“Application of UV-VIS-NIR spectroscopy in membrane separation processes for fast quantitative compositional analysis: A case study of egg products” LINK
“Non-Destructive Detection of Meat Quality Based on Multiple Spectral Dimension Reduction Methods by Near-Infrared Spectroscopy” LINK
” … Discrimination of Sunflower Seeds with Different Internal Mildew Grades by Fusion of Near-Infrared Diffuse Reflectance and Transmittance Spectra …” | LINK
“Improved Soil Organic Carbon Prediction in a Forest Area by Near-Infrared Spectroscopy: Spiking of a Soil Spectral Library” | LINK
“Application of Near Infrared Spectroscopy to Monitor the Quality Change of Sour Cherry Stored under Modified Atmosphere Conditions” | LINK
“Foods : Evaluating the Use of a Similarity Index (SI) Combined with near Infrared (NIR) Spectroscopy as Method in Meat Species Authenticity” | LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
“Prediction of quality traits in dry pepper powder using visible and near-infrared spectroscopy” LINK
“Pendugaan Kandungan Kimia Minyak Goreng Menggunakan Near Infrared Spectroscopy” LINK
Hyperspectral Imaging (HSI)
“Hyperspectral image-based measurement of total flavonoid content of leaf-use Ginkgo biloba L.” LINK
“Visual Monitoring of Fatty Acid Degradation during Green Tea Storage by Hyperspectral Imaging” | LINK
“Combination of hyperspectral imaging and entropy weight method for the comprehensive assessment of antioxidant enzyme activity in Tan mutton” LINK
“Raw Beef Patty Analysis Using Near-Infrared Hyperspectral Imaging: Identification of Four Patty Categories” LINK
“Structure and near-infrared spectral properties of mesoporous silica for hyperspectral camouflage materials” | LINK
“Deep Spectral-Spatial Features of Near Infrared Hyperspectral Images for Pixel-Wise Classification of Food Products” | LINK
Spectral Imaging
“Nutritional monitoring of boron in Eucalyptus spp. in the Brazilian cerrado by multispectral bands of the MSI sensor (Sentinel-2)” LINK
Chemometrics and Machine Learning
“Sensors : A Chemiresistor Sensor Array Based on Graphene Nanostructures: From the Detection of Ammonia and Possible Interfering VOCs to Chemometric Analysis” | LINK
“Metabolites : Comprehensive Metabolomic Fingerprinting Combined with Chemometrics Identifies Species- and Variety-Specific Variation of Medicinal Herbs: An Ocimum Study” | LINK
“Sex classification of silkworm pupae from different varieties by near infrared spectroscopy combined with chemometrics” | LINK
“Evaluation of Optimized Preprocessing and Modeling Algorithms for Prediction of Soil Properties Using VIS-NIR Spectroscopy” | LINK
“Development of nondestructive models to predict oil content and fatty acid composition of Gomenzer (Ethiopian mustard) using nearinfrared reflectance spectroscopy” LINK
“Remote Sensing : Hyperspectral Inversion Model of Relative Heavy MetalContent in Pennisetum sinese Roxb via EEMD-db3 Algorithm” LINK
“Develop Non‐Destructive Models to Predict Oil Content and Fatty Acid Composition in Gomenzer (Ethiopian Mustard) Using Near‐Infrared Reflectance Spectroscopy” LINK
Optics for Spectroscopy
“ChalcogenideBased Narrowband Photodetectors for Imaging and Light Communication” LINK
AI News
Is today an AI day? – Google opens up their Bard LLM. – NVIDIA launches cloud tools for Generative AI. – Adobe announces Firefly, an AI image creator. – Microsoft unveils Bing Image Creator. LINK
Research on Spectroscopy
“Polymers : The Potential of Visible Spectroscopy as a Tool for the In-Line Monitoring of Lignin Methylolation” | LINK
Environment NIR-Spectroscopy Application
“Remote Sensing : Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet” | LINK
“Animals : Lipid Characteristics of the Muscle and Perirenal Fat in Young Tudanca Bulls Fed on Different Levels of Grass Silage” | LINK
“Agronomy : Estimation of Relative Chlorophyll Content in Spring Wheat Based on Multi-Temporal UAV Remote Sensing” | LINK
“Biosynthesis of Melanin Nanoparticles for Photoacoustic Imaging Guided Photothermal Therapy” LINK
“Agriculture : The Effect of Enzyme Activity on Carbon Sequestration and the Cycle of Available Macro- (P, K, Mg) and Microelements (Zn, Cu) in Phaeozems” | LINK
“Near infrared spectroscopy for fast characterization of animal by products feedstocks for biogas production: Calibration of a handheld device” LINK
Food & Feed Industry NIR Usage
“Rapid determination of protein, starch and moisture contents in wheat flour by near-infrared hyperspectral imaging” LINK
Laboratory and NIR-Spectroscopy
“Analytical strategies for herbal Cannabis samples in forensic applications: A comprehensive review” LINK
“Substituent Controllable Assembly of Anthracene Donors and TCNQ Acceptors in Charge Transfer Cocrystals” LINK
“Foods : Feature Reduction for the Classification of Bruise Damage to Apple Fruit Using a Contactless FT-NIR Spectroscopy with Machine Learning” | LINK
“Perbedaan Nilai Near Infrared Spectroscopic terhadap Posisi Head Up 15o dan Head Up 30 O pada Pasien yang Dirawat di Ruang Intensive Care Unit” LINK
“Broad learning system with Takagi-Sugeno fuzzy subsystem for tobacco origin identification based on near infrared spectroscopy” LINK
“Rapid and Non-Invasive Detection of Aedes aegypti Co-Infected with Zika and Dengue Viruses Using Near Infrared Spectroscopy” LINK
“Multivariate Curve Resolution Applied to Near Infrared Spectroscopic Data Acquired Throughout the Cooking Process to Monitor Evolving Béchamel Sauces” | LINK
“Limited usefulness of visible-near-infrared spectroscopy in soils: The picture gets much clearer” LINK
“Application of Near Infrared Spectroscopy to Monitor the Quality Change of Sour Cherry Stored under Modified Atmosphere Conditions” LINK
“Assessment of integrated freshness index of different varieties of eggs using the visible and near-infrared spectroscopy” LINK
“Super Broadband Near‐Infrared Solid Solution Phosphors with Adjustable Peak Wavelengths from 1165 to 875 nm for NIR Spectroscopy Applications” LINK
“Determination of the ADF and IVOMD Content of Sugarcane Using Near Infrared Spectroscopy Coupled with Chemometrics” LINK
“Evaluating the Use of a Similarity Index (SI) Combined with near Infrared (NIR) Spectroscopy as Method in Meat Species Authenticity” | LINK
“Rapid Determination of Geniposide and Baicalin in Lanqin Oral Solution by Near-Infrared Spectroscopy with Chemometric Algorithms during Alcohol Precipitation” LINK
“Applied Sciences : Determination of Coniferous Wood’s Compressive Strength by SE-DenseNet Model Combined with Near-Infrared Spectroscopy” LINK
“Estimation of Proximate, Fatty Acid, Mineral Content and Proline Level in Amaranth using Near Infrared Reflectance Spectroscopy” LINK
“Non-invasive measurement of shear force in chicken meat using near infrared spectroscopy supported by neural network analysis” LINK
“Application of NIRS adaptive technology for prediction of in vitro digestibility parameters of feed ingredients from Fermented Cocoa Pods” LINK
“Visible-Near Infrared Reflectance Spectroscopy for Rhodamine B Detection in Chili Paste Using Principal Component Analysis” LINK
“Foods : Lipids in a Nutshell: Quick Determination of Lipid Content in Hazelnuts with NIR Spectroscopy” | LINK
“Near-infrared spectroscopy for early selection of waxy cassava clones via seed analysis” | LINK
“An Efficient Perovskite‐Like Phosphor with Peak Emission Wavelength at 850 nm for High‐Performance NIR LEDs” LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
“Sensors : Towards a Miniaturized Photoacoustic Detector for the Infrared Spectroscopic Analysis of SO2F2 and Refrigerants” | LINK
“Correlating near infrared data for improved polyolefin recycling” LINK
“NON-DESTRUCTIVE ASSESSMENT OF SOIL ORGANIC CARBON USING NEAR INFRARED TECHNOLOGY” | Mechram.pdf LINK
“FullColor Emissive DDA Carbazole Luminophores: Red to NearInfrared Mechanofluorochromism, AggregationInduced NearInfrared Emission, and Photodynamic Therapy Application” LINK
Hyperspectral Imaging (HSI)
“Applied Sciences : Rapid Estimation of Moisture Content in Unpeeled Potato Tubers Using Hyperspectral Imaging” | LINK
“Remote Sensing : Applying Variable Selection Methods and Preprocessing Techniques to Hyperspectral Reflectance Data to Estimate Tea Cultivar Chlorophyll Content” LINK
Chemometrics and Machine Learning
“Remote Sensing : Maize Yield Prediction with Machine Learning, Spectral Variables and Irrigation Management” | LINK
“Improving the Performance of a Spectral Model to Estimate Total Nitrogen Content with Small Soil Samples Sizes” LINK
“Rapid quantification of goat milk adulteration with cow milk using Raman spectroscopy and chemometrics” LINK
“Determination of Diclofenac Diethylamine Levels in Emulgel Preparations Using NIR Spectroscopy Combined with Chemometrics” LINK
“Selection of reference samples for updating multivariate calibration models used in the analysis of pig faeces” LINK
“Chemosensors : How Chemometrics Revives the UV-Vis Spectroscopy Applications as an Analytical Sensor for Spectralprint (Nontargeted) Analysis” | LINK
“An integrated approach utilizing raman spectroscopy and chemometrics for authentication and detection of adulteration of agarwood essential oils” | LINK
“Miniaturized NIR spectroscopy and chemometrics: A smart combination to solve food authentication challenges” | LINK
“In Vitro Validation of a New Tissue Oximeter Using Visible Light” | LINK
Optics for Spectroscopy
“Investigation of PAN: Hemp Stems Nanofibers Produced by Electrospinning Method” LINK
Equipment for Spectroscopy
“FeDoped Carbon Dots as NIRII Fluorescence Probe for In Vivo Gastric Imaging and pH Detection” LINK
“Chemosensors : Wavelet Transform Makes Water an Outstanding Near-Infrared Spectroscopic Probe” | LINK
Environment NIR-Spectroscopy Application
“Comparison of soil salinity indices based on satellite imagery analysis in Syrdarya province, Uzbekistan” | LINK
“Colorants : Andy Warhol and His Amazing Technicolor Shoes: Characterizing the Synthetic Dyes Found in Dr. Ph. Martin’s Synchromatic Transparent Watercolors and Used in À la Recherche du Shoe Perdu” | LINK
Agriculture NIR-Spectroscopy Usage
“Variation in potential feeding value of triticale forage among plant fraction, maturity stage, growing season and genotype” LINK
“Effect of nps fertilizer and harvesting stage on biomass yield and quality parameters of bracharia grass under supplementary irrigation in Southern Ethiopia” LINK
“Analysis of Seasonal Effects on Nutritive Value of Native Forages in the Southern Great Plains and Its Relationship to Sampling Method” LINK
“Non-Destructive Evaluation of Moisture Content in Single Soybean Seed Using Vis-NIR Spectroscopy” LINK
“Rootstock’s and Cover-Crops’ Influence on Grape: A NIR-Based ANN Classification Model” LINK
“Classification of Heavy Metal Contamination Risk in Typical Agricultural Soils by Visible and Near Infrared Reflectance Spectroscopy” LINK
“Effect of Ustilago maydis on the Nutritive Value and Aerobic Deterioration of Maize Silage” | LINK
“IJMS : Label-Free Characterization of Macrophage Polarization Using Raman Spectroscopy” | LINK
Food & Feed Industry NIR Usage
“Animals : Licury Cake in Diets for Lactating Goats: Qualitative Aspects of Milk and Cheese” | LINK
“Wheat yield estimation using remote sensing data based on machine learning approaches” | LINK
Other
“Applications of High-Throughput Phenotypic Phenomics” | LINK
“Evaluation of dry matter content in intact potatoes using different optical sensing modes” | LINK
“Doped potassium dihydrogen phosphate single crystals with enhanced second-harmonic generation efficiency: An investigation of phase purity, nonlinear …” LINK
"Rapid Determination of Cellulose and Hemicellulose Contents in Corn
Stover Using Near-Infrared Spectroscopy Combined with Wavelength
Selection" LINK
"A Rapid Recognition Method of Auricularia Auricula Varieties based on Near-Infrared Spectral Characteristics" LINK
"Study on robust model construction method of Multi-batch Fruit Online Sorting by near-infrared spectroscopy" LINK
"Foods : Effects of Orientations and Regions on Performance of Online
Soluble Solids Content Prediction Models Based on Near-Infrared
Spectroscopy for Peaches" LINK
"In Vivo Near-Infrared Noninvasive Glucose Measurement and Detection in Humans" LINK
"Monitoring freshness of crayfish (Prokaryophyllus clarkii) through the
combination of near-infrared spectroscopy and chemometric method" | LINK
"Identification of Five Similar Cinnamomum Wood Species Using Portable Near-Infrared Spectroscopy" LINK
"Rapid determination of urea formaldehyde resin content in wood fiber mat using near-infrared spectroscopy" LINK
"Portable/Handheld NIR sebagai Teknologi Evaluasi Mutu Bahan Pertanian secara Non-Destruktif" LINK
"Detection of Chilling Injury Symptoms of Salak Pondoh Fruit during Cold Storage with Near Infrared Spectroscopy (NIRS)" LINK
"21st International Conference on Near Infrared Spectroscopy (NIR 2023)" LINK
"A First attempt to combine NIRS and plenoptic cameras for the
assessment of grasslands functional diversity and species composition" LINK
"Establishment of NIRS Model for Oil Content in Single Seed of Oil Peony" LINK
"Effect of Biochar on Desert Soil Wind Erosion Using Sweep Model and Vis-Nir Spectroscopy Technique" LINK
"Probing 1D convolutional neural network adapted to near-infrared spectroscopy for efficient classification of mixed fish" LINK
"Agronomic characterization of anaerobic digestates with near-infrared spectroscopy" LINK
"Rapid Detection of Green Sichuan Pepper Geographic Origin Based on Near-Infrared Spectroscopy" LINK
" Identification of multiple raisins by feature fusion combined with NIR spectroscopy" LINK
"Portable FT-NIR spectroscopic sensor for detection of chemical
precursors of explosives using advanced prediction algorithms" | LINK
"Research on High-throughput Crop Authenticity Identification Method
Based on Near-infrared Spectroscopy and InResSpectra model" LINK
"Differences between chemical analysis and portable near-infrared reflectance spectrometry in maize hybrids" LINK
"Organic resources from Madagascar: Dataset of chemical and near-infrared spectroscopy measurements" | LINK
"Establishment of a rapid detection model for the sensory quality and
components of Yuezhou Longjing tea using near-infrared spectroscopy" LINK
"Breed authentication in Iberian pork meat analysed in situ using Near Infrared Spectroscopy" LINK
"Canopy VIS-NIR spectroscopy and self-learning artificial intelligence
for a generalised model of predawn leaf water potential in Vitis
vinifera" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Prediction of water relaxation time using near infrared spectroscopy" LINK
"Determination of ash content in silicon dioxide filled epoxy-phenolic prepreg using near infrared spectroscopy" LINK
"Online near-infrared spectroscopy for automatic polymeric material identification" Additives LINK
"Review of near infrared hyperspectral imaging applications related to wood and wood products" LINK
"Lamb-dip saturated-absorption cavity ring-down rovibrational molecular spectroscopy in the near-infrared" LINK
"Modulation of Thermally Stable Photoluminescence in Cr3+-Based Near-Infrared Phosphors" LINK
Raman Spectroscopy
"A comparative study based on serum SERS spectra in and on the coffee ring for high precision breast cancer detection" LINK
"Foods : Recent Developments in Surface-Enhanced Raman Spectroscopy and
Its Application in Food Analysis: Alcoholic Beverages as an Example" LINK
"Raman spectroscopy biochemical characterisation of bladder cancer cisplatin resistance regulated by FDFT1: a review" | LINK
"Broadband Nanoscale SurfaceEnhanced Raman Spectroscopy by Multiresonant
Nanolaminate Plasmonic Nanocavities on Vertical Nanopillars" LINK
Hyperspectral Imaging (HSI)
"Mid-infrared speckle reduction technique for hyperspectral imaging" LINK
"Rapid and accurate detection of starch content in mixed sorghum by hyperspectral imaging combined with data fusion technology" LINK
" A decision fusion method based on hyperspectral imaging and electronic
nose techniques for moisture content prediction in frozen-thawed pork" LINK
"A novel high-throughput hyperspectral scanner and analytical methods
for predicting maize kernel composition and physical traits" LINK
"Hyperspectral imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends" LINK
"Applied Sciences : Hyperspectral Identification of Ginseng Growth Years
and Spectral Importance Analysis Based on Random Forest" LINK
"Nondestructive detection of anthocyanin content in fresh leaves of purple maize using hyperspectral data" LINK
"Application of Principal Component Analysis to Hyperspectral Data for Potassium Concentration Classification on Peach leaves" LINK
Chemometrics and Machine Learning
"Aberrant brain network and eye gaze patterns during natural social
interaction predict multi-domain social-cognitive behaviors in girls
with fragile X syndrome" | LINK
"Agronomy : Tomato Comprehensive Quality Evaluation and Irrigation Mode
Optimization with Biogas Slurry Based on the Combined Evaluation Model" LINK
"Comparison of polynomial and machine learning regression models to
predict LDPE, PET, and ABS concentrations in beach sediment based on
spectral ..." LINK
"Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A
Review of the Recent Literature" openaccess MachineLearning DeepLearning
LINK
" NIR Validation and Calibration of Van Soest cell wall constituents
(ADF, NDF, and ADL) of Available Corn Silage in Bangladesh" LINK
"Sensors : Rapid Detection and Quantification of Adulterants in Fruit
Juices Using Machine Learning Tools and Spectroscopy Data" LINK
"Applied Sciences : Prediction of Soil Shear Strength Parameters Using Combined Data and Different Machine Learning Models" LINK
"Prediction and visualization of fat content in polythene-packed meat
using near-infrared hyperspectral imaging and chemometrics" LINK
"Comparison of Spectroscopy-Based Methods and Chemometrics to Confirm Classification of Specialty Coffees" LINK
"Proximal sensor data fusion and auxiliary information for tropical soil property prediction: Soil texture" LINK
Environment NIR-Spectroscopy Application
"Comparison of natural and technogenic soils developed on volcanic ash by Vis-NIR spectroscopy" LINK
"Hranidbena vrijednost kukuruznih silaža Sisačko-moslavačke županije" LINK
"Vibrational spectroscopic evaluation of hydrophilic or hydrophobic properties of oxide surfaces" LINK
"Controlled Synthesis, Spectral Studies, and Catalytic Activity of
Silver and Gold Nanoparticles Biosynthesized Using Ficus sycomorus Leaf
Extract" | LINK
NIR Calibration-Model Services
How to Develop Near-Infrared Spectroscopy Calibrations in the 21st Century? | Chemometrics LINK
Spectroscopy and Chemometrics News Weekly 28, 2022 | NIRS NIR
Spectroscopy MachineLearning Spectrometer Spectrometric Analytical
Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software
IoT Sensors QA QC Testing Quality LINK
"Rapid Determination of Cellulose and Hemicellulose Contents in Corn
Stover Using Near-Infrared Spectroscopy Combined with Wavelength
Selection" LINK
"A Rapid Recognition Method of Auricularia Auricula Varieties based on Near-Infrared Spectral Characteristics" LINK
"Study on robust model construction method of Multi-batch Fruit Online Sorting by near-infrared spectroscopy" LINK
"Foods : Effects of Orientations and Regions on Performance of Online
Soluble Solids Content Prediction Models Based on Near-Infrared
Spectroscopy for Peaches" LINK
"In Vivo Near-Infrared Noninvasive Glucose Measurement and Detection in Humans" LINK
"Monitoring freshness of crayfish (Prokaryophyllus clarkii) through the
combination of near-infrared spectroscopy and chemometric method" | LINK
"Identification of Five Similar Cinnamomum Wood Species Using Portable Near-Infrared Spectroscopy" LINK
"Rapid determination of urea formaldehyde resin content in wood fiber mat using near-infrared spectroscopy" LINK
"Portable/Handheld NIR sebagai Teknologi Evaluasi Mutu Bahan Pertanian secara Non-Destruktif" LINK
"Detection of Chilling Injury Symptoms of Salak Pondoh Fruit during Cold Storage with Near Infrared Spectroscopy (NIRS)" LINK
"21st International Conference on Near Infrared Spectroscopy (NIR 2023)" LINK
"A First attempt to combine NIRS and plenoptic cameras for the
assessment of grasslands functional diversity and species composition" LINK
"Establishment of NIRS Model for Oil Content in Single Seed of Oil Peony" LINK
"Effect of Biochar on Desert Soil Wind Erosion Using Sweep Model and Vis-Nir Spectroscopy Technique" LINK
"Probing 1D convolutional neural network adapted to near-infrared spectroscopy for efficient classification of mixed fish" LINK
"Agronomic characterization of anaerobic digestates with near-infrared spectroscopy" LINK
"Rapid Detection of Green Sichuan Pepper Geographic Origin Based on Near-Infrared Spectroscopy" LINK
" Identification of multiple raisins by feature fusion combined with NIR spectroscopy" LINK
"Portable FT-NIR spectroscopic sensor for detection of chemical
precursors of explosives using advanced prediction algorithms" | LINK
"Research on High-throughput Crop Authenticity Identification Method
Based on Near-infrared Spectroscopy and InResSpectra model" LINK
"Differences between chemical analysis and portable near-infrared reflectance spectrometry in maize hybrids" LINK
"Organic resources from Madagascar: Dataset of chemical and near-infrared spectroscopy measurements" | LINK
"Establishment of a rapid detection model for the sensory quality and
components of Yuezhou Longjing tea using near-infrared spectroscopy" LINK
"Breed authentication in Iberian pork meat analysed in situ using Near Infrared Spectroscopy" LINK
"Canopy VIS-NIR spectroscopy and self-learning artificial intelligence
for a generalised model of predawn leaf water potential in Vitis
vinifera" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Prediction of water relaxation time using near infrared spectroscopy" LINK
"Determination of ash content in silicon dioxide filled epoxy-phenolic prepreg using near infrared spectroscopy" LINK
"Online near-infrared spectroscopy for automatic polymeric material identification" Additives LINK
"Review of near infrared hyperspectral imaging applications related to wood and wood products" LINK
"Lamb-dip saturated-absorption cavity ring-down rovibrational molecular spectroscopy in the near-infrared" LINK
"Modulation of Thermally Stable Photoluminescence in Cr3+-Based Near-Infrared Phosphors" LINK
Raman Spectroscopy
"A comparative study based on serum SERS spectra in and on the coffee ring for high precision breast cancer detection" LINK
"Foods : Recent Developments in Surface-Enhanced Raman Spectroscopy and
Its Application in Food Analysis: Alcoholic Beverages as an Example" LINK
"Raman spectroscopy biochemical characterisation of bladder cancer cisplatin resistance regulated by FDFT1: a review" | LINK
"Broadband Nanoscale SurfaceEnhanced Raman Spectroscopy by Multiresonant
Nanolaminate Plasmonic Nanocavities on Vertical Nanopillars" LINK
Hyperspectral Imaging (HSI)
"Mid-infrared speckle reduction technique for hyperspectral imaging" LINK
"Rapid and accurate detection of starch content in mixed sorghum by hyperspectral imaging combined with data fusion technology" LINK
" A decision fusion method based on hyperspectral imaging and electronic
nose techniques for moisture content prediction in frozen-thawed pork" LINK
"A novel high-throughput hyperspectral scanner and analytical methods
for predicting maize kernel composition and physical traits" LINK
"Hyperspectral imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends" LINK
"Applied Sciences : Hyperspectral Identification of Ginseng Growth Years
and Spectral Importance Analysis Based on Random Forest" LINK
"Nondestructive detection of anthocyanin content in fresh leaves of purple maize using hyperspectral data" LINK
"Application of Principal Component Analysis to Hyperspectral Data for Potassium Concentration Classification on Peach leaves" LINK
Chemometrics and Machine Learning
"Aberrant brain network and eye gaze patterns during natural social
interaction predict multi-domain social-cognitive behaviors in girls
with fragile X syndrome" | LINK
"Agronomy : Tomato Comprehensive Quality Evaluation and Irrigation Mode
Optimization with Biogas Slurry Based on the Combined Evaluation Model" LINK
"Comparison of polynomial and machine learning regression models to
predict LDPE, PET, and ABS concentrations in beach sediment based on
spectral ..." LINK
"Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A
Review of the Recent Literature" openaccess MachineLearning DeepLearning
LINK
" NIR Validation and Calibration of Van Soest cell wall constituents
(ADF, NDF, and ADL) of Available Corn Silage in Bangladesh" LINK
"Sensors : Rapid Detection and Quantification of Adulterants in Fruit
Juices Using Machine Learning Tools and Spectroscopy Data" LINK
"Applied Sciences : Prediction of Soil Shear Strength Parameters Using Combined Data and Different Machine Learning Models" LINK
"Prediction and visualization of fat content in polythene-packed meat
using near-infrared hyperspectral imaging and chemometrics" LINK
"Comparison of Spectroscopy-Based Methods and Chemometrics to Confirm Classification of Specialty Coffees" LINK
"Proximal sensor data fusion and auxiliary information for tropical soil property prediction: Soil texture" LINK
Environment NIR-Spectroscopy Application
"Comparison of natural and technogenic soils developed on volcanic ash by Vis-NIR spectroscopy" LINK
"Hranidbena vrijednost kukuruznih silaža Sisačko-moslavačke županije" LINK
"Vibrational spectroscopic evaluation of hydrophilic or hydrophobic properties of oxide surfaces" LINK
"Controlled Synthesis, Spectral Studies, and Catalytic Activity of
Silver and Gold Nanoparticles Biosynthesized Using Ficus sycomorus Leaf
Extract" | LINK
NIR Calibration-Model Services
How to Develop Near-Infrared Spectroscopy Calibrations in the 21st Century? | Chemometrics LINK
Spectroscopy and Chemometrics News Weekly 28, 2022 | NIRS NIR
Spectroscopy MachineLearning Spectrometer Spectrometric Analytical
Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software
IoT Sensors QA QC Testing Quality LINK
"Rapid Determination of Cellulose and Hemicellulose Contents in Corn
Stover Using Near-Infrared Spectroscopy Combined with Wavelength
Selection" LINK
"A Rapid Recognition Method of Auricularia Auricula Varieties based on Near-Infrared Spectral Characteristics" LINK
"Study on robust model construction method of Multi-batch Fruit Online Sorting by near-infrared spectroscopy" LINK
"Foods : Effects of Orientations and Regions on Performance of Online
Soluble Solids Content Prediction Models Based on Near-Infrared
Spectroscopy for Peaches" LINK
"In Vivo Near-Infrared Noninvasive Glucose Measurement and Detection in Humans" LINK
"Monitoring freshness of crayfish (Prokaryophyllus clarkii) through the
combination of near-infrared spectroscopy and chemometric method" | LINK
"Identification of Five Similar Cinnamomum Wood Species Using Portable Near-Infrared Spectroscopy" LINK
"Rapid determination of urea formaldehyde resin content in wood fiber mat using near-infrared spectroscopy" LINK
"Portable/Handheld NIR sebagai Teknologi Evaluasi Mutu Bahan Pertanian secara Non-Destruktif" LINK
"Detection of Chilling Injury Symptoms of Salak Pondoh Fruit during Cold Storage with Near Infrared Spectroscopy (NIRS)" LINK
"21st International Conference on Near Infrared Spectroscopy (NIR 2023)" LINK
"A First attempt to combine NIRS and plenoptic cameras for the
assessment of grasslands functional diversity and species composition" LINK
"Establishment of NIRS Model for Oil Content in Single Seed of Oil Peony" LINK
"Effect of Biochar on Desert Soil Wind Erosion Using Sweep Model and Vis-Nir Spectroscopy Technique" LINK
"Probing 1D convolutional neural network adapted to near-infrared spectroscopy for efficient classification of mixed fish" LINK
"Agronomic characterization of anaerobic digestates with near-infrared spectroscopy" LINK
"Rapid Detection of Green Sichuan Pepper Geographic Origin Based on Near-Infrared Spectroscopy" LINK
" Identification of multiple raisins by feature fusion combined with NIR spectroscopy" LINK
"Portable FT-NIR spectroscopic sensor for detection of chemical
precursors of explosives using advanced prediction algorithms" | LINK
"Research on High-throughput Crop Authenticity Identification Method
Based on Near-infrared Spectroscopy and InResSpectra model" LINK
"Differences between chemical analysis and portable near-infrared reflectance spectrometry in maize hybrids" LINK
"Organic resources from Madagascar: Dataset of chemical and near-infrared spectroscopy measurements" | LINK
"Establishment of a rapid detection model for the sensory quality and
components of Yuezhou Longjing tea using near-infrared spectroscopy" LINK
"Breed authentication in Iberian pork meat analysed in situ using Near Infrared Spectroscopy" LINK
"Canopy VIS-NIR spectroscopy and self-learning artificial intelligence
for a generalised model of predawn leaf water potential in Vitis
vinifera" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Prediction of water relaxation time using near infrared spectroscopy" LINK
"Determination of ash content in silicon dioxide filled epoxy-phenolic prepreg using near infrared spectroscopy" LINK
"Online near-infrared spectroscopy for automatic polymeric material identification" Additives LINK
"Review of near infrared hyperspectral imaging applications related to wood and wood products" LINK
"Lamb-dip saturated-absorption cavity ring-down rovibrational molecular spectroscopy in the near-infrared" LINK
"Modulation of Thermally Stable Photoluminescence in Cr3+-Based Near-Infrared Phosphors" LINK
Raman Spectroscopy
"A comparative study based on serum SERS spectra in and on the coffee ring for high precision breast cancer detection" LINK
"Foods : Recent Developments in Surface-Enhanced Raman Spectroscopy and
Its Application in Food Analysis: Alcoholic Beverages as an Example" LINK
"Raman spectroscopy biochemical characterisation of bladder cancer cisplatin resistance regulated by FDFT1: a review" | LINK
"Broadband Nanoscale SurfaceEnhanced Raman Spectroscopy by Multiresonant
Nanolaminate Plasmonic Nanocavities on Vertical Nanopillars" LINK
Hyperspectral Imaging (HSI)
"Mid-infrared speckle reduction technique for hyperspectral imaging" LINK
"Rapid and accurate detection of starch content in mixed sorghum by hyperspectral imaging combined with data fusion technology" LINK
" A decision fusion method based on hyperspectral imaging and electronic
nose techniques for moisture content prediction in frozen-thawed pork" LINK
"A novel high-throughput hyperspectral scanner and analytical methods
for predicting maize kernel composition and physical traits" LINK
"Hyperspectral imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends" LINK
"Applied Sciences : Hyperspectral Identification of Ginseng Growth Years
and Spectral Importance Analysis Based on Random Forest" LINK
"Nondestructive detection of anthocyanin content in fresh leaves of purple maize using hyperspectral data" LINK
"Application of Principal Component Analysis to Hyperspectral Data for Potassium Concentration Classification on Peach leaves" LINK
Chemometrics and Machine Learning
"Aberrant brain network and eye gaze patterns during natural social
interaction predict multi-domain social-cognitive behaviors in girls
with fragile X syndrome" | LINK
"Agronomy : Tomato Comprehensive Quality Evaluation and Irrigation Mode
Optimization with Biogas Slurry Based on the Combined Evaluation Model" LINK
"Comparison of polynomial and machine learning regression models to
predict LDPE, PET, and ABS concentrations in beach sediment based on
spectral ..." LINK
"Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A
Review of the Recent Literature" openaccess MachineLearning DeepLearning
LINK
" NIR Validation and Calibration of Van Soest cell wall constituents
(ADF, NDF, and ADL) of Available Corn Silage in Bangladesh" LINK
"Sensors : Rapid Detection and Quantification of Adulterants in Fruit
Juices Using Machine Learning Tools and Spectroscopy Data" LINK
"Applied Sciences : Prediction of Soil Shear Strength Parameters Using Combined Data and Different Machine Learning Models" LINK
"Prediction and visualization of fat content in polythene-packed meat
using near-infrared hyperspectral imaging and chemometrics" LINK
"Comparison of Spectroscopy-Based Methods and Chemometrics to Confirm Classification of Specialty Coffees" LINK
"Proximal sensor data fusion and auxiliary information for tropical soil property prediction: Soil texture" LINK
Environment NIR-Spectroscopy Application
"Comparison of natural and technogenic soils developed on volcanic ash by Vis-NIR spectroscopy" LINK
"Hranidbena vrijednost kukuruznih silaža Sisačko-moslavačke županije" LINK
"Vibrational spectroscopic evaluation of hydrophilic or hydrophobic properties of oxide surfaces" LINK
"Controlled Synthesis, Spectral Studies, and Catalytic Activity of
Silver and Gold Nanoparticles Biosynthesized Using Ficus sycomorus Leaf
Extract" | LINK
Custom NIR Calibration Models development for a large list of NIR
spectrometers NIRS spectroscopy spectrometer chemometrics
MachineLearning DigitalTransformation miniaturization mobileDevices
MobileSpectrometers NIRanalysis Laboratoires LINK
Do you use a near-infrared Spectrometer with Chemometric Methods? This
will save you time | NIR NIRS SWIR MIR NIT LINK
Spectroscopy and Chemometrics News Weekly 15, 2022 | NIRS NIR
Spectroscopy MachineLearning Spectrometer Spectrometric Analytical
Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software
IoT Sensors QA QC Testing Quality LINK
"Development of a calibration model for near infrared spectroscopy using a convolutional neural network" LINK
"PhotoReduction with NIR Light of Nucleus Targeting Pt(IV) Nanoparticles
for Combined TumorTargeted Chemotherapy and Photodynamic Immunotherapy"
LINK
New spectra dataset released: | Vis-NIR reflectance spectra of basalts (raw pieces and powders) | ROMA database LINK
"Novel fNIRS study on homogeneous symmetric feature-based transfer learning for brain-computer interface" | LINK
"Hybrid motion artifact detection and correction approach for functional near-infrared spectroscopy measurements" LINK
"Broadband Near-Field Near-Infrared Spectroscopy and Imaging with a Laser-Driven Light Source" LINK
"... of Malperfused Areas in an Irradiated Random Pattern Skin Flap
Model Using Indocyanine Green Angiography and Near-Infrared
Reflectance-Based Imaging and ..." LINK
"Variation of properties of two contrasting Oxisols enhanced by pXRF and Vis-NIR" | LINK
"Carbon Footprint Reduction by Utilizing Real Time NIR Spectroscopy for RVP Measurement in Natural Gas Condensate Stabilizers" LINK
"Near Infrared Spectroscopy Detects Change of Tissue Hemoglobin and
Water Levelsin Kawasaki Disease and Coronary Artery Lesions" LINK
"Cortical Activation of Swallowing Using fNIRS: A Proof of Concept Study with Healthy Adults" | LINK
"Sensors : A Compact Fiber-Coupled NIR/MIR Laser Absorption Instrument
for the Simultaneous Measurement of Gas-Phase Temperature and CO, CO2,
and H2O Concentration" LINK
"Predicting Soil Organic Carbon Mineralization Rates Using δ13C,
Assessed by Near-Infrared Spectroscopy, in Depth Profiles Under
Permanent Grassland Along a ..." | LINK
"FGANet: fNIRS-guided Attention Network for Hybrid EEG-fNIRS Brain-Computer Interfaces" LINK
"Gasoline octane number prediction from near-infrared spectroscopy with an ANN-based model" LINK
"Non-destructive Measurements of Toona sinensis Chlorophyll and Nitrogen
Content Under Drought Stress Using Near Infrared Spectroscopy" | LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Electrochromism of Nanographenes in the NearInfrared Region" LINK
"Energies : Fast Quantitative Modelling Method for Infrared Spectrum Gas
Logging Based on Adaptive Step Sliding Partial Least Squares" LINK
"Discrimination of Commercial Ibuprofen Tablets by using a Button Sample Holder and Mid-Infrared Spectroscopy" LINK
"Sensors : Extending Effective Dynamic Range of Hyperspectral Line Cameras for Short Wave Infrared Imaging" LINK
"Induced NearInfrared Emission and Controlled Photooxidation Based on Sulfonated Crown Ether in Water" LINK
Hyperspectral Imaging (HSI)
"Transcriptome and hyperspectral profiling allows assessment of phosphorus nutrient status in rice under field conditions" LINK
"Unsupervised Spatial-Spectral CNN-Based Feature Learning for Hyperspectral Image Classification" LINK
"Determination of viability and vigor of naturally-aged rice seeds using hyperspectral imaging with machine learning" LINK
"Diagnostical Accuracy of Hyperspectral Imaging After Free Flap Surgery" LINK
Spectral Imaging
"Remote Sensing : Genetic Programming Approach for the Detection of
Mistletoe Based on UAV Multispectral Imagery in the Conservation Area of
Mexico City" LINK
Chemometrics and Machine Learning
"Foods : Discriminant Analysis of Pu-Erh Tea of Different Raw Materials Based on Phytochemicals Using Chemometrics" LINK
"Algorithm of Stability-Analysis-Based Feature Selection for NIR Calibration Transfer" LINK
"Chemosensors : Nonlinear Multivariate Regression Algorithms for
Improving Precision of Multisensor Potentiometry in Analysis of Spent
Nuclear Fuel Reprocessing Solutions" LINK
"Blood Glucose Prediction Using Machine Learning on Jetson Nanoplatform" LINK
"Machine Learning-Powered Models for Near-Infrared Spectrometers: Prediction of Protein in Multiple Grain Cereals" LINK
"Cognitive spectroscopy for the classification of rice varieties: a
comparison of machine learning and deep learning approaches in analysing
long-wave near-infrared ..." LINK
"Foods : Fatty Acid Profiling in Kernels Coupled with Chemometric
Analyses as a Feasible Strategy for the Discrimination of Different
Walnuts" LINK
"Remote Sensing : A Machine Learning Framework for the Classification of
Natura 2000 Habitat Types at Large Spatial Scales Using MODIS Surface
Reflectance Data" LINK
"Non-destructive detection of chilling injury in kiwifruit using a
dual-laser scanning system with a principal component analysis - back
propagation neural network" LINK
Research on Spectroscopy
"Applied Sciences : Pre-Disinfection of Poly-Methyl-Methacrylate (PMMA)
Reduces Volatile Sulfides Compounds (VSC) Production in Experimental
Biofilm In Vitro" LINK
"Remote Sensing : Study of Atmospheric Carbon Dioxide Retrieval Method Based on Normalized Sensitivity" LINK
Equipment for Spectroscopy
"Mid-IR spectroscopy with NIR grating spectrometers" LINK
Environment NIR-Spectroscopy Application
"Antibacterial Copolypeptoids with Potent Activity against Drug
Resistant Bacteria and Biofilms, Excellent Stability, and Recycling
Property" LINK
"Determining Water Transport Kinetics in Limestone by Dual-Wavelength Cavity Ring-Down Spectroscopy" LINK
"Verifying the predictive performance for soil organic carbon when
employing field Vis-NIR spectroscopy and satellite imagery obtained
using two different sampling ..." LINK
Agriculture NIR-Spectroscopy Usage
"Agriculture : A 1D-SP-Net to Determine Early Drought Stress Status of
Tomato (Solanum lycopersicum) with Imbalanced Vis/NIR Spectroscopy Data"
LINK
"Remote Sensing : Spectral-Based Classification of Plant Species Groups
and Functional Plant Parts in Managed Permanent Grassland" LINK
"Assessment of macronutrients and alpha-galactosides of texturized vegetable proteins by near infrared hyperspectral imaging" LINK
"A fuzzy multi-criteria decision-making approach for the assessment of
forest health applying hyper spectral imageries: A case study from
Ramsar forest, North of Iran" LINK
"Feasibility of near-infrared spectroscopic rapid detection method for
the water content of vermiculite substrates in desert facility
agriculture" | LINK
"Evaluation of Methods for Measuring Fusarium-Damaged Kernels of Wheat" LINK
"Effects of free-air temperature increase on grain yield and greenhouse gas emissions in a double rice cropping system" LINK
"The use of milk Fourier-transform mid-infrared spectroscopy to diagnose
pregnancy and determine spectral regional associations with pregnancy
in US dairy cows" LINK
"Nutrients : Analysis of the Correlation between Meal Frequency and Obesity among Chinese Adults Aged 18-59 Years in 2015" LINK
"Agriculture : N2O Emission and Nitrification/Denitrification Bacterial
Communities in Upland Black Soil under Combined Effects of Early and
Immediate Moisture" LINK
"Bovine fecal chemistry changes with progression of Southern Cattle
Tick, Rhipicephalus (Boophilus) microplus (Acari: Ixodidae) infestation"
LINK
Food & Feed Industry NIR Usage
"Effects of Irrigation Strategy and Plastic Film Mulching on Soil N 2 O Emissions and Fruit Yields of Greenhouse Tomato" LINK
Chemical Industry NIR Usage
"Polymers : π-Conjugated Polymers and Their Application in Organic and Hybrid Organic-Silicon Solar Cells" LINK
Pharma Industry NIR Usage
"Applied Sciences : Design of Two-Mode Spectroscopic Sensor for
Biomedical Applications: Analysis and Measurement of Relative Intensity
Noise through Control Mechanism" LINK
Other
"How This A.I. Draws Anything You Describe [Dall-E 2]" LINK
"Asphaltene Precipitation Onsets in Relation to the Critical Dilution of Athabasca Bitumen in Paraffinic Solvents" LINK
"Design Meets Neuroscience: A Preliminary Review of Design Research Using Neuroscience Tools" LINK
"Dissimilatory nitrate reduction in urban lake ecosystems: a comparison study between closed and open lakes in Chengdu, China" LINK
"[Exclusive] Elon Musk: A future worth getting excited about" LINK
NIR Calibration-Model Services
Custom NIR Calibration Models development for a large list of NIR
spectrometers NIRS spectroscopy spectrometer chemometrics
MachineLearning DigitalTransformation miniaturization mobileDevices
MobileSpectrometers NIRanalysis Laboratoires LINK
Do you use a near-infrared Spectrometer with Chemometric Methods? This
will save you time | NIR NIRS SWIR MIR NIT LINK
Spectroscopy and Chemometrics News Weekly 15, 2022 | NIRS NIR
Spectroscopy MachineLearning Spectrometer Spectrometric Analytical
Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software
IoT Sensors QA QC Testing Quality LINK
"Development of a calibration model for near infrared spectroscopy using a convolutional neural network" LINK
"PhotoReduction with NIR Light of Nucleus Targeting Pt(IV) Nanoparticles
for Combined TumorTargeted Chemotherapy and Photodynamic Immunotherapy"
LINK
New spectra dataset released: | Vis-NIR reflectance spectra of basalts (raw pieces and powders) | ROMA database LINK
"Novel fNIRS study on homogeneous symmetric feature-based transfer learning for brain-computer interface" | LINK
"Hybrid motion artifact detection and correction approach for functional near-infrared spectroscopy measurements" LINK
"Broadband Near-Field Near-Infrared Spectroscopy and Imaging with a Laser-Driven Light Source" LINK
"... of Malperfused Areas in an Irradiated Random Pattern Skin Flap
Model Using Indocyanine Green Angiography and Near-Infrared
Reflectance-Based Imaging and ..." LINK
"Variation of properties of two contrasting Oxisols enhanced by pXRF and Vis-NIR" | LINK
"Carbon Footprint Reduction by Utilizing Real Time NIR Spectroscopy for RVP Measurement in Natural Gas Condensate Stabilizers" LINK
"Near Infrared Spectroscopy Detects Change of Tissue Hemoglobin and
Water Levelsin Kawasaki Disease and Coronary Artery Lesions" LINK
"Cortical Activation of Swallowing Using fNIRS: A Proof of Concept Study with Healthy Adults" | LINK
"Sensors : A Compact Fiber-Coupled NIR/MIR Laser Absorption Instrument
for the Simultaneous Measurement of Gas-Phase Temperature and CO, CO2,
and H2O Concentration" LINK
"Predicting Soil Organic Carbon Mineralization Rates Using δ13C,
Assessed by Near-Infrared Spectroscopy, in Depth Profiles Under
Permanent Grassland Along a ..." | LINK
"FGANet: fNIRS-guided Attention Network for Hybrid EEG-fNIRS Brain-Computer Interfaces" LINK
"Gasoline octane number prediction from near-infrared spectroscopy with an ANN-based model" LINK
"Non-destructive Measurements of Toona sinensis Chlorophyll and Nitrogen
Content Under Drought Stress Using Near Infrared Spectroscopy" | LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Electrochromism of Nanographenes in the NearInfrared Region" LINK
"Energies : Fast Quantitative Modelling Method for Infrared Spectrum Gas
Logging Based on Adaptive Step Sliding Partial Least Squares" LINK
"Discrimination of Commercial Ibuprofen Tablets by using a Button Sample Holder and Mid-Infrared Spectroscopy" LINK
"Sensors : Extending Effective Dynamic Range of Hyperspectral Line Cameras for Short Wave Infrared Imaging" LINK
"Induced NearInfrared Emission and Controlled Photooxidation Based on Sulfonated Crown Ether in Water" LINK
Hyperspectral Imaging (HSI)
"Transcriptome and hyperspectral profiling allows assessment of phosphorus nutrient status in rice under field conditions" LINK
"Unsupervised Spatial-Spectral CNN-Based Feature Learning for Hyperspectral Image Classification" LINK
"Determination of viability and vigor of naturally-aged rice seeds using hyperspectral imaging with machine learning" LINK
"Diagnostical Accuracy of Hyperspectral Imaging After Free Flap Surgery" LINK
Spectral Imaging
"Remote Sensing : Genetic Programming Approach for the Detection of
Mistletoe Based on UAV Multispectral Imagery in the Conservation Area of
Mexico City" LINK
Chemometrics and Machine Learning
"Foods : Discriminant Analysis of Pu-Erh Tea of Different Raw Materials Based on Phytochemicals Using Chemometrics" LINK
"Algorithm of Stability-Analysis-Based Feature Selection for NIR Calibration Transfer" LINK
"Chemosensors : Nonlinear Multivariate Regression Algorithms for
Improving Precision of Multisensor Potentiometry in Analysis of Spent
Nuclear Fuel Reprocessing Solutions" LINK
"Blood Glucose Prediction Using Machine Learning on Jetson Nanoplatform" LINK
"Machine Learning-Powered Models for Near-Infrared Spectrometers: Prediction of Protein in Multiple Grain Cereals" LINK
"Cognitive spectroscopy for the classification of rice varieties: a
comparison of machine learning and deep learning approaches in analysing
long-wave near-infrared ..." LINK
"Foods : Fatty Acid Profiling in Kernels Coupled with Chemometric
Analyses as a Feasible Strategy for the Discrimination of Different
Walnuts" LINK
"Remote Sensing : A Machine Learning Framework for the Classification of
Natura 2000 Habitat Types at Large Spatial Scales Using MODIS Surface
Reflectance Data" LINK
"Non-destructive detection of chilling injury in kiwifruit using a
dual-laser scanning system with a principal component analysis - back
propagation neural network" LINK
Research on Spectroscopy
"Applied Sciences : Pre-Disinfection of Poly-Methyl-Methacrylate (PMMA)
Reduces Volatile Sulfides Compounds (VSC) Production in Experimental
Biofilm In Vitro" LINK
"Remote Sensing : Study of Atmospheric Carbon Dioxide Retrieval Method Based on Normalized Sensitivity" LINK
Equipment for Spectroscopy
"Mid-IR spectroscopy with NIR grating spectrometers" LINK
Environment NIR-Spectroscopy Application
"Antibacterial Copolypeptoids with Potent Activity against Drug
Resistant Bacteria and Biofilms, Excellent Stability, and Recycling
Property" LINK
"Determining Water Transport Kinetics in Limestone by Dual-Wavelength Cavity Ring-Down Spectroscopy" LINK
"Verifying the predictive performance for soil organic carbon when
employing field Vis-NIR spectroscopy and satellite imagery obtained
using two different sampling ..." LINK
Agriculture NIR-Spectroscopy Usage
"Agriculture : A 1D-SP-Net to Determine Early Drought Stress Status of
Tomato (Solanum lycopersicum) with Imbalanced Vis/NIR Spectroscopy Data"
LINK
"Remote Sensing : Spectral-Based Classification of Plant Species Groups
and Functional Plant Parts in Managed Permanent Grassland" LINK
"Assessment of macronutrients and alpha-galactosides of texturized vegetable proteins by near infrared hyperspectral imaging" LINK
"A fuzzy multi-criteria decision-making approach for the assessment of
forest health applying hyper spectral imageries: A case study from
Ramsar forest, North of Iran" LINK
"Feasibility of near-infrared spectroscopic rapid detection method for
the water content of vermiculite substrates in desert facility
agriculture" | LINK
"Evaluation of Methods for Measuring Fusarium-Damaged Kernels of Wheat" LINK
"Effects of free-air temperature increase on grain yield and greenhouse gas emissions in a double rice cropping system" LINK
"The use of milk Fourier-transform mid-infrared spectroscopy to diagnose
pregnancy and determine spectral regional associations with pregnancy
in US dairy cows" LINK
"Nutrients : Analysis of the Correlation between Meal Frequency and Obesity among Chinese Adults Aged 18-59 Years in 2015" LINK
"Agriculture : N2O Emission and Nitrification/Denitrification Bacterial
Communities in Upland Black Soil under Combined Effects of Early and
Immediate Moisture" LINK
"Bovine fecal chemistry changes with progression of Southern Cattle
Tick, Rhipicephalus (Boophilus) microplus (Acari: Ixodidae) infestation"
LINK
Food & Feed Industry NIR Usage
"Effects of Irrigation Strategy and Plastic Film Mulching on Soil N 2 O Emissions and Fruit Yields of Greenhouse Tomato" LINK
Chemical Industry NIR Usage
"Polymers : π-Conjugated Polymers and Their Application in Organic and Hybrid Organic-Silicon Solar Cells" LINK
Pharma Industry NIR Usage
"Applied Sciences : Design of Two-Mode Spectroscopic Sensor for
Biomedical Applications: Analysis and Measurement of Relative Intensity
Noise through Control Mechanism" LINK
Other
"How This A.I. Draws Anything You Describe [Dall-E 2]" LINK
"Asphaltene Precipitation Onsets in Relation to the Critical Dilution of Athabasca Bitumen in Paraffinic Solvents" LINK
"Design Meets Neuroscience: A Preliminary Review of Design Research Using Neuroscience Tools" LINK
"Dissimilatory nitrate reduction in urban lake ecosystems: a comparison study between closed and open lakes in Chengdu, China" LINK
"[Exclusive] Elon Musk: A future worth getting excited about" LINK
NIR Calibration-Model Services
Custom NIR Calibration Models development for a large list of NIR
spectrometers NIRS spectroscopy spectrometer chemometrics
MachineLearning DigitalTransformation miniaturization mobileDevices
MobileSpectrometers NIRanalysis Laboratoires LINK
Do you use a near-infrared Spectrometer with Chemometric Methods? This
will save you time | NIR NIRS SWIR MIR NIT LINK
Spectroscopy and Chemometrics News Weekly 15, 2022 | NIRS NIR
Spectroscopy MachineLearning Spectrometer Spectrometric Analytical
Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software
IoT Sensors QA QC Testing Quality LINK
"Development of a calibration model for near infrared spectroscopy using a convolutional neural network" LINK
"PhotoReduction with NIR Light of Nucleus Targeting Pt(IV) Nanoparticles
for Combined TumorTargeted Chemotherapy and Photodynamic Immunotherapy"
LINK
New spectra dataset released: | Vis-NIR reflectance spectra of basalts (raw pieces and powders) | ROMA database LINK
"Novel fNIRS study on homogeneous symmetric feature-based transfer learning for brain-computer interface" | LINK
"Hybrid motion artifact detection and correction approach for functional near-infrared spectroscopy measurements" LINK
"Broadband Near-Field Near-Infrared Spectroscopy and Imaging with a Laser-Driven Light Source" LINK
"... of Malperfused Areas in an Irradiated Random Pattern Skin Flap
Model Using Indocyanine Green Angiography and Near-Infrared
Reflectance-Based Imaging and ..." LINK
"Variation of properties of two contrasting Oxisols enhanced by pXRF and Vis-NIR" | LINK
"Carbon Footprint Reduction by Utilizing Real Time NIR Spectroscopy for RVP Measurement in Natural Gas Condensate Stabilizers" LINK
"Near Infrared Spectroscopy Detects Change of Tissue Hemoglobin and
Water Levelsin Kawasaki Disease and Coronary Artery Lesions" LINK
"Cortical Activation of Swallowing Using fNIRS: A Proof of Concept Study with Healthy Adults" | LINK
"Sensors : A Compact Fiber-Coupled NIR/MIR Laser Absorption Instrument
for the Simultaneous Measurement of Gas-Phase Temperature and CO, CO2,
and H2O Concentration" LINK
"Predicting Soil Organic Carbon Mineralization Rates Using δ13C,
Assessed by Near-Infrared Spectroscopy, in Depth Profiles Under
Permanent Grassland Along a ..." | LINK
"FGANet: fNIRS-guided Attention Network for Hybrid EEG-fNIRS Brain-Computer Interfaces" LINK
"Gasoline octane number prediction from near-infrared spectroscopy with an ANN-based model" LINK
"Non-destructive Measurements of Toona sinensis Chlorophyll and Nitrogen
Content Under Drought Stress Using Near Infrared Spectroscopy" | LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
"Electrochromism of Nanographenes in the NearInfrared Region" LINK
"Energies : Fast Quantitative Modelling Method for Infrared Spectrum Gas
Logging Based on Adaptive Step Sliding Partial Least Squares" LINK
"Discrimination of Commercial Ibuprofen Tablets by using a Button Sample Holder and Mid-Infrared Spectroscopy" LINK
"Sensors : Extending Effective Dynamic Range of Hyperspectral Line Cameras for Short Wave Infrared Imaging" LINK
"Induced NearInfrared Emission and Controlled Photooxidation Based on Sulfonated Crown Ether in Water" LINK
Hyperspectral Imaging (HSI)
"Transcriptome and hyperspectral profiling allows assessment of phosphorus nutrient status in rice under field conditions" LINK
"Unsupervised Spatial-Spectral CNN-Based Feature Learning for Hyperspectral Image Classification" LINK
"Determination of viability and vigor of naturally-aged rice seeds using hyperspectral imaging with machine learning" LINK
"Diagnostical Accuracy of Hyperspectral Imaging After Free Flap Surgery" LINK
Spectral Imaging
"Remote Sensing : Genetic Programming Approach for the Detection of
Mistletoe Based on UAV Multispectral Imagery in the Conservation Area of
Mexico City" LINK
Chemometrics and Machine Learning
"Foods : Discriminant Analysis of Pu-Erh Tea of Different Raw Materials Based on Phytochemicals Using Chemometrics" LINK
"Algorithm of Stability-Analysis-Based Feature Selection for NIR Calibration Transfer" LINK
"Chemosensors : Nonlinear Multivariate Regression Algorithms for
Improving Precision of Multisensor Potentiometry in Analysis of Spent
Nuclear Fuel Reprocessing Solutions" LINK
"Blood Glucose Prediction Using Machine Learning on Jetson Nanoplatform" LINK
"Machine Learning-Powered Models for Near-Infrared Spectrometers: Prediction of Protein in Multiple Grain Cereals" LINK
"Cognitive spectroscopy for the classification of rice varieties: a
comparison of machine learning and deep learning approaches in analysing
long-wave near-infrared ..." LINK
"Foods : Fatty Acid Profiling in Kernels Coupled with Chemometric
Analyses as a Feasible Strategy for the Discrimination of Different
Walnuts" LINK
"Remote Sensing : A Machine Learning Framework for the Classification of
Natura 2000 Habitat Types at Large Spatial Scales Using MODIS Surface
Reflectance Data" LINK
"Non-destructive detection of chilling injury in kiwifruit using a
dual-laser scanning system with a principal component analysis - back
propagation neural network" LINK
Research on Spectroscopy
"Applied Sciences : Pre-Disinfection of Poly-Methyl-Methacrylate (PMMA)
Reduces Volatile Sulfides Compounds (VSC) Production in Experimental
Biofilm In Vitro" LINK
"Remote Sensing : Study of Atmospheric Carbon Dioxide Retrieval Method Based on Normalized Sensitivity" LINK
Equipment for Spectroscopy
"Mid-IR spectroscopy with NIR grating spectrometers" LINK
Environment NIR-Spectroscopy Application
"Antibacterial Copolypeptoids with Potent Activity against Drug
Resistant Bacteria and Biofilms, Excellent Stability, and Recycling
Property" LINK
"Determining Water Transport Kinetics in Limestone by Dual-Wavelength Cavity Ring-Down Spectroscopy" LINK
"Verifying the predictive performance for soil organic carbon when
employing field Vis-NIR spectroscopy and satellite imagery obtained
using two different sampling ..." LINK
Agriculture NIR-Spectroscopy Usage
"Agriculture : A 1D-SP-Net to Determine Early Drought Stress Status of
Tomato (Solanum lycopersicum) with Imbalanced Vis/NIR Spectroscopy Data"
LINK
"Remote Sensing : Spectral-Based Classification of Plant Species Groups
and Functional Plant Parts in Managed Permanent Grassland" LINK
"Assessment of macronutrients and alpha-galactosides of texturized vegetable proteins by near infrared hyperspectral imaging" LINK
"A fuzzy multi-criteria decision-making approach for the assessment of
forest health applying hyper spectral imageries: A case study from
Ramsar forest, North of Iran" LINK
"Feasibility of near-infrared spectroscopic rapid detection method for
the water content of vermiculite substrates in desert facility
agriculture" | LINK
"Evaluation of Methods for Measuring Fusarium-Damaged Kernels of Wheat" LINK
"Effects of free-air temperature increase on grain yield and greenhouse gas emissions in a double rice cropping system" LINK
"The use of milk Fourier-transform mid-infrared spectroscopy to diagnose
pregnancy and determine spectral regional associations with pregnancy
in US dairy cows" LINK
"Nutrients : Analysis of the Correlation between Meal Frequency and Obesity among Chinese Adults Aged 18-59 Years in 2015" LINK
"Agriculture : N2O Emission and Nitrification/Denitrification Bacterial
Communities in Upland Black Soil under Combined Effects of Early and
Immediate Moisture" LINK
"Bovine fecal chemistry changes with progression of Southern Cattle
Tick, Rhipicephalus (Boophilus) microplus (Acari: Ixodidae) infestation"
LINK
Food & Feed Industry NIR Usage
"Effects of Irrigation Strategy and Plastic Film Mulching on Soil N 2 O Emissions and Fruit Yields of Greenhouse Tomato" LINK
Chemical Industry NIR Usage
"Polymers : π-Conjugated Polymers and Their Application in Organic and Hybrid Organic-Silicon Solar Cells" LINK
Pharma Industry NIR Usage
"Applied Sciences : Design of Two-Mode Spectroscopic Sensor for
Biomedical Applications: Analysis and Measurement of Relative Intensity
Noise through Control Mechanism" LINK
Other
"How This A.I. Draws Anything You Describe [Dall-E 2]" LINK
"Asphaltene Precipitation Onsets in Relation to the Critical Dilution of Athabasca Bitumen in Paraffinic Solvents" LINK
"Design Meets Neuroscience: A Preliminary Review of Design Research Using Neuroscience Tools" 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)
" CARACTERIZACIÓN QUIMICA DE SUELOS VOLCANICOS UTILIZANDO ESPECTROSCOPIA DE INFRARROJO CERCANO (NIRS)" LINK
"Development of an FT-NIR Method to Predict Process Cheese Functionality" LINK
"An effective classification framework for brain-computer interface system design based on combining of fNIRS and EEG signals" LINK
"Coronary artery disease and its impact on the pulsatile brain: A functional NIRS study" LINK
"Predicting anemia using NIR spectrum of spent dialysis fluid in hemodialysis patients" | LINK
"Automated Detection of Tetranychus urticae Koch in Citrus Leaves Based on Colour and VIS/NIR Hyperspectral Imaging" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
" A novel spectral index for estimating fractional cover of non-photosynthetic vegetation using near-infrared bands of Sentinel satellite" LINK
"Spatial distribution of total polyphenols in multi-type of tea using near-infrared hyperspectral imaging" LINK
"Investigation on the Mechanisms of Mg(OH)2 Dehydration and MgO Hydration by Near-Infrared Spectroscopy" LINK
"Near Infrared Reflectance Spectroscopy Analysis to Predict Diet Composition of a Mountain Ungulate Species" LINK
"Nondestructive determination of SSC in Korla Fragrant Pear using a portable near-infrared spectroscopy system" LINK
"Applied Sciences, Vol. 11, Pages 4717: 808-Nm Near-Infrared Laser Photobiomodulation versus Switched-Off Laser Placebo in Major Aphthae Management: A Randomized Double-Blind Controlled Trial" LINK
"Titration of Inspired Oxygen in Preterm Infants with Hypoxemic Respiratory Failure Using Near Infrared Spectroscopy and Pulse Oximetry: A New Approach" LINK
"Shedding light on neuroscience: Two decades of functional nearinfrared spectroscopy applications and advances from a bibliometric perspective" LINK
Hyperspectral Imaging (HSI)
"Hyperspectral Detection and Monitoring of Salt Stress in Pomegranate Cultivars" Agronomy LINK
"Applied Sciences, Vol. 11, Pages 4588: Beef Quality Grade Classification Based on Intramuscular Fat Content Using Hyperspectral Imaging Technology" LINK
Spectral Imaging
"Artificial Intelligence Empowered Multispectral Vision Based System for Non-Contact Monitoring of Large Yellow Croaker (Larimichthys crocea) Fillets" Foods LINK
Chemometrics and Machine Learning
" Assessment of chicken breast shelf life based on bench-top and portable near-infrared spectroscopy tools coupled with chemometrics" LINK
" Prediction of the particle size and flow characteristics of powder blends for tableting by near-infrared spectroscopy and chemometrics" LINK
"Antibacterial Activity of Moroccan Zantaz Honey and the Influence of Its Physicochemical Parameters Using Chemometric Tools" AppliedSciences LINK
"Predicting pectin performance strength using nearinfrared spectroscopic data: A comparative evaluation of 1D convolutional neural network, partial least squares, and ridge regression modeling" LINK
"Sequential and orthogonalized PLS (SOPLS) regression for path analysis: Order of blocks and relations between effects" LINK
"The Impacts of Spatial Resolution, Viewing Angle, and Spectral Vegetation Indices on the Quantification of Woody Mediterranean Species Seasonality Using Remote Sensing" LINK
"Partial least squares and silver nanoparticles in spectrophotometric prediction of total hardness of water" LINK
"Genetic robust kernel sample selection for chemometric data analysis" LINK
Equipment for Spectroscopy
"Nearinfrared triggered drug delivery of Imatinib Mesylate by molybdenum disulfide nanosheets grafted copolymers as thermosensitive nanocarriers" LINK
Process Control and NIR Sensors
"IQR CUSUM charts: An efficient approach for monitoring variations in aquatic toxicity" LINK
Environment NIR-Spectroscopy Application
"Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments" LINK
"Ecometabolic mixture design-fingerprints from exploratory multi-block data analysis in Coffea arabica beans from climate changes: Elevated carbon dioxide and reduced soil water availability" LINK
Agriculture NIR-Spectroscopy Usage
"Integrating Straw Management and Seeding to Improve Seed Yield and Reduce Environmental Impacts in Soybean Production" Agronomy LINK
" Soil N 2 O flux and nitrification and denitrification gene responses to feed-induced differences in the composition of dairy cow faeces" | LINK
Food & Feed Industry NIR Usage
"Pulsed Electric Field (PEF) Processing of Chilled and Frozen-Thawed Lamb Meat Cuts: Relationships between Sensory Characteristics and Chemical Composition of Meat" Foods LINK
Other
"Racial Differences in Hemodynamic Responses to Lower Body Negative Pressure: The Effects of Capsaicin" LINK
"Quantitative vibrational spectroscopy on liquid mixtures: concentration units matter" LINK
"Enhanced light harvesting in dyesensitized solar cells enabled by TiO2:Er3+, Yb3+ upconversion phosphor particles as solar spectral converter and light scattering medium" LINK
NIR Calibration-Model Services
Spectroscopy and Chemometrics News Weekly 24, 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
Get the Spectroscopy and Chemometrics News Weekly in real time on Twitter @ CalibModel and follow us.
Near-Infrared Spectroscopy (NIRS)
" CARACTERIZACIÓN QUIMICA DE SUELOS VOLCANICOS UTILIZANDO ESPECTROSCOPIA DE INFRARROJO CERCANO (NIRS)" LINK
"Development of an FT-NIR Method to Predict Process Cheese Functionality" LINK
"An effective classification framework for brain-computer interface system design based on combining of fNIRS and EEG signals" LINK
"Coronary artery disease and its impact on the pulsatile brain: A functional NIRS study" LINK
"Predicting anemia using NIR spectrum of spent dialysis fluid in hemodialysis patients" | LINK
"Automated Detection of Tetranychus urticae Koch in Citrus Leaves Based on Colour and VIS/NIR Hyperspectral Imaging" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
" A novel spectral index for estimating fractional cover of non-photosynthetic vegetation using near-infrared bands of Sentinel satellite" LINK
"Spatial distribution of total polyphenols in multi-type of tea using near-infrared hyperspectral imaging" LINK
"Investigation on the Mechanisms of Mg(OH)2 Dehydration and MgO Hydration by Near-Infrared Spectroscopy" LINK
"Near Infrared Reflectance Spectroscopy Analysis to Predict Diet Composition of a Mountain Ungulate Species" LINK
"Nondestructive determination of SSC in Korla Fragrant Pear using a portable near-infrared spectroscopy system" LINK
"Applied Sciences, Vol. 11, Pages 4717: 808-Nm Near-Infrared Laser Photobiomodulation versus Switched-Off Laser Placebo in Major Aphthae Management: A Randomized Double-Blind Controlled Trial" LINK
"Titration of Inspired Oxygen in Preterm Infants with Hypoxemic Respiratory Failure Using Near Infrared Spectroscopy and Pulse Oximetry: A New Approach" LINK
"Shedding light on neuroscience: Two decades of functional nearinfrared spectroscopy applications and advances from a bibliometric perspective" LINK
Hyperspectral Imaging (HSI)
"Hyperspectral Detection and Monitoring of Salt Stress in Pomegranate Cultivars" Agronomy LINK
"Applied Sciences, Vol. 11, Pages 4588: Beef Quality Grade Classification Based on Intramuscular Fat Content Using Hyperspectral Imaging Technology" LINK
Spectral Imaging
"Artificial Intelligence Empowered Multispectral Vision Based System for Non-Contact Monitoring of Large Yellow Croaker (Larimichthys crocea) Fillets" Foods LINK
Chemometrics and Machine Learning
" Assessment of chicken breast shelf life based on bench-top and portable near-infrared spectroscopy tools coupled with chemometrics" LINK
" Prediction of the particle size and flow characteristics of powder blends for tableting by near-infrared spectroscopy and chemometrics" LINK
"Antibacterial Activity of Moroccan Zantaz Honey and the Influence of Its Physicochemical Parameters Using Chemometric Tools" AppliedSciences LINK
"Predicting pectin performance strength using nearinfrared spectroscopic data: A comparative evaluation of 1D convolutional neural network, partial least squares, and ridge regression modeling" LINK
"Sequential and orthogonalized PLS (SOPLS) regression for path analysis: Order of blocks and relations between effects" LINK
"The Impacts of Spatial Resolution, Viewing Angle, and Spectral Vegetation Indices on the Quantification of Woody Mediterranean Species Seasonality Using Remote Sensing" LINK
"Partial least squares and silver nanoparticles in spectrophotometric prediction of total hardness of water" LINK
"Genetic robust kernel sample selection for chemometric data analysis" LINK
Equipment for Spectroscopy
"Nearinfrared triggered drug delivery of Imatinib Mesylate by molybdenum disulfide nanosheets grafted copolymers as thermosensitive nanocarriers" LINK
Process Control and NIR Sensors
"IQR CUSUM charts: An efficient approach for monitoring variations in aquatic toxicity" LINK
Environment NIR-Spectroscopy Application
"Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments" LINK
"Ecometabolic mixture design-fingerprints from exploratory multi-block data analysis in Coffea arabica beans from climate changes: Elevated carbon dioxide and reduced soil water availability" LINK
Agriculture NIR-Spectroscopy Usage
"Integrating Straw Management and Seeding to Improve Seed Yield and Reduce Environmental Impacts in Soybean Production" Agronomy LINK
" Soil N 2 O flux and nitrification and denitrification gene responses to feed-induced differences in the composition of dairy cow faeces" | LINK
Food & Feed Industry NIR Usage
"Pulsed Electric Field (PEF) Processing of Chilled and Frozen-Thawed Lamb Meat Cuts: Relationships between Sensory Characteristics and Chemical Composition of Meat" Foods LINK
Other
"Racial Differences in Hemodynamic Responses to Lower Body Negative Pressure: The Effects of Capsaicin" LINK
"Quantitative vibrational spectroscopy on liquid mixtures: concentration units matter" LINK
"Enhanced light harvesting in dyesensitized solar cells enabled by TiO2:Er3+, Yb3+ upconversion phosphor particles as solar spectral converter and light scattering medium" LINK
NIR Calibration-Model Services
Spectroscopy and Chemometrics News Weekly 24, 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
Get the Spectroscopy and Chemometrics News Weekly in real time on Twitter @ CalibModel and follow us.
Near-Infrared Spectroscopy (NIRS)
" CARACTERIZACIÓN QUIMICA DE SUELOS VOLCANICOS UTILIZANDO ESPECTROSCOPIA DE INFRARROJO CERCANO (NIRS)" LINK
"Development of an FT-NIR Method to Predict Process Cheese Functionality" LINK
"An effective classification framework for brain-computer interface system design based on combining of fNIRS and EEG signals" LINK
"Coronary artery disease and its impact on the pulsatile brain: A functional NIRS study" LINK
"Predicting anemia using NIR spectrum of spent dialysis fluid in hemodialysis patients" | LINK
"Automated Detection of Tetranychus urticae Koch in Citrus Leaves Based on Colour and VIS/NIR Hyperspectral Imaging" LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
" A novel spectral index for estimating fractional cover of non-photosynthetic vegetation using near-infrared bands of Sentinel satellite" LINK
"Spatial distribution of total polyphenols in multi-type of tea using near-infrared hyperspectral imaging" LINK
"Investigation on the Mechanisms of Mg(OH)2 Dehydration and MgO Hydration by Near-Infrared Spectroscopy" LINK
"Near Infrared Reflectance Spectroscopy Analysis to Predict Diet Composition of a Mountain Ungulate Species" LINK
"Nondestructive determination of SSC in Korla Fragrant Pear using a portable near-infrared spectroscopy system" LINK
"Applied Sciences, Vol. 11, Pages 4717: 808-Nm Near-Infrared Laser Photobiomodulation versus Switched-Off Laser Placebo in Major Aphthae Management: A Randomized Double-Blind Controlled Trial" LINK
"Titration of Inspired Oxygen in Preterm Infants with Hypoxemic Respiratory Failure Using Near Infrared Spectroscopy and Pulse Oximetry: A New Approach" LINK
"Shedding light on neuroscience: Two decades of functional nearinfrared spectroscopy applications and advances from a bibliometric perspective" LINK
Hyperspectral Imaging (HSI)
"Hyperspectral Detection and Monitoring of Salt Stress in Pomegranate Cultivars" Agronomy LINK
"Applied Sciences, Vol. 11, Pages 4588: Beef Quality Grade Classification Based on Intramuscular Fat Content Using Hyperspectral Imaging Technology" LINK
Spectral Imaging
"Artificial Intelligence Empowered Multispectral Vision Based System for Non-Contact Monitoring of Large Yellow Croaker (Larimichthys crocea) Fillets" Foods LINK
Chemometrics and Machine Learning
" Assessment of chicken breast shelf life based on bench-top and portable near-infrared spectroscopy tools coupled with chemometrics" LINK
" Prediction of the particle size and flow characteristics of powder blends for tableting by near-infrared spectroscopy and chemometrics" LINK
"Antibacterial Activity of Moroccan Zantaz Honey and the Influence of Its Physicochemical Parameters Using Chemometric Tools" AppliedSciences LINK
"Predicting pectin performance strength using nearinfrared spectroscopic data: A comparative evaluation of 1D convolutional neural network, partial least squares, and ridge regression modeling" LINK
"Sequential and orthogonalized PLS (SOPLS) regression for path analysis: Order of blocks and relations between effects" LINK
"The Impacts of Spatial Resolution, Viewing Angle, and Spectral Vegetation Indices on the Quantification of Woody Mediterranean Species Seasonality Using Remote Sensing" LINK
"Partial least squares and silver nanoparticles in spectrophotometric prediction of total hardness of water" LINK
"Genetic robust kernel sample selection for chemometric data analysis" LINK
Equipment for Spectroscopy
"Nearinfrared triggered drug delivery of Imatinib Mesylate by molybdenum disulfide nanosheets grafted copolymers as thermosensitive nanocarriers" LINK
Process Control and NIR Sensors
"IQR CUSUM charts: An efficient approach for monitoring variations in aquatic toxicity" LINK
Environment NIR-Spectroscopy Application
"Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments" LINK
"Ecometabolic mixture design-fingerprints from exploratory multi-block data analysis in Coffea arabica beans from climate changes: Elevated carbon dioxide and reduced soil water availability" LINK
Agriculture NIR-Spectroscopy Usage
"Integrating Straw Management and Seeding to Improve Seed Yield and Reduce Environmental Impacts in Soybean Production" Agronomy LINK
" Soil N 2 O flux and nitrification and denitrification gene responses to feed-induced differences in the composition of dairy cow faeces" | LINK
Food & Feed Industry NIR Usage
"Pulsed Electric Field (PEF) Processing of Chilled and Frozen-Thawed Lamb Meat Cuts: Relationships between Sensory Characteristics and Chemical Composition of Meat" Foods LINK
Other
"Racial Differences in Hemodynamic Responses to Lower Body Negative Pressure: The Effects of Capsaicin" LINK
"Quantitative vibrational spectroscopy on liquid mixtures: concentration units matter" LINK
"Enhanced light harvesting in dyesensitized solar cells enabled by TiO2:Er3+, Yb3+ upconversion phosphor particles as solar spectral converter and light scattering medium" 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
Beside the free NIR-Predictor software with Windows user interface,
the real-time Predictor Engine is also available
for embedded integration in application, cloud and instrument-software (ICT).
As a light-weigt single library file (DLL) with application programming interface (API),
documentation and software development kit (SDK)
including sample source code (C#).
Easy integration and deployment,
no software license protection (no serial key, no dongle).
Put your spectrum as an array into the multivariate predictor,
no specific file format needed.
Fast prediction speed and low latency because of compiled code library (direct call, no cloud API).
Protected prediction results with outlier detection information.
Minimal System Requirements
Windows 7 Starter 32Bit, 1.6 GHz, 2 GB RAM, non-Administrator account
Installation
There are no administrator rights required,
unpack the zip file to a folder "NIR-Predictor" in your documents or on your desktop.
Read the ReadMe.txt and double click the NIR-Predictor.exe file.
Upgrade
If you have installed an older version of NIR-Predictor then unpack into a different folder named e.g. "NIR-PredictorVx.y".
All versions can run side-by-side. Copy the Calibrations in use to the new version into the "Calibration" folder. That's all.
Uninstall
Make sure to backup your reports and calibrations inside your "NIR-Predictor" folder.
Delete the "NIR-Predictor" folder.
Neben der kostenlosen NIR-Predictor-Software mit Windows-Benutzeroberfläche
ist die Echtzeit-Predictor-Engine auch verfügbar
für die eingebettete Integration in Applikations-, Cloud- und Geräte-Software (ICT).
Als leichtgewichtige Einzelbibliotheksdatei (DLL)
mit Anwendungsprogrammier-Schnittstelle (API),
Dokumentation und Software Development Kit (SDK)
inklusive Beispiel-Quellcode (C#).
Einfache Integration und Bereitstellung,
kein Software-Lizenzschutz (kein Serienschlüssel, kein Dongle).
Geben Sie Ihr Spektrum als Array in den multivariaten Prädiktor ein,
es ist kein spezielles Dateiformat erforderlich.
Schnelle Vorhersagegeschwindigkeit und niedrige Latenz aufgrund der kompilierten Code-Bibliothek (direkter Aufruf, keine Cloud-API).
Geschützte Vorhersageergebnisse mit Informationen zur Ausreißererkennung.
Minimal System Requirements
Windows 7 Starter 32Bit, 1.6 GHz, 2 GB RAM, non-Administrator account
Installation
There are no administrator rights required,
unpack the zip file to a folder "NIR-Predictor" in your documents or on your desktop.
Read the ReadMe.txt and double click the NIR-Predictor.exe file.
Upgrade
If you have installed an older version of NIR-Predictor then unpack into a different folder named e.g. "NIR-PredictorVx.y".
All versions can run side-by-side. Copy the Calibrations in use to the new version into the "Calibration" folder. That's all.
Uninstall
Make sure to backup your reports and calibrations inside your "NIR-Predictor" folder.
Delete the "NIR-Predictor" folder.
Oltre al software gratuito NIR-Predictor con interfaccia utente Windows,
il Predictor Engine in tempo reale è disponibile anche
per l'integrazione embedded in applicazioni, cloud e strumenti-software (ICT).
Come un singolo file di libreria leggera (DLL)
con interfaccia di programmazione dell'applicazione (API),
documentazione e kit di sviluppo del software (SDK)
incluso il codice sorgente di esempio (C#).
Facile integrazione e distribuzione,
nessuna protezione della licenza software (nessuna chiave seriale, nessun dongle).
Inserisci il tuo spettro come array nel predittore multivariato,
non è necessario alcun formato di file specifico.
Velocità di predizione veloce e bassa latenza grazie alla libreria di codice compilata (chiamata diretta, nessuna API cloud).
Risultati di predizione protetti con informazioni di rilevamento degli outlier.
Minimal System Requirements
Windows 7 Starter 32Bit, 1.6 GHz, 2 GB RAM, non-Administrator account
Installation
There are no administrator rights required,
unpack the zip file to a folder "NIR-Predictor" in your documents or on your desktop.
Read the ReadMe.txt and double click the NIR-Predictor.exe file.
Upgrade
If you have installed an older version of NIR-Predictor then unpack into a different folder named e.g. "NIR-PredictorVx.y".
All versions can run side-by-side. Copy the Calibrations in use to the new version into the "Calibration" folder. That's all.
Uninstall
Make sure to backup your reports and calibrations inside your "NIR-Predictor" folder.
Delete the "NIR-Predictor" folder.