Spectroscopy and Chemometrics / Machine Learning News Weekly #29, 2022

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

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 28, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 28, 2022 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near-Infrared Spectroscopy (NIRS)

“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

“Vis-NIR 초분광 영상을 이용한 딸기 잿빛 곰팡이 감염 조기 검출” 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

“Soils of the World ” LINK

“Remote Sensing : Estimation of Pb Content Using Reflectance Spectroscopy in Farmland Soil Near Metal Mines, Central China” LINK




Agriculture NIR-Spectroscopy Usage

“Agriculture : Tea Category Identification Using Wavelet Signal Reconstruction of Hyperspectral Imagery and Machine Learning” LINK

“Plants : Decontamination and Germination of Buckwheat Grains upon Treatment with Oxygen Plasma Glow and Afterglow” LINK

“Near-infrared hyperspectral imaging evaluation of Fusarium damage and DON in single wheat kernels” LINK

“Spectral Indices for Imaging Diesel and Gasoline Polluted Soils Derived from Close-Range Hyperspectral Data” LINK

“Molecular Spectroscopy Market Size is projected to reach USD 3.85 Billion by 2030, growing at a CAGR of 4.28%: Straits Research” LINK

“Remote Sensing : Hyperspectral Reflectance Proxies to Diagnose In-Field Fusarium Head Blight in Wheat with Machine Learning” LINK

“Prediction of milk protein content based on improved sparrow search algorithm and optimized back propagation neural network” | LINK

“Technological Innovations for the Management of Insect-Pests in Stored Grains” | LINK

“Current progress on innovative pest detection techniques for stored cereal grains and thereof powders” FoodSecurity LINK

“Determination of Forage Quality by Near-Infrared Reflectance Spectroscopy in Sweet Sorghum (Sorghum bicolor var. saccharatum (L.) Mohlenbr.)” LINK

“Analysis of Protein Denaturation, and Chemical Visualization, of Frozen Grass Carp Surimi Containing Soluble Soybean Polysaccharides” LINK

“Remote Sensing : Prototyping Crop Traits Retrieval Models for CHIME: Dimensionality Reduction Strategies Applied to PRISMA Data” LINK




Horticulture NIR-Spectroscopy Applications

“Development of portable nondestructive detection device for mango internal Diseases fruits NonDestructive Testing LINK

“Global model for in-field monitoring of sugar content and color of melon pulp with comparative regression approach” | LINK




Food & Feed Industry NIR Usage

“Foods : Saponification Value of Fats and Oils as Determined from 1H-NMR Data: The Case of Dairy Fats” LINK




Pharma Industry NIR Usage

“Pharmaceutical tablet compression: measuring temporal and radial concentration profiles to better assess segregation” LINK




Medicinal Spectroscopy

“Evaluation of the Effect of Del Nido and Cold Blood Cardioplegia on Renal Functions in the Surgery of Congenital Heart Diseases” LINK




Other

“近赤外分光法を用いた脂質研究の動向と将来展望” LINK

“Minerals : Fluid Inclusion and Chemical Composition Characteristics of Emeralds from Rajasthan Area, India” LINK

“On-chip complex refractive index detection at multiple wavelengths for selective sensing” | LINK

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




Spectroscopy and Chemometrics/Machine-Learning News Weekly #6, 2022

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 5, 2022 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 5, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 5, 2022 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl 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)

“Heat impact control in flash pasteurization by estimation of applied pasteurization units using near infrared spectroscopy” LINK

“Rapid and real-time detection of moisture in black tea during withering using micro-near-infrared spectroscopy” LINK

“Rapid Quality Discrimination of Grape Seed Oil Using an Extreme Machine Learning Approach with Near-Infrared (NIR) Spectroscopy” LINK

“Non-destructive prediction of the hotness of fresh pepper with a single scan using portable near infrared spectroscopy and a variable selection strategy” LINK

“Hyperspectral remote sensing for foliar nutrient detection in forestry: A near-infrared perspective” LINK

“High Near-Infrared Reflective Zn1-xAxWO4 Pigments with Various Hues Facilely Fabricated by Tuning Doped Transition Metal Ions (A = Co, Mn, and Fe)” LINK

“Identifying the main drivers of the seasonal decline of near-infrared reflectance of a temperate deciduous forest” LINK




Hyperspectral Imaging (HSI)

“Estimation of soil organic matter content using selected spectral subset of hyperspectral data” LINK

“Applied Sciences : Detection of Foreign Materials on Broiler Breast Meat Using a Fusion of Visible Near-Infrared and Short-Wave Infrared Hyperspectral Imaging” LINK

“Feasibility Study of Wood-leaf Separation Based on Hyperspectral LiDAR Technology in Indoor Circumstances” LINK

“Calibration of Short-Wave InfraRed (SWIR) hyperspectral imaging using Diffuse Reflectance infrared Fourier Transform spectroscopy (DRIFTS) to obtain continuous …” LINK




Chemometrics and Machine Learning

“Remote Sensing : Estimation of Salinity Content in Different Saline-Alkali Zones Based on Machine Learning Model Using FOD Pretreatment Method” LINK




Research on Spectroscopy

“Portable vibrational spectroscopic methods can discriminate between grass-fed and grain-fed beef” LINK

“Dataanalysis method for material optimization by forecasting longterm chemical stability” LINK




Process Control and NIR Sensors

“Gaussian Process Inspired Neural Networks for Spectral Unmixing Dataset Augmentation” LINK

“Copper (I) selenocyanate (CuSeCN): Eco-friendly solution-processable deposition of hole transport layer for organic solar cells” LINK




Environment NIR-Spectroscopy Application

“Soil erodibility prediction by Vis-NIR spectra and environmental covariates coupled with GIS, regression and PLSR in a watershed scale, Iran” LINK

“Remote Sensing : Retrieval of Phytoplankton Pigment Composition from Their In Vivo Absorption Spectra” LINK




Agriculture NIR-Spectroscopy Usage

“Remote Sensing : Using Sentinel-2 Images for Soil Organic Carbon Content Mapping in Croplands of Southwestern France. The Usefulness of Sentinel-1/2 Derived Moisture Maps and Mismatches between Sentinel Images and Sampling Dates” LINK

“Rapid Directed Molecular Evolution of Fluorescent Proteins in Mammalian Cells” LINK

“First identification of sudoite in Caribbean Ceramic-Age lapidary craftsmanship” LINK

“Elevated carbon dioxide stimulates nitrous oxide emission in agricultural soils: A global meta-analysis”LINK




Food & Feed Industry NIR Usage

“Photonics : Raman Spectroscopy Enables Non-Invasive Identification of Mycotoxins р. Fusarium of Winter Wheat Seeds” LINK




Pharma Industry NIR Usage

“The relationship between renal oxygen saturation and renal function in patients with and without diabetes following coronary artery bypass grafting surgery” LINK




Medicinal Spectroscopy

“Evaluation of Functional Capacity and Muscle Metabolism in Individuals with Peripheral Arterial Disease with and without Diabetes” | LINK




Other

“Global meta-analysis of terrestrial nitrous oxide emissions and associated functional genes under nitrogen addition” LINK

“CATCH the Wave of Coronary Atherosclerotic Plaque MRI” LINK

“Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture. (arXiv:2112.08534v1 [cs.LG])” LINK

“The Biologically Relevant Coordination Chemistry of Iron and Nitric Oxide: Electronic Structure and Reactivity” LINK





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Spectroscopy and Chemometrics/Machine-Learning News Weekly #5, 2022

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 4, 2022 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 4, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 4, 2022 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

This week’s NIR news Weekly is sponsored by Your-Company-Name-Here – NIR-spectrometers. Check out their product page … link

Get the Spectroscopy and Chemometrics News Weekly in real time on Twitter @ CalibModel and follow us.




Near-Infrared Spectroscopy (NIRS)

“Use of Near-Infrared Spectroscopy combined with chemometrics for authentication and traceability of intact lemon fruits” LINK

“Application of NIR Spectroscopy and Multivariate Analysis for Non-destructive Evaluation of Apple Moisture Content during Ultrasonic Drying” LINK

“FT-NIR spectroscopy analysis for monitoring the microbial production of 2-phenylethanol using crude glycerol as carbon source” LINK

“Animals : Assessment of the Effectiveness of a Portable NIRS Instrument in Controlling the Mixer Wagon Tuning and Ration Management” LINK

“Scarcity Mindset Neuro Network Decoding With Reward: A Tree-Based Model and Functional Near-Infrared Spectroscopy Study” LINK

“Effectiveness of near-infrared spectroscopy as a non-invasive tool to discriminate spectral profiles of in vitro cultured oocytes from goats” LINK

“In-vivo quantification of lactate using Near Infrared reflectance spectroscopy” LINK

“Brain and renal oxygenation measured by NIRS related to patent ductus arteriosus in preterm infants: a prospective observational study” | LINK

“Rapid Quantitative Determination of Chemical Oxygen Demand in Different Water Systems Based on Near-Infrared Spectroscopy Combined with Binary Grey Wolf …” LINK

“Classification of fNIRS data with LDA and SVM: a proof-of-concept for application in infant studies” LINK

“Chemosensors : Detection of Monilia Contamination in Plum and Plum Juice with NIR Spectroscopy and Electronic Tongue” LINK




Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)

“Radiometric calibration accuracy and stability of GOES-16 ABI Infrared radiance” | LINK

“Monte Carlo Characterization of Short-Wave Infrared Optical Wavelengths for Biosensing Applications” LINK




Hyperspectral Imaging (HSI)

“Agriculture : Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm” LINK

“Agronomy : Non-invasive Monitoring of Berry Ripening Using On-the-Go Hyperspectral Imaging in the Vineyard” LINK

“Prediction of Aqueous Glucose Concentration Using Hyperspectral Imaging” LINK




Spectral Imaging

“Agriculture : A Handheld Grassland Vegetation Monitoring System Based on Multispectral Imaging” LINK




Chemometrics and Machine Learning

“Multivariate analysis of food fraud: A review of NIR based instruments in tandem with chemometrics” LINK

“Recent Chemometric Opportunities in Criminalistics” LINK

“Non-Invasive Methods for Predicting the Quality of Processed Horticultural Food Products, with Emphasis on Dried Powders, Juices and Oils: A Review” LINK

“Hyperspectral inversion of nitrogen content in phyllostachys pubescens based on partial least squares regression model” LINK

“Applied Sciences : Identification of a Suitable Machine Learning Model for Detection of Asymptomatic Ganoderma boninense Infection in Oil Palm Seedlings Using Hyperspectral Data” LINK




Research on Spectroscopy

“A variable selection method based on mutual information and variance inflation factor” LINK




Environment NIR-Spectroscopy Application

“Remote Sensing : Reducing Scaling Effect on Downscaled Land Surface Temperature Maps in Heterogenous Urban Environments” LINK




Agriculture NIR-Spectroscopy Usage

“Biogenic and physicogenic aggregates: formation pathways, assessment techniques, and influence on soil properties” LINK

“Research on the transmission performance of multi-layer simulated mural surface by imaging spectrum” LINK

“Estimation of genetic parameters for carcass grading traits, image analysis traits, and monounsaturated fatty acids in Japanese Black cattle from Hyogo Prefecture” LINK




Pharma Industry NIR Usage

“CONDITION OF RENAL OXYGENATION IN PRETERM INFANTS WITH HEMODINAMICALLY SIGNIFICANT PATENT DUCTUS ARTERIOSUS” LINK

“Extent of lipid core plaque in patients with Achilles tendon xanthoma undergoing percutaneous coronary intervention for coronary artery disease” LINK




Other

“Textural and compositional effects of ilmenite on the spectra of high-titanium lunar basalts” LINK





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Spectroscopy and Chemometrics News Weekly #9, 2021

NIR Calibration-Model Services

How to Develop Chemometric Near-Infrared Spectroscopy Calibrations in the 21st Century? LINK

Spectroscopy and Chemometrics News Weekly 8, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 8, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 8, 2021 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl 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)

” Profiling Mannheimia haemolytica infection in dairy calves using near infrared spectroscopy (NIRS) and multivariate analysis (MVA)” LINK

“Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes” LINK

“Prediction of Physicochemical Properties in Honeys with Portable Near-Infrared (microNIR) Spectroscopy Combined with Multivariate Data Processing” LINK

“Monitoring the Bacterial Response to Antibiotic and Time Growth Using Near-infrared Spectroscopy Combined with MachineLearning” LINK

“Reducing the Moisture Effect and Improving the Prediction of Soil Organic Matter With VIS-NIR Spectroscopy in Black Soil Area” LINK

“Nirs tools for prediction of main extractives compounds of teak (Tectona grandis L.) heartwood” LINK




Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)

“Using near-infrared spectroscopy to determine moisture content, gel strength, and viscosity of gelatin” LINK

“Forensic potentiality of near-infrared in the geographical discrimination of Astrocaryum aculeatum Oil” LINK

“Anomaly detection during milk processing by autoencoder neural network based on near-infrared spectroscopy” LINK

“Infrared Spectroscopy of Blood” LINK

“Using a Portable Near-infrared Spectroscopy Device to Estimate The Second Ventilatory Threshold.” LINK

“Prediction of Specialty Coffee Flavors Based on NearInfrared Spectra Using Machine and Deep Learning Methods” LINK

“Applications of near infrared spectroscopy for fish and fish products quality: a review” LINK

“Prediction of Diet Quality in Mongolian Livestock with Portable near Infrared Spectroscopy Offeces” LINK

“Application of Fourier Transform Near-infrared Spectroscopy combined with Gas Chromatograpy in rapid and simultaneous determination of essential components in …” LINK

“Evaluation of swelling properties and drug release from mechanochemical pre-gelatinized glutinous rice starch matrix tablets by near infrared spectroscopy” LINK

“Rapid Screening of Mentha spicata Essential Oil and l-Menthol in Mentha piperita Essential Oil by ATR-FTIR Spectroscopy Coupled with Multivariate Analyses” LINK

“Application of Fourier transform near-infrared spectroscopy combined with GC in rapid and simultaneous determination of essential components in Amomum villosum.” LINK

“A Hybrid Variable Selection Strategy of Near Infrared Spectroscopy for Detection the Ratio of Tea Polyphenols to Amino Acids in Green Tea Infusion” LINK




Hyperspectral Imaging (HSI)

“Identification of Weeds Based on Hyperspectral Imaging and Machine Learning” | LINK

“AI-based hyperspectral and VOCs assessment approach to identify adulterated extra virgin olive oil” | LINK




Chemometrics and Machine Learning

“Trends in artificial intelligence, machine learning, and chemometrics applied to chemical data” LINK

“Detection of camellia oil adulteration using chemometrics based on fatty acids GC fingerprints and phytosterols GC-MS fingerprints” LINK

“Exploring two-trace two-dimensional (2T2D) correlation spectroscopy as an effective approach to improve accuracy of discriminant analysis by highlighting asynchronous features in two separate spectra of a sample” LINK

“Self-Cleaning-Mediated SERS Chip Coupled Chemometric Algorithms for Detection and Photocatalytic Degradation of Pesticides in Food” LINK

“Chemometrics and Experimental Design for the Quantification of Nitrate Salts in Nitric Acid: Near-Infrared Spectroscopy Absorption Analysis” LINK




Agriculture NIR-Spectroscopy Usage

“Biochemical methane potential prediction for mixed feedstocks of straw and manure in anaerobic co-digestion” LINK

” Effective Quantification of Tannin Content in Sorghum Grains Using Near-infrared Spectroscopy” LINK

“Continuous blending monitored and feedback controlled by machine vision-based PAT tool.” LINK

“Near-Infrared Hyperspectral Imaging Spectroscopy to Detect Microplastics and Pieces of Plastic in Almond Flour” | in-almond-flour LINK




Horticulture NIR-Spectroscopy Applications

“Fast, simultaneous and contactless assessment of intact mango fruit by means of near infrared spectroscopy [J]” LINK




Food & Feed Industry NIR Usage

“Predictive Ability of Four SmallScale Quality Tests for Dough Rheological Properties and Baking Quality in Hard Red Spring Wheat” LINK

“Improved prediction of minced pork meat chemical properties with near-infrared spectroscopy by a fusion of scatter- correction techniques” LINK




Laboratory and NIR-Spectroscopy

“Improving performance: a collaborative strategy for the multi-data fusion of electronic nose and hyperspectral to track the quality difference of rice” LINK




Other

“Effect of different levels of fertilization on bean biochemical composition in Coffea arabica cv. Rubi” LINK





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Spectroscopy and Chemometrics News Weekly #4, 2021

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 3, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 3, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 3, 2021 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl 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)

“Transcutaneous Near Infra-Red Spectroscopy (NIRS) for monitoring paediatric renal allograft perfusion” LINK

“Multivariate calibration: Identification of phenolic compounds in PROPOLIS using FTNIR” LINK

“Feasibility study of detecting palm oil adulteration with recycled cooking oil using a handheld NIR spectroscopy” LINK

“PREDICTION OF BIOACTIVE COMPOUNDS IN BARLEY BY NEAR-INFRARED REFLECTANCE SPECTROSCOPY (NIRS)” LINK

“A Systematic and Consistent Assay for High-throughput Characterization of Stalk Quality in Sugarcane by Near-infrared Spectroscopy” LINK

“Ultraviolet-visible/near infrared spectroscopy and hyperspectral imaging to study the different types of raw cotton” LINK




Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)

“Rapid qualitative identification and quantitative analysis of Flos Mume based on fourier transform near infrared spectroscopy” LINK

” Distinction of surgically resected gastrointestinal stromal tumor by near-infrared hyperspectral imaging” LINK

” Dataset of chemical and near-infrared spectroscopy measurements of fresh and dried poultry and cattle manure” LINK

“Utilizing near infrared hyperspectral imaging for quantitatively predicting adulteration in tapioca starch” LINK

“Near-infrared hyperspectral imaging for detection and visualization of offal adulteration in ground pork” LINK

“Comparative study of degradation between the carbon black back coat layer and magnetic layer in magnetic audio tapes using attenuated total reflectance Fourier transform infrared spectroscopy and machine learning techniques” LINK




Hyperspectral Imaging (HSI)

“Near-infrared hyperspectral imaging (NIR-HSI) and normalized difference image (NDI) data processing: an advanced method to map collagen in archaeological …” LINK

“Effect of curvature on hyperspectral reflectance images of cereal seed-sized objects” LINK

” Application of hyperspectral imaging to detect toxigenic Fusarium infection on cornmeal” LINK




Chemometrics and Machine Learning

“Determination of Conformational Changes of Polyphenol Oxidase and Peroxidase in Peach Juice during Mild Heat Treatment Using FTIR Spectroscopy Coupled with Chemometrics” LINK

“Model-Based Pre-Processing in Vibrational Spectroscopy” LINK

“Challenging handheld NIR spectrometers with moisture analysis in plant matrices: performance of PLSR vs. GPR vs. ANN modelling” LINK

“Maturity determination of single maize seed by using near-infrared hyperspectral imaging coupled with comparative analysis of multiple classification models” LINK

“Comparison of Soil Total Nitrogen Content Prediction Models Based on Vis-NIR Spectroscopy.” LINK

“Machine Learning-Based Presymptomatic Detection of Rice Sheath Blight Using Spectral Profiles.” LINK




Equipment for Spectroscopy

“Green analytical assay for the quality assessment of tea by using pocket-sized NIR spectrometer.” Caffeine Catechins Theanine LINK




Process Control and NIR Sensors

” Impact of Processing and Extraction on the Minor Components of Green Spanish-Style Gordal Table Olive Fat, as Assessed by Innovative Approaches” LINK




Environment NIR-Spectroscopy Application

“National-scale spectroscopic assessment of soil organic carbon in forests of the Czech Republic” LINK

“Image and Point Data Fusion for Enhanced Discrimination of Ore and Waste in Mining” LINK




Agriculture NIR-Spectroscopy Usage

” Evaluation of the physicochemical content and solid-state fermentation stage of Zhenjiang aromatic vinegar using near-infrared spectroscopy” LINK

“Non-invasive spectroscopic and imaging systems for prediction of beef quality in a meat processing pilot plant” LINK

“Compositional Analyses Reveal Relationships among Components of Blue Maize Grains” LINK

“Rapid Detection of the Quality of Miso (Japanese Fermented Soybean Paste) Using Visible/Near-Infrared Spectroscopy” LINK




Horticulture NIR-Spectroscopy Applications

“Optical Absorption and Scattering Properties at 900-1650 nm and Their Relationships with Soluble Solid Content and Soluble Sugars in Apple Flesh during Storage” LINK




Food & Feed Industry NIR Usage

“Feasibility of fraud detection in rice using a handheld near-infrared spectroscopy” LINK

“Feasibility of fraud detection in milk powder using a handheld near-infrared spectroscopy” LINK

“Wild barley, a resource to optimize yield stability and quality of elite barley: kumulative Dissertation” LINK

” Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning” LINK




Laboratory and NIR-Spectroscopy

” Identification of peat type and humification by laboratory VNIR/SWIR hyperspectral imaging of peat profiles with focus on fen-bog transition in aapa mires” LINK





Digitization in the field of NIR spectroscopy (smart sensors)

Digitalization is advancing, also in NIR spectroscopy, which enables trainable miniature smart sensors e.g. for analyses in the food&feed, chemical and pharmaceutical sectors.

The calibration is the core of a NIR spectroscopy sensor, it enables the numerous applications and should therefore not be the weakest link in the measurement chain.

The development of calibrations that turn NIR spectrometers into smart sensors is done manually by experts (NIR specialist, chemometrician, data scientist) with so-called chemometrics software.

This is very time-consuming (time to market) and the result is person-dependent and thus suboptimal, because each expert has his own preferred way of proceeding. In addition, the calibrations have to be maintained, as new data has been collected in the meantime, which can be used to extend and improve the calibrations.

This is where our automated service comes in, combining the knowledge and good practices of NIR spectroscopy and chemometrics collected in one software and using machine learning to generate optimal calibrations.

Based on this, we have developed a complete technology platform (Time to Market) that covers the entire process from sending NIR + Lab data, to NIR Calibration as a Service, from online purchase of calibrations, to NIR Predictor software that directly evaluates newly measured NIR data locally and generates result reports.

Besides the free desktop version with user interface, the NIR Predictor can also be integrated (OEM). This can be integrated in parallel as a complement to your current Predictor, allowing the user to choose how they want to calibrate. And give them the advantage in NIR feasibility studies and NIR spectrometer evaluations to quickly provide the customer with a solid and accurate calibration that will make their NIR system deliver better results.

Advantages for your NIR users (internal or external)
  • no initial costs (no chemometrics software license required),
  • calculable operating costs (fixed amount instead of time and hourly rate) (calibration development, calibration maintenance)
  • easy to use (no chemometrics and software training),
  • quicker to use (no calibration development work) and
  • better calibrations (precision, accuracy, robustness, …)


Our chargeable service is based on the calibration development and the annual calibration use. Calibration development and calibration use can also be carried out separately (manufacturer / user).

For you as a spectrometer manufacturer, this means that you can deliver your system pre-calibrated for certain applications without incurring software license costs. And without your application specialists having to provide additional calibration services.

The unique advantages of our calibration service together with the free NIR Predictor are:
  • no software license costs (chemometrics software, predictor software, OEM integration)
  • no chemometrics know-how necessary
  • no time needed to develop optimal NIR calibrations.


If interested in using/evaluating the service :

About CalibrationModel.com : Time and knowledge intensive creation and optimization of chemometric evaluation methods for spectrometers as a service to enable more accurate analysis and measurement results.



see also

Paradigm Change in NIR

Five Mistakes to avoid on Digitalization in NIR

NIR – Total cost of ownership (TCO)

OEM / White Label Software

White Paper



Spectroscopy and Chemometrics News Weekly #48, 2020

NIR Calibration-Model Services

Do you use Molecular Spectroscopy with Multivariate Regression Models? That will save you development time LINK

Using cost saving NIR-Spectroscopy Analysis? You can Save even more Costs and Time! How? Read here | VIS NIR NIRS Spectroscopy LabManager Analysis Labs QualityControl CostSaving foodindustry foodproduct Spectrometer Sensor Analytics IoT LINK

Spectroscopy and Chemometrics News Weekly 47, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Get the Spectroscopy and Chemometrics News Weekly in real time on Twitter @ CalibModel and follow us.



Near-Infrared Spectroscopy (NIRS)

“Influence of forage particle size and residual moisture on near infrared reflectance spectroscopy (NIRS) calibration accuracy for macro-mineral determination” LINK

“Rapid detection of adulteration in Dendrobium Huoshanense using NIR spectroscopy coupled with chemometric methods” LINK

“Using near-infrared spectroscopy to discriminate closely related species: A case study of neotropical ferns” LINK

“Temperature-dependent, VIS-NIR reflectance spectroscopy of sodium sulfates” LINK




Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)

“Using near-infrared spectroscopy to determine intramuscular fat and fatty acids of beef applying different prediction approach.” LINK

“Estimating the sensory qualities of tomatoes using visible and near-infrared spectroscopy and interpretation based on gas chromatography–mass …” LINK

“Near Infrared Spectroscopy-Based Evaluation of Patellar Tendon and Knee Ligaments” LINK

“Predictive capacity of some wood properties by near-infrared spectroscopy” LINK

“Predicting Marian Plum Fruit Quality without Environmental Condition Impact by Handheld Visible–Near-Infrared Spectroscopy” LINK

“Near infrared reflectance spectroscopy to quantify Perkinsus marinus infecting Crassostrea virginica” LINK

“Application of genetic algorithm and multivariate methods in detection and measurement of milk‐surfactant adulteration by attenuated total reflection and near‐infrared spectroscopy” LINK

“Classifying Thermal Degradation of Polylactic Acid by Using Machine Learning Algorithms Trained on Fourier Transform Infrared Spectroscopy Data” LINK

“Classification Option for Korean Traditional Paper Based on Type of Raw Materials, Using Near-infrared Spectroscopy and Multivariate Statistical Methods” LINK

“Rapid and simultaneous quality analysis of the three active components in Lonicerae Japonicae Flos by near-infrared spectroscopy” LINK

“Determination of Adenosine and Cordycepin Concentrations in Cordyceps militaris Fruiting Bodies Using Near-Infrared Spectroscopy” LINK

“Application of near-infrared hyperspectral imaging for variety identification of coated maize kernels with deep learning” LINK




Hyperspectral Imaging (HSI)

Konica Minolta to acquire Specim, the leading global supplier of hyperspectral imaging.   “Konica Minolta shares our vision and values and will greatly support our business through improved sell-through,” said Tapio Kallonen, CEO of Specim LINK

“Research on Citrus grandis Granulation Determination Based on Hyperspectral Imaging through Deep Learning” LINK

Wavelength Selection Method Based on Partial Least Square from Hyperspectral Unmanned Aerial Vehicle Orthomosaic of Irrigated Olive Orchards” LINK




Chemometrics and Machine Learning

“Fractional order modeling and recognition of nitrogen content level of rubber tree foliage” LINK

“Development of multi-product calibration models of various root and tuber powders by fourier transform near infra-red (FT-NIR) spectroscopy for the quantification of …” LINK

“Detection and Quantification of Tomato Paste Adulteration Using Conventional and Rapid Analytical Methods” LINK

“Prediction of Acidity Level of Avomango (Gadung Klonal 21) Using Local Polynomial Estimator” LINK

“A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra” LINK

“Spectrometric Classification of Bamboo Shoot Species by Comparison of Different Machine Learning Methods” LINK

Supporting the Sensory Panel to Grade Virgin Olive Oils: An In-House-Validated Screening Tool by Volatile Fingerprinting and Chemometrics” LINK




Process Control and NIR Sensors

“A Process Analytical Concept for In-Line FTIR Monitoring of Polysiloxane Formation” Polymers LINK




Agriculture NIR-Spectroscopy Usage

“Vis–NIR spectroscopy: from leaf dry mass production estimate to the prediction of macro-and micronutrients in soybean crops” LINK

“Comparison of benchtop and handheld near‐infrared spectroscopy devices to determine forage nutritive value” LINK




Food & Feed Industry NIR Usage

“Rapid Authentication of 100% Italian Durum Wheat Pasta by FT-NIR Spectroscopy Combined with Chemometric Tools” LINK

“Identification of Corn Seeds with Different Freezing Damage Degree Based on Hyperspectral Reflectance Imaging and Deep Learning Method” LINK

“Near-and mid-infrared determination of some quality parameters of cheese manufactured from the mixture of different milk species” LINK

“Portable NIR spectrometer for quick identification of fat bloom in chocolates.” LINK




Laboratory and NIR-Spectroscopy

“A Novel Spectral Matching Approach for Pigment: Spectral Subsection Identification Considering Ion Absorption Characteristics” LINK




Other

“Development of a novel quantitative function between spectral value and metmyoglobin content in Tan mutton” LINK

” 基于稀疏网络的可见光/近红外反射光谱土壤有机质含量估算” LINK

“基于可见-近红外光谱的茄子叶绿素荧光参数 Fv/Fm 预测方法” LINK

“A Miniaturized and Fast System for Thin Film Thickness Measurement” LINK

“Molecular spectroscopy with optical frequency combs” LINK





NIR-Predictor – Manual


NIR-Predictor – Manual

Predicting Spectra

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:

  1. Open the demo Spectra folder by using the Menu > Open Demo Spectra or press F8.
    There are files with spectra from different Vendors.

  2. Drag & drop a spectra file onto the NIR-Predictor window (or press Ctrl+O as for ’Open some files).

  3. 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

  1. You have measured your samples with you NIR-Instrument Software.
    And got the Lab-values of these samples.

    samples
    -> measured NIR-spectra
    -> Lab-references analytics

  2. Now you need to combine these data.

    NIR-spectra + Lab-references
    -> PropertiesBySamples

    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!

  3. 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.

  4. 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.

  5. Email the CalibrationRequest.zip file
    to info@CalibrationModel.com to develop the calibrations.

  6. 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:

  1. in NIR-Predictor : Menu > Open Calibrations (F9)

  2. an explorer window is opened where the calibrations are located

  3. create a folder for your application, choose a name

  4. copy the calibration file(s) (*.cm) into that folder

  5. in NIR-Predictor : Menu > Search and load Applications (F4)

Usage:

  1. in NIR-Predictor : open the Application drop down list, and select your application by name

  2. if all is fine, the calibration file is valid and not expired, it shows : Calibration “1 valid calibation”

  3. the NIR-Predictor is now ready to predict

  4. 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.

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

  1. 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

  2. Use your favorite editor or spreadsheet program to enter and copy&paste the Lab Values into the columns and save the file.

  3. 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.

  4. 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.

The file header line contains :

Sample Replicates Names Prop1 Prop2 Prop3 DateFirst DateLast Hashes

Where Name and Date describes the spectrum.

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

Further References