Spectroscopy and Chemometrics/Machine-Learning News Weekly #22, 2022

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 21, 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 21, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 21, 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)

“NIRSCAM: A Mobile Near-Infrared Sensing System for Food Calorie Estimation” LINK

“Nutritional Components of Beverage Granules by Near-Infrared Spectroscopy Based on PLS Model” | LINK

“A new concept of acousto-optic tunable filter-based near-infrared hyperspectral imager for planetary surface exploration” LINK

“Supplementary Materials Determination of the Geographical Origin of Walnuts (Juglans regia L.) Using Near-Infrared Spectroscopy and Chemometrics” LINK

“Characteristic wavelengths optimization improved the predictive performance of near-infrared spectroscopy models for determination of aflatoxin B1 in maize” LINK

“DETERMINATION OF QUALITY AND RIPENING STAGES OF ‘PACOVAN’BANANAS USING VIS-NIR SPECTROSCOPY AND MACHINE LEARNING” LINK

“Molecules : Identification of Stingless Bee Honey Adulteration Using Visible-Near Infrared Spectroscopy Combined with Aquaphotomics” LINK

“A review of visible and near-infrared (Vis-NIR) spectroscopy application in plant stress detection” LINK

“Predicting the performance of handheld near-infrared photonic sensors from a master benchtop device” LINK

“A novel handheld FT-NIR spectroscopic approach for real-time screening of major cannabinoids content in hemp” LINK

“Chemometric studies of hops degradation at different storage forms using UV-Vis, NIRS and UPLC analyses” LINK

“Comparison of VIS/NIR spectral curves plus RGB images with hyperspectral images for the identification of Pterocarpus species” | LINK

“Etruscan Fine Ware Pottery: Near-Infrared (NIR) Spectroscopy as a Tool for the Investigation of Clay Firing Temperature and Atmosphere” LINK

“Penilaian Sejawat: Fast and contactless assessment of intact mango fruit quality attributes using near infrared spectroscopy (NIRS). IOP EES.” | EES Mango Kusumiyati (Gabungan).pdf LINK

“Segregation of ‘Hayward’kiwifruit for storage potential using Vis-NIR spectroscopy” LINK

“Portable Near-Infrared Spectroscopy as a Screening Test of Corrosive Solutions Concealed in Plastic Containers” | LINK

“General model of multi-quality detection for apple from different origins by Vis/NIR transmittance spectroscopy” | LINK

“Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview” LINK

“Application of near-infrared spectroscopy to agriculture and forestry” | LINK




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

“NearInfrared LightDriven ThreeDimensional Soft Photonic Crystals Loaded with Upconversion Nanoparticles” LINK

“Gaming behavior and brain activation using functional nearinfrared spectroscopy, Iowa gambling task, and machine learning techniques” LINK

“Metaheuristic algorithms in visible and near infrared spectra to detect excess nitrogen content in tomato plants” LINK




Hyperspectral Imaging (HSI)

“Non-destructive age estimation of biological fluid stains: An integrated analytical strategy based on near-infrared hyperspectral imaging and multivariate regression” LINK

“Prediction of peroxidase activity using near infrared hyperspectral imaging in red delicious apple fruit during storage time” LINK

“Remote Sensing : Detection of Apple Valsa Canker Based on Hyperspectral Imaging” LINK




Spectral Imaging

“Sensors : Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery” LINK




Chemometrics and Machine Learning

“PENGEMBANGAN MODEL PARTIAL LEAST SQUARE REGRESSION (PLSR) UNTUK MEMPREDIKSI KEASAMAN (pH) DAN KADAR AIR BIJI KAKAO (Theobroma …” LINK

“Unsupervised detection of ash dieback disease (Hymenoscyphus fraxineus) using diffusion-based hyperspectral image clustering” LINK

“IAI SPECIAL EDITION: Infrared spectroscopy chemometric model for determination of phenolic content of plant leaf powder” LINK

“Sensors : Accuracy and Reproducibility of Laboratory Diffuse Reflectance Measurements with Portable VNIR and MIR Spectrometers for Predictive Soil Organic Carbon Modeling” LINK

“Coatings : Nondestructive Evaluation of Thermal Barrier Coatings Thickness Using Terahertz Technique Combined with PCA-GA-ELM Algorithm” LINK

“Prediction of topsoil organic carbon content with Sentinel-2 imagery and spectroscopic measurements under different conditions using an ensemble model approach …” LINK




Optics for Spectroscopy

“Artificial Intelligence in Classical and Quantum Photonics” LINK




Research on Spectroscopy

” A dataset for spectral radiative properties of black poly (methyl methacrylate)” LINK




Process Control and NIR Sensors

“Pharmaceutics : Tailoring Rational Manufacturing of Extemporaneous Compounding Oral Dosage Formulations with a Low Dose of Minoxidil” LINK




Environment NIR-Spectroscopy Application

“Polymers : Antioxidant and Anti-Aging Activity of Freeze-Dried Alcohol-Water Extracts from Common Nettle (Urtica dioica L.) and Peppermint (Mentha piperita L.) in Elastomer Vulcanizates” LINK

“Particle densities of cultivated south greenlandic soils can be explained by a threecompartment model, pedotransfer functions, and a visNIR spectroscopy model” LINK




Agriculture NIR-Spectroscopy Usage

“Influence of ingredient quality and diet formulation on amino acid digestibility and growth performance of poultry and swine” LINK

“Guidelines for Optimal Use of NIRSC Forage and Feed Calibrations in Membership Laboratories” LINK

“Linear Support Vector Machine Classification of Plant Stress From Soybean Aphid (Hemiptera: Aphididae) Using Hyperspectral Reflectance” LINK

“Goat milk authentication by one-class classification of digital image-based fingerprint signatures: detection of adulteration with cow milk” LINK

“Agronomy : Crop Monitoring Strategy Based on Remote Sensing Data (Sentinel-2 and Planet), Study Case in a Rice Field after Applying Glycinebetaine” LINK

“Estimation of Vertisols Soil Nutrients by Hyperion Satellite Data: Case Study in Deccan Plateau of India” | LINK

“Real-time milk analysis integrated with stacking ensemble learning as a tool for the daily prediction of cheese-making traits in Holstein cattle” LINK

“The effect of nitrogen fertility rate and seeding rate on yield, nutritive value and economics of forage corn in a low corn heat unit region of Western Canada” LINK




Food & Feed Industry NIR Usage

“Near-infrared techniques for fraud detection in dairy products: A review” | LINK




Laboratory and NIR-Spectroscopy

“Digital technologies to assess yoghurt quality traits and consumers acceptability” LINK




Other

“Tantalum – 2D Light Transport” | optics physically simulation spectroscopy spectrum prism lens mirror light lighttransport multiple scattering LINK

“Characterizing tourniquet induced hemodynamics during total knee arthroplasty using diffuse optical spectroscopy” LINK





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Spectroscopy and Chemometrics/Machine-Learning News Weekly #21, 2022

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 20, 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 20, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 20, 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)

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

“Blood discrimination based on NIR spectroscopy and BP neural network combined with genetic algorithm” LINK

“Automated surface mapping via unsupervised learning and classification of Mercury Visible-Near-Infrared reflectance spectra” LINK

“At-line and inline prediction of droplet size in mayonnaise with near-infrared spectroscopy” LINK

“Use of near-infrared spectroscopy and chemometrics for fast discrimination of Sargassum fusiforme” LINK

” Chemometric studies of hops degradation at different storage forms using UVVis, NIRS and UPLC analyses” LINK

“Portable NIR spectroscopy and PLS based variable selection for adulteration detection in quinoa flour” LINK

“Foods : Assessment of Pumpkin Seed Oil Adulteration Supported by Multivariate Analysis: Comparison of GC-MS, Colourimetry and NIR Spectroscopy Data” LINK

“Near infrared spectroscopy to evaluate the effect of a hybrid exercise programme on peripheral muscle metabolism in patients with intermittent claudication: an …” LINK

“Plants : Prediction and Comparisons of Turpentine Content in Slash Pine at Different Slope Positions Using Near-Infrared Spectroscopy” LINK

“Teknologi Near Infrared Reflectance Spectroscopy (NIRS) dan Metode Kemometri untuk Deteksi Pemalsuan Minyak Nilam” LINK

“Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue—A Salmon Case Study” LINK

“Multi-information based on ATR-FTIR and FT-NIR for identification and evaluation for different parts and harvest time of Dendrobium officinale with chemometrics” LINK

“Application of near infrared spectroscopy to predict contents of various lactones in chromatographic process of Ginkgo Folium” LINK

“A feasibility study on improving the non-invasive detection accuracy of bottled Shuanghuanglian oral liquid using near infrared spectroscopy” LINK

“The application of NIR spectroscopy in moisture determining of vegetable seeds” | seedtesting seedquality NIRS methodDevelopment Calibration solution content analysis seed grain grains LINK

“Aplicación de imágenes hiperespectrales (HSI-NIR) para la determinación de estrés hídrico en hojas de patata.” LINK

“Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks” | LINK

“Near-infrared spectroscopy and machine learning-based technique to predict quality-related parameters in instant tea” | LINK

“Prediction of Wheat Quality Parameters Combining Raman, Fluorescence and Near‐Infrared Spectroscopy (NIRS)” LINK




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

“Extending Effective Dynamic Range of Hyperspectral Line Cameras for Short Wave Infrared Imaging” | LINK

“Single-domain nearinfrared protein provides a scaffold for antigen-dependent fluorescent nanobodies” LINK

“Analyzing the Water Confined in Hydrogel Using Near-Infrared Spectroscopy” LINK

“Application of infrared spectroscopic techniques to cheese authentication: A review” LINK

“Estimation of grain quality parameters in rice for highthroughput screening with nearinfrared spectroscopy and deep learning” LINK




Hyperspectral Imaging (HSI)

“Multispectral camera system design for replacement of hyperspectral cameras for detection of aflatoxin B1” LINK

“Hyperspectral Imaging for cherry tomato” LINK

“A novel 3D convolutional neural network model with supervised spectral regression for recognition of hyperspectral images of colored wool fiber” LINK

“Channel and band attention embedded 3D CNN for model development of hyperspectral image in object-scale analysis” LINK

“A novel high-throughput hyperspectral scanner and analytical methods for predicting maize kernel composition and physical traits” LINK

“Learning Multiscale Temporal-Spatial-Spectral Features via a Multi-path Convolutional LSTM Neural Network for Change Detection with Hyperspectral Images” LINK

“The relationship between the spatial pattern of lakeside wetlands and water quality utilizing UAV hyperspectral remote sensing” LINK

“Building spectral catalogue for salt marsh vegetation, hyperspectral and multispectral remote sensing” LINK

“Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review” LINK

“A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications” LINK

“Using hyperspectral imaging technology for assessing internal quality parameters of persimmon fruits during the drying process” LINK

“Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview” | LINK




Chemometrics and Machine Learning

“Chemometrics for Raman Spectroscopy Harmonization” LINK

“Determination of active ingredients in alcoholbased gel by spectroscopic techniques and chemometric analysis” LINK

“Effect of variable selection algorithms on model performance for predicting moisture content in biological materials using spectral data” LINK

Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding project page: sota FID(7.27 on COCO), without ever training on COCO, human raters find Imagen samples to be on par with the COCO data itself in image-text alignment LINK

“Validação prática de modelos de infravermelho próximo para tomate: sólidos solúveis e acidez” LINK




Facts

“Does active sitting provide more physiological changes than traditional sitting and standing workstations?” LINK




Research on Spectroscopy

“Destructive and rapid non-invasive methods used to detect adulteration of dried powdered horticultural products: A review” LINK

“Development of a spectroscopic approach for non-destructive and rapid screening of cucumbers based on maximum limit of nitrate accumulation” LINK




Equipment for Spectroscopy

“Review of portable near infrared spectrometers: Current status and new techniques” LINK

“Discrimination of the Red Jujube Varieties Using a Portable NIR Spectrometer and Fuzzy Improved Linear Discriminant Analysis” | LINK




Process Control and NIR Sensors

“Applied Sciences : Process Monitoring Using Kernel PCA and Kernel Density Estimation-Based SSGLR Method for Nonlinear Fault Detection” LINK

“Distinctive Microbial Processes and Controlling Factors Related to Indirect N2O Emission from Agricultural and Urban Rivers in Taihu Watershed” LINK




Environment NIR-Spectroscopy Application

“Monitoring the Concentrations of Cd, Cu, Pb, Ni, Cr, Zn, Mn and Fe in Cultivated Haplic Luvisol Soils Using Near-Infrared Reflectance Spectroscopy” LINK

“Plants : Variations in Total Phenolic, Total Flavonoid Contents, and Free Radicals’ Scavenging Potential of Onion Varieties Planted under Diverse Environmental Conditions” LINK

“Vis-NIR-spectroscopy-and loss-on-ignition-based functions to estimate organic matter content of calcareous soils” | LINK

“Remote Sensing : Mapping Soil Properties with Fixed Rank Kriging of Proximally Sensed Soil Data Fused with Sentinel-2 Biophysical Parameter” LINK

“Effects of hyperspectral data with different spectral resolutions on the estimation of soil heavy metal content: From ground-based and airborne data to satellite …” LINK

“Comparing Two Different Development Methods of External Parameter Orthogonalization for Estimating Organic Carbon from Field-Moist Intact Soils by Reflectance …” LINK

“Towards recycling of challenging waste fractions: Identifying flame retardants in plastics with optical spectroscopic techniques” LINK




Agriculture NIR-Spectroscopy Usage

“Plants : Comparative Analysis of the NDVI and NGBVI as Indicators of the Protective Effect of Beneficial Bacteria in Conditions of Biotic Stress” LINK

“Agriculture : Online Detection and Classification of Moldy Core Apples by Vis-NIR Transmittance Spectroscopy” LINK

“Aggregate size distribution of arid and semiarid laboratory soils (< 2 mm) as predicted by VIS-NIR-SWIR spectroscopy” LINK

“Application of near-infrared spectroscopy to agriculture and forestry” LINK

“Dry heating, moist heating, and microwave irradiation of coldclimateadapted barley grainEffects on ruminantrelevant carbohydrate and molecular structural spectral profiles” LINK

“Nutrient digestibility and predicting the energy content of pig feeds” LINK

“Comparative study of different wavelength selection methods in the transfer of crop kernel qualitive near-infrared models” LINK

“Agronomy : Monitoring of Nitrogen Indices in Wheat Leaves Based on the Integration of Spectral and Canopy Structure Information” LINK

“… visualization and quantification of total acid and reducing sugar contents in fermented grains by combining spectral and color data through hyperspectral imaging” LINK

“Biomarkers and biosensors for the diagnosis of noncompliant pH, dark cutting beef predisposition, and welfare in cattle” LINK




Forestry and Wood Industry NIR Usage

“A spectral analysis of stem bark for boreal and temperate tree species” | LINK

“Optical properties of transparent wood composites prepared using transverse sections of poplar wood” | LINK




Food & Feed Industry NIR Usage

“Foods : Oxidative Stability and Antioxidant Activity of Selected Cold-Pressed Oils and Oils Mixtures” LINK

“Predicting Single Kernel Moisture and Protein Content of Mushroom Popcorn Using NIR Spectroscopy: Tool for Determining Their Effect on Popping Performance” LINK

“Research on Physicochemical Properties, Microscopic Characterization and Detection of Different Freezing-damaged Corn Seeds” LINK

“Estimating cadmium-lead concentrations in rice blades through fractional order derivatives of foliar spectra” LINK

“Recent advances in emerging techniques for non-destructive detection of seed viability: A review” seedtesting seedGermination seedquality NDT nondestructiveTesting seeds LINK




Laboratory and NIR-Spectroscopy

“Optimizing a Standard Spectral Measurement Protocol to Enhance the Quality of Soil Spectra: Exploration of Key Variables in Lab-Based VNIR-SWIR Spectral …” LINK




Other

“A Robust Functional Partial Least Squares for ScalaronMultipleFunction Regression” LINK

“Planetary Terrestrial Analogues Library Project: 3. Characterization of Samples With MicrOmega” LINK

“LASER-BASED SORTING OF CONSTRUCTION AND DEMOLITION WASTE FOR THE CIRCULAR ECONOMY” LINK

“A robust functional partial least squares for scalaronmultiplefunction regression” LINK

“Facebook trained AI to fool facial recognition systems, and it works on live video” deepfakes deidentification LINK

“Optical properties and novelty preparation PVA/PVP doping with Cu as surface plasmonic ions” LINK





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Spectroscopy and Chemometrics/Machine-Learning News Weekly #42, 2021

NIR Calibration-Model Services

Spectroscopy and Chemometrics/Machine-Learning News Weekly 41, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensor QA QC Testing Quality LINK

Spektroskopie und Chemometrie/Machine-Learning Neuigkeiten Wöchentlich 41, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT sensors Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria/Machine-Learning Weekly News 41, 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)

“Development and Submission of Near Infrared Analytical Procedures Guidance for Industry” FDA LINK

“Identification prediction moisture content of Thai coconut sugar (Cocos nucifera L.) using FT-NIR spectroscopy” LINK

“Blood identification of NIR spectroscopy based on BP neural network combined with particle swarm optimization” LINK

“A preliminary study on the utilisation of near infrared spectroscopy to predict age and in vivo human metabolism” LINK

“Near-infrared guidance finalized for small molecule testing, with biologics to come” RAPS LINK

“Wavelength Selection Method for Near Infrared Spectroscopy Based on Iteratively Retains Informative Variables and Successive Projections Algorithm” LINK

“Near-infrared spectroscopy calibration strategies to predict multiple nutritional parameters of pasture species from different” LINK

” Comparison of metabolites and variety authentication of Amomum tsao-ko and Amomum paratsao-ko using GC-MS and NIR spectroscopy” LINK

“Hyperfine-Resolved Near-Infrared Spectra of H(2)(17)O” LINK

“Geographical Differentiation of Hom Mali Rice Cultivated in Different Regions of Thailand Using FTIR-ATR and NIR Spectroscopy” LINK

“Seasonal Snowpack Classification Based on Physical Properties Using Near-Infrared Proximal Hyperspectral Data” | LINK

“NIR-based sensing system for non-Invasive detection of Hemoglobin for point-of-care applications” LINK

“A promising inorganic YFeO3 pigments with high near-infrared reflectance and infrared emission” LINK

“Classification of Softwoods using Wood Extract Information and Near Infrared Spectroscopy.” LINK

“Rapid determination and origin identification of total polysaccharides contents in Schisandra chinensis by near-infrared spectroscopy” LINK

“Near-Infrared Spectroscopy Technology in Food” | LINK

“Postharvest ripeness assessment of ‘Hass’ avocado based on development of a new ripening index and Vis-NIR spectroscopy” LINK




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

“Utility of near‐infrared spectroscopy to detect the extent of lipid core plaque leading to periprocedural myocardial infarction” LINK

“Scaling up Sagebrush Chemistry with Near-Infrared Spectroscopy and Uas-Acquired Hyperspectral Imagery” LINK




Hyperspectral Imaging (HSI)

“Spatially Resolved Spectroscopic Characterization of Nanostructured Films by Hyperspectral Dark-Field Microscopy” LINK

“Visual attention-driven framework to incorporate spatial-spectral features for hyperspectral anomaly detection” LINK




Spectral Imaging

“Spectral Super-Resolution of Multispectral Images Using Spatial-Spectral Residual Attention Network” LINK




Chemometrics and Machine Learning

“Feasibility of a chromameter and chemometric techniques to discriminate pure and mixed organic and conventional red pepper powders: A pilot study” LINK

“Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage” LINK

“Applied Sciences : A Novel Principal Component Analysis Integrating Long Short-Term Memory Network and Its Application in Productivity Prediction of Cutter Suction Dredgers” LINK

“Plants : Morpho-Physiological Classification of Italian Tomato Cultivars (Solanum lycopersicum L.) According to Drought Tolerance during Vegetative and Reproductive Growth” LINK

“Automatic food and beverage authentication and adulteration detection by classification hybrid fusion” LINK

“Remote Sensing : Improving Accuracy of Herbage Yield Predictions in Perennial Ryegrass with UAV-Based Structural and Spectral Data Fusion and Machine Learning” LINK

“Estimating Fat Components of Potato Chips Using Visible and Near-Infrared Spectroscopy and a Compositional Calibration Model” LINK

“Wavelengths selection based on genetic algorithm (GA) and successive projections algorithms (SPA) combine with PLS regression for determination the soluble …” LINK

“Comparison of PLS and SVM models for soil organic matter and particle size using vis-NIR spectral libraries” LINK

“Verified the rapid evaluation of the edible safety of wild porcini mushrooms, using deep learning and PLS‐DA” LINK




Optics for Spectroscopy

“Scientists Teach AI Cameras to See Depth in Photos Better” AI Camera Depth LINK




Facts

“Deep learning accelerates super-resolution microscopy by up to ten times” | DeepLearning microscopy LINK

“Statistical Learning to Operationalize a Domain Agnostic Data Quality Scoring. (arXiv:2108.08905v1 [cs.LG])” LINK




Research on Spectroscopy

“Foods : Instrumentation for Routine Analysis of Acrylamide in French Fries: Assessing Limitations for Adoption” LINK




Process Control and NIR Sensors

“NIR spectroscopy for monitoring of the critical manufacturing steps and quality attributes of paliperidone prolonged release tablets” LINK




Environment NIR-Spectroscopy Application

“Spatial Differentiation Analysis of Water Quality in Dianchi Lake Based on GF-5 NDVI Characteristic Optimization” LINK

“Remote Sensing : Using Sentinel-2 for Simplifying Soil Sampling and Mapping: Two Case Studies in Umbria, Italy” LINK

“Sensors : Ground-Based Hyperspectral Remote Sensing for Estimating Water Stress in Tomato Growth in Sandy Loam and Silty Loam Soils” LINK

“Potential of high-spectral resolution for field phenotyping in plant breeding: Application to maize under water stress” LINK




Agriculture NIR-Spectroscopy Usage

“Study the Genetic Diversity in Protein, Zinc and Iron in Germplasm Pools of Desi Type Chickpeas as Implicated in Quality Breeding” LINK

“Additives and soy detection in powder rice beverage by vibrational spectroscopy as an alternative method for quality and safety control” LINK

“Remote Sensing : Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel” LINK

“Fodder biomass, nutritive value, and grain yield of dual‐purpose Pearl Millet, Sorghum and Maize cultivars across different agro‐ecologies in Burkina Faso” LINK

“Agronomy : Effects of the Foliar Application of Potassium Fertilizer on the Grain Protein and Dough Quality of Wheat” LINK

“Remote Sensing : Sentinel-2 and Landsat-8 Multi-Temporal Series to Estimate Topsoil Properties on Croplands” LINK

“Sensors : Assessment of Grain Harvest Moisture Content Using Machine Learning on Smartphone Images for Optimal Harvest Timing” LINK

“Using UAV image data to monitor the effects of different nitrogen application rates on tea quality” LINK

“A single calibration of near-infrared spectroscopy to determine the quality of forage for multiple species” LINK




Forestry and Wood Industry NIR Usage

“Teakwood Chemistry and Natural Durability” LINK




Food & Feed Industry NIR Usage

“Foods : Temporal Sensory Perceptions of Sugar-Reduced 3D Printed Chocolates” LINK

“Foods : Rapid Nondestructive Simultaneous Detection for Physicochemical Properties of Different Types of Sheep Meat Cut Using Portable Vis/NIR Reflectance Spectroscopy System” LINK

“Foods : Real-Time Gauging of the Gelling Maturity of Duck Eggs Pickled in Strong Alkaline Solutions” LINK




Chemical Industry NIR Usage

“Polymers : Drug Amorphous Solid Dispersions Based on Poly(vinyl Alcohol): Evaluating the Effect of Poly(propylene Succinate) as Plasticizer” LINK




Pharma Industry NIR Usage

” Effects of acetazolamide and furosemide on ventilation and cerebral blood volume in normocapnic and hypercapnic COPD patients” LINK




Other

“Advances, challenges and perspectives of quantum chemical approaches in molecular spectroscopy of the condensed phase” LINK

“Calidad composicional y sensorial de la carne bovina y su determinación mediante infrarrojo cercano” LINK

“Bioinspired StimuliResponsive Hydrogel with Reversible Switching and Fluorescence Behavior Served as LightControlled Soft Actuators” LINK

“Neural Efficiency in Athletes: A Systematic Review” LINK

“Tailored Chiral Copper Selenide Nanochannels for Ultrasensitive Enantioselective Recognition and Detection” LINK

“In vivo diffuse reflectance spectroscopic analysis of fatty liver with inflammation in mice” LINK

“Métodos de análise da composição química e valor nutricional de alimentos para ruminantes” LINK

“Dissociation between exercise intensity thresholds: mechanistic insights from supine exercise” LINK





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 #28, 2020

NIR Calibration-Model Services

Services for professional Development of Near-Infra-Red Spectroscopy Calibration Methods | NIRS Lab testing method food LINK

Spectroscopy and Chemometrics News Weekly 27, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Analytical Chemistry ag Food Dairy Analysis Lab Labs Laboratories Laboratory IoT Sensors QA QC material testing quality safety LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 27, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 27, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near-Infrared Spectroscopy (NIRS)

“NEAR-INFRARED SPECTROSCOPY AS A RAPID AND SIMULTANEOUS ASSESSMENT OF AGRICULTURAL GROUNDWATER QUALITY PARAMETERS / NEAR INFRARED SPECTROSCOPY SEBAGAI METODE CEPAT DAN SIMULTAN UNTUK PREDIKSI KUALITAS AIR TANAH LAHAN PERTANIAN” LINK

“How well can near infrared reflectance spectroscopy (NIRS) measure sediment organic matter in multiple lakes?” LINK

“Assessment of Embryonic Bioactivity through Changes in the Water Structure Using Near-Infrared Spectroscopy and Imaging” LINK




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

“Applied Sciences, Vol. 10, Pages 3722: FTIR-ATR Spectroscopy Combined with Multivariate Regression Modeling as a Preliminary Approach for Carotenoids Determination in Cucurbita Spp.” LINK

“At-line Prediction of Gelatinized Starch and Fiber Fractions in Extruded Dry Dog Food Using Different Near-Infrared Spectroscopy Technologies” LINK

“Near-infrared Prediction of Edible Oil Frying Times Based on Bayesian Ridge Regression” LINK

“Estimating wood moisture by near infrared spectroscopy: Testing acquisition methods and wood surfaces qualities” LINK




Chemometrics and Machine Learning

“Determination of Loline Alkaloids and Mycelial Biomass in Endophyte-Infected Schedonorus Pratensis by Near-Infrared Spectroscopy and Chemometrics” LINK

“Quantification of extra virgin olive oil adulteration using smartphone videos.” LINK

“Discrimination of legal and illegal Cannabis spp . according to European legislation using near infrared spectroscopy and chemometrics” LINK

“A comparison of chemometrics classification tools for identification of perirenal fat in lambs.” LINK

“Simultaneous Quantitative Analysis of K + and Tl + in Serum and Drinking Water Based on UV-Vis Spectra and Chemometrics” LINK

“Combination of spectra and texture data of hyperspectral imaging for prediction and visualization of palmitic acid and oleic acid contents in lamb meat” LINK

“NIR hyperspectral imaging coupled with chemometrics for nondestructive assessment of phosphorus and potassium contents in tea leaves” LINK

“Non-destructive genotypes classification and oil content prediction using near-infrared spectroscopy and chemometric tools in soybean breeding program” LINK

“Predicting milk mid-infrared spectra from first-parity Holstein cows using a test-day mixed model with the perspective of herd management” LINK




Research on Spectroscopy

“An efficient method to quantitatively detect competitive adsorption of DNA on single-walled carbon nanotube surfaces” LINK




Equipment for Spectroscopy

“Determination of Ethanol in Alcoholic Drinks: Flow Injection Analysis with Amperometric Detection Versus Portable Raman Spectrometer” LINK

“Micro-Electro-Mechanical System Fourier Transform Infrared (MEMS FT-IR) Spectrometer Under ModulatedPulsed Light Source Excitation” LINK

“Theae nigrae folium: Comparing the analytical performance of benchtop and handheld near-infrared spectrometers” LINK




Agriculture NIR-Spectroscopy Usage

“Fermentation, Vol. 6, Pages 56: Beer Aroma and Quality Traits Assessment Using Artificial Intelligence” LINK

“Optimization of modeling conditions for near infrared measurement of protein content in milk by orthogonal array design.” LINK

“Soil organic matter in various land uses and management, and its accuracy measurement using near infrared technology” LINK

“Lettuce plant health assessment using UAV-based hyperspectral sensor and proximal sensors” LINK

“Effects of planting density on nutritive value, dry matter yield, and predicted milk yield of dairy cows from 2 brown midrib forage sorghum hybrids” LINK




Horticulture NIR-Spectroscopy Applications

“Non-Destructive Detection of Strawberry Quality Using Multi-Features of Hyperspectral Imaging and Multivariate Methods” LINK

“A simple and nondestructive approach for the analysis of soluble solid content in citrus by using portable visible to near-infrared spectroscopy.” LINK




Food & Feed Industry NIR Usage

“Hyperspectral monitor on chlorophyll density in winter wheat (Triticum aestivum L.) under water stress” LINK

“Assessment of Biochemical and Seed Quality Traits in Hulless Barley Germplasm” LINK




Beverage and Drink Industry NIR Usage

“Online determination of coffee roast degree toward controlling acidity” LINK




Other

“Improvement on curing performance and morphology of E5I/TPGDA mixture in a free radical-cationic hybrid photopolymerization system” LINK

“Color analysis and detection of Fe minerals in multi-mineral mixtures from acid-alteration environments” LINK

“Growth and maturity of Longnose Skates (Raja rhina) along the North American West Coast” LINK





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Spectroscopy and Chemometrics News Weekly #25, 2020

NIR Calibration-Model Services

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

Machine Learning for NIR Spectroscopy as a Service, a Game Changer for Productivity and Accuracy/Precision! Use the free NIR-Predcitor software to combine NIRS + Lab data and send your Calibration Request. LabManager Analysis MachineLearning LINK

Spectroscopy and Chemometrics News Weekly 24, 2020 | 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 24, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 24, 2020 | 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 Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us.




Near-Infrared Spectroscopy (NIRS)

“Near-Infrared (NIR) Spectroscopy to Differentiate Longissimus thoracis et lumborum (LTL) Muscles of Game Species” LINK

“Estimation of Harumanis (Mangifera indica L.) Sweetness using Near-Infrared (NIR) Spectroscopy” LINK

“Handheld Near-Infrared Spectrometers: Reality and Empty Promises” miniaturization NIRS FTNIR MEMS MOEMS LVFs LINK

BESTCentreLTU research hot off the press: | In collaboration with Assoc. Prof. Kellie Tuck from , we’ve developed new near-infrared emissive electrochemiluminophores for sensing in NIR transparent biological media. LINK

“Near-Infrared Emitter for Bioanalytical Applications” NIR ECL electrochemiluminescence LINK

“Fault detection with moving window PCA using NIRS spectra for the monitoring of anaerobic digestion process” LINK

“New applications of visnir spectroscopy for the prediction of soil properties” LINK

“Simultaneous determination of quality parameters in yerba mate (Ilex paraguariensis) samples by application of near-infrared (NIR) spectroscopy and partial least …” LINK

“Control of ascorbic acid in fortified powdered soft drinks using near-infrared spectroscopy (NIRS) and multivariate analysis.” LINK




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

“Non-Invasive Blood Glucose Monitoring using Near-Infrared Spectroscopy based on Internet of Things using Machine Learning” LINK

“Investigating the Quality of Antimalarial Generic Medicines Using Portable Near-Infrared Spectroscopy” LINK

“Rapid quantitative detection of mineral oil contamination in vegetable oil by near-infrared spectroscopy” LINK

“THE DETERMINATION OF FATTY ACIDS IN CHEESES OF VARIABLE COMPOSITION (COW, EWE’S, AND GOAT) BY MEANS OF NEAR INFRARED SPECTROSCOPY” LINK

“Detection of melamine and sucrose as adulterants in milk powder using near-infrared spectroscopy with DD-SIMCA as one-class classifier and MCR-ALS as a means to provide pure profiles of milk and of both adulterants with forensic evidence” LINK

“Protein, weight, and oil prediction by singleseed nearinfrared spectroscopy for selection of seed quality and yield traits in pea (Pisum sativum)” LINK

“Modeling bending strength of oil-heat-treated wood by near-infrared spectroscopy” LINK

“ripening stages monitoring of Lamuyo pepper using a new‐generation near‐infrared spectroscopy sensor” LINK

“Should the Past Define the Future of Interpretation of Infrared and Raman Spectra?” LINK

“Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interface Using Regularized Linear Discriminant Analysis Ensemble Classifier Based on Bootstrap Aggregating.” LINK

“Continuously measurement of the dry matter content using near-infrared spectroscopy” LINK

“Rapid identification of Lilium species and polysaccharide contents based on near infrared spectroscopy and weighted partial least square method.” LINK

“A Rapid and Nondestructive Approach for the Classification of Different-Age Citri Reticulatae Pericarpium Using Portable Near Infrared Spectroscopy.” LINK




Hyperspectral Imaging (HSI)

“Rapid detection of quality index of postharvest fresh tea leaves using hyperspectral imaging” LINK

“Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms” LINK

“A deep learning based regression method on hyperspectral data for rapid prediction of cadmium residue in lettuce leaves” LINK

“Deep learning applied to hyperspectral endoscopy for online spectral classification” DOI:10.1038/s41598-020-60574-6 LINK

“Detection of fish fillet substitution and mislabeling using multimode hyperspectral imaging techniques” LINK




Chemometrics and Machine Learning

“Molecules, Vol. 25, Pages 1453: Characterization, Quantification and Quality Assessment of Avocado (Persea americana Mill.) Oils” LINK

“Comprehensive Chemometrics – Chemical and Biochemical Data Analysis Reference Work • 2nd Edition • 2020” | books Chemometrics DataAnalysis Chemical Biochemical LINK

“Identification of invisible biological traces in forensic evidences by hyperspectral NIR imaging combined with chemometrics” LINK




Research on Spectroscopy

“Automatisierte und digitale Dokumentation der Applikation organischer Düngemittel” LINK

“Plenary Lecture Methods and Tools for Sensors Information Processing” LINK




Equipment for Spectroscopy

Using NIR scanner to assess grass in sward for composition prior to baling and wrapping for EU LIFE Farm4More project. Thanks to Dinamica Generale for providing the equipment LINK

“Determination of soluble solids content in Prunus avium by Vis/NIR equipment using linear and non-linear regression methods” LINK

“Characterization of Deep Green Infection in Tobacco Leaves Using a Hand-Held Digital Light Projection Based Near-Infrared Spectrometer and an Extreme Learning …” LINK




Agriculture NIR-Spectroscopy Usage

“Hyperspectral imaging using multivariate analysis for simulation and prediction of agricultural crops in Ningxia, China” LINK

“Placing Soil Information in the Hands of Farmers” LINK

“Robustness of visible/near and midinfrared spectroscopic models to changes in the quantity and quality of crop residues in soil” LINK

“Complex Food Recognition using Hyper-Spectral Imagery” LINK




Horticulture NIR-Spectroscopy Applications

” The Effect of Spent Mushroom Substrate and Cow Slurry on Sugar Content and Digestibility of Alfalfa Grass Mixtures” LINK




Laboratory and NIR-Spectroscopy

“The influence analysis of reflectance anisotropy of canopy on the prediction accuracy of Cu stress based on laboratory multi-directional measurement” LINK




Other

LINK



Spectroscopy and Chemometrics News Weekly #24, 2020

NIR Calibration-Model Services

Machine Learning for NIR Spectroscopy as a Service, a Game Changer for Productivity and Accuracy/Precision! Use the free NIR-Predcitor software to combine NIRS + Lab data and send your Calibration Request. LabManager Analysis MachineLearning LINK

“Food quality digitized at the “speed of light” ” : Food Sample -> measured with a NIRS spectrometer -> spectral data -> ⚖️ predicted with a NIRPredictor & CalibrationModel -> % quantitative results -> quality decision -> LINK

Spectroscopy and Chemometrics News Weekly 23, 2020 | 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 23, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 23, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near-Infrared Spectroscopy (NIRS)

“Fiber Content Determination of Linen/Viscose Blends Using NIR Spectroscopy” LINK

“Characterization of a high power time-domain NIRS device: towards faster and deeper investigation of biological tissues” LINK

“… chamosite from an hydrothermalized oolitic ironstone (Saint-Aubin-des-Châteaux, Armorican Massif, France): crystal chemistry, Vis-NIR spectroscopy (red variety) and …” LINK

“Study on evolution methods for the optimization of machine learning models based on FT-NIR spectroscopy” LINK

“Vibrational coupling to hydration shell – Mechanism to performance enhancement of qualitative analysis in NIR spectroscopy of carbohydrates in aqueous environment.” LINK

” RAPID EVALUATION OF DRY WHITE KIDNEY BEANS COOKING CHARACTERISTICS BY NEAR-INFRARED (NIR) SPECTROSCOPY” LINK

For food analysts, how to choose between a ‘classic’ method and a ‘modern’ technique such as FT-NIR or RMN? Our recently available paper tries to answer that question based on error evaluation: LINK

“FT-NIR combined with chemometrics versus classic chemical methods as accredited analytical support for decision-making: application to chemical compositional compliance of feedingstuffs” LINK




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

“Functional Classification of Feed Items in Pampa Grassland, Based on Their Near-Infrared Spectrum” LINK

“A Rapid and Nondestructive Approach for the Classification of Different-Age Citri Reticulatae Pericarpium Using Portable Near Infrared Spectroscopy” LINK

“Near-infrared spectroscopy as a new method for post-harvest monitoring of white truffles” LINK

“Rapid Prediction of Apparent Amylose, Total Starch, and Crude Protein by Near‐Infrared Reflectance Spectroscopy for Foxtail Millet (Setaria italica)” LINK

“New Induced Mutation Genetic Algorithm for Spectral Variables Selection in Near Infrared Spectroscopy” LINK

“Quantification of Plant Root Species Composition in Peatlands Using FTIR Spectroscopy” LINK

“Functional classification of feed items in pampa grassland, based on their near-infrared spectrum” LINK

“A feasibility of nondestructive rapid detection of total volatile basic nitrogen content in frozen pork based on portable near-infrared spectroscopy” LINK

” Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy” LINK

“Has the time come to use near-infrared spectroscopy in your science classroom?” LINK

“Feasibility of using near-infrared measurements to detect changes in water quality” LINK

“A novel CC-tSNE-SVR model for rapid determination of diesel fuel quality by near infrared spectroscopy” LINK

“Optimizing analysis of coal property using laser-induced breakdown and near-infrared reflectance spectroscopies” LINK

“Probing Active Sites and Reaction Intermediates of Electrocatalysis Through Confocal Near-Infrared Photoluminescence Spectroscopy: A Perspective.” LINK

“Determination of in situ ruminal degradation of phytate phosphorus from single and compound feeds in dairy cows using chemical analysis and near-infrared spectroscopy” LINK

“Non-destructive assessment of moisture content and modulus of rupture of sawn timber Hevea wood using near infrared spectroscopy technique” LINK

“Accurate prediction of glucose concentration and identification of major contributing features from hardly distinguishable near-infrared spectroscopy” LINK

” Multiblock PLS-DA on fecal and plasma visible-near-infrared spectra for discriminating young bulls according to their efficiency. Preliminary results” LINK

“Examining the Utility of Visible Near-Infrared and Optical Remote Sensing for the Early Detection of Rapid ‘Ōhi‘a Death” LINK

“Developing deep learning based regression approaches for determination of chemical compositions in dry black goji berries (Lycium ruthenicum Murr.) using near-infrared hyperspectral …” LINK

” RAPID, NONDESTRUCTIVE AND SIMULTANEOUS PREDICTIONS OF SOIL CONTENT IN WULING MOUNTAIN AREA USING NEAR INFRARED …” LINK




Raman Spectroscopy

“Differentiating cancer cells using Raman spectroscopy (Conference Presentation)” LINK

“Applied Sciences, Vol. 10, Pages 3545: Raman Spectral Analysis for Quality Determination of Grignard Reagent” LINK

“Surfaceenhanced Raman spectroscopy for onsite analysis: A review of recent developments” LINK




Hyperspectral Imaging (HSI)

“Estimating leaf mercury content in Phragmites australis based on leaf hyperspectral reflectance” LINK

“A hyperspectral microscope based on a birefringent ultrastable common-path interferometer (Conference Presentation)” LINK

“Hyperspectral imaging of beet seed germination prediction” LINK

“Hyperspectral imaging for discrimination of Keemun black tea quality categories: Multivariate calibration analysis and data fusion” LINK

“Potential of deep learning and snapshot hyperspectral imaging for classification of species in meat” LINK

“Performance of Fluorescence and Diffuse Reflectance Hyperspectral Imaging for Characterization of Lutefisk: A Traditional Norwegian Fish Dish” LINK




Spectral Imaging

“Identify the ripening stage of avocado by multispectral camera using semi-supervised learning on small dataset” LINK

“Multispectral imaging for predicting the water status in mushroom during hotair dehydration” LINK




Chemometrics and Machine Learning

“Sample selection, calibration and validation of models developed from a large dataset of near infrared spectra of tree leaves” Eucalyptus forage quality LINK

“Determination of Loline Alkaloids and Mycelial Biomass in Endophyte-Infected Schedonorus Pratensis by Near-Infrared Spectroscopy and Chemometrics” LINK

“Detection and Assessment of Nitrogen Effect on Cold Tolerance for Tea by Hyperspectral Reflectance with PLSR, PCR, and LM Models” LINK

“Application of vibrationnal spectroscopy and chemometrics to access the quality of Locally produced antimalarial medicines in the Democratic Republic of Congo.” LINK

“Predicting total petroleum hydrocarbons in field soils with VisNIR models developed on laboratoryconstructed samples” LINK

“National spectral data and learning algorithms for potentially toxic elements modelling in forest soil horizons” LINK

“Rapid determination of the textural properties of silver carp (Hypophthalmichthys molitrix) using near-infrared reflectance spectroscopy and chemometrics” LINK

“Vibrational spectroscopy and chemometrics for quantifying key bioactive components of various plum cultivars grown in New Zealand” LINK




Equipment for Spectroscopy

“NearInfrared Multipurpose LanthanideImaging Nanoprobes” LINK




Process Control and NIR Sensors

“Non-invasive measurement of quality attributes of processed pomegranate products” LINK




Environment NIR-Spectroscopy Application

“Spectral Feature Selection Optimization for Water Quality Estimation.” LINK

“Remote Sensing, Vol. 12, Pages 931: Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters” LINK

“Estimation of total nitrogen and organic carbon contents in mine soils with NIR reflectance spectroscopy and various chemometric methods” LINK




Agriculture NIR-Spectroscopy Usage

“Development of a compact multimodal imaging system for rapid characterisation of intrinsic optical properties of freshly excised tissue (Conference Presentation)” LINK

“Agriculture, Vol. 10, Pages 181: Grafting and ShadingThe Influence on Postharvest Tomato Quality” LINK

“Remote Sensing, Vol. 12, Pages 940: Editorial for the Special Issue Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”” LINK

“Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm.” LINK

“The development of models to predict the nutritional value of feedstuffs and feed mixture using NIRS” LINK

“Permafrost soil complexity evaluated by laboratory imaging Vis‐NIR spectroscopy” LINK




Horticulture NIR-Spectroscopy Applications

“Recent advances in imaging techniques for bruise detection in fruits and vegetables” LINK




Forestry and Wood Industry NIR Usage

“Nutritional characterization of trees from the Amazonian piedmont, Putumayo department, Colombia” LINK




Food & Feed Industry NIR Usage

“Approaches, applications, and future directions for hyperspectral vegetation studies: An emphasis on yieldlimiting factors in wheat” LINK

“Beef Nutritional Quality Testing and Food Packaging” LINK




Laboratory and NIR-Spectroscopy

“UV Irradiation and Near Infrared Characterization of Laboratory Mars Soil Analog Samples: the case of Phthalic Acid, Adenosine 5′-Monophosphate, L-Glutamic Acid …” molecular biosignatures; spectroscopy; lifedetection LINK




Other

LINK

“Effect of substrate temperature on the microstructural and optical properties of RF sputtered grown ZnO thin films” LINK

Using near-infrared light to 3-D print an ear inside the body LINK

“Eco-friendly dye sensitized solar cell using natural dye with solid polymer electrolyte as hole transport material” solarcell 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

NIR-Predictor Download

The free NIR-Predictor software
  • comes with demo data, so you can predict sample spectra with demo calibrations.
  • has no functional limitations, no nagging, no ads and needs no license-key.
  • you need no account and no registration to download and use.
  • runs on Microsoft Windows 10/8/7 (Starter, Basic, Professional) (32 bit / 64 bit).
  • no data is ever transmitted from your local machine. We don’t even collect usage data.
See more Videos



Beside the free NIR-Predictor software with Windows user interface,
the real-time Predictor Engine is also available
  • for embedded integration in application, cloud and instrument-software (ICT).
  • As a light-weigt single library file (DLL)
    with application programming interface (API),
    documentation and software development kit (SDK)
    including sample source code (C#).
  • Easy integration and deployment,
    no software license protection (no serial key, no dongle).
  • Put your spectrum as an array into the multivariate predictor,
    no specific file format needed.
  • Fast prediction speed and low latency
    because of compiled code library (direct call, no cloud API).
  • Protected prediction results with outlier detection information.
See NIR Method Development Service for Labs and NIR-Vendors (OEM, White-Label)



Software Size Date Comment
NIR-Predictor V2.6.0.2 (download)

What’s new, see Release Notes

By downloading and/or using the software
you accept the Software License Agreement (EULA)
3.7 MB 18.08.2021 public release

Minimal System Requirements
Windows 7 Starter 32Bit, 1.6 GHz, 2 GB RAM, non-Administrator account

Installation
There are no administrator rights required, unpack the zip file to a folder “NIR-Predictor” in your documents or on your desktop.
Read the ReadMe.txt and double click the NIR-Predictor.exe file.

Upgrade
If you have installed an older version of NIR-Predictor then unpack into a different folder named e.g. “NIR-PredictorVx.y”. All versions can run side-by-side. Copy the Calibrations in use to the new version into the “Calibration” folder. That’s all.

Uninstall
Make sure to backup your reports and calibrations inside your “NIR-Predictor” folder. Delete the “NIR-Predictor” folder.


Start Calibrate

See also: