barium Hexa-Ferrite nanoparticles for the shielding against EMI, NIR, and thermal ..." LINK
Exposing ears to near infrared light (NIR) could prevent noise-induced hearing loss, according to studies by and US scientists. Shining NIR into the ears of mice for 10 minutes prevented cell death, and hearing loss was reduced. EARA LINK
.
NIR Calibration-Model Services
Spectroscopy and Chemometrics News Weekly 19, 2021 | NIRS NIR Spectroscopy MachineLearning spectrometer Spectrometric Analytical Chemistry chemical analysis labs laboratories laboratory IoT material sensor QAlab QClab 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)
"Absolute calibration of the spectral responsivity of thermal detectors in the near-infrared (NIR) and mid-infrared (MIR) regions by using blackbody radiation" LINK
"Quantifying the minerals abundances on planetary surfaces using VIS-NIR spectroscopy, what uncertainties should we expect? General results and application to the ..." LINK
"Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy" LINK
"Fast at-line characterization of solid organic waste: Comparing analytical performance of different compact near infrared spectroscopic systems with different measurement configurations" LINK
"Developing and testing a new quantitative near infrared spectroscopy online tracking measuring system for soil detection during automatic dishwashing" LINK
"Near infrared spectroscopy (NIRS) as tool for classification into official commercial categories and shelf-life storage times of pre-sliced modified atmosphere packaged Iberian dry-cured loin" LINK
"Relating Near-Infrared Light Path-Length Modifications to the Water Content of Scattering Media in Near-Infrared Spectroscopy: Toward a New Bouguer-Beer-Lambert Law" LINK
"Non-invasive identification of dyed textiles by using VIS-NIR FORS and hyperspectral imaging techniques" LINK
"The potential of handheld near infrared spectroscopy to detect food adulteration: Results of a global, multi-instrument inter-laboratory study" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Hyperspectral and Infrared Image Collaborative Classification Based on Morphology Feature Extraction" LINK
"Generic models for rapid detection of vanillin and melamine adulterated in infant formulas from diverse brands based on near-infrared hyperspectral imaging" LINK
"Rapid determination of fructooligosaccharide in solar-dried banana syrup by using near-infrared spectroscopy" LINK
"Sampling Optimization for Blend Monitoring of a Low Dose Formulation in a Tablet Press Feed Frame Using Spatially Resolved Near-Infrared Spectroscopy" LINK
"Data Fusion of UV-Vis and FTIR Spectra Combined with Principal Component Analysis for Distinguishing of Andrographis paniculata Extracts Based on Cultivation Ages and Solvent Extraction" LINK
"Evaluation of swelling properties and drug release from mechanochemical pre-gelatinized glutinous rice starch matrix tablets by near infrared spectroscopy" LINK
" Research Note: Non-destructive Detection of Super Grade Chick Embryos or Hatchlings using Near-infrared Spectroscopy" LINK
"Defining Eucalyptus urophylla with its hybrid and the rules of genetic recombination using near infrared spectroscopy" LINK
"Sensitivity of Near-Infrared Permanent Laser Scanning Intensity for Retrieving Soil Moisture on a Coastal Beach: Calibration Procedure Using In Situ Data" LINK
"Authentication of Antibiotics Using Portable Near-Infrared Spectroscopy and Multivariate Data Analysis" LINK
"Qualitative identification of waste textiles based on near-infrared spectroscopy and the back propagation artificial neural network" LINK
"Sampling Optimization for Blend Monitoring of a Low Dose Formulation in a Tablet Press Feed Frame Using Spatially Resolved Near-Infrared Spectroscopy." LINK
"Development of a Novel Green Tea Quality Roadmap and the Complex Sensory-associated Characteristics exploration using Rapid Near-Infrared Spectroscopy ..." LINK
Hyperspectral Imaging (HSI)
"A Scalable Approach for the Efficient Segmentation of Hyperspectral Images" LINK
"Hyperspectral image classification via principal component analysis, 2D spatial convolution, and support vector machines" LINK
Chemometrics and Machine Learning
"Comparison of methods to predict feed intake and residual feed intake using behavioral and metabolite data in addition to classical performance variables"LINK
"Insights into chemometric algorithms for quality attributes and hazards detection in foodstuffs using Raman/surface enhanced Raman spectroscopy" LINK
"A conjunction of <em>sn-</em>2 fatty acids and overall fatty acid composition combined with chemometric techniques increase the effectiveness of lard detection in fish feed" LINK
Equipment for Spectroscopy
"Physicochemical Analysis and Adulteration Detection in Malaysia Stingless Bee Honey Using a Handheld NearInfrared Spectrometer" LINK
"A Near-Infrared “Matchbox Size” Spectrometer to Detect and Quantify Malaria Parasitemia" LINK
Process Control and NIR Sensors
"A Hybrid NIR-Soft Sensor Method for Real Time In-Process Control During Continuous Direct Compression Manufacturing Operations" LINK
"Aging of polypropylene probed by near infrared spectroscopy" LINK
Environment NIR-Spectroscopy Application
"Satellite image processing based on percolation for physicochemical analysis of soil cover of industrial waste facilities" LINK
Agriculture NIR-Spectroscopy Usage
"progress update towards an international standard for push-broom hyper-spectral imagers" IEE LINK
"Hyperspectral imaging for the determination of the main unsaturated fatty acid distribution in shelled almonds analysed in bulk" LINK
"Nutrient Prediction for Tef (Eragrostis tef) Plant and Grain with HyperspectralData and Partial Least Squares Regression: Replicating Methods and Results across Environments" LINK
"Rapid and real-time detection of black tea fermentation quality by using an inexpensive data fusion system" LINK
"The application of spectroscopy techniques for diagnosis of malaria parasites and arboviruses and surveillance of mosquito vectors: A systematic review and critical appraisal of evidence" LINK
"Near infrared spectroscopy of plantation forest soil nutrients in Sabah, Malaysia, and the potential for microsite assessment" LINK
Food & Feed Industry NIR Usage
"Simultaneous assessment of nitrogen and water status in winter wheat using hyperspectral and thermal sensors" LINK
"Detection of Meat Adulteration Using Spectroscopy-Based Sensors" Foods LINK
"Assessment of Bulk Composition of Heterogeneous Food Matrices Using Raman Spectroscopy" LINK
"Composition and properties of bovine milk: A case study from dairy farms in Northern Sweden; Part II. Effect of monthly variation" LINK
"Characterization of the Triacylglycerol Fraction of Italian and Extra-European Hemp Seed Oil" Foods LINK
"Nondestructive Determination of Kiwifruit SSC using Visible/Near-Infrared
Spectroscopy with Genetic Algorithm" LINK
"Hyperspectral assessment of leaf nitrogen accumulation for winter wheat using different regression modeling" | LINK
"Study of Spectral Response Characteristics of Oilseed Rape (Brassica napus) to Particulate Matters Based on Hyper-Spectral Technique" LINK
Other
"Analysing spectroscopy data using two-step group penalized partial least squares regression" LINK
"Influence of type of video contents and display resolution on physiological and psychological evaluation" LINK
"PVC based flexible nanocomposites with the incorporation of Polyaniline and Barium Hexa-Ferrite nanoparticles for the shielding against EMI, NIR, and thermal ..." LINK
Exposing ears to near infrared light (NIR) could prevent noise-induced hearing loss, according to studies by and US scientists. Shining NIR into the ears of mice for 10 minutes prevented cell death, and hearing loss was reduced. EARA LINK
.
NIR Calibration-Model Services
Spectroscopy and Chemometrics News Weekly 19, 2021 | NIRS NIR Spectroscopy MachineLearning spectrometer Spectrometric Analytical Chemistry chemical analysis labs laboratories laboratory IoT material sensor QAlab QClab 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)
"Absolute calibration of the spectral responsivity of thermal detectors in the near-infrared (NIR) and mid-infrared (MIR) regions by using blackbody radiation" LINK
"Quantifying the minerals abundances on planetary surfaces using VIS-NIR spectroscopy, what uncertainties should we expect? General results and application to the ..." LINK
"Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy" LINK
"Fast at-line characterization of solid organic waste: Comparing analytical performance of different compact near infrared spectroscopic systems with different measurement configurations" LINK
"Developing and testing a new quantitative near infrared spectroscopy online tracking measuring system for soil detection during automatic dishwashing" LINK
"Near infrared spectroscopy (NIRS) as tool for classification into official commercial categories and shelf-life storage times of pre-sliced modified atmosphere packaged Iberian dry-cured loin" LINK
"Relating Near-Infrared Light Path-Length Modifications to the Water Content of Scattering Media in Near-Infrared Spectroscopy: Toward a New Bouguer-Beer-Lambert Law" LINK
"Non-invasive identification of dyed textiles by using VIS-NIR FORS and hyperspectral imaging techniques" LINK
"The potential of handheld near infrared spectroscopy to detect food adulteration: Results of a global, multi-instrument inter-laboratory study" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Hyperspectral and Infrared Image Collaborative Classification Based on Morphology Feature Extraction" LINK
"Generic models for rapid detection of vanillin and melamine adulterated in infant formulas from diverse brands based on near-infrared hyperspectral imaging" LINK
"Rapid determination of fructooligosaccharide in solar-dried banana syrup by using near-infrared spectroscopy" LINK
"Sampling Optimization for Blend Monitoring of a Low Dose Formulation in a Tablet Press Feed Frame Using Spatially Resolved Near-Infrared Spectroscopy" LINK
"Data Fusion of UV-Vis and FTIR Spectra Combined with Principal Component Analysis for Distinguishing of Andrographis paniculata Extracts Based on Cultivation Ages and Solvent Extraction" LINK
"Evaluation of swelling properties and drug release from mechanochemical pre-gelatinized glutinous rice starch matrix tablets by near infrared spectroscopy" LINK
" Research Note: Non-destructive Detection of Super Grade Chick Embryos or Hatchlings using Near-infrared Spectroscopy" LINK
"Defining Eucalyptus urophylla with its hybrid and the rules of genetic recombination using near infrared spectroscopy" LINK
"Sensitivity of Near-Infrared Permanent Laser Scanning Intensity for Retrieving Soil Moisture on a Coastal Beach: Calibration Procedure Using In Situ Data" LINK
"Authentication of Antibiotics Using Portable Near-Infrared Spectroscopy and Multivariate Data Analysis" LINK
"Qualitative identification of waste textiles based on near-infrared spectroscopy and the back propagation artificial neural network" LINK
"Sampling Optimization for Blend Monitoring of a Low Dose Formulation in a Tablet Press Feed Frame Using Spatially Resolved Near-Infrared Spectroscopy." LINK
"Development of a Novel Green Tea Quality Roadmap and the Complex Sensory-associated Characteristics exploration using Rapid Near-Infrared Spectroscopy ..." LINK
Hyperspectral Imaging (HSI)
"A Scalable Approach for the Efficient Segmentation of Hyperspectral Images" LINK
"Hyperspectral image classification via principal component analysis, 2D spatial convolution, and support vector machines" LINK
Chemometrics and Machine Learning
"Comparison of methods to predict feed intake and residual feed intake using behavioral and metabolite data in addition to classical performance variables"LINK
"Insights into chemometric algorithms for quality attributes and hazards detection in foodstuffs using Raman/surface enhanced Raman spectroscopy" LINK
"A conjunction of <em>sn-</em>2 fatty acids and overall fatty acid composition combined with chemometric techniques increase the effectiveness of lard detection in fish feed" LINK
Equipment for Spectroscopy
"Physicochemical Analysis and Adulteration Detection in Malaysia Stingless Bee Honey Using a Handheld NearInfrared Spectrometer" LINK
"A Near-Infrared “Matchbox Size” Spectrometer to Detect and Quantify Malaria Parasitemia" LINK
Process Control and NIR Sensors
"A Hybrid NIR-Soft Sensor Method for Real Time In-Process Control During Continuous Direct Compression Manufacturing Operations" LINK
"Aging of polypropylene probed by near infrared spectroscopy" LINK
Environment NIR-Spectroscopy Application
"Satellite image processing based on percolation for physicochemical analysis of soil cover of industrial waste facilities" LINK
Agriculture NIR-Spectroscopy Usage
"progress update towards an international standard for push-broom hyper-spectral imagers" IEE LINK
"Hyperspectral imaging for the determination of the main unsaturated fatty acid distribution in shelled almonds analysed in bulk" LINK
"Nutrient Prediction for Tef (Eragrostis tef) Plant and Grain with HyperspectralData and Partial Least Squares Regression: Replicating Methods and Results across Environments" LINK
"Rapid and real-time detection of black tea fermentation quality by using an inexpensive data fusion system" LINK
"The application of spectroscopy techniques for diagnosis of malaria parasites and arboviruses and surveillance of mosquito vectors: A systematic review and critical appraisal of evidence" LINK
"Near infrared spectroscopy of plantation forest soil nutrients in Sabah, Malaysia, and the potential for microsite assessment" LINK
Food & Feed Industry NIR Usage
"Simultaneous assessment of nitrogen and water status in winter wheat using hyperspectral and thermal sensors" LINK
"Detection of Meat Adulteration Using Spectroscopy-Based Sensors" Foods LINK
"Assessment of Bulk Composition of Heterogeneous Food Matrices Using Raman Spectroscopy" LINK
"Composition and properties of bovine milk: A case study from dairy farms in Northern Sweden; Part II. Effect of monthly variation" LINK
"Characterization of the Triacylglycerol Fraction of Italian and Extra-European Hemp Seed Oil" Foods LINK
"Nondestructive Determination of Kiwifruit SSC using Visible/Near-Infrared
Spectroscopy with Genetic Algorithm" LINK
"Hyperspectral assessment of leaf nitrogen accumulation for winter wheat using different regression modeling" | LINK
"Study of Spectral Response Characteristics of Oilseed Rape (Brassica napus) to Particulate Matters Based on Hyper-Spectral Technique" LINK
Other
"Analysing spectroscopy data using two-step group penalized partial least squares regression" LINK
"Influence of type of video contents and display resolution on physiological and psychological evaluation" LINK
"PVC based flexible nanocomposites with the incorporation of Polyaniline and Barium Hexa-Ferrite nanoparticles for the shielding against EMI, NIR, and thermal ..." LINK
Exposing ears to near infrared light (NIR) could prevent noise-induced hearing loss, according to studies by and US scientists. Shining NIR into the ears of mice for 10 minutes prevented cell death, and hearing loss was reduced. EARA LINK
It’s easy to use with NIR-Predictor,
just drag & drop your data for getting the prediction results.
It supports an automatic file format detection.
So you don’t need to specify the instrument type and settings! See the list of supported formats and NIR Vendors: NIR-Predictor supported Spectral Data File Formats
Use the included data to checkout how it feels:
Open the demo Spectra folder by using the Menu > Open Demo Spectra or press F8.
There are files with spectra from different Vendors.
Drag & drop a spectra file onto the NIR-Predictor window (or press Ctrl+O as for ’Open some files).
The spectra will be
loaded
pre-processed
predicted and
reported
Note:
All the steps are fully automatic.
All calibrations that are compatible with the spectra, will produce prediction results in one go.
To select specific calibrations choose the Application. Where the " " empty means use all the calibrations.
To define a Application read more in chapter “Applications”
Hint:
To get access to Statistics of Predictions and Reports use the Menu > Show more/less (Ctrl+M) or you can simply resize the window. Here you can also re-do the Analyze step manually with changed inputs (e.g. Result Ordering).
Creating your own Calibrations
How it works - step by step
You have measured your samples with you NIR-Instrument Software.
And got the Lab-values of these samples.
Note: If you combined these data already in your NIR software used,
and you can export it as a JCAMP-DX file then use
Menu > Create Request File .req ... (F2)
and read the “Help.html” and NIR-Predictor JCAMP.
Else proceed as below.
The NIR-Predictor provides tooling for that:
Menu > Create Properties File... (F6)
Select the folder with your NIR spectra measured for an application.
NIR-Predictor creates a customized Properties file template for that data to enter the Lab values.
Note: You don’t need to specify your instrument or vendor or an application. It’s all done automatically. And also the sample spectra are detected and grouped automatically!
Use your favorite editor or spreadsheet program to enter and copy&paste
the Lab-references Values into the columns “Prop1”, “Prop2” etc. and save the file.
A final check of your entered data is done by NIR-Predictor,
to make sure your data ist complete and all is fine.
Menu > Create Calibration Request... (F7)
Select the folder with the filled file.
A CalibrationRequest.zip is created with the necessary data
if enougth diverse Lab values are entered.
Email the CalibrationRequest.zip file
to info@CalibrationModel.com to develop the calibrations.
When your calibrations are ready, you will receive an email with a link
to the CalibrationModel WebShop where
you can purchase and download the calibration files,
that work with our free NIR-Predictor software without internet access.
Note: Your sent NIR data is deleted after processing.
We do not collect your NIR data!
Note: Further details can be found under “Create Properties File” and “Create Calibration Request”.
Configure the Calibrations for prediction usage
Configuration:
in NIR-Predictor : Menu > Open Calibrations (F9)
an explorer window is opened where the calibrations are located
create a folder for your application, choose a name
copy the calibration file(s) (*.cm) into that folder
in NIR-Predictor : Menu > Search and load Applications (F4)
Usage:
in NIR-Predictor : open the Application drop down list, and select your application by name
if all is fine, the calibration file is valid and not expired, it shows : Calibration “1 valid calibation”
the NIR-Predictor is now ready to predict
to switch the application, goto 6.
Applications
The Application concept allows to group multiple Calibrations together for an Application. By selecting an Application before prediction, only the Calibrations belonging to the Application will be used for Prediction. In the Demo Data this is used to have multiple spectrometer as Application. This can be used easily as e.g. as Application “Meat Products” containing Fat and Moisture Calibration.
To create an Application, create a folder with the Application’s name inside the Calibrations folder, and move/copy all the Calibrations files to this Application folder. To remove a Calibration from the Application, remove the Calibration file from the Application folder.
After creating an new Application folder, press menu Search and load Applications (F4) to update the NIR-Predictor dialog where the Application can be selected via the dropdown list. You don’t need to close the NIR-Predictor.
After moving Calibration files around, press menu Search and load Calibrations (F5) to update the NIR-Predictor dialog.
The use-all case
In the NIR-Predictor dialog where the Application can be selected via the dropdown list, the empty "" name means that all (yes all) valid Calibrations will be used for prediction.
Note: The Prediction Report will contain only results from spectral compatible Calibrations with the given spectra. That allows to automatically handle the multi vendor NIR instrument usage.
Prediction Result Report
Histograms of Prediction Values per Property
Shows the distribution of the predicted results per calibration. The histogram range contains the range of the calibrated property and includes the predicted results.
The histogram bar (bin) color is defined as follow:
blue : all predictions inside calibration range.
red : all predictions outside calibration range.
orange : some overlaps with calibration range.
So not all spectra in a orange bin are outside calibration range.
Histograms
Note: Predicted values are always shown in Histogram table and Prediction Value List table, even if the spectrum does not fit into model (spectrum different to model, aka Residual Outlier) shown as Out = X.
Note: Old browsers like Microsoft Internet Explorer 11 don’t support the grafics for Histogram charts. Use an current browser like Firefox or Chrome or Edge.
Note: If your browser opens the report too slow, try to deactivate some browser plugins, because they can filter what you look at and some add-ons are really slow.
Spectra Plot Thumbnail on the Prediction Report
Visualizes the min,median,max spectrum of the spectra dropped as files on the NIR-Predictor. This gives a minimal and good spectral overview of the predicted property results.
Spectra Plot color legend: min,median,max spectrum by predicted property or if no calibration is available by spectral intensity.
The min,median,max is determined from the predicted properties or if not available from the intensity of the spectra.
Beside the histogram of the predicted properties, where the distribution can be seen, the spectra shown are the ones from min,median,max predicted property.
This gives a minimal and good spectral overview of the predicted property results.
The “Spectral Range” and number of datapoints is shown in the Prediction Report Header below the listed spectra files.
To zoom the spectra plot a little, zoom the report in the browser (hold ctrl + mouse wheel, or pinch on touch screen).
The spectra plots and histograms are stored with the report and can be archived.
Note
Note that the spectra are shown in the raw values that are loaded, they are not shown pre-processed as the calibration model uses them to make the prediction.
Note that the median property spectrum is the median from the predicted property pobulation and not the “median” of the calibration property range.
Note that in the multi calibration prediction case, the spectra are selected for each property based on the related predicted property values and so the spectra plots shows typical different spectra.
Spectra Plot
Outlier Detection
To safeguard the prediction results, outliers are automatically checked for each individual prediction. This is based on limits that are determined when creating the calibration with the base data. Thus, a strange spectral measurement can be detected and signaled as an outlier even without base data only by means of the calibration and the NIR predictor. A prediction result with outlier warning is to be distrusted. How the various outlier tests are interpreted and how to avoid them in practice is described here.
The spectrum is an outlier to the model, if the spectrum is not similar with the spectra and lab-values the model is built with.
This legend is shown on each NIR-Predictor prediction report below the results:
Outlier (Out) Symbol Description
“X” : spectrum does not fit into model (spectrum different to model)
“O” : spectrum is wide outside model center (spectrum similar to model but far away)
“=” : prediction is outside upper or lower range of model (property outside model range)
“-” : spectrum is incompatible to calibration
Note: A prediction result with outlier warning is to be distrusted.
There are 3 outlier cases (X, O, =) and the incompatible data case “-”.
The bad case is “X”
the medium case is “O”
and the soft case is “=”.
The technical names in literature correspond to:
“X” : Spectral Residual Outlier
“O” : Leverage Outlier
“=” : Property Range Outlier
These 3 outlier cases can appear in combinations, like “XO=” or “XO” or “O=” or “X=”. The more outlier marker are shown the more likely the spectrum is an Outlier.
The default setting in NIR-Predictor Menu > “Report with Simplified Outlier Symbols”
is ON, that will show only the worst case instead of all combinations to have a simplified minimal information.
if OFF, that will show the combinations (e.g. “XO=” or “XO” or “O=” or “X=”), which is more informative for analyzing problem cases.
Some hints to avoid these Outliers:
“X” : spectrum does not fit into model (spectrum different to model)
Check if the spectrum is noise only, or has no proper signal. That can happen when measured past the sample or measured into the air or at a different substance. If you have multiple NIR instruments of the same type, use spectra measured with different instruments for the calibration.
“O” : spectrum is wide outside model center (spectrum similar to model but far away) Sample temperature has an effect on NIR spectra shape, use spectra measured at different (typical use) temperatures (sample temperature, instrument temperature).
“=” : prediction is outside upper or lower range of model (property outside model range)
Use more spectra for the calibration in the Lab value region where your special interest is. If the predicted value is only a little bit out of the calibration range, it can be Ok. Add these spectra to the calibration spectra (with the Lab values), to extend the prediction range of the calibration.
“-” : spectrum is incompatible to calibration
The spectra (from the NIR instrument) has a different wavelength range or a different resolution than the spectra used for calibration. Check Instrument settings (wavelength range, resolution)
Result Ordering
To change the ordering, a drop-down-box is located below the Analyze button. If there is an analysis from the current session, and the Result Ordering is changed, the data is re-Analyzed and reported with the new Result Ordering setting. That allows to compare the different orderings. The Result Ordering is listed in the Prediction Report above the Prediction Value List and stored in the settings.
The order/sorting of the prediction results of the spectra can be defined:
GivenOrder (default) the given order of the spectra from file select dialog or drag&drop
*) sorted : ascending sort
Date_Name sorted by Date (if any) and then by Name
Name_Date sorted by Name and then by Date
Date_NamesWithNumbers sorted by Date (if any) and then by Name with number logic
NamesWithNumbers_Date sorted by Name with number logic (e.g. “ABC1” is before “ABC002” ) and then by Date
*) as above but sorted Rev : reverse sort = descending sort
Rev_Date_Name
Rev_Name_Date
Rev_Date_NamesWithNumbers
Rev_NamesWithNumbers_Date
E.g. with reverse sort by Rev_Date_Name, the newest spectra appear on top.
Print to PDF
Depending on how many calibrations are used the result table is getting broader. To print the report (e.g. to Adobe PDF, FreePDF or Microsoft XPS), sometimes the landscape format is shorter in number of pages or in portrait a scale of 80% fits nicely. Or try another internet browser (Mozilla Firefox, Google Chrome, Microsoft Edge, …) to print the report and set the browser as your default browser so it will be opened by default.
Archiving Reports
Each report is contained in one file only, including the grafics. To save storage space the report file folder can be compressed to a zip file (.zip, .7z).
Enter lab values to NIR spectra
Entering the laboratory reference values for NIR calibrations
We have developed specialized tools into NIR-Predictor to combine the NIR and Lab data is a sample-based safe manner.
The main target is to improve Data Quality during the step of combining of the Lab data and the NIR data, because to model a good reliable calibration the data that build the base needs to be of high quality.
It also simplifies to enter the lab values manually to the corresponding NIR data, because of automatically grouping repeated NIR measurements of the same sample, so the lab values can be entered sample based and not by spectrum.
It helps to avoid false reference data, because of the broken relation of NIR spectra and reference values, data entry on the wrong position in the table.
And Helps to detect errors of duplicated or multiple copies of spectra files, and checks for inconsistencies in Date-Time and Sample-Naming. It also checks for missing values.
That all increases the Data Quality for the next step of Calibration Development, and makes data entry a less time consuming and less risky work.
How it works
Menu > Create Properties File... (F6) select the folder with your NIR spectra measured for an application. NIR-Predictor creates a Properties file template for that data : PropertiesBySamples.csv.txt
Use your favorite editor or spreadsheet program to enter and copy&paste the Lab Values into the columns and save the file.
Menu > Create Calibration Request... (F7) select the folder with the filled file for a last check and a Calibration Request file is created with the needed data as a single zip file.
Email the Calibration Request file to info@CalibrationModel.com to develop the calibrations.
Ok that is it, the NIR-Predictor guides you through the steps needed. And if you need to know more details, the Chapter “Create Properties File” is for you.
Create Properties File
Note:
If you have (exported) JCAMP-DX files containing the Lab-Values, you don’t need to do this step.
You can send the JCAMP file with your Request (.req) file directly to the calibration service at info@CalibrationModel.com.
If your JCAMP-DX files does NOT contain Lab-Values, this is a way to go.
For calibrating the spectra to the lab-values you need to assign the lab-values to the spectra. The easiest way is to have a table where each spectrum (row) is linked to multiple lab-values (columns). This function Create Properties File build such a table for the selected spectra folder automatically!
This table is stored in the file PropertiesBySamples.csv.txt. This can be created for any spectra folder you like. The file extension is .csv.txt to make it easy to edit in a text editor and also in a spreadsheet (excel). The columns are standard TAB separated.
Prop1, Prop2, Prop3 are the place to enter the Lab Reference Concentrations properties corresponding to each spectrum. It can be extended to Prop4, Prop5, … etc. Of course you can enter real word names like “Fat (%)” instead of “Prop1”. It’s recommended to put the measurement unit beside the name.
Replicates is the number of replicated or repeated spectra of a sample that is grouped together in the Sample based property file. Sample name and the DateFirst / DateLast between the sample spectra are measured.
Date format is ISO-8601. Missing Dates are 0002-02-02T00:00:00.0000000.
If the file PropertiesBySamples.csv.txt already exist in the selected folder, the user will be notified (it will not be overwritten, because the file may contain user entered Lab-values). The Lab Reference Concentrations values are initialized to 0 (zero) and needed to be changed.
Note: 0 is not interpreted as missing value! If you have a 0 concentration value, put in 0 or 0.0 .
The entry of properties is as easy as possible, because it’s organized by Sample (and not by Spectra), so it’s like your Lab-Value Table that is sample based. The sample rows are sorted in a special way by Sample name. Sorting by Date or alphabetically by Sample can done easily in a spreadsheet program.
Note: when coping lab values to the samples make sure they correspond, so that there are no gaps and the sorting is the same.
The Spectra (rows) are initially sorted by name (and date) to have the replicates/repeats together. You can sort for your convenience in a spreadsheet program.
Enter the Lab Reference Concentrations to the spectra/sample.
Enter the Lab-Values in spreadsheet (e.g. Excel) or a text editor (e.g. Notepad++). If done, use the next menu Create Calibration Request.
Hints: Data handling:
The NIR-Predictor creates the PropertiesBySamples.csv.txt once, after that the user is responsible for its content. That means NIR-Predictor does not change this file anymore.
You can remove entire rows (spectra) in the property file. You don’t need to remove the spectra files. The NIR-Predictor is aware of the relation, the PropertiesBySamples.csv.txt defines what will be calibrated.
How to add more spectra files?
The additional spectra can be handled in a separate folder, create the property file and copy the spectra to the other folder and copy/merge the property files together in your editor or spreadsheet.
Or
Copy the spectra into the folder, rename the PropertiesBySamples.csv.txt to e.g. “PropertiesBySamples-Part1.csv.txt” and use Create Properties File to create a new PropertiesBySamples.csv.txt with all the spectra. You can copy/merge the content of the Properties files together in your editor or spreadsheet.
What happens with possible duplicate rows? It does no harm to the Calibration because we do an exact checking and data cleaning in the calibration process.
What happens to duplicate spectra names? The spectra names are not relevant for the calibration process. The spectra names are helpful to assign the lab-values to the corresponding spectrum entry. That’s why the table is initially sorted by name. The spectra names can be edited by the user.
Create Calibration Request
The menu function Create Calibration Request packs a created Properties file (see “Create Properties File”) and it’s linked spectra files in a compressed ZIP file for sending to the CalibrationModel.com Service.
Please note that the number of measured quantitative samples need to be at least 60 . That means you need at least 60 different spectra (not counting the replicate/repeated measurements).
It shows additional property information about the data you have entered, like - the property type (Quantitative) - it’s range (min - max) and - the number of unique values and - if the Lab-values are enough diverse to get calibrated.
First select the folder with the PropertiesBySamples.csv.txt and measured spectra files of samples you have Lab-values. The data is checked and you get notified what is missing or might be wrong. If something needs to be changed, edit the PropertiesBySamples.csv.txt and do Create Calibration Request again. Your last selected folder is remembered, so you can press return in the folder selection dialog.
Hint: The keyboard shortcuts for redoing it after you edited some entries is : F7 Return - that allows you to get the property information quickly.
Hint: If you open the PropertiesBySamples.csv.txt in a spreadsheet program, you can create Histogram plots of the entered Lab-values, to see in which range are to less samples measurements.
When all is fine
When all is fine the “CalibrationRequest.zip” file is created for that data.
The ZIP file contains:
your PropertiesBySamples.csv.txt
your personal REQuest file for your computer system, that looks like
e.g. “337dcdc06b2d6dfb0b5c4bba578642312edf2ae84d909281624d7e26283e8b07 WIN-GB0PB48GSK4.req”
the spectra data files
Note: If the CalibrationRequest.zip file is already created and you change the PropertiesBySamples.csv.txt make sure to delete the old CalibrationRequest.zip file first! In the dialog it states if it was successfully created or NOT because it already exist. So you are always on the safe side.
Note:CalibrationRequest.zip file name contains the property names to know what would be calibrated and at the end an identification number for referencing the file. E.g. “CalibrationRequest ‘Prop1’ - ‘Prop2’ h31T3wOH.zip”
Program Settings
The users program settings are stored in UserSettings.json
The program counters are stored in GlobalCounters.json
It’s easy to use with NIR-Predictor,
just drag & drop your data for getting the prediction results.
It supports an automatic file format detection.
So you don’t need to specify the instrument type and settings! See the list of supported formats and NIR Vendors: NIR-Predictor supported Spectral Data File Formats
Use the included data to checkout how it feels:
Open the demo Spectra folder by using the Menu > Open Demo Spectra or press F8.
There are files with spectra from different Vendors.
Drag & drop a spectra file onto the NIR-Predictor window (or press Ctrl+O as for ’Open some files).
The spectra will be
loaded
pre-processed
predicted and
reported
Note:
All the steps are fully automatic.
All calibrations that are compatible with the spectra, will produce prediction results in one go.
To select specific calibrations choose the Application. Where the " " empty means use all the calibrations.
To define a Application read more in chapter “Applications”
Hint:
To get access to Statistics of Predictions and Reports use the Menu > Show more/less (Ctrl+M) or you can simply resize the window. Here you can also re-do the Analyze step manually with changed inputs (e.g. Result Ordering).
Creating your own Calibrations
How it works - step by step
You have measured your samples with you NIR-Instrument Software.
And got the Lab-values of these samples.
Note: If you combined these data already in your NIR software used,
and you can export it as a JCAMP-DX file then use
Menu > Create Request File .req ... (F2)
and read the “Help.html” and NIR-Predictor JCAMP.
Else proceed as below.
The NIR-Predictor provides tooling for that:
Menu > Create Properties File... (F6)
Select the folder with your NIR spectra measured for an application.
NIR-Predictor creates a customized Properties file template for that data to enter the Lab values.
Note: You don’t need to specify your instrument or vendor or an application. It’s all done automatically. And also the sample spectra are detected and grouped automatically!
Use your favorite editor or spreadsheet program to enter and copy&paste
the Lab-references Values into the columns “Prop1”, “Prop2” etc. and save the file.
A final check of your entered data is done by NIR-Predictor,
to make sure your data ist complete and all is fine.
Menu > Create Calibration Request... (F7)
Select the folder with the filled file.
A CalibrationRequest.zip is created with the necessary data
if enougth diverse Lab values are entered.
Email the CalibrationRequest.zip file
to info@CalibrationModel.com to develop the calibrations.
When your calibrations are ready, you will receive an email with a link
to the CalibrationModel WebShop where
you can purchase and download the calibration files,
that work with our free NIR-Predictor software without internet access.
Note: Your sent NIR data is deleted after processing.
We do not collect your NIR data!
Note: Further details can be found under “Create Properties File” and “Create Calibration Request”.
Configure the Calibrations for prediction usage
Configuration:
in NIR-Predictor : Menu > Open Calibrations (F9)
an explorer window is opened where the calibrations are located
create a folder for your application, choose a name
copy the calibration file(s) (*.cm) into that folder
in NIR-Predictor : Menu > Search and load Applications (F4)
Usage:
in NIR-Predictor : open the Application drop down list, and select your application by name
if all is fine, the calibration file is valid and not expired, it shows : Calibration “1 valid calibation”
the NIR-Predictor is now ready to predict
to switch the application, goto 6.
Applications
The Application concept allows to group multiple Calibrations together for an Application. By selecting an Application before prediction, only the Calibrations belonging to the Application will be used for Prediction. In the Demo Data this is used to have multiple spectrometer as Application. This can be used easily as e.g. as Application “Meat Products” containing Fat and Moisture Calibration.
To create an Application, create a folder with the Application’s name inside the Calibrations folder, and move/copy all the Calibrations files to this Application folder. To remove a Calibration from the Application, remove the Calibration file from the Application folder.
After creating an new Application folder, press menu Search and load Applications (F4) to update the NIR-Predictor dialog where the Application can be selected via the dropdown list. You don’t need to close the NIR-Predictor.
After moving Calibration files around, press menu Search and load Calibrations (F5) to update the NIR-Predictor dialog.
The use-all case
In the NIR-Predictor dialog where the Application can be selected via the dropdown list, the empty "" name means that all (yes all) valid Calibrations will be used for prediction.
Note: The Prediction Report will contain only results from spectral compatible Calibrations with the given spectra. That allows to automatically handle the multi vendor NIR instrument usage.
Prediction Result Report
Histograms of Prediction Values per Property
Shows the distribution of the predicted results per calibration. The histogram range contains the range of the calibrated property and includes the predicted results.
The histogram bar (bin) color is defined as follow:
blue : all predictions inside calibration range.
red : all predictions outside calibration range.
orange : some overlaps with calibration range.
So not all spectra in a orange bin are outside calibration range.
Histograms
Note: Predicted values are always shown in Histogram table and Prediction Value List table, even if the spectrum does not fit into model (spectrum different to model, aka Residual Outlier) shown as Out = X.
Note: Old browsers like Microsoft Internet Explorer 11 don’t support the grafics for Histogram charts. Use an current browser like Firefox or Chrome or Edge.
Note: If your browser opens the report too slow, try to deactivate some browser plugins, because they can filter what you look at and some add-ons are really slow.
Spectra Plot Thumbnail on the Prediction Report
Visualizes the min,median,max spectrum of the spectra dropped as files on the NIR-Predictor. This gives a minimal and good spectral overview of the predicted property results.
Spectra Plot color legend: min,median,max spectrum by predicted property or if no calibration is available by spectral intensity.
The min,median,max is determined from the predicted properties or if not available from the intensity of the spectra.
Beside the histogram of the predicted properties, where the distribution can be seen, the spectra shown are the ones from min,median,max predicted property.
This gives a minimal and good spectral overview of the predicted property results.
The “Spectral Range” and number of datapoints is shown in the Prediction Report Header below the listed spectra files.
To zoom the spectra plot a little, zoom the report in the browser (hold ctrl + mouse wheel, or pinch on touch screen).
The spectra plots and histograms are stored with the report and can be archived.
Note
Note that the spectra are shown in the raw values that are loaded, they are not shown pre-processed as the calibration model uses them to make the prediction.
Note that the median property spectrum is the median from the predicted property pobulation and not the “median” of the calibration property range.
Note that in the multi calibration prediction case, the spectra are selected for each property based on the related predicted property values and so the spectra plots shows typical different spectra.
Spectra Plot
Outlier Detection
To safeguard the prediction results, outliers are automatically checked for each individual prediction. This is based on limits that are determined when creating the calibration with the base data. Thus, a strange spectral measurement can be detected and signaled as an outlier even without base data only by means of the calibration and the NIR predictor. A prediction result with outlier warning is to be distrusted. How the various outlier tests are interpreted and how to avoid them in practice is described here.
The spectrum is an outlier to the model, if the spectrum is not similar with the spectra and lab-values the model is built with.
This legend is shown on each NIR-Predictor prediction report below the results:
Outlier (Out) Symbol Description
“X” : spectrum does not fit into model (spectrum different to model)
“O” : spectrum is wide outside model center (spectrum similar to model but far away)
“=” : prediction is outside upper or lower range of model (property outside model range)
“-” : spectrum is incompatible to calibration
Note: A prediction result with outlier warning is to be distrusted.
There are 3 outlier cases (X, O, =) and the incompatible data case “-”.
The bad case is “X”
the medium case is “O”
and the soft case is “=”.
The technical names in literature correspond to:
“X” : Spectral Residual Outlier
“O” : Leverage Outlier
“=” : Property Range Outlier
These 3 outlier cases can appear in combinations, like “XO=” or “XO” or “O=” or “X=”. The more outlier marker are shown the more likely the spectrum is an Outlier.
The default setting in NIR-Predictor Menu > “Report with Simplified Outlier Symbols”
is ON, that will show only the worst case instead of all combinations to have a simplified minimal information.
if OFF, that will show the combinations (e.g. “XO=” or “XO” or “O=” or “X=”), which is more informative for analyzing problem cases.
Some hints to avoid these Outliers:
“X” : spectrum does not fit into model (spectrum different to model)
Check if the spectrum is noise only, or has no proper signal. That can happen when measured past the sample or measured into the air or at a different substance. If you have multiple NIR instruments of the same type, use spectra measured with different instruments for the calibration.
“O” : spectrum is wide outside model center (spectrum similar to model but far away) Sample temperature has an effect on NIR spectra shape, use spectra measured at different (typical use) temperatures (sample temperature, instrument temperature).
“=” : prediction is outside upper or lower range of model (property outside model range)
Use more spectra for the calibration in the Lab value region where your special interest is. If the predicted value is only a little bit out of the calibration range, it can be Ok. Add these spectra to the calibration spectra (with the Lab values), to extend the prediction range of the calibration.
“-” : spectrum is incompatible to calibration
The spectra (from the NIR instrument) has a different wavelength range or a different resolution than the spectra used for calibration. Check Instrument settings (wavelength range, resolution)
Result Ordering
To change the ordering, a drop-down-box is located below the Analyze button. If there is an analysis from the current session, and the Result Ordering is changed, the data is re-Analyzed and reported with the new Result Ordering setting. That allows to compare the different orderings. The Result Ordering is listed in the Prediction Report above the Prediction Value List and stored in the settings.
The order/sorting of the prediction results of the spectra can be defined:
GivenOrder (default) the given order of the spectra from file select dialog or drag&drop
*) sorted : ascending sort
Date_Name sorted by Date (if any) and then by Name
Name_Date sorted by Name and then by Date
Date_NamesWithNumbers sorted by Date (if any) and then by Name with number logic
NamesWithNumbers_Date sorted by Name with number logic (e.g. “ABC1” is before “ABC002” ) and then by Date
*) as above but sorted Rev : reverse sort = descending sort
Rev_Date_Name
Rev_Name_Date
Rev_Date_NamesWithNumbers
Rev_NamesWithNumbers_Date
E.g. with reverse sort by Rev_Date_Name, the newest spectra appear on top.
Print to PDF
Depending on how many calibrations are used the result table is getting broader. To print the report (e.g. to Adobe PDF, FreePDF or Microsoft XPS), sometimes the landscape format is shorter in number of pages or in portrait a scale of 80% fits nicely. Or try another internet browser (Mozilla Firefox, Google Chrome, Microsoft Edge, …) to print the report and set the browser as your default browser so it will be opened by default.
Archiving Reports
Each report is contained in one file only, including the grafics. To save storage space the report file folder can be compressed to a zip file (.zip, .7z).
Enter lab values to NIR spectra
Entering the laboratory reference values for NIR calibrations
We have developed specialized tools into NIR-Predictor to combine the NIR and Lab data is a sample-based safe manner.
The main target is to improve Data Quality during the step of combining of the Lab data and the NIR data, because to model a good reliable calibration the data that build the base needs to be of high quality.
It also simplifies to enter the lab values manually to the corresponding NIR data, because of automatically grouping repeated NIR measurements of the same sample, so the lab values can be entered sample based and not by spectrum.
It helps to avoid false reference data, because of the broken relation of NIR spectra and reference values, data entry on the wrong position in the table.
And Helps to detect errors of duplicated or multiple copies of spectra files, and checks for inconsistencies in Date-Time and Sample-Naming. It also checks for missing values.
That all increases the Data Quality for the next step of Calibration Development, and makes data entry a less time consuming and less risky work.
How it works
Menu > Create Properties File... (F6) select the folder with your NIR spectra measured for an application. NIR-Predictor creates a Properties file template for that data : PropertiesBySamples.csv.txt
Use your favorite editor or spreadsheet program to enter and copy&paste the Lab Values into the columns and save the file.
Menu > Create Calibration Request... (F7) select the folder with the filled file for a last check and a Calibration Request file is created with the needed data as a single zip file.
Email the Calibration Request file to info@CalibrationModel.com to develop the calibrations.
Ok that is it, the NIR-Predictor guides you through the steps needed. And if you need to know more details, the Chapter “Create Properties File” is for you.
Create Properties File
Note:
If you have (exported) JCAMP-DX files containing the Lab-Values, you don’t need to do this step.
You can send the JCAMP file with your Request (.req) file directly to the calibration service at info@CalibrationModel.com.
If your JCAMP-DX files does NOT contain Lab-Values, this is a way to go.
For calibrating the spectra to the lab-values you need to assign the lab-values to the spectra. The easiest way is to have a table where each spectrum (row) is linked to multiple lab-values (columns). This function Create Properties File build such a table for the selected spectra folder automatically!
This table is stored in the file PropertiesBySamples.csv.txt. This can be created for any spectra folder you like. The file extension is .csv.txt to make it easy to edit in a text editor and also in a spreadsheet (excel). The columns are standard TAB separated.
Prop1, Prop2, Prop3 are the place to enter the Lab Reference Concentrations properties corresponding to each spectrum. It can be extended to Prop4, Prop5, … etc. Of course you can enter real word names like “Fat (%)” instead of “Prop1”. It’s recommended to put the measurement unit beside the name.
Replicates is the number of replicated or repeated spectra of a sample that is grouped together in the Sample based property file. Sample name and the DateFirst / DateLast between the sample spectra are measured.
Date format is ISO-8601. Missing Dates are 0002-02-02T00:00:00.0000000.
If the file PropertiesBySamples.csv.txt already exist in the selected folder, the user will be notified (it will not be overwritten, because the file may contain user entered Lab-values). The Lab Reference Concentrations values are initialized to 0 (zero) and needed to be changed.
Note: 0 is not interpreted as missing value! If you have a 0 concentration value, put in 0 or 0.0 .
The entry of properties is as easy as possible, because it’s organized by Sample (and not by Spectra), so it’s like your Lab-Value Table that is sample based. The sample rows are sorted in a special way by Sample name. Sorting by Date or alphabetically by Sample can done easily in a spreadsheet program.
Note: when coping lab values to the samples make sure they correspond, so that there are no gaps and the sorting is the same.
The Spectra (rows) are initially sorted by name (and date) to have the replicates/repeats together. You can sort for your convenience in a spreadsheet program.
Enter the Lab Reference Concentrations to the spectra/sample.
Enter the Lab-Values in spreadsheet (e.g. Excel) or a text editor (e.g. Notepad++). If done, use the next menu Create Calibration Request.
Hints: Data handling:
The NIR-Predictor creates the PropertiesBySamples.csv.txt once, after that the user is responsible for its content. That means NIR-Predictor does not change this file anymore.
You can remove entire rows (spectra) in the property file. You don’t need to remove the spectra files. The NIR-Predictor is aware of the relation, the PropertiesBySamples.csv.txt defines what will be calibrated.
How to add more spectra files?
The additional spectra can be handled in a separate folder, create the property file and copy the spectra to the other folder and copy/merge the property files together in your editor or spreadsheet.
Or
Copy the spectra into the folder, rename the PropertiesBySamples.csv.txt to e.g. “PropertiesBySamples-Part1.csv.txt” and use Create Properties File to create a new PropertiesBySamples.csv.txt with all the spectra. You can copy/merge the content of the Properties files together in your editor or spreadsheet.
What happens with possible duplicate rows? It does no harm to the Calibration because we do an exact checking and data cleaning in the calibration process.
What happens to duplicate spectra names? The spectra names are not relevant for the calibration process. The spectra names are helpful to assign the lab-values to the corresponding spectrum entry. That’s why the table is initially sorted by name. The spectra names can be edited by the user.
Create Calibration Request
The menu function Create Calibration Request packs a created Properties file (see “Create Properties File”) and it’s linked spectra files in a compressed ZIP file for sending to the CalibrationModel.com Service.
Please note that the number of measured quantitative samples need to be at least 60 . That means you need at least 60 different spectra (not counting the replicate/repeated measurements).
It shows additional property information about the data you have entered, like - the property type (Quantitative) - it’s range (min - max) and - the number of unique values and - if the Lab-values are enough diverse to get calibrated.
First select the folder with the PropertiesBySamples.csv.txt and measured spectra files of samples you have Lab-values. The data is checked and you get notified what is missing or might be wrong. If something needs to be changed, edit the PropertiesBySamples.csv.txt and do Create Calibration Request again. Your last selected folder is remembered, so you can press return in the folder selection dialog.
Hint: The keyboard shortcuts for redoing it after you edited some entries is : F7 Return - that allows you to get the property information quickly.
Hint: If you open the PropertiesBySamples.csv.txt in a spreadsheet program, you can create Histogram plots of the entered Lab-values, to see in which range are to less samples measurements.
When all is fine
When all is fine the “CalibrationRequest.zip” file is created for that data.
The ZIP file contains:
your PropertiesBySamples.csv.txt
your personal REQuest file for your computer system, that looks like
e.g. “337dcdc06b2d6dfb0b5c4bba578642312edf2ae84d909281624d7e26283e8b07 WIN-GB0PB48GSK4.req”
the spectra data files
Note: If the CalibrationRequest.zip file is already created and you change the PropertiesBySamples.csv.txt make sure to delete the old CalibrationRequest.zip file first! In the dialog it states if it was successfully created or NOT because it already exist. So you are always on the safe side.
Note:CalibrationRequest.zip file name contains the property names to know what would be calibrated and at the end an identification number for referencing the file. E.g. “CalibrationRequest ‘Prop1’ - ‘Prop2’ h31T3wOH.zip”
Program Settings
The users program settings are stored in UserSettings.json
The program counters are stored in GlobalCounters.json
It’s easy to use with NIR-Predictor,
just drag & drop your data for getting the prediction results.
It supports an automatic file format detection.
So you don’t need to specify the instrument type and settings! See the list of supported formats and NIR Vendors: NIR-Predictor supported Spectral Data File Formats
Use the included data to checkout how it feels:
Open the demo Spectra folder by using the Menu > Open Demo Spectra or press F8.
There are files with spectra from different Vendors.
Drag & drop a spectra file onto the NIR-Predictor window (or press Ctrl+O as for ’Open some files).
The spectra will be
loaded
pre-processed
predicted and
reported
Note:
All the steps are fully automatic.
All calibrations that are compatible with the spectra, will produce prediction results in one go.
To select specific calibrations choose the Application. Where the " " empty means use all the calibrations.
To define a Application read more in chapter “Applications”
Hint:
To get access to Statistics of Predictions and Reports use the Menu > Show more/less (Ctrl+M) or you can simply resize the window. Here you can also re-do the Analyze step manually with changed inputs (e.g. Result Ordering).
Creating your own Calibrations
How it works - step by step
You have measured your samples with you NIR-Instrument Software.
And got the Lab-values of these samples.
Note: If you combined these data already in your NIR software used,
and you can export it as a JCAMP-DX file then use
Menu > Create Request File .req ... (F2)
and read the “Help.html” and NIR-Predictor JCAMP.
Else proceed as below.
The NIR-Predictor provides tooling for that:
Menu > Create Properties File... (F6)
Select the folder with your NIR spectra measured for an application.
NIR-Predictor creates a customized Properties file template for that data to enter the Lab values.
Note: You don’t need to specify your instrument or vendor or an application. It’s all done automatically. And also the sample spectra are detected and grouped automatically!
Use your favorite editor or spreadsheet program to enter and copy&paste
the Lab-references Values into the columns “Prop1”, “Prop2” etc. and save the file.
A final check of your entered data is done by NIR-Predictor,
to make sure your data ist complete and all is fine.
Menu > Create Calibration Request... (F7)
Select the folder with the filled file.
A CalibrationRequest.zip is created with the necessary data
if enougth diverse Lab values are entered.
Email the CalibrationRequest.zip file
to info@CalibrationModel.com to develop the calibrations.
When your calibrations are ready, you will receive an email with a link
to the CalibrationModel WebShop where
you can purchase and download the calibration files,
that work with our free NIR-Predictor software without internet access.
Note: Your sent NIR data is deleted after processing.
We do not collect your NIR data!
Note: Further details can be found under “Create Properties File” and “Create Calibration Request”.
Configure the Calibrations for prediction usage
Configuration:
in NIR-Predictor : Menu > Open Calibrations (F9)
an explorer window is opened where the calibrations are located
create a folder for your application, choose a name
copy the calibration file(s) (*.cm) into that folder
in NIR-Predictor : Menu > Search and load Applications (F4)
Usage:
in NIR-Predictor : open the Application drop down list, and select your application by name
if all is fine, the calibration file is valid and not expired, it shows : Calibration “1 valid calibation”
the NIR-Predictor is now ready to predict
to switch the application, goto 6.
Applications
The Application concept allows to group multiple Calibrations together for an Application. By selecting an Application before prediction, only the Calibrations belonging to the Application will be used for Prediction. In the Demo Data this is used to have multiple spectrometer as Application. This can be used easily as e.g. as Application “Meat Products” containing Fat and Moisture Calibration.
To create an Application, create a folder with the Application’s name inside the Calibrations folder, and move/copy all the Calibrations files to this Application folder. To remove a Calibration from the Application, remove the Calibration file from the Application folder.
After creating an new Application folder, press menu Search and load Applications (F4) to update the NIR-Predictor dialog where the Application can be selected via the dropdown list. You don’t need to close the NIR-Predictor.
After moving Calibration files around, press menu Search and load Calibrations (F5) to update the NIR-Predictor dialog.
The use-all case
In the NIR-Predictor dialog where the Application can be selected via the dropdown list, the empty "" name means that all (yes all) valid Calibrations will be used for prediction.
Note: The Prediction Report will contain only results from spectral compatible Calibrations with the given spectra. That allows to automatically handle the multi vendor NIR instrument usage.
Prediction Result Report
Histograms of Prediction Values per Property
Shows the distribution of the predicted results per calibration. The histogram range contains the range of the calibrated property and includes the predicted results.
The histogram bar (bin) color is defined as follow:
blue : all predictions inside calibration range.
red : all predictions outside calibration range.
orange : some overlaps with calibration range.
So not all spectra in a orange bin are outside calibration range.
Histograms
Note: Predicted values are always shown in Histogram table and Prediction Value List table, even if the spectrum does not fit into model (spectrum different to model, aka Residual Outlier) shown as Out = X.
Note: Old browsers like Microsoft Internet Explorer 11 don’t support the grafics for Histogram charts. Use an current browser like Firefox or Chrome or Edge.
Note: If your browser opens the report too slow, try to deactivate some browser plugins, because they can filter what you look at and some add-ons are really slow.
Spectra Plot Thumbnail on the Prediction Report
Visualizes the min,median,max spectrum of the spectra dropped as files on the NIR-Predictor. This gives a minimal and good spectral overview of the predicted property results.
Spectra Plot color legend: min,median,max spectrum by predicted property or if no calibration is available by spectral intensity.
The min,median,max is determined from the predicted properties or if not available from the intensity of the spectra.
Beside the histogram of the predicted properties, where the distribution can be seen, the spectra shown are the ones from min,median,max predicted property.
This gives a minimal and good spectral overview of the predicted property results.
The “Spectral Range” and number of datapoints is shown in the Prediction Report Header below the listed spectra files.
To zoom the spectra plot a little, zoom the report in the browser (hold ctrl + mouse wheel, or pinch on touch screen).
The spectra plots and histograms are stored with the report and can be archived.
Note
Note that the spectra are shown in the raw values that are loaded, they are not shown pre-processed as the calibration model uses them to make the prediction.
Note that the median property spectrum is the median from the predicted property pobulation and not the “median” of the calibration property range.
Note that in the multi calibration prediction case, the spectra are selected for each property based on the related predicted property values and so the spectra plots shows typical different spectra.
Spectra Plot
Outlier Detection
To safeguard the prediction results, outliers are automatically checked for each individual prediction. This is based on limits that are determined when creating the calibration with the base data. Thus, a strange spectral measurement can be detected and signaled as an outlier even without base data only by means of the calibration and the NIR predictor. A prediction result with outlier warning is to be distrusted. How the various outlier tests are interpreted and how to avoid them in practice is described here.
The spectrum is an outlier to the model, if the spectrum is not similar with the spectra and lab-values the model is built with.
This legend is shown on each NIR-Predictor prediction report below the results:
Outlier (Out) Symbol Description
“X” : spectrum does not fit into model (spectrum different to model)
“O” : spectrum is wide outside model center (spectrum similar to model but far away)
“=” : prediction is outside upper or lower range of model (property outside model range)
“-” : spectrum is incompatible to calibration
Note: A prediction result with outlier warning is to be distrusted.
There are 3 outlier cases (X, O, =) and the incompatible data case “-”.
The bad case is “X”
the medium case is “O”
and the soft case is “=”.
The technical names in literature correspond to:
“X” : Spectral Residual Outlier
“O” : Leverage Outlier
“=” : Property Range Outlier
These 3 outlier cases can appear in combinations, like “XO=” or “XO” or “O=” or “X=”. The more outlier marker are shown the more likely the spectrum is an Outlier.
The default setting in NIR-Predictor Menu > “Report with Simplified Outlier Symbols”
is ON, that will show only the worst case instead of all combinations to have a simplified minimal information.
if OFF, that will show the combinations (e.g. “XO=” or “XO” or “O=” or “X=”), which is more informative for analyzing problem cases.
Some hints to avoid these Outliers:
“X” : spectrum does not fit into model (spectrum different to model)
Check if the spectrum is noise only, or has no proper signal. That can happen when measured past the sample or measured into the air or at a different substance. If you have multiple NIR instruments of the same type, use spectra measured with different instruments for the calibration.
“O” : spectrum is wide outside model center (spectrum similar to model but far away) Sample temperature has an effect on NIR spectra shape, use spectra measured at different (typical use) temperatures (sample temperature, instrument temperature).
“=” : prediction is outside upper or lower range of model (property outside model range)
Use more spectra for the calibration in the Lab value region where your special interest is. If the predicted value is only a little bit out of the calibration range, it can be Ok. Add these spectra to the calibration spectra (with the Lab values), to extend the prediction range of the calibration.
“-” : spectrum is incompatible to calibration
The spectra (from the NIR instrument) has a different wavelength range or a different resolution than the spectra used for calibration. Check Instrument settings (wavelength range, resolution)
Result Ordering
To change the ordering, a drop-down-box is located below the Analyze button. If there is an analysis from the current session, and the Result Ordering is changed, the data is re-Analyzed and reported with the new Result Ordering setting. That allows to compare the different orderings. The Result Ordering is listed in the Prediction Report above the Prediction Value List and stored in the settings.
The order/sorting of the prediction results of the spectra can be defined:
GivenOrder (default) the given order of the spectra from file select dialog or drag&drop
*) sorted : ascending sort
Date_Name sorted by Date (if any) and then by Name
Name_Date sorted by Name and then by Date
Date_NamesWithNumbers sorted by Date (if any) and then by Name with number logic
NamesWithNumbers_Date sorted by Name with number logic (e.g. “ABC1” is before “ABC002” ) and then by Date
*) as above but sorted Rev : reverse sort = descending sort
Rev_Date_Name
Rev_Name_Date
Rev_Date_NamesWithNumbers
Rev_NamesWithNumbers_Date
E.g. with reverse sort by Rev_Date_Name, the newest spectra appear on top.
Print to PDF
Depending on how many calibrations are used the result table is getting broader. To print the report (e.g. to Adobe PDF, FreePDF or Microsoft XPS), sometimes the landscape format is shorter in number of pages or in portrait a scale of 80% fits nicely. Or try another internet browser (Mozilla Firefox, Google Chrome, Microsoft Edge, …) to print the report and set the browser as your default browser so it will be opened by default.
Archiving Reports
Each report is contained in one file only, including the grafics. To save storage space the report file folder can be compressed to a zip file (.zip, .7z).
Enter lab values to NIR spectra
Entering the laboratory reference values for NIR calibrations
We have developed specialized tools into NIR-Predictor to combine the NIR and Lab data is a sample-based safe manner.
The main target is to improve Data Quality during the step of combining of the Lab data and the NIR data, because to model a good reliable calibration the data that build the base needs to be of high quality.
It also simplifies to enter the lab values manually to the corresponding NIR data, because of automatically grouping repeated NIR measurements of the same sample, so the lab values can be entered sample based and not by spectrum.
It helps to avoid false reference data, because of the broken relation of NIR spectra and reference values, data entry on the wrong position in the table.
And Helps to detect errors of duplicated or multiple copies of spectra files, and checks for inconsistencies in Date-Time and Sample-Naming. It also checks for missing values.
That all increases the Data Quality for the next step of Calibration Development, and makes data entry a less time consuming and less risky work.
How it works
Menu > Create Properties File... (F6) select the folder with your NIR spectra measured for an application. NIR-Predictor creates a Properties file template for that data : PropertiesBySamples.csv.txt
Use your favorite editor or spreadsheet program to enter and copy&paste the Lab Values into the columns and save the file.
Menu > Create Calibration Request... (F7) select the folder with the filled file for a last check and a Calibration Request file is created with the needed data as a single zip file.
Email the Calibration Request file to info@CalibrationModel.com to develop the calibrations.
Ok that is it, the NIR-Predictor guides you through the steps needed. And if you need to know more details, the Chapter “Create Properties File” is for you.
Create Properties File
Note:
If you have (exported) JCAMP-DX files containing the Lab-Values, you don’t need to do this step.
You can send the JCAMP file with your Request (.req) file directly to the calibration service at info@CalibrationModel.com.
If your JCAMP-DX files does NOT contain Lab-Values, this is a way to go.
For calibrating the spectra to the lab-values you need to assign the lab-values to the spectra. The easiest way is to have a table where each spectrum (row) is linked to multiple lab-values (columns). This function Create Properties File build such a table for the selected spectra folder automatically!
This table is stored in the file PropertiesBySamples.csv.txt. This can be created for any spectra folder you like. The file extension is .csv.txt to make it easy to edit in a text editor and also in a spreadsheet (excel). The columns are standard TAB separated.
Prop1, Prop2, Prop3 are the place to enter the Lab Reference Concentrations properties corresponding to each spectrum. It can be extended to Prop4, Prop5, … etc. Of course you can enter real word names like “Fat (%)” instead of “Prop1”. It’s recommended to put the measurement unit beside the name.
Replicates is the number of replicated or repeated spectra of a sample that is grouped together in the Sample based property file. Sample name and the DateFirst / DateLast between the sample spectra are measured.
Date format is ISO-8601. Missing Dates are 0002-02-02T00:00:00.0000000.
If the file PropertiesBySamples.csv.txt already exist in the selected folder, the user will be notified (it will not be overwritten, because the file may contain user entered Lab-values). The Lab Reference Concentrations values are initialized to 0 (zero) and needed to be changed.
Note: 0 is not interpreted as missing value! If you have a 0 concentration value, put in 0 or 0.0 .
The entry of properties is as easy as possible, because it’s organized by Sample (and not by Spectra), so it’s like your Lab-Value Table that is sample based. The sample rows are sorted in a special way by Sample name. Sorting by Date or alphabetically by Sample can done easily in a spreadsheet program.
Note: when coping lab values to the samples make sure they correspond, so that there are no gaps and the sorting is the same.
The Spectra (rows) are initially sorted by name (and date) to have the replicates/repeats together. You can sort for your convenience in a spreadsheet program.
Enter the Lab Reference Concentrations to the spectra/sample.
Enter the Lab-Values in spreadsheet (e.g. Excel) or a text editor (e.g. Notepad++). If done, use the next menu Create Calibration Request.
Hints: Data handling:
The NIR-Predictor creates the PropertiesBySamples.csv.txt once, after that the user is responsible for its content. That means NIR-Predictor does not change this file anymore.
You can remove entire rows (spectra) in the property file. You don’t need to remove the spectra files. The NIR-Predictor is aware of the relation, the PropertiesBySamples.csv.txt defines what will be calibrated.
How to add more spectra files?
The additional spectra can be handled in a separate folder, create the property file and copy the spectra to the other folder and copy/merge the property files together in your editor or spreadsheet.
Or
Copy the spectra into the folder, rename the PropertiesBySamples.csv.txt to e.g. “PropertiesBySamples-Part1.csv.txt” and use Create Properties File to create a new PropertiesBySamples.csv.txt with all the spectra. You can copy/merge the content of the Properties files together in your editor or spreadsheet.
What happens with possible duplicate rows? It does no harm to the Calibration because we do an exact checking and data cleaning in the calibration process.
What happens to duplicate spectra names? The spectra names are not relevant for the calibration process. The spectra names are helpful to assign the lab-values to the corresponding spectrum entry. That’s why the table is initially sorted by name. The spectra names can be edited by the user.
Create Calibration Request
The menu function Create Calibration Request packs a created Properties file (see “Create Properties File”) and it’s linked spectra files in a compressed ZIP file for sending to the CalibrationModel.com Service.
Please note that the number of measured quantitative samples need to be at least 60 . That means you need at least 60 different spectra (not counting the replicate/repeated measurements).
It shows additional property information about the data you have entered, like - the property type (Quantitative) - it’s range (min - max) and - the number of unique values and - if the Lab-values are enough diverse to get calibrated.
First select the folder with the PropertiesBySamples.csv.txt and measured spectra files of samples you have Lab-values. The data is checked and you get notified what is missing or might be wrong. If something needs to be changed, edit the PropertiesBySamples.csv.txt and do Create Calibration Request again. Your last selected folder is remembered, so you can press return in the folder selection dialog.
Hint: The keyboard shortcuts for redoing it after you edited some entries is : F7 Return - that allows you to get the property information quickly.
Hint: If you open the PropertiesBySamples.csv.txt in a spreadsheet program, you can create Histogram plots of the entered Lab-values, to see in which range are to less samples measurements.
When all is fine
When all is fine the “CalibrationRequest.zip” file is created for that data.
The ZIP file contains:
your PropertiesBySamples.csv.txt
your personal REQuest file for your computer system, that looks like
e.g. “337dcdc06b2d6dfb0b5c4bba578642312edf2ae84d909281624d7e26283e8b07 WIN-GB0PB48GSK4.req”
the spectra data files
Note: If the CalibrationRequest.zip file is already created and you change the PropertiesBySamples.csv.txt make sure to delete the old CalibrationRequest.zip file first! In the dialog it states if it was successfully created or NOT because it already exist. So you are always on the safe side.
Note:CalibrationRequest.zip file name contains the property names to know what would be calibrated and at the end an identification number for referencing the file. E.g. “CalibrationRequest ‘Prop1’ - ‘Prop2’ h31T3wOH.zip”
Program Settings
The users program settings are stored in UserSettings.json
The program counters are stored in GlobalCounters.json