Spectroscopy and Chemometrics/Machine-Learning News Weekly #13, 2022

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Spectroscopy and Chemometrics News Weekly 12, 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 12, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 12, 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)

” In ovo sexing of eggs from brown breeds with a gender-specific color using visible-near-infrared spectroscopy: effect of incubation day and measurement …” LINK

“Toward real time release testing of Shuxuening injection based on near infrared spectroscopy and accuracy profile” LINK

“Do-it-yourself VIS/NIR pushbroom hyperspectral imager with C-mount optics” LINK

“Near-Infrared Spectroscopy for Prediction of Potentially Toxic Elements in Soil and Sediments from a Semiarid and Coastal Humid Tropical Transitional River Basin” LINK

“Establishment of Near Infrared Spectroscopy Model for Predicting Sucrose Content of Peanut Seed and Application in Mutants Selection” LINK

“Near infrared spectroscopic evaluation of biochemical and crimp properties of knee joint ligaments and patellar tendon” LINK

“Abnormal Oxidative Metabolism in the Cuprizone Mouse Model of Demyelination: an in vivo NIRS-MRI Study” LINK

“Fourier-transform near-infrared spectroscopy as a fast screening tool for the verification of the geographical origin of grain maize (Zea mays L.)” LINK

“Optimization and compensation of models on tomato soluble solids content assessment with online Vis/NIRS diffuse transmission system” LINK

“Low-cost, handheld near-infrared spectroscopy for root dry matter content prediction in cassava” | breeding plantbreeding phenotyping handheld NIR spectrometers drymatter LINK

“Application of near-infrared spectroscopy to agriculture and forestry” QualityMonitoring lowcostDevices agricultural organic compounds NIRSpectroscopy LINK

“… coupled with a classifier to increase transparency in the seafood value chain: Bioelectrical impedance analysis (BIA), near-infrared spectroscopy (NIR) and time …” LINK

“Identification of informative spectral ranges for predicting major chemical constituents in corn using NIR spectroscopy” LINK

“A novel methodology for determining effectiveness of preprocessing methods in reducing undesired spectral variability in near infrared spectra” LINK

“Rapid and comprehensive quality assessment of Bupleuri Radix through near-infrared spectroscopy combined with chemometrics” LINK

“Multi-Modal Integration of EEG-fNIRS for Characterization of Brain Activity Evoked by Preferred Music” LINK

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

“A primer on soil analysis using visible and near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy” LINK

“Near-Infrared Spectroscopy (NIRS) as a Method for Biological Sex Discrimination in the Endangered Houston Toad (Anaxyrus houstonensis)” LINK

“Broadband Near-Field Near-Infrared Spectroscopy and Imaging with a Laser-Driven Light Source” LINK

“Determination of aflatoxin B1 level in rice (Oryza sativa L.) through near-infrared spectroscopy and an improved simulated annealing variable selection method” LINK




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

“Development and Application of Web-based Near Infrared Crude Oil Fast-Evaluation Technology” LINK

“Near-infrared analysis of nanofibrillated cellulose aerogel manufacturing” LINK




Hyperspectral Imaging (HSI)

“Spectral simulation and method design of camouflage textiles for concealment of hyperspectral imaging in UV-Vis-IR against multidimensional combat background” | LINK




Chemometrics and Machine Learning

“Foods : 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

“Chemosensors : Comparison of Various Signal Processing Techniques and Spectral Regions for the Direct Determination of Syrup Adulterants in Honey Using Fourier Transform Infrared Spectroscopy and Chemometrics” LINK

“Prediction of soil organic matter content based on characteristic band selection method” LINK

“Variable Selection Based on Gray Wolf Optimization Algorithm for the Prediction of Saponin Contents in Xuesaitong Dropping Pills Using NIR Spectroscopy” | LINK

“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

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

“Improved understanding and prediction of pear fruit firmness with variation partitioning and sequential multi-block modelling” LINK




Spectroscopy

“Agricultural Potentials of Molecular Spectroscopy and Advances for Food Authentication: An Overview. Processes 2022, 10, 214” LINK




Optics for Spectroscopy

“Detection of Organosulfur and Organophosphorus Compounds Using a Hexafluorobutyl Acrylate-Coated Tapered Optical Fibers” | LINK


Research on Spectroscopy

“Polymethine dyes-loaded solid lipid nanoparticles (SLN) as promising photosensitizers for biomedical applications” LINK

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




Process Control and NIR Sensors

“At-line and inline prediction of droplet size in mayonnaise with nearinfrared spectroscopy” | | ProcessMonitoring SpectralSensing PAT process analytical technologies LINK




Environment NIR-Spectroscopy Application

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

“Person-specific connectivity mapping uncovers differences of bilingual language experience on brain bases of attention in children” LINK

“Remote Sensing : Fine-Scale Mapping of Natural Ecological Communities Using Machine Learning Approaches” LINK




Agriculture NIR-Spectroscopy Usage

“Multimodal Imaging, OCT B-Scan Localization, and En Face OCT Detection of Macular Hyperpigmentation in Eyes with Intermediate AMD” LINK

“Transforming Passive into Active: Multimodal PheophytinBased Carbon Dots Customize Protein Corona to Target Metastatic Breast Cancer” LINK

” A review of hyperspectral remote sensing of crops” LINK

“Plants : Phytochemical Composition and Antioxidant Activity of Passiflora spp. Germplasm Grown in Ecuador” LINK

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

“Aquaphotomics Research of Cold Stress in Soybean Cultivars with Different Stress Tolerance Ability: Early Detection of Cold Stress Response” LINK




Horticulture NIR-Spectroscopy Applications

“NONDESTRUCITVE QUALITY EVALUATION TECHNOLOGIES FOR FRUITS AND VEGETABLES” | Quality Evaluation Technology for Fruits and Vegetables_0.pdf LINK




Food & Feed Industry NIR Usage

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

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

“Foods : Uses of FT-MIR Spectroscopy and Multivariate Analysis in Quality Control of Coffee, Cocoa, and Commercially Important Spices” LINK




Pharma Industry NIR Usage

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




Medicinal Spectroscopy

“Engineered ProteinAu Bioplaster for Efficient Skin Tumor Therapy” LINK




Other

“Investigation of Spectroscopic and Lasing Characteristics of Er3+-Doped Alkaline Titanium Borate Glasses” LINK

“Development of a high-accuracy autonomous sensing system for a field scouting robot” LINK

“Synthesis and Electrochemical Behavior of Ferrocenyl β‐Ketoamines FcC (O) CH= C (NH (C6H4‐4‐R ‘) R” LINK

“Taming salophen in rare earth metallocene chemistry” LINK

“KineticsRegulated Interfacial Selective Superassembly of Asymmetric Smart Nanovehicles with Tailored Topological Hollow Architectures” LINK

“Using the extract of pomegranate peel as a natural indicator for colorimetric detection and simultaneous determination of Fe3+ and Fe2+ by partial least squaresartificial neural network” LINK

“Sensors : Simultaneous Sensitive Determination of δ13C, δ18O, and δ17O in Human Breath CO2 Based on ICL Direct Absorption Spectroscopy” LINK

“Splanchnic oxygen saturation during reoxygenation with 21% or 100% O” LINK

“Measuring Nd(III) Solution Concentration in the Presence of Interfering Er(III) and Cu(II) Ions: A Partial Least Squares Analysis of Ultraviolet-Visible Spectra ” LINK

“Toxics : Determination of Prenatal Substance Exposure Using Meconium and Orbitrap Mass Spectrometry” LINK

“Metal-Based Linear Light Upconversion Implemented in Molecular Complexes: Challenges and Perspectives” LINK

“Highly effective gene delivery based on cyclodextrin multivalent assembly in target cancer cells” LINK

“Sensors : Free-Space Transmission and Detection of Variously Polarized Near-IR Beams Using Standard Communication Systems with Embedded Singular Phase Structures” LINK

“Measuring Nd(III) Solution Concentration in the Presence of Interfering Er(III) and Cu(II) Ions: A Partial Least Squares Analysis of Ultraviolet-Visible Spectra” LINK

Spectroscopy and Chemometrics/Machine-Learning News Weekly #10, 2022

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Spectroscopy and Chemometrics News Weekly 9, 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 9, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 9, 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)

“Near-infrared solar reflectance and chromaticity properties of novel green ceramic pigment Cr-doped Y3Al5O12” LINK

“Near-Infrared Spectroscopy (NIRS) as a Method for Biological Sex Discrimination in the Endangered Houston Toad (Anaxyrus houstonensis)” LINK

“Kernel Flow: a high channel count scalable time-domain functional near-infrared spectroscopy system” LINK

“Model Development of Non-Destructive Coffee Beans Moisture Content Determination Using Modified Near Infrared Spectroscopy Instrument” LINK

“Machine Learning Approach Using a Handheld Near-Infrared (NIR) Device to Predict the Effect of Storage Conditions on Tomato Biomarkers” LINK

“Adaptively Optimized Gas Analysis Model with Deep Learning for Near-Infrared Methane Sensors” LINK

“Development of non-invasive blood glucose regression based on near-infrared spectroscopy combined with a deep learning method” LINK

“Assessing the Spectral Characteristics of Dye-and Pigment-Based Inkjet Prints by VNIR Hyperspectral Imaging” LINK

“Fusion of a Low-cost Electronic Nose and Near Infrared Spectroscopy for Qualitative and Quantitative Detection of Beef Adulterated with Duck” LINK

“NEAR INFRARED SPECTROSCOPY (NIRS) COUPLED WITH CHEMOMETRIC TOOLS USED FOR FOOD PRODUCTS ADULTERATION DETECTION” LINK

” A Nondestructive Identification Method of Producing Regions of Citrus Based on Near Infrared Spectroscopy” LINK

“Detecting the content of the bright blue pigment in cream based on deep learning and near-infrared spectroscopy” LINK

“A NIR Study on Hydrogen Bonds of Bamboo-Based Cellulose Ⅱ” LINK

“Vis-NIR Spectra Discriminant of Pesticide Residues on the Hami Melon Surface by GADF and Multi-Scale CNN” LINK

“Pedometric tools for classification of southwestern Amazonian soils: A quali-quantitative interpretation incorporating visible-near infrared spectroscopy” LINK

“… Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time …” LINK

“NIRS and Aquaphotomics Trace Robusta-to-Arabica Ratio in Liquid Coffee Blends” LINK

“Effect of Soil Particle Size on Prediction of Soil Total Nitrogen Using Discrete Wavelength NIR Spectral Data” LINK

“Research on Construction of Visible-Near Infrared Spectroscopy Analysis Model for Soluble Solid Content in Different Colors of Jujube” LINK

“A Nondestructive Identification Method of Producing Regions of Citrus Based on Near Infrared Spectroscopy” LINK




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

“Accessing High-Power Near-Infrared Spectroscopy Using Cr3+-Substituted Metal Phosphate Phosphors” LINK

“Can Grassland Chemical Quality Be Quantified Using Transform Near-Infrared Spectroscopy?” LINK

“System Design and Preliminary Analysis of the UQ Near Infrared Spectroscopy Data of the Hayabusa2 Re-entry” LINK

“A fast method to measure the degree of oxidation of dialdehyde celluloses using multivariate calibration and infrared spectroscopy” LINK

” In-Line Identification of Different Grades of GPPS Based on Near-Infrared Spectroscopy” LINK

“Eu2+Doped Layered Double Borate Phosphor with Ultrawide NearInfrared Spectral Distribution in Response to UltravioletBlue Light Excitation” LINK

“Near-Infrared Spectroscopy Applied to the Detection of Multiple Adulterants in Roasted and Ground Arabica Coffee” LINK




Raman Spectroscopy

“RAMANMETRIX: a delightful way to analyze Raman spectra. (arXiv:2201.07586v1 [physics.data-an])” LINK




Hyperspectral Imaging (HSI)

“Rapid and nondestructive determination of sorghum purity combined with deep forest and near-infrared hyperspectral imaging” LINK

“Intraoperative hyperspectral imaging (HSI) as a new diagnostic tool for the detection of cartilage degeneration” | LINK

“Application of Visible/Near-Infrared Hyperspectral Imaging with Convolutional Neural Networks to Phenotype Aboveground Parts to Detect Cabbage Plasmodiophora …” LINK

“Detection of nutshells in cumin powder using NIR Hyperspectral Imaging and chemometrics tools” LINK

“Gastric Cancer Detection by Two-step Learning in Near-Infrared Hyperspectral Imaging” LINK

“A Spectral and Spatial Attention Network for Change Detection in Hyperspectral Images” LINK




Chemometrics and Machine Learning

“Analysis and classification of peanuts with fungal diseases based on real-time spectral processing” LINK

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

“PLS-R Calibration Models for Wine Spirit Volatile Phenols Prediction by Near-Infrared Spectroscopy” LINK

“Development and assessment of spectroscopy methodologies and chemometrics strategies to detect pharmaceuticals blend endpoint in a pharmaceutical …” LINK

“Quick Measurement Method of Condensation Point of Diesel Based on Temperature-Compensation Model” LINK

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

“Evaluation of a robust regression method (RoBoost-PLSR) to predict biochemical variables for agronomic applications: Case study of grape berry maturity monitoring” LINK




Facts

“Nondestructive internal quality evaluation of pears using X-ray imaging and Machine Learning” LINK




Research on Spectroscopy

“Mechanical-based and Optical-based Methods for Nondestructive Evaluation of Fruit Firmness” | LINK




Equipment for Spectroscopy

“Application of a portable near-infrared spectrometer for rapid, non-destructive evaluation of moisture content in Para rubber timber” | LINK

“The Feasibility of Two Handheld Spectrometers for Meat Speciation Combined with Chemometric Methods and Its Application for Halal Certification” LINK

“Rapid identification of the storage age of dried tangerine peel using a hand-held near infrared spectrometer and machine learning” LINK

“pType NearInfrared Transparent Delafossite Thin Films with Ultrahigh Conductivity” LINK




Agriculture NIR-Spectroscopy Usage

“Agricultural practices of perennial energy crops affect nitrogen cycling microbial communities” LINK

“The Simultaneous Prediction of Soil Properties and Vegetation Coverage from Vis-NIR Hyperspectral Data with a One-Dimensional Convolutional Neural Network: A …” LINK

“Alginate-enabled green synthesis of S/Ag1. 93S nanoparticles, their photothermal property and in-vitro assessment of their anti-skin-cancer effects augmented by a …” LINK

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

“Compact meta-spectral image sensor for mobile applications” | LINK

“Detection of insect damaged rice grains using visible and near infrared hyperspectral imaging technique” LINK

“Terrain analysis, erosion simulations, and sediment fingerprinting: a case study assessing the erosion sensitivity of agricultural catchments in the border of the …” | LINK

” THE QUALITY OF FRESH AND ENSILED BIOMASS OF Brassica napus oleifera AND PROSPECTS OF ITS USE” LINK




Horticulture NIR-Spectroscopy Applications

“Interactions of Linearly Polarized and Unpolarized Light on Kiwifruit Using Aquaphotomics” LINK




Beverage and Drink Industry NIR Usage

“Beyond Beers Law: Quasi-Ideal Binary Liquid Mixtures” LINK




Other

” From Polymorph Screening to Dissolution Testing” LINK

“Charge and Spin Delocalization in Mixed-Valent Vinylruthenium-Triarylamine-Conjugates with Planarized Triarylamines” LINK

“РАЗРАБОТКА МЕТОДИКИ ВЫЯВЛЕНИЯ ФАЛЬСИФИКАЦИИ ЛЬНЯНОГО МАСЛА МЕТОДОМ БИК-СПЕКТРОСКОПИИ С ПРИМЕНЕНИЕМ …” LINK

“Bioelectrochemical Partial-Denitrification Coupled with Anammox for Autotrophic Nitrogen Removal” LINK

“Broadband Optical Phase Modulation by Colloidal CdSe Quantum Wells” LINK





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Spectroscopy and Chemometrics/Machine-Learning News Weekly #45, 2021


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Spectroscopy and Chemometrics News Weekly 44, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

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Near-Infrared Spectroscopy (NIRS)

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

“Uji Karakteristik Biochar dengan Pendekatan Near Infrared Spectroscopy (NIRS)” LINK

“Establishment of online quantitative model for moisture content determination of hydroxychloroquine sulfate particles by near infrared spectroscopy” LINK

“Nondestructive detection model of soluble solids content of an apple using visible/near-infrared spectroscopy combined with CARS and MPGA” LINK

“Detecting cadmium contamination in loessal soils using near-infrared spectroscopy in the Xiaoqinling gold area” LINK

“Egg Freshness Evaluation Using Transmission and Reflection of NIR Spectroscopy Coupled Multivariate Analysis” LINK

“Determination of Alcohol Content in Beers of Different Styles Based on Portable Near-Infrared Spectroscopy and Multivariate Calibration” | LINK

“Analysing the Water Spectral Pattern by Near-Infrared Spectroscopy and Chemometrics as a Dynamic Multidimensional Biomarker in Preservation: Rice Germ …” LINK

“Remote Sensing : The Application of NIRS to Determine Animal Physiological Traits for Wildlife Management and Conservation” LINK

“Wavelength selection method for near-infrared spectroscopy based on standard-sample calibration transfer of mango and apple” LINK

“A portable NIR-system for mixture powdery food analysis using deep learning” LINK




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

“Compositional and sensory quality of beef and its determination by near infrared” LINK

“Butyrylcholinesterase responsive supramolecular prodrug with targeted nearinfrared cellular imaging property” LINK




Raman Spectroscopy

“Quantitative analysis of binary and ternary organo-mineral solid dispersions by Raman spectroscopy for robotic planetary exploration missions on Mars” | OpenAccess LINK




Hyperspectral Imaging (HSI)

“A shallow network for hyperspectral image classification using an autoencoder with convolutional neural network” | LINK

“Hyperspectral camera development on an unmanned aerial vehicle” LINK

“Direct reflectance transformation methodology for drone-based hyperspectral imaging” LINK




Spectral Imaging

“Remote Sensing : Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves” LINK




Chemometrics and Machine Learning

“Sensors : Adaboost-Based Machine Learning Improved the Modeling Robust and Estimation Accuracy of Pear Leaf Nitrogen Concentration by In-Field VIS-NIR Spectroscopy” LINK

“Discrimination of Manufacturers Origin of Oxytetracycline Using Terahertz Time-Domain Spectroscopy with Chemometric Methods” LINK




Spectroscopy

“Sensors : Non-Invasive Monitoring of Ethanol and Methanol Levels in Grape-Derived Pisco Distillate by Vibrational Spectroscopy” LINK




Equipment for Spectroscopy

“Improving the thermoelectric performances of polymer via synchronously realizing of chemical doping and side-chain cleavage” LINK




Environment NIR-Spectroscopy Application

“Determining physical and mechanical volcanic rock properties via reflectance spectroscopy” LINK

“Unauthorized landfills of solid household and industrial wastes detection in the Arctic and Subarctic territories using remote sensing technologies” LINK

“Evaluating the effects of distinct water saturation states on the light penetration depths of sand-textured soils” LINK




Agriculture NIR-Spectroscopy Usage

“Sorghum Grains Grading for Food, Feed, and Fuel Using NIR Spectroscopy” LINK

“Estimation of leaf area index at the late growth stage of crops using unmanned aerial vehicle hyperspectral images” LINK

“Analysis of Spectral Reflectance Characteristics Using Hyperspectral Sensor at Diverse Phenological Stages of Soybeans” LINK




Horticulture NIR-Spectroscopy Applications

“Data fusion for fruit quality authentication: combining non-destructive sensing techniques to predict quality parameters of citrus cultivars” | LINK




Food & Feed Industry NIR Usage

“Buckwheat Identification by Combined UV-VIS-NIR Spectroscopy and Multivariate Analysis” LINK




Other

“Effect of the annealing temperature on the growth of the silver nanoparticles synthesized by physical route” LINK



Spectroscopy and Chemometrics News Weekly #26, 2021

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Spectroscopy and Chemometrics News Weekly 25, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK


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Near-Infrared Spectroscopy (NIRS)

“Thermally Stable CaLu2Mg2Si3O12:Cr3+ Phosphors for NIR LEDs” LINK

“A New Statistical Approach for fNIRS Hyperscanning to Predict Brain Activity of Preschoolers’ Using Teacher’s” LINK

“NIR: 21st-Century Innovations” LINK




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

“Suitability of the muscle O2 resaturation parameters most used for assessing reactive hyperemia: a near-infrared spectroscopy study” LINK

“Simultaneous Quantification of 14 Compounds in Achillea millefolium by GC-MS Analysis and Near-Infrared Spectroscopy Combined with Multivariate …” | LINK

“Rapid Determination of the Total Phosphorus and the Nitrate Nitrogen in Denitrifying Phosphorus Removal with iPLS and Near Infrared Spectroscopy” LINK

“Estimation of Andrographolides and Gradation of Andrographis paniculata Leaves Using Near Infrared Spectroscopy Together With Support Vector Machine” LINK

“Nondestructive Phenolic Compounds Measurement and Origin Discrimination of Peated Barley Malt using Near-infrared Hyperspectral Imagery and Machine …” LINK

” Suitability of the muscle O2 resaturation parameters most used for assessing reactive hyperemia: a near-infrared spectroscopy study” LINK

“New perspective for the in-field analysis of cannabis samples using handheld near-infrared spectroscopy: A case study focusing on the determination of Δ9 …” LINK




Hyperspectral Imaging (HSI)

“Hyperspectral Imaging Assessment of Systemic Sclerosis Using the Soft Abundance Score and Band Selection” LINK

“Comparison of Texture Feature Extraction Methods for Hyperspectral Imagery Classification” LINK

“Hyperspectral Estimation of Heavy Metal Pb Concentration in Vineyard Soil in Turpan Basin” LINK




Chemometrics and Machine Learning

“Spectroscopic Fingerprinting and Chemometrics for the Discrimination of Italian Emmer Landraces” LINK

“Determination of extractive content in Cupressus sempervirens wood through a NIRS-PLSR model and its correlation with durability” LINK

“Optimization by Means of Chemometric Tools of an Ultrasound-Assisted Method for the Extraction of Betacyanins from Red Dragon Fruit (Hylocereus polyrhizus)” LINK

“The use of vibrational spectroscopy to predict vitamin C in Kakadu plum powders” LINK

“Fast quantification of total volatile basic nitrogen (TVB-N) content in beef and pork by near-infrared spectroscopy: Comparison of SVR and PLS model” LINK

“Combination of machine learning and VIRS for predicting soil organic matter” | LINK




Facts

“Hybrid Machine Learning for Scanning Near-field Optical Spectroscopy.” LINK




Research on Spectroscopy

“Rapid Synthesis of RedEmitting Sr2Sc0.5Ga1.5O5:Eu2+ Phosphors and the Tunable Photoluminescence Via Sr/Ba Substitution” LINK




Equipment for Spectroscopy

“MEMS and MOEMS Based Visible and NearInfrared Spectrometers” LINK




Process Control and NIR Sensors

“Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland” LINK




Agriculture NIR-Spectroscopy Usage

“Usage of Airborne Hyperspectral Imaging Data for Identifying Spatial Variability of Soil Nitrogen Content” LINK

“Determination of acid value during edible oil storage using a portable” LINK




Food & Feed Industry NIR Usage

“Identifying the best rice physical form for non-destructive prediction of protein content utilising near-infrared spectroscopy to support digital phenotyping” LINK

“Bioactive Phenolic Metabolites from Adriatic Brown Algae Dictyota dichotoma and Padina pavonica (Dictyotaceae)” Foods LINK

“Spectroscopic approaches for non-destructive shell egg quality and freshness evaluation: opportunities and challenges” LINK




Medicinal Spectroscopy

“Towards Development of LED-based Time-Domain Near-IR Spectroscopy System for Delineating Breast Cancer from Adjacent Normal Tissue” LINK




Other

“The Effect of Principal Component Analysis Parameters on Solar-Induced Chlorophyll Fluorescence Signal Extraction” LINK

“Er3+doped antimonysilica glass and fiber for broadband optical amplification” LINK

“Newly developed glass samples containing P2O5-B2O3-Bi2O3-Li2O-CdO and their performance in optical and radiation attenuation applications” LINK

“Discrimination and Quantitation of Biologically Relevant Carboxylate Anions Using A [DyePAMAM] Complex” Sensors LINK


Spectroscopy and Chemometrics News Weekly #12, 2021

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Spectroscopy and Chemometrics News Weekly 11, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Application Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC QualityTesting LINK

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Near-Infrared Spectroscopy (NIRS)

“An Evaluation of Different NIR-Spectral Pre-Treatments to Derive the Soil Parameters C and N of a Humus-Clay- Rich Soil” | LINK

“Assessment of frying oil quality by FT-NIR spectroscopy” LINK

“Differentiation between Fresh and Thawed Cephalopods Using NIR Spectroscopy and Multivariate Data Analysis” LINK

“A High Efficiency Trivalent Chromium-Doped Near-Infrared-Emitting Phosphor and Its NIR Spectroscopy Application” LINK

“Machine learning applied as an in-situ monitoring technique for the water content in oil recovered by means of NIR spectroscopy” LINK

“Prediction of a wide range of compounds concentration in raw coffee beans using NIRS, PLS and variable selection” LINK

“Fast and nondestructive determination of simultaneous physicochemical parameters of Manihot esculenta flour using FTNIR spectroscopy and multivariate analysis” LINK

“Intramuscular fat prediction of the semimembranosus muscle in hot lamb carcases using NIR.” LINK

“Fast and non‐destructive determination of simultaneous physicochemical parameters of Manihot esculenta flour using FT‐NIR spectroscopy and multivariate analysis” LINK

“Application of ANOVA-simultaneous component analysis to quantify and characterise effects of age, temperature, syrup adulteration and irradiation on near-infrared (NIR) spectral data of honey” LINK

“Near‐Infrared Spectroscopy (NIRS) and Optical Sensors for Estimating Protein and Fiber in Dryland Mediterranean Pastures” LINK

” Genetic variation of seed phosphorus concentration in winter oilseed rape and development of a NIRS calibration” | LINK

“… condition of leaves during black tea processing via the fusion of electronic eye (E-eye), colorimetric sensing array (CSA), and micro-near-infrared spectroscopy (NIRS)” LINK

“A review of the application of near-infrared spectroscopy (NIRS) in forestry” LINK




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

“Development of a passive optical heterodyne radiometer for near and mid-infrared spectroscopy” LINK

“Investigation of Active Compounds in Black Galingale (Kaempferia parviflora Wall. ex Baker) Using Color Analysis and Near Infrared Spectroscopy Techniques” LINK

” Quality control of milk powder with near-infrared spectroscopy” LINK

” An empirical investigation of deviations from the Beer-Lambert law in near-infrared spectroscopy: A case study of lactate in aqueous solutions, serum, blood and …” LINK

“Understanding the effect of urea on the phase transition of poly (N-isopropylacrylamide) in aqueous solution by temperature- dependent near-infrared spectroscopy” LINK

“Mechanically robust amino acid crystals as fiber-optic transducers and wide bandpass filters for optical communication in the near-infrared” LINK

“Optimal Combination of Band-Pass Filters for Theanine Content Prediction using Near-Infrared Spectroscopy” LINK

“Unlocking the Secrets of Terminalia Kernels Using Near-Infrared Spectroscopy” LINK

“Identification of tomatoes with early decay using visible and near infrared hyperspectral imaging and image‐spectrum merging technique” LINK

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

“Predicting grapevine canopy nitrogen status using proximal sensors and nearinfrared reflectance spectroscopy” LINK

“Sensors, Vol. 21, Pages 1413: The Use of a Micro Near Infrared Portable Instrument to Predict Bioactive Compounds in a Wild Harvested FruitKakadu Plum (Terminalia ferdinandiana)” LINK

“Prediction of specialty coffee flavors based on nearinfrared spectra using machine and deeplearning methods” LINK

“Soil characterization by near-infrared spectroscopy and principal component analysis” LINK

“DISCRIMINATION OF INFECTED SILKWORM CHRYSALISES USING NEAR INFRARED SPECTROSCOPY COMBINED WITH MULTIVARIATE ANALYSIS DURING …” LINK

“Evaluation of melon drying using hyperspectral imaging technique in the near infrared region” LINK




Raman Spectroscopy

“Raman spectroscopy in the detection of adulterated essential oils: The case of nonvolatile adulterants” LINK




Hyperspectral Imaging (HSI)

“Combining Multi-Dimensional Convolutional Neural Network (CNN) With Visualization Method for Detection of Aphis gossypii Glover Infection in Cotton Leaves Using Hyperspectral Imaging” LINK




Chemometrics and Machine Learning

“Detection and quantification of cow milk adulteration using portable near-infrared spectroscopy combined with chemometrics” LINK

” Realizing transfer learning for updating deep learning models of spectral data to be used in a new scenario” LINK

“Employing supervised classification techniques in determining playability status of polyesterurethane magnetic audio tapes” LINK

“Detection of Adulteration in Honey by Infrared Spectroscopy and Chemometrics: Effect on Human Health” LINK

“Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics” LINK

“Application of electronic nose with chemometrics methods to the detection of juices fraud” LINK

“Anisotropic effect on the predictability of intramuscular fat content in pork by hyperspectral imaging and chemometrics” LINK

“Nondestructive qualitative and quantitative analysis of Yaobitong capsule using near-infrared spectroscopy in tandem with chemometrics.” LINK




Research on Spectroscopy

“Efficient utilization of date palm waste for the bioethanol production through Saccharomyces cerevisiae strain” LINK

” Spectral data analysis methods for soil properties assessment using remote sensing” DOI: 10.9790/0661-2301011418 LINK




Equipment for Spectroscopy

“Handheld arduino-based near infrared spectrometer for non-destructive quality evaluation of siamese oranges” LINK




Environment NIR-Spectroscopy Application

“Estimation of Soil Salt and Ion Contents Based on Hyperspectral Remote Sensing Data: A Case Study of Baidunzi Basin, China. Water 2021, 13, 559” LINK

“Spectroscopic anatomical mapping of left atrium endocardial substrate and lesion using an optically integrated mapping catheter”LINK

“Predicting the contents of soil salt and major water-soluble ions with fractional-order derivative spectral indices and variable selection” LINK

“Mid-Infrared Spectroscopy Supports Identification of the Origin of Organic Matter in Soils. Land 2021, 10, 215” LINK




Agriculture NIR-Spectroscopy Usage

“Identification and classification of Asian soybean rust using leaf-based hyperspectral reflectance” LINK

“Diagnosis of early blight disease in tomato plant based on visible/near-infrared spectroscopy and principal components analysis-artificial neural network prior to visual …” LINK

“Fourier Transform Infrared Spectroscopy as a Tool to Study Milk Composition Changes in Dairy Cows Attributed to Housing Modifications to Improve Animal Welfare” LINK

“Detection of soil organic matter using hyperspectral imaging sensor combined with multivariate regression modeling procedures” LINK

“Corrigendum to: Optimisation of dry matter and nutrients in feed rations through use of a near-infrared spectroscopy system mounted on a self-propelled feed mixer” LINK

“staling of white wheat bread crumb and effect of maltogenic α-amylases. part 3: spatial evolution of bread staling with time by near infrared hyperspectral imaging” LINK

” Prediction performance of portable near infrared reflectance instruments using preprocessed dried, ground forage samples” LINK




Food & Feed Industry NIR Usage

“Near Infrared (NIR) Spectroscopy as a Tool to Assess Blends Composition and Discriminate Antioxidant Activity of Olive Pomace Cultivars” LINK

“Enhanced moisture loss and oil absorption of deep-fried food by blending extra virgin olive oil in rapeseed oil” LINK




Pharma Industry NIR Usage

“GRAZING PATTERNS, DIET QUALITY, AND PERFORMANCE OF COW-CALF PAIRS GRAZING SHORT GRASS PRAIRIE USING CONTINUOUS OR HIGH …” LINK

“PENDUGAAN KADAR PATCHOULI ALCOHOL PADA MINYAK NILAM HASIL FRAKSINASI MENGGUNAKAN METODE PRINCIPAL COMPONENT REGRESSION” LINK




Medicinal Spectroscopy

“Noninvasive Monitoring of Glucose Using Near-Infrared Reflection Spectroscopy of Skin-Constraints and Effective Novel Strategy in Multivariate Calibration” LINK



Spectroscopy and Chemometrics News Weekly #11, 2021

NIR Calibration-Model Services

Custom NIR Equations for better accuracy and precision for QA QC Testing Analysis Lab PAT NIRS NIR ag food LINK

Improve Accuracy of fast Nondestructive NIR Measurements by Optimal Calibration | foodquality agtech Lab biotech LINK

Spectroscopy and Chemometrics News Weekly 10, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 10, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 10, 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.

br>

Near-Infrared Spectroscopy (NIRS)

“Fast and contactless assessment of intact mango fruit quality attributes using near infrared spectroscopy (NIRS)” LINK

“NON-INVASIVE IDENTIFICATION OF COMMERCIAL GREEN TEA BLENDS USING NIR SPECTROSCOPY AND SUPPORT VECTOR MACHINE” LINK

” Mango internal defect detection based on optimal wavelength selection method using NIR spectroscopy” LINK

“Application of Fourier Transform Near-Infrared (FT-NIR) spectroscopy for detection of adulteration in palm sugar” LINK

“Quick assessment of chicken spoilage based on hyperspectral NIR spectra combined with partial least squares regression” LINK

“PENDUGAAN KANDUNGAN GIZI AMPAS TEBU (Bagasse) MENGGUNAKAN NIRS (Near Infrared Reflectance Spectroscopy)” LINK

“… condition of leaves during black tea processing via the fusion of electronic eye (E-eye), colorimetric sensing array (CSA), and micro-near-infrared spectroscopy (NIRS)” LINK

” NIR spectroscopy of natural medicines supported by novel instrumentation and methods for data analysis and interpretation” LINK




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

“A method using near infrared hyperspectral imaging to highlight the internal quality of apple fruit slices” LINK

“Use of near-infrared spectroscopy on predicting wastewater constituents to facilitate the operation of a membrane bioreactor” LINK

“Textile Recognition and Sorting for Recycling at an Automated Line Using Near Infrared Spectroscopy” LINK

“Identification of tomatoes with early decay using visible and near infrared hyperspectral imaging and imagespectrum merging technique” LINK

“Mineral equilibrium in commercial curd and predictive ability of near-infrared spectroscopy” LINK

“Organic fertilizer from agricultural waste: determination of phosphorus content using near infrared reflectance” LINK

“Robust fraudulence detection of patchouli oil plant using near infrared spectroscopy” LINK

“Rapid quantification of epigoitrin in the extraction process of Radix Isatidis using near infrared spectroscopy” LINK




Hyperspectral Imaging (HSI)

“Nondestructive detection for egg freshness based on hyperspectral imaging technology combined with harris hawks optimization support vector regression” LINK




Chemometrics and Machine Learning

“Rapid screening of apple juice quality using ultraviolet, visible, and near infrared spectroscopy and chemometrics: A comparative study” LINK

“Portable Spectroscopy Calibration with Inexpensive and Simple Sampling Reference Alternatives for Dry Matter and Total Carotenoid Contents in Cassava Roots” LINK

“Gravity mass powder flow through conical hoppers–Part I: A mathematical model predicting the radial velocity profiles of free‐flowing granular systems as a function of …” LINK

“Simultaneous sex and species classification of silkworm pupae by” LINK

“Application of near infrared spectroscopy in chemometric modeling of tannin content and stiasny number of Pinus caribaea bark” LINK

“Monitoring UV-accelerated alteration processes of paintings by means of hyperspectral micro-FTIR imaging and chemometrics” LINK




Equipment for Spectroscopy

“Principle and optimum analysis of small near-infrared spectrometers based on digital micromirror device” LINK

We are excited to announce the release of the VIS-NIR version of our ultra compact PEBBLE spectrometer platform. The PEBBLE VIS-NIR is ideal for fluorescence and absorption spectroscopy. Read about our ultra compact PEBBLE VIS-NIR spectrometer here: LINK




Environment NIR-Spectroscopy Application

“Transfer learning strategy for plastic pollution detection in soil: Calibration transfer from high-throughput HSI system to NIR sensor” LINK

“UAS-Based Hyperspectral Environmental Monitoring of Acid Mine Drainage Affected Waters” Minerals LINK




Agriculture NIR-Spectroscopy Usage

” Comparison of UAV RGB imagery and hyperspectral remote-sensing data for monitoring winter-wheat growth” LINK

” Soil nutrient information extraction model based on transfer learning and near infrared spectroscopy” LINK

“Machine Learning-Based Prediction of Selected Parameters of Commercial Biomass Pellets Using Line Scan Near Infrared- Hyperspectral Image” LINK




Horticulture NIR-Spectroscopy Applications

” Nondestructive Assessment of Citrus Fruit Quality and Ripening by Visible–Near Infrared Reflectance Spectroscopy” LINK




Food & Feed Industry NIR Usage

“An Alternative Approach to Evaluate the Quality of Protein-Based Raw Materials for Dry Pet Food” LINK

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




Other

“Sequential Data Fusion Techniques for the Authentication of the P.G.I. Senise (Crusco) Bell Pepper” 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 #40, 2020

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 39, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK




Near-Infrared Spectroscopy (NIRS)

“Development of a Near Infrared Reflectance Spectroscopy (NIRS) Platform for Rapid Wheat Quality Analysis” LINK

Near infrared spectroscopy (NIRS), however, is relatively cheap (once you have the machine), and non-destructive. In this article, we demonstrate that adequate calibrations can be obtained for total terpene content and some specific terpenoids for pines, spruces and thuja LINK

“Special Issue on Brain Machine/Computer Interface and its Application” fNIRS LINK

“Performance of near-infrared (NIR) spectroscopy in pork shoulder as a predictor for pork belly softness” LINK

“Omega-3 and Omega-6 Determination in Nile Tilapia’s Fillet Based on MicroNIR Spectroscopy and Multivariate Calibration” LINK

“Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt) Seed Viability with Multivariate Data Analysis” LINK

“Untargeted classification for paprika powder authentication using visible–Near infrared spectroscopy (VIS-NIRS)” LINK

“Monitoring the composition, authenticity and quality dynamics of commercially available Nigerian fat-filled milk powders under inclement conditions using NIRS, chemometrics, packaging and …” LINK

“Predicting total petroleum hydrocarbons in field soils with Vis–NIR models developed on laboratory‐constructed samples” LINK

“Modeling mass loss of biomass by NIRspectrometry during the torrefaction process” LINK




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

“Rapid determination of the chemical compositions of peanut seed (Arachis hypogaea.) using portable Near-Infrared Spectroscopy” LINK

“Simultaneous detection of trace adulterants in food based on multi-molecular infrared (MM-IR) spectroscopy” LINK

“Monitoring the quality of ethanol-based hand sanitizers by low-cost near-infrared spectroscopy” LINK

“Prediction of neutral detergent fiber content in corn stover using near-infrared spectroscopy technique” LINK

“Applied Sciences, Vol. 10, Pages 5801: Potential Use of Near-Infrared Spectroscopy to Predict Fatty Acid Profile of Meat from Different European Autochthonous Pig Breeds” LINK

“Rapid and non-destructive seed viability prediction using near-infrared hyperspectral imaging coupled with a deep learning approach” LINK

“Multivariate classification for the direct determination of cup profile in coffee blends via handheld near-infrared spectroscopy” LINK

“QUANTITATIVE CHARACTERIZATION OF SUSTAINED RELEASE TABLETS WITH DICLOFENAC SODIUM BY MEANS OF NEAR-INFRARED SPECTROSCOPY AND …” LINK




Raman Spectroscopy

“Raman spectroscopy and machine-learning for edible oils evaluation” LINK




Hyperspectral Imaging (HSI)

“Application of hyperspectral imaging for detecting and visualizing leaf lard adulteration in minced pork” LINK

“Detection of Shape Characteristics of Kiwifruit Based on Hyperspectral Imaging Technology” LINK




Chemometrics and Machine Learning

“A rapid food chain approach for authenticity screening: the development, validation and transferability of a chemometric model using two handheld near infrared spectroscopy …” LINK




Equipment for Spectroscopy

“Principles and applications of miniaturized nearinfrared (NIR) spectrometers” LINK

“A comparison of different optical instruments and machine learning techniques to identify sprouting activity in potatoes during storage” | LINK




Environment NIR-Spectroscopy Application

“Water-based measured-value fuzzification improves the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy.” LINK




Agriculture NIR-Spectroscopy Usage

“In situ effective snow grain size mapping using a compact hyperspectral imager” LINK

“Estimation of soil nitrogen in agricultural regions by VNIR reflectance spectroscopy” LINK




Food & Feed Industry NIR Usage

“Rapid screening of DON contamination in whole wheat meals by Vis/NIR spectroscopy and computer vision coupling technology” LINK

“Quantitative Analysis of Perennial Buckwheat Leaves Protein and GABA Using Near Infrared Spectroscopy” LINK




Laboratory and NIR-Spectroscopy

“Near-infrared laboratory measurements of feldspathic rocks as a reference for hyperspectral Martian remote sensing data interpretation.” LINK




Other

“Determinación de la calidad de carne bovina y la aceptación por parte del consumidor mediante el uso de pruebas con base en infrarrojo cercano” LINK

“Validación de un algoritmo de procesamiento de imágenes Red Green Blue (RGB), para la estimación de proteína cruda en gramíneas vs la tecnología de …” LINK

“IonQ claims it has built the most powerful quantum computer yet” QuantumComputing LINK

“D-Wave’s 5,000-qubit quantum computing platform handles 1 million variables” LINK

“The Sample, the Spectra and the Maths-The Critical Pillars in the Development of Robust and Sound Applications of Vibrational Spectroscopy.” 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