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 #17, 2022

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

How to improve your NIRS analysis, get the free White Paper | NIR infrared ag lab QAQC QC quality measurement LINK

Spectroscopy and Chemometrics News Weekly 16, 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 16, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 16, 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)

“Penilaian Sejawat: Fast, simultaneous and contactless assessment of intact mango fruit by means of near infrared spectroscopy. AIMS Press.” | Agriculture (Gabungan Nilai).PDF LINK

“Relevance of Near infrared (NIR) spectroscopy in the determination of intrinsic Rheological properties of crude oil components from Asabor Platform, Nigeria.” LINK

“Feasibility of Near-Infrared Spectroscopy for Rapid Detection of Available Nitrogen in Vermiculite Substrates in Desert Facility Agriculture” LINK

“Establishment of a Nondestructive Analysis Method for Lignan Content in Sesame using Near Infrared Reflectance Spectroscopy” LINK

“Near Infrared Spectroscopy: A useful technique for inline monitoring of the enzyme catalyzed biosynthesis of third-generation biodiesel from waste cooking oil” LINK

“A Study on Nitrogen Concentration Detection Model of Rubber Leaf Based on Spatial-Spectral Information with NIR Hyperspectral Data” LINK

“Design and Performance of a Near-Infrared Spectroscopy Measurement System for In-Field Alfalfa Moisture Measurement” LINK

“Estimating Forest Soil Properties for Humus Assessment—Is Vis-NIR the Way to Go?” LINK

“Association and solubility of chlorophenols in CCl4: MIR/NIR spectroscopic and DFT study” LINK

“Prediction of rhodinol content in Java citronella oil using NIR spectroscopy in the initial stage developing a spectral smart sensor system” | LINK

“Classification of Cocoa Beans Based on Fermentation Level Using PLS-DA Combined with Visible Near-Infrared (VIS-NIR) Spectroscopy” LINK

“Carbon Footprint Reduction by Utilizing Real Time NIR Spectroscopy for RVP Measurement in Natural Gas Condensate Stabilizers” LINK

“Determination of Total Saccharide Content in Auricularia auricula Based on Near-Infrared Spectroscopy” | LINK

“Prediction of crosslink density of prevulcanised latex using NIR Spectroscopy based on combination of fractional order derivative (FOD) and variable selection …” LINK

“Determination of Acid Level (pH) and Moisture Content of Cocoa Beans at Various Fermentation Level Using Visible Near-Infrared (Vis-NIR) Spectroscopy” LINK

“Near infrared spectroscopy and machine learning classifier of crosslink density level of prevulcanized natural rubber latex” LINK

“Development of a calibration model for near infrared spectroscopy using a convolutional neural network” 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

“Predicting Soil Organic Carbon Mineralization Rates Using δ13C, Assessed by Near-Infrared Spectroscopy, in Depth Profiles Under Permanent Grassland Along a …” | LINK

“Sensors : A Multiblock Approach to Fuse Process and Near-Infrared Sensors for On-Line Prediction of Polymer Properties” LINK




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

“Determination of Storage Period of Harvested Plums by NearInfrared Spectroscopy and Quality attributes” LINK

“Performance improvement in a supercontinuum fiber-coupled system for near infrared absorption spectroscopy” LINK




Hyperspectral Imaging (HSI)

“New Zealand Honey Botanical Origin Classification with Hyperspectral Imaging” LINK

“Scanning Hyperspectral Imaging for In Situ Biogeochemical Analysis of Lake Sediment Cores: Review of Recent Developments” LINK




Chemometrics and Machine Learning

“Non-destructive near infrared spectroscopy externally validated using large number sets for creation of robust calibration models enabling prediction of apple firmness” LINK

“Foods : Predicting Satiety from the Analysis of Human Saliva Using Mid-Infrared Spectroscopy Combined with Chemometrics” LINK

“A NIR,1H-NMR, LC-MS and chemometrics pilot study on the origin of Carvedilol drug substance: a tool for discovering falsified active pharmaceutical ingredients” LINK

“Algorithm of Stability-Analysis-Based Feature Selection for NIR Calibration Transfer” LINK

“Improving TVB-N prediction in pork using portable spectroscopy with just-in-time learning model updating method” LINK

“Method Development and Validation of a Near-infrared spectroscopic method for In-line API Quantification during Fluidized Bed Granulation” LINK

“Non-destructive follow-up of ‘Jintao’kiwifruit ripening through VIS-NIR spectroscopy-individual vs. average calibration model’s predictions” LINK

“Machine Learning-Powered Models for Near-Infrared Spectrometers: Prediction of Protein in Multiple Grain Cereals” LINK

“A hybrid variable selection and modeling strategy for the determination of target compounds in different spectral datasets” LINK

“Sensors : A Data-Driven Model with Feedback Calibration Embedded Blood Pressure Estimator Using Reflective Photoplethysmography” LINK

“Non-Destructive Genotyping of Cultivars and Strains of Sesame through NIR Spectroscopy and Chemometrics” | LINK




Research on Spectroscopy

“Quadruple Functionalization of a Tetraphenylethylene Aromatic Scaffold with Ynamides or Tetracyanobutadienes: Synthesis and Optical Properties” LINK




Environment NIR-Spectroscopy Application

“Marriage of Heterobuckybowls with Triptycene: Molecular Waterwheels for Separating C60 and C70” LINK

“Performance of hyperspectral data in predicting and mapping zinc concentration in soil” LINK




Agriculture NIR-Spectroscopy Usage

“Study of neurophysiological responses associated with the application of magnetic fields to the brain” LINK

“PLANT DISEASE DETECTION USING IMAGE SENSORS: ASTEP TOWARDS PRECISION AGRICULTURE” LINK

“Linking long-term soil phosphorus management to microbial communities involved in nitrogen reactions” | LINK

“Bovine fecal chemistry changes with progression of Southern Cattle Tick, Rhipicephalus (Boophilus) microplus (Acari: Ixodidae) infestation” LINK

“Remote Sensing : The Accuracy of Winter Wheat Identification at Different Growth Stages Using Remote Sensing” LINK




Horticulture NIR-Spectroscopy Applications

“Accurate nondestructive prediction of soluble solids content in citrus by nearinfrared diffuse reflectance spectroscopy with characteristic variable selection” LINK




Food & Feed Industry NIR Usage

“Verifying the Geographical Origin and Authenticity of Greek Olive Oils by Means of Optical Spectroscopy and Multivariate Analysis” LINK

“Analysis of wheat flour-insect powder mixtures based on their near infrared spectra” LINK




Laboratory and NIR-Spectroscopy

“Utilization of Pollution Indices, Hyperspectral Reflectance Indices, and Data-Driven Multivariate Modelling to Assess the Bottom Sediment Quality of Lake Qaroun …” LINK

“Oxygen vacancies and defects tailored microstructural, optical and electrochemical properties of Gd doped CeO2 nanocrystalline thin films” LINK




Other

“Simultaneous removal of aromatic pollutants and nitrate at high concentrations by hypersaline denitrification: Long-term continuous experiments investigation” LINK

“Study of sub-band state formation in the optical band gap of CuGaS2 thin films by electronic excitations” LINK

“A sensor combination based automatic sorting system for waste washing machine parts” LINK

Spectroscopy and Chemometrics/Machine-Learning News Weekly #12, 2022

NIR Calibration-Model Services

“Combining NIR spectroscopy with automation in chemometric prediction model development-towards a low cost food analysis system.” price cost NIR NIRS spectroscopy chemometrics software tools DataScientist LINK

Increase Your Profit with optimized NIRS Spectroscopy Accuracy Beverage Processing Dairy milk meat nutrition LINK

Spectroscopy and Chemometrics News Weekly 11, 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 11, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 11, 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)

“Synaptosomal-Associated Protein 25 Gene Polymorphisms Affect Treatment Efficiency of Methylphenidate in Children With Attention-Deficit Hyperactivity Disorder: An fNIRS Study” | LINK

“Applied Sciences : Numerical Study of Near-Infrared Light Propagation in Aqueous Alumina Suspensions Using the Steady-State Radiative Transfer Equation and Dependent Scattering Theory” LINK

“Reducing false discoveries in resting-state functional connectivity using short channel correction: an fNIRS study” | LINK

“Diffuse Optical Tomography Using fNIRS Signals Measured from the Skull Surface of the Macaque Monkey” LINK

“Prediction of cellulose nanofibril (CNF) amount of CNF/polypropylene composite using near infrared spectroscopy” LINK

“Near infraredbased process analytical technology module for estimating gelatinization optimal point” LINK

“A Novel NIR-Based Strategy for Rapid Freshness Assessment of Preserved Eggs” | LINK

“Machine Learning-Based Assessment of Cognitive Impairment Using Time-Resolved Near-Infrared Spectroscopy and Basic Blood Test” | LINK

“Rapid Detection Method of Pleurotus Eryngii Mycelium based on Near Infrared Spectral Characteristics” LINK

“Research on moisture content detection method during green tea processing based on machine vision and near-infrared spectroscopy technology” LINK

“Use of Artificial Neural Networks and NIR Spectroscopy for Non-Destructive Grape Texture Prediction” LINK

“NIRS for vicine and convicine content of faba bean seed allowed GWAS to prepare for marker-assisted adjustment of seed quality of German winter faba beans” LINK

“Evidence that large vessels do affect near infrared spectroscopy” | LINK

“Use of LC-Orbitrap MS and FT-NIRS with multivariate analysis to determine geographic origin of Boston butt pork” LINK

“Prediction of the intramuscular fat and protein content of freeze dried ground meat from cattle and sheep using Near‐Infrared Spectroscopy (NIRS)” LINK

“Prediction of quality of total mixed ration for dairy cows by near infrared reflectance spectroscopy and empirical equations” LINK

“Analysis and Model Comparison of Carbon and Nitrogen Concentrations in Sediments of the Yellow Sea and Bohai Sea by Visible-Near Infrared Spectroscopy” | LINK

“Non-destructive measurement and real-time monitoring of apple hardness during ultrasonic contact drying via portable NIR spectroscopy and machine learning” LINK

“Polymers : A NIR-Light-Driven Twisted and Coiled Polymer Actuator with a PEDOT-Tos/Nylon-6 Composite for Durable and Remotely Controllable Artificial Muscle” LINK

“Invited review: A comprehensive review of visible and near-infrared spectroscopy for predicting the chemical composition of cheese” LINK

“Chemometrics: An Excavator in Temperature-Dependent Near-Infrared Spectroscopy” LINK

“Are NIRS tools useful to assess digestible energy of pig feedstuffs?” | LINK




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

“Biosensors : Haemoprocessor: A Portable Platform Using Rapid Acoustically Driven Plasma Separation Validated by Infrared Spectroscopy for Point-of-Care Diagnostics” LINK

“Estimating texture and organic carbon of an Oxisol by near infrared spectroscopy” LINK

“PhotomultiplicationType Organic Photodetectors for NearInfrared Sensing with High and BiasIndependent Specific Detectivity” LINK




Hyperspectral Imaging (HSI)

“Detection and discrimination of disease and insect stress of tea plants using hyperspectral imaging combined with wavelet analysis” LINK

“Evaluation of the benefits of combined reflection and transmission hyperspectral imaging data through disease detection and quantification in plant-pathogen …” | LINK

“Multi-layer Cascade Screening Strategy for Semi-supervised Change Detection in Hyperspectral Images” LINK

“Comparison of near-infrared, mid-infrared, Raman spectroscopy and near-infrared hyperspectral imaging to determine chemical, structural and rheological properties …” LINK

“Developing an affordable hyperspectral imaging system for rapid identification of Escherichia coli O157:H7 and Listeria monocytogenes in dairy products” LINK




Chemometrics and Machine Learning

“Simultaneous Monitoring of the Evolution of Chemical Parameters in the Fermentation Process of Pineapple Fruit Wine Using the Liquid Probe for Near-Infrared Coupled with Chemometrics” LINK

“Agronomy : Decision Support System (DSS) for Managing a Beef Herd and Its Grazing Habitat’s Sustainability: Biological/Agricultural Basis of the Technology and Its Validation” LINK

“Prediction of Entire Tablet Formulations From Pure Powder Components Spectra via a Two-Step Non-Linear Optimization Methodology” LINK




Optics for Spectroscopy

“Polymers : Development and Characterization of Plantain (Musa paradisiaca) Flour-Based Biopolymer Films Reinforced with Plantain Fibers” LINK




Research on Spectroscopy

“Understanding water dynamics in different subcellular environments by combining multiplexing imaging and dimethylamino naphthalene fluorescent derivatives”LINK




Environment NIR-Spectroscopy Application

“Performance of hyperspectral data in predicting and mapping zinc concentration in soil” LINK

“Prediction of soil carbon and nitrogen contents using visible and near infrared diffuse reflectance spectroscopy in varying salt-affected soils in Sine Saloum (Senegal)” LINK

“Machine Learning Framework for Intelligent Detection of Wastewater Pollution by IoT-Based Spectral Technology” | LINK




Agriculture NIR-Spectroscopy Usage

“Applied Sciences : Combining Hyperspectral Reflectance and Multivariate Regression Models to Estimate Plant Biomass of Advanced Spring Wheat Lines in Diverse Phenological Stages under Salinity Conditions” LINK

“Applied Sciences : Mineral Content Estimation of Lunar Soil of Lunar Highland and Lunar Mare Based on Diagnostic Spectral Characteristic and Partial Least Squares Method” LINK

“Extraction of rock and alteration geons by FODPSO segmentation and GP regression on the HyMap imagery: a case study of SW Birjand, eastern Iran” LINK

“The Relative Performance of a Benchtop Scanning Monochromator and Handheld Fourier Transform Near-Infrared Reflectance Spectrometer in Predicting Forage …” LINK

“Non-destructive assessment of amylose content in rice using a quality inspection system at grain elevators” LINK

“Remote Sensing : Predicting the Chlorophyll Content of Maize over Phenotyping as a Proxy for Crop Health in Smallholder Farming Systems” LINK




Horticulture NIR-Spectroscopy Applications

“Correction of Temperature Variation with Independent Water Samples to Predict Soluble Solids Content of Kiwifruit Juice Using NIR Spectroscopy” LINK




Forestry and Wood Industry NIR Usage

“Study of Variability of Waste Wood Samples Collected in a Panel Board Industry” | LINK

“PREDICTION FOR TOTAL MOISTURE CONTENT IN WOOD PELLETS BY NEAR INFRARED SPECTROSCOPY NIRS” LINK




Chemical Industry NIR Usage

“Enhancement of Photostability through Side Chain Tuning in Dioxythiophene-Based Conjugated Polymers” LINK




Petro Industry NIR Usage

“Towards energy discretization for muon scattering tomography in GEANT4 simulations: A discrete probabilistic approach. (arXiv:2201.08804v1 [physics.comp-ph])” LINK




Pharma Industry NIR Usage

“Revealing the Effect of Heat Treatment on the Spectral Pattern of Unifloral Honeys Using Aquaphotomics” LINK

“Spectroscopic Characteristics of Xeloda Chemodrug” LINK

“Bilateral acute macular neuroretinopathy in a young woman after the first dose of Oxford-AstraZeneca COVID-19 vaccine” LINK

“Spatial particle size distribution at intact sample surfaces of a Dystric Cambisol under forest use” LINK




Other

“Chemosensors : Development of Cyanine 813-Based Doped Supported Devices for Divalent Metal Ions Detection” LINK

“Spectral actinometry at SMEAR-Estonia. (arXiv:2202.06132v1 [physics.ao-ph])” LINK

“Experimental and theoretical investigation on the nonlinear optical properties of LDS 821 dye in different solvents and DNA” LINK

“Domain invariant covariate selection (Di-CovSel) for selecting generalized features across domains” LINK

“Structural, optical analysis, and Poole-Frenkel emission in NiO/CMC-PVP: Bio-nanocomposites for optoelectronic applications” LINK





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Spectroscopy and Chemometrics/Machine-Learning News Weekly #9, 2022

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 8, 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 8, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 8, 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)

“Research of Parameter Optimization of Preprocessing and Feature Extraction for NIRS Qualitative Analysis Based on PSO Method” LINK

“Fusion of a low-cost electronic nose and Fourier transform near-infrared spectroscopy for qualitative and quantitative detection of beef adulterated with duck.” LINK

“Assessment oil composition and species discrimination of Brassicas seeds based on hyperspectral imaging and portable near infrared (NIR) spectroscopy tools and …” LINK

“Prediksi Kualitas Buah Jambu Biji “Kristal” Secara NonDestruktif Menggunakan Portable Near Infrared Spectrometer (Nirs)” LINK

“Quantitative Analysis of Blend Uniformity within a Three-Chamber Feed Frame using Simultaneously Raman and Near-Infrared Spectroscopy” LINK

“Rapid Identification of Peucedanum Praeruptorum Dunn and its Adulterants by Hand-Held near-Infrared Spectroscopy” | LINK

“Methods of Detecting Multiple Chemical Substances Based on Near-Infrared Colloidal Quantum Dot Array and Spectral Reconstruction Algorithm” LINK

“Near-Infrared Spectroscopy Detection of Cotton/Polyester Content Based on Dropout Deep Belief Network” LINK

“Determining Pasture Biodiversity with NIRS” 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

“NIR Calibration Transfer Method Based on Minimizing Mean Distribution Discrepancy” LINK

“Near infrared spectroscopy reveals instability in retinal mitochondrial metabolism and haemodynamics with blue light exposure at environmental levels” LINK

“Study on Characteristic Wavelength Extraction Method for Near Infrared Spectroscopy Identification Based on Genetic Algorithm” LINK

“Discrimination of Transgenic Canola (Brassica napus L.) and their Hybrids with B. rapa using Vis-NIR Spectroscopy and Machine Learning Methods” LINK

“Recent Advances in Application of Near-Infrared Spectroscopy for Quality Detections of Grapes and Grape Products” LINK

“Detection of calcium chloride salts in rubber cublum using Near-Infrared Spectroscopy Technique” LINK

“PLS-DA and Vis-NIR spectroscopy based discrimination of abdominal tissues of female rabbits” LINK

“Sparse Reconstruction using Block Sparse Bayesian Learning with Fast Marginalized Likelihood Maximization for Near-Infrared Spectroscopy” LINK

“Study on Online Detection Method of “Yali” Pear Black Heart Disease Based on Vis-Near Infrared Spectroscopy and AdaBoost Integrated Model” LINK

“Detection of Umami Substances and Umami Intensity Based on Near-Infrared Spectroscopy” LINK

“Farklı ana materyal üzerinde oluşmuş toprakların adli bilim için VNIRS tekniği ile spektral karakterizasyonu ve özelliklerinin tahmin edilmesi” LINK

“Prediction of soil hydraulic properties using VIS-NIR spectral data in semi-arid region of Northern Karnataka Plateau” LINK

“Rapid Assessment of Fresh Beef Spoilage Using Portable Near-Infrared Spectroscopy” LINK

“The Relationship Between Genetic Variations and NIRs Differences of Eucalyptus Pellita Provenances” LINK

“Geographical Origin Discrimination of Taiping Houkui Tea Using Convolutional Neural Network and Near-Infrared Spectroscopy” LINK

“Assessment of soil quality using VIS-NIR spectra in invaded coastal wetlands” | LINK

“Relationship Between Visible/Near Infrared Spectral Data and Fertilization Information at Different Positions of Hatching Eggs” LINK




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

“Near Infrared Spectral Wavelength Selection Based on Improved Team Progress Algorithm” LINK

“Coupling ATR-FTIR Spectroscopy and Chemometric Analysis for Rapid and Non-Destructive Ink Discrimination of Forensic Documents” LINK

“Model-based mid-infrared spectroscopy for on-line monitoring of volatile fatty acids in the anaerobic digester” LINK

“Depthdependent hydration dynamics in human skin: Vehiclecontrolled efficacy assessment of a functional 10% urea plus NMF moisturizer by nearinfrared confocal spectroscopic imaging (KOSIM IR) and capacitance method complemented by volunteer perception” LINK

“Detection of Umami Substances and Umami Intensity Based on Near-Infrared Spectroscopy” LINK

“Early Detection of Cauliflower Gray Mold Based on Near-Infrared Spectrum Feature Extraction” LINK




Hyperspectral Imaging (HSI)

“Hyperspectral imaging for non-destructive detection of honey adulteration” LINK

“Study on the Spectral Characteristics of Ground Objects in Land-Based Hyperspectral Imaging” LINK

“Combine Hyperspectral Imaging and Machine Learning to Identify the Age of Cotton Seeds” LINK

“Nondestructive detection of total soluble solids in grapes using VMD‐RC and hyperspectral imaging” LINK

“Nondestructive Detection of Codling Moth Infestation in Apples Using Pixel-Based NIR Hyperspectral Imaging with Machine Learning and Feature Selection” LINK

“Research on Rich Borer Detection Methods Based on Hyperspectral Imaging Technology” LINK




Chemometrics and Machine Learning

“A Data Fusion Model to Merge the Spectra Data of Intact and Powdered Cayenne Pepper for the Fast Inspection of Antioxidant Properties” LINK

“Rapid Determination of β-Glucan Content of Hulled and Naked Oats Using near Infrared Spectroscopy Combined with Chemometrics” LINK

“Prediction Model of TVB-N Concentration in Mutton Based on Near Infrared Characteristic Spectra” LINK

“Optimization of Fruit Pose and Modeling Method for Online Spectral Detection of Apple Moldy Core” LINK




Research on Spectroscopy

“Local Preserving Projection Similarity Measure Method Based on Kernel Mapping and Rank-Order Distance” LINK

“A Method for Detecting Sucrose in Living Sugarcane With Visible-NIR Transmittance Spectroscopy” LINK




Process Control and NIR Sensors

“Cognitive and linguistic dysfunction after thalamic stroke and recovery process: possible mechanism” LINK




Environment NIR-Spectroscopy Application

“Empower Innovations in Routine Soil Testing” LINK




Agriculture NIR-Spectroscopy Usage

“Preharvest phenotypic prediction of grain quality and yield of durum wheat using multispectral imaging” LINK

“Cove-Edged Graphene Nanoribbons with Incorporation of Periodic Zigzag-Edge Segments” LINK

“Utilizing near infra-red spectroscopy to identify physiologic variations during digital retinal imaging in preterm infants” | LINK




Forestry and Wood Industry NIR Usage

“Spectrometric prediction of nitrogen content in different tissue types of trees 2” LINK

“Spectrometric Prediction of Nitrogen Content in Different Tissues of Slash Pine Trees” LINK




Food & Feed Industry NIR Usage

“Optical techniques in non-destructive detection of wheat quality: A review” LINK




Chemical Industry NIR Usage

“Quantifying Polaron Mole Fractions and Interpreting Spectral Changes in Molecularly Doped Conjugated Polymers” LINK




Pharma Industry NIR Usage

“Quantitative Changes in Muscular and Capillary Oxygen Desaturation Measured by Optical Sensors during Continuous Positive Airway Pressure Titration for …” LINK




Medicinal Spectroscopy

“406: PERIOPERATIVE NONINVASIVE NEUROMONITORING IN INFANTS WITH CONGENITAL HEART DISEASE” LINK




Laboratory and NIR-Spectroscopy

“Available on line at Directory of Open Access Journals” LINK




Other

“A Spectroscopic Study of Dysprosium-Doped TlPb2Br5 for Development of Novel Mid-IR Gain Media” LINK

“No differences in splenic emptying during on-transient supine cycling between aerobically trained and untrained participants” | LINK

“Influence of Substrate Temperature on Structural and Optical Properties of Co-Evaporated Cu2SnS3/ITO Thin Films” LINK

“Synthesis and characterization of new multinary selenides Sn4In5Sb9Se25 and Sn6. 13Pb1. 87In5. 00Sb10. 12Bi2. 88Se35” LINK





.

Spectroscopy and Chemometrics/Machine-Learning News Weekly #3, 2022

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 2, 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 2, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 2, 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)

“Qualitative Discrimination of Intact Tobacco Leaves Based on Near-Infrared Technology” | LINK

“Compositionally complex (Ca, Sr, Ba) ZrO3 fibrous membrane with excellent structure stability and NIR reflectance” LINK

“Applicability of Near Infrared Reflectance Spectroscopy to Predict Amylose Contents of Single-Grain Maize” LINK

“Potential of Near-Infrared (NIR) spectroscopy technique for early detection of Insidious Fruit Rot (IFR) disease in Harumanis mango” LINK

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

“Interferometric near Infrared Spectroscopy (iNIRS): From Conception to Human Brain Imaging” LINK

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

“… Square, Artificial Neural Network and Support Vector Regressions for real time monitoring of CHO cell culture processes using in situ Near Infrared spectroscopy” LINK

“Phonetic versus spatial processes during motor-oriented imitations of visuo-labial and visuo-lingual speech: a functional near-infrared spectroscopy study” LINK

” The Relationship Between Genetic Variations and NIRs Differences of Eucalyptus Pellita Provenances” LINK

“Sensors : Field Detection of Rhizoctonia Root Rot in Sugar Beet by Near Infrared Spectrometry” LINK

“Use of visible-near infrared spectroscopy to predict nutrient composition of poultry excreta” LINK

“Use of Functional Near-Infrared Spectroscopy to Predict and Measure Cochlear Implant Outcomes: A Scoping Review” LINK

“Applied Sciences : Potencial Use of Near Infrared Spectroscopy (NIRS) to Categorise Chorizo Sausages from Iberian Pigs According to Several Quality Standards” LINK




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

“NearInfrared Light Responsive TiO2 for Efficient Solar Energy Utilization” LINK

“Hydration of LiOH and LiCl─ Near-Infrared Spectroscopic Analysis” LINK

“NEAR INFRARED-HYPER SPECTRAL IMAGING APPLICATIONS IN FISHERIES” LINK

“Optimization of sweet basil harvest time and cultivar characterization using near‐infrared spectroscopy, liquid and gas chromatography, and chemometric statistical …” LINK

“Fiber-based source of 500 kW mid-infrared solitons” LINK




Hyperspectral Imaging (HSI)

“Hyperspectral Image Classification Using Factor Analysis and Convolutional Neural Networks” | LINK

“Pre-Launch Calibration of the HYPSO-1 Cubesat Hyperspectral Imager” LINK

“Potential of in-field Vis/NIR hyperspectral imaging to monitor quality parameters of alfalfa” LINK

“A comparison of machine learning algorithms for mapping soil iron parameters indicative of pedogenic processes by hyperspectral imaging of intact soil profiles” LINK

“Inversion modeling of rice canopy nitrogen content based on MPA-GA-ELM UAV hyperspectral remote sensing” LINK

“Sensors : Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging” LINK

“Interleaved attention convolutional compression network: An effective data mining method for the fusion system of gas sensor and hyperspectral” LINK




Chemometrics and Machine Learning

“Remote Sensing : Machine Learning Classification of Endangered Tree Species in a Tropical Submontane Forest Using WorldView-2 Multispectral Satellite Imagery and Imbalanced Dataset” LINK

“Sensors : Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine” LINK

“Image analysis for predicting phenolics in Arabidopsis” LINK

“Ultravioletvisual spectroscopy estimation of nitrate concentrations in surface waters via machine learning” LINK

“Near-infrared calibration models for estimating volatile fatty acids and methane production from in vitro rumen fermentation of different total mixed rations” LINK

“Foods : Tea and Chicory Extract Characterization, Classification and Authentication by Non-Targeted HPLC-UV-FLD Fingerprinting and Chemometrics” LINK




Optics for Spectroscopy

“Perovskites Enabled Highly Sensitive and Fast Photodetectors” LINK




Facts

“Tracing the aqueous alteration history between Isidis and Hellas Planitiae on Mars” LINK

“Remote Sensing : Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques?” | LINK




Research on Spectroscopy

“Sustainability : Finger Millet Production in Ethiopia: Opportunities, Problem Diagnosis, Key Challenges and Recommendations for Breeding” LINK

“Foods : Online Detection of Watercore Apples by Vis/NIR Full-Transmittance Spectroscopy Coupled with ANOVA Method” LINK

“Biosensors : Reflectance Spectroscopy with Multivariate Methods for NondestructiveDiscrimination of Edible Oil Adulteration” LINK




Environment NIR-Spectroscopy Application

“Environmental assessment of soil quality indices using near infrared reflectance spectroscopy” LINK

“Remote Sensing : Ground Observations and Environmental Covariates Integration for Mapping of Soil Salinity: A Machine Learning-Based Approach” LINK




Agriculture NIR-Spectroscopy Usage

“Animals : Optimizing Near Infrared Reflectance Spectroscopy to Predict Nutritional Quality of Chickpea Straw for Livestock Feeding” LINK

“Foods : Development of Nano Soy Milk through Sensory Attributes and Consumer Acceptability” LINK

“Progression of macular atrophy in patients receiving long-term anti-VEGF therapy for age-related macular degeneration; Real Life Data” LINK

“Complexity Assessment of Chronic Pain in Elderly Knee Osteoarthritis Based on Neuroimaging Recognition Techniques” | LINK

“Integrating Genomic and Phenomic Breeding Selection Tools with Field Practices to Improve Seed Composition Quality Traits in Soybean” LINK

“Impact of Forage Diversity on Forage Productivity, Nutritive Value, Beef Cattle Performance and Enteric Methane Emissions.” LINK

“Ecological effects on the nutritional value of bromeliads, and its influence on Andean bears’ diet selection” LINK




Horticulture NIR-Spectroscopy Applications

“Correction of Temperature Variation with Independent Water Samples to Predict Soluble Solids Content of Kiwifruit Juice Using NIR Spectroscopy” Kiwi LINK




Food & Feed Industry NIR Usage

“Real-Time Detection of Rice Growth Phase Transition for Panicle Nitrogen Application Timing Assessment” LINK

“Prediction of pork meat quality parameters with existing hyperspectral devices” LINK




Pharma Industry NIR Usage

“First report of rapid, non-invasive, and reagent-free detection of malaria through the skin of patients with a beam of infrared light” LINK




Laboratory and NIR-Spectroscopy

“Yield and Quality Response of Alfalfa Varieties to High Salinity Irrigation” LINK

“Physical and Chemical Characterisation of the Pigments of a 17th-Century Mural Painting in the Spanish Caribbean” LINK




Other

“A Learned SVD approach for Inverse Problem Regularization in Diffuse Optical Tomography. (arXiv:2111.13401v1 [math.NA])” LINK

” การ วัด ปริมาณ ของแข็ง ที่ ละลาย น้า ได้ ของ เนื อ ทุเรียน พันธุ์ หมอนทอง โดย เนีย ร์ อินฟราเรด สเปก โทร ส โก ปี แบบ ออนไลน์ และ ออฟ ไลน์” LINK




NIR-Predictor – Frequently Asked Questions (FAQ)


NIR-Predictor – FAQ

Please also refer to the NIR-Predictor – Manual and check the Hints and Notes.


How to Configure / Load / Import / Activate / Setup / Use the Calibrations (*.cm) in NIR-Predictor?

Chapter “Configure the Calibrations for prediction usage” – NIR-Predictor – Manual


Do you have a calibration file for XY ?

We create custom calibrations out of your NIR + Lab measurements of XY.
We do not sell off-the-shelf calibrations.


I have downloaded the software but can’t see it?

Please note, that the download time will be very short, because of the small file size.
Check your browser’s download folder. The download is the file “NIR-PredictorVx.y.zip”


Why a .zip and no installer (Setup.exe) ?

Because a .zip deploy keeps it simple for all:

  • easy : no Administrator rights needed to install, delete it to uninstall
  • harmless : no system changes during setup
  • transparency : you see what you get

Is there a command line (CLI) version of NIR-Predictor ?

If you want to customize it in all details, our OEM API for NIR-instrument-software (White-Label) integration gives you full access. If you are an NIR-Vendor (or similar) please contact us via email info@CalibrationModel.com


The free NIR-Predictor does not create a model, what is wrong ?

As the name says, the NIR-Predictor just predicts NIR data with a model. To create a model you need to send your data to the CalibrationModel service, after development process you get an email with a link to the calibration where it can be purchased and downloaded.


Why does the creation of the PropertyFile.txt take so long with hundrets of spectra files?

It normally takes only 1-10 seconds not minutes.
Make sure that the spectra data files are stored locally on your main drive
and not on a cloud-drive or network storage or slow USB thumb drive or SD-Card.


Is there a way to use converted ASCII spectrum data to be used in NIR-Predictor?

Yes, this is the simplest ASCII CSV file format the NIR-Predictor supports.
And there are other formats supported.


Can you convert old calibration data from vendor A to be integrated to our new vendor B NIRS calibration data in our instrument?

No, we don’t do model or spectra conversion / transformation (aka model transfer).
We build optimized models with wavelength compatible data.


Does NIR-Predictor contain any malware, spyware or adware?

No, NIR-Predictor does not contain any malware, spyware or adware.


How to copy the prediction results from the table in the browser?

Copy selected columns from the table.
By holding down the Ctrl key, rectangular areas in the table can be selected with the mouse and copied to the clipboard with Ctrl+C and then copied to a spreadsheet program with Ctrl+V.


Will an expired calibration still work?

No. Until you extend the usage time.
The expired calibration file will be moved to the CalibrationExpired folder on the next start of NIR-Predictor or “Search and load Calibrations” menu function.

Selected Calibration files from the folder CalibrationExpired can be send to info@CalibrationModel.com with your Request file (.req) files for extending their usage time.

There is the possibility to get a perpetual usage, which means their is no expiration (valid until 2050).

That way you can get the time extended calibration back, that behaves exactly as the one before with extended usage time.


What does the calibration Expiration date mean exactly on the Prediction Report?

The Expiration date, it is the final day when the calibration will be valid (similar to credit cards).


What does the (number) in brackets after the [range] mean?

During the creation of a Calibration Request the NIR-Predictor shows a message containing
” ‘Prop1’ / “Quantitative [1.40 – 2.90] (154) ”
here 154 is the number of unique values in the property range.


How long does it take to create of the property file and calibration request file from 500 spectra files?

Use a local folder on your computer (not a network drive) for your spectra files then the property file and the calibration request file is created in around 1-3 seconds. (measured on a system with SSD drive, Intel i7, 2.4 GHz)


I tried to create the calibration request and got the error message: The number of Property Values of all spectra are different?

Use the generated PropertiesBySpectra (Note: Spectra not Sample) template file.
Do not reformat the generated template file, just fill in the Property values and save as Text CSV (*.csv) file (not as Excel file “.xlsx”).


Are you able to create the calibration if we only have 20 spectra?

To create a reliable quantitative calibration you need measured spectra of at least 48..60 (more is better) different samples with different Lab-values in the required measuring range!
The NIR-Predictor will check for that automatically.


Same sample measured multiple times (replicate measurements), how to enter the Lab-value only once in Property file?

Use the created PropertiesBySample template (not the PropertiesBySpectra).


Different samples with exact the same Lab-value, how to enter the Lab-Values?

Entering the Lab-values into the generated PropertiesBySpectra template the NIR-Predictor detects them as the same Sample and as a result we have too less different values to build a calibration. But in my case these are different samples with exact the same Lab-value, how to do?

You have to cheat a little bit to make NIR-Predictor do not detect same sample as measured multiple times. This is because most NIR users do replicate measurements on the same sample and NIR-Predictor looks for that. In PropertiesBySpectra modify the values a little bit to make them different, e.g.
0.18
0.18001
0.18002


Is the free NIR-Predictor software you provide for anyone to download and use?

Yes, if downloaded directly from our homepage by the user.
See also Software License Agreement


What is an Outlier?

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.

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.
See also manual chapter Outliers.


If something is wrong, please tell us

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