Spectroscopy and Chemometrics/Machine-Learning News Weekly #19, 2022

NIR Calibration-Model Services

Efficient development of new quantitative prediction equations for multivariate NIR spectra data VIS-NIRS NIT SWIR LINK

Spectroscopy and Chemometrics News Weekly 18, 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 18, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 18, 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)

“Physicochemical mechanisms of FT-NIRS age prediction in fish otoliths” LINK

“Quality Assessment of Red Wine Grapes through NIR Spectroscopy” LINK

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

“Assessment of senior drivers’ internal state in the event of simulated unexpected vehicle motion based on near-infrared spectroscopy” LINK

“Interoceptive Attentiveness Induces Significantly More PFC Activation during a Synchronized Linguistic Task Compared to a Motor Task as Revealed by Functional Near-Infrared Spectroscopy” LINK

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

“Hidden Information in Uniform Design for Visual and Near-Infrared Spectrum and for Inkjet Printing of Clothing on Canvas to Enhance Urban Security” LINK

“Remote Sensing : Near-Infrared Spectroscopy and Mode Cloning (NIR-MC) for In-Situ Analysis of Crude Protein in Bamboo” LINK

“Reorganization of prefrontal network in stroke patients with dyskinesias: evidence from resting-state functional near-infrared spectroscopy” LINK

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

“Near-Infrared Spectral Characteristic Extraction and Qualitative Analysis Method for Complex Multi-Component Mixtures Based on TRPCA-SVM” LINK

“Home-based monitoring of lower urinary tract health: simultaneous measures using wearable near infrared spectroscopy and linked wireless scale” LINK

“NIRS-derived muscle V̇O2 kinetics after moderate running exercise in healthy males: reliability and associations with parameters of aerobic fitness” LINK

“Broadband NIR-emitting Te Cluster-Doped Glass for Smart Light Source towards Night-Vision and NIR Spectroscopy Applications” LINK

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

“Home-based monitoring of lower urinary tract health: simultaneous measures using wearable near infrared spectroscopy and linked wireless scale” | LINK

“Prediction of formaldehyde and residual methanol concentration in formalin using near infrared spectroscopy” LINK

“Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains” LINK

“Performance and reproducibility assessment across multiple time-domain near-infrared spectroscopy device replicas” | LINK

“Predicting bleachability of Eucalyptus mechanical pulp by moisture content-dependent near-infrared spectroscopy” LINK

“A Standard-Free Calibration Transfer Strategy for a Discrimination Model of Apple Origins Based on Near-Infrared Spectroscopy” LINK

“Carbon Storage of Technosols Developed on Volcanic Ash Assessed with Xrf and Vis-Nir Spectroscopy” LINK

“Evaluation of portable near-infrared spectroscopy for authentication of mRNA based COVID-19 vaccines” LINK

“Visible and Near-Infrared Spectroscopic Discriminant Analysis Applied to Identification of Soy Sauce Adulteration” | LINK

” … of smoke-derived compounds from bushfires in Cabernet-Sauvignon grapes, must, and wine using Near-Infrared spectroscopy and machine learning …” LINK

“Study on the detection of heavy metal lead (Pb) in mussels based on near-infrared spectroscopy technology and a REELM classifier” LINK

“Rapid quantification of alkaloids, sugar and yield of tobacco (Nicotiana tabacum L.) varieties by using Vis-NIR-SWIR spectroradiometry” LINK

“The quantitative detection of botanical trashes contained in seed cotton with near infrared spectroscopy method” LINK

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

“Near-infrared spectroscopy-based nondestructive at-line analysis of physicochemical properties of atorvastatin calcium hydrate after grinding” LINK




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

“UltraEfficient GAGG:Cr3+ Ceramic PhosphorConverted Laser Diode: A Promising HighPower Compact NearInfrared Light Source Enabling Clear Imaging” LINK

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

“Reorganization of prefrontal network in stroke patients with dyskinesias: evidence from restingstate functional nearinfrared spectroscopy” LINK

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




Hyperspectral Imaging (HSI)

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

“Soluble Solids Content prediction for Korla fragrant pears using hyperspectral imaging and GsMIA” LINK




Chemometrics and Machine Learning

“Remote Sensing : The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing” LINK

“Exploration of compressive sensing in the classification of frozen fish based on two-dimensional correlation spectrum” LINK

“Method development and validation of a near-infrared spectroscopic method for in-line API quantification during fluidized bed granulation” LINK




Optics for Spectroscopy

“Sensors : Effect of Surface Morphology Changes on Optical Properties of Silicon Nanowire Arrays” LINK




Research on Spectroscopy

“Laser-driven white light with tunable low-colour temperature based on novel ZrO2-doped (Gd, Lu) 2O3: Eu red-emitting transparent ceramics” LINK




Equipment for Spectroscopy

“Rapid authentication of the geographical origin of milk using portable nearinfrared spectrometer and fuzzy uncorrelated discriminant transformation” LINK




Future topics in Spectroscopy

“Quality analysis and authentication of nutraceuticals using near IR (NIR) spectroscopy: A comprehensive review of novel trends and applications” LINK




Process Control and NIR Sensors

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




Environment NIR-Spectroscopy Application

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

“Remote Sensing : Enhancing Front-Vehicle Detection in Large Vehicle Fleet Management” LINK




Agriculture NIR-Spectroscopy Usage

“Effects of supplementation rate of an extruded dried distillers’ grains cube fed to growing heifers on voluntary intake and digestibility of bermudagrass hay” LINK

“Comprehensive Study of Traditional Plant Ground Ivy (Glechoma hederacea L.) Grown in Croatia in Terms of Nutritional and Bioactive Composition” LINK

“Agronomy : Calibration of Near-Infrared Spectra for Phosphorus Fractions in Grassland Soils on the Tibetan Plateau” LINK

“Impact of preparation pH and temperature on amino acid stability of highly concentrated cell culture feed media” LINK

“Use of spectroscopic sensors in meat and livestock industries” LINK




Horticulture NIR-Spectroscopy Applications

“Non‐destructive prediction of total soluble solids in strawberry using near infrared spectroscopy” LINK




Food & Feed Industry NIR Usage

“Making Cocoa Origin Traceable” LINK

” An Experimental Model for Assessing the Storage Life of Chilled Chicken Meat Through NIR Spectroscopy” LINK




Pharma Industry NIR Usage

“Rapid Pentosan Polysulfate Sodium (PPS) Maculopathy Progression” LINK




Other

“of the Potato Association of America Winnipeg, Manitoba Canada July” LINK

“Constructing visible light induced direct dual Z scheme heterostructure for photodegradation of organic pollutants” LINK

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

“Enhanced photoluminescence in Dy3+/Au co-doped bismuth borosilicate glass” LINK





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

NIR Calibration-Model Services

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

Knowledge-Based Variable Selection and Model Selection for near-infrared spectroscopy NIRS | PLSR PCA PCR PLS SVR ANN LINK

Rapid NIR method Development for the quantitative analysis of | predictive sensors Industry40 Industry4.0 LINK

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 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 #22, 2020

NIR Calibration-Model Services

New Free NIR-Predictor V2.6 software is released – Reads and predicts *.spc spectra file format (Thermo-Scientific / Galactic GRAMS) – Spectra Plots on the Prediction Reports NIRS NIR Spectroscopy Spectrometer QualityControl Lab Laboratory Analysis LINK
Spectra Plot


Spectroscopy and Chemometrics News Weekly 21, 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 21, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 21, 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)

“NIR spectroscopy application for determination caffeine content of Arabica green bean coffee” LINK

“Review of NIR spectroscopy methods for nondestructive quality analysis of oilseeds and edible oils” LINK

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

“Determination of metmyoglobin in cooked tan mutton using Vis/NIR hyperspectral imaging system” LINK

“Prediction of water content in Lintong green bean coffee using FT-NIRS and PLS method” LINK

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

“A system using in situ NIRS sensors for the detection of product failing to meet quality standards and the prediction of optimal postharvest shelf-life in the case of oranges kept in cold storage” LINK

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

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




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

“Rapid and Non-destructive Detecting Frying Times of Peanut Oil Based on Near Infrared Reflectance Spectroscopy” LINK

“Different Supervised and unsupervised classification approaches based on Visible/Near infrared spectral analysis for discrimination of microbial contaminated lettuce …” LINK

“Nondestructive determination of lignin content in Korla fragrant pear based on near-infrared spectroscopy” LINK

“Monitoring the Progress and Healing Status of Burn Wounds Using 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 … forensic evidence” LINK

“Differentiating Between Malignant Mesothelioma and Other Pleural Lesions Using Fourier Transform Infrared Spectroscopy” LINK

“Confirmation of brand identification in infant formulas by near-infrared spectroscopy fingerprints” LINK

“Near-infrared spectroscopy of the placenta for monitoring fetal oxygenation during labour.” LINK

“Impact of H2O on atmospheric CH4 measurement in near-infrared absorption spectroscopy.” LINK

“Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds” LINK

“Protein, weight, and oil prediction by single-seed near-infrared spectroscopy for selection of seed quality and yield traits in pea (Pisum sativum).” phenotyping LINK

“Multiple-depth Modeling of Soil Organic Carbon using Visible–Near Infrared Spectroscopy” LINK

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

“Simultaneous determination of antioxidant properties and total phenolic content of Siraitia grosvenorii by near infrared spectroscopy” LINK

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

“Estimating δ15N and δ13C in Barley and Pea Mixtures Using Near-Infrared Spectroscopy with Genetic Algorithm Based Partial Least Squares Regression” LINK

“Investigating the Quality of Antimalarial Generic Medicines Using Portable 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

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




Raman Spectroscopy

“Raman Technology for Today’s Spectroscopists” LINK

“Diagnosis of Citrus Greening using Raman Spectroscopy-Based Pattern Recognition” LINK




Hyperspectral Imaging (HSI)

“Classification of Hyperspectral Endocrine Tissue Images Using Support Vector Machines.” LINK

“Using dual-channel CNN to classify hyperspectral image based on spatial-spectral information” LINK

“Diagnosis of Late Blight of Potato Leaves Based on Deep Learning Hyperspectral Images” LINK

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

“Applied Sciences, Vol. 10, Pages 2259: Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves” LINK

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

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




Chemometrics and Machine Learning

“Rapid detection of saffron (Crocus sativus L.) Adulterated with lotus stamens and corn stigmas by near-infrared spectroscopy and chemometrics” LINK

“Simultaneous quantification of active constituents and antioxidant capability of green tea using NIR spectroscopy coupled with swarm intelligence algorithm” LINK

“Comparison of CNN Algorithms on Hyperspectral Image Classification in Agricultural Lands” LINK

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

“Identification of Tea Diseases Based on Spectral Reflectance and Machine Learning” LINK

“Machine learning estimators for the quantity and quality of grass swards used for silage production using drone-based imaging spectrometry and …” LINK




Research on Spectroscopy

“Lanthanide complexes with N-(2, 6-dimethylphenyl) oxamate: Synthesis, characterisation and cytotoxicity” LINK

“Automatisierte und digitale Dokumentation der Applikation organischer Düngemittel” LINK




Equipment for Spectroscopy

“Evaluation of Depth Measurement Method Based on Spectral Characteristics Using Hyperspectrometer” LINK

“Monitoring wine fermentation deviations using an ATR-MIR spectrometer and MSPC charts” LINK




Process Control and NIR Sensors

“Process analytical technology tools for process control of roller compaction in solid pharmaceuticals manufacturing.” LINK




Agriculture NIR-Spectroscopy Usage

“The effect of bubble formation within carbonated drinks on the brewage foamability, bubble dynamics and sensory perception by consumers” LINK

“Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis” LINK

“Remote Sensing, Vol. 12, Pages 1256: Crop Separability from Individual and Combined Airborne Imaging Spectroscopy and UAV Multispectral Data” LINK

“Portable IoT NIR Spectrometer for Detecting Undesirable Substances in Forages of Dairy Farms” LINK

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

“Automatisierte und digitale Dokumentation der Applikation organischer Düngemittel” LINK




Horticulture NIR-Spectroscopy Applications

” Nondestructive determining the soluble solids content of citrus using near infrared transmittance technology combined with the variable selection algorithm” LINK




Food & Feed Industry NIR Usage

“Statistical Analysis of Protein Content in Wheat Germplasm Based on Near-infrared Reflectance Spectroscopy” LINK

“Prediction of infertile chicken eggs before hatching by the Naïve-Bayes method combined with visible near infrared transmission spectroscopy” LINK




Other

“Microsoft lays off journalists to replace them with AI” LINK





Spectroscopy and Chemometrics News Weekly #16, 2020

NIR Calibration-Model Services

Feed your favorite spectral Food Scanner sensor with customized specialized analysis models by downloading the free NIR-Predictor software. Collect Laboratory data and build your calibrations! | spectroscopy analyzer food dairy quality IoT LINK

Ho to improve your Near Infra Red (NIR) Analyzer Precision Accuracy Performance | nearIR Lab NIRS Light LINK

How to improve your NIRS analysis, get the free White Paper | infrared QAQC foodquality ingredients constituents LINK

Improve Accuracy of fast Nondestructive NIRS Measurements by Optimal Calibration | Feed Lab prediction Sensor LINK

Increase Your Profit with optimized NIR Accuracy Chocolate Bakery Tea Meat LINK

Spectroscopy and Chemometrics News Weekly 15, 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 15, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 15, 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)

“Authentication of Iberian pork official quality categories using a portable near infrared spectroscopy (NIRS) instrument” meat pig LINK

“NIR SPECTROSCOPY METHOD FOR FATTY ACID CONTENT OF OILSEEDS” LINK

“Proximate composition determination in goat cheese whey by near infrared spectroscopy (NIRS).” LINK

“Soil Organic Carbon Prediction by Vis-NIR Spectroscopy: Case Study the Kur-Aras Plain, Azerbaijan” LINK

“Near infrared spectroscopy (NIRS) data analysis for a rapid and simultaneous prediction of feed nutritive parameters.” LINK




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

“Identification of human brown/beige adipose tissue using near-infrared time-resolved spectroscopy” LINK

“Visible near infrared reflectance molecular chemical imaging of human ex vivo carcinomas and murine in vivo carcinomas” LINK

“Data analysis on near infrared spectroscopy as a part of technology adoption for cocoa farmer in Aceh Province, Indonesia.” LINK

“Sensor Development for Soil-Property Detection Using Near Infrared Spectroscopy” LINK

“Classifying maize kernels naturally infected by fungi using near-infrared hyperspectral imaging” LINK

“Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance” LINK




Hyperspectral Imaging (HSI)

“Novel Deep-Learning-Based Spatial-Spectral Feature Extraction For Hyperspectral Remote Sensing Applications” LINK

“Estimation of grapevine predawn leaf water potential based on hyperspectral reflectance data in Douro wine region” LINK




Chemometrics and Machine Learning

“Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks.” LINK

“Analysis of the Influence of Substrate Formulations on the Bioactive Chemical Profile of Lingzhi or Reishi Medicinal Mushroom, Ganoderma lucidum (Agaricomycetes) by Conventional and Chemometrics Methods.” LINK

“Use of precision farming practices and crop modelling for enhancing water and phosphorus efficiency” LINK

“A chemometric strategy to evaluate the comparability of PLS models obtained from quartz cuvettes and disposable glass vials in the determination of extra virgin olive …” LINK




Research on Spectroscopy

Classical Least Squares Method for Quantitative Spectral Analysis with Python LINK

“Method for Quantitative Broadband Diffuse Optical Spectroscopy of Tumor-Like Inclusions” LINK




Process Control and NIR Sensors

“Monitoring composting process of olive oil solid waste using FT-NIR spectroscopy” LINK

“Monitoring of CO2 Absorption Solvent in Natural Gas Process Using Fourier Transform Near-Infrared Spectrometry.” LINK




Agriculture NIR-Spectroscopy Usage

“Development of sugarcane and trash identification system in sugar production using hyperspectral imaging” LINK

“Smart Agriculture: The Age of Drones in Agriculture” LINK

“Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data” LINK

“Hyperspectral imaging in assessing the condition of plants: strengths and weaknesses” LINK

“Detection and identification of fungal growth on freeze-dried Agaricus bisporus using spectrum and olfactory sensor.” LINK

“Effect of strain and nutritional density of the diet on the water-protein ratio, fat and collagen levels in the breast and legs of broilers slaughtered at different …” LINK

“Detecting Bruise Damage and Level of Severity IN APPLES USING A CONTACTLESS NIR SPECTROMETER” LINK

“Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images” LINK

“Non-destructive estimation of winter wheat leaf moisture content using near-ground hyperspectral imaging technology” LINK




Food & Feed and Beverage Industry NIR Usage

“Chemometric tools for food fraud detection: the role of target class in non-targeted analysis” LINK

“Utilization of text mining as a big data analysis tool for food science and nutrition” LINK





Spectroscopy and Chemometrics News Weekly #14, 2020

CalibrationModel.com

NIR User? Get better results faster | Food Science QC Lab Laboratory Manager chemist LabWork Chemie analytik LINK

NIR-Predictor Software supports spectral file formats out of the box from: and others – Mobile spectroscopy NIRS portable Analyzers H2020 LINK

Timesaving Calibration Modeling Method: for near-infrared NIR Spectroscopy (NIRS) Multivariate Quantitative Prediction. food quality laboratory LINK

Spectroscopy and Chemometrics News Weekly 13, 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 13, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse analytic LINK

Spettroscopia e Chemiometria Weekly News 13, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

We have updated the free NIR-Predictor-Software Spectral Data format support list for many mobile and benchtop NIR Spectroscopy Sensors. | Used in QualityControl for Food Fruits Milk Meat 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)

“Aplicação da espectroscopia de reflectância no infravermelho próximo (NIRS) na determinação do potencial bioquímico de metano–Revisão” LINK

“Prediction of soil organic matter and clay contents by near-infrared spectroscopy-NIRS” LINK

“Fast detection and quantification of pork meat in other meats by reflectance FT-NIR spectroscopy and multivariate analysis” LINK

“Improved GA-SVM Algorithm and Its Application of NIR Spectroscopy in Orange Growing Location Identification” LINK

“Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors.” Tobacco LINK

“Data analysis on near infrared spectroscopy as a part of technology adoption for cocoa farmer in Aceh Province, Indonesia” LINK

“Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors” LINK

“Identification of a Glass Substrate to Study Cells Using Fourier Transform Infrared Spectroscopy: Are We Closer to Spectral Pathology?” LINK

“Raman-Infrared spectral fusion combined with partial least squares (PLS) for quantitative analysis of polycyclic aromatic hydrocarbons in soil” LINK

“Identification metliod of ginger-processed Pinelliaternata based on infrared spectroscopy data fusion.” LINK

“Terahertz Time of Flight Spectroscopy as a Coating Thickness Reference Method for Partial Least Squares Near Infrared Spectroscopy Models” LINK

“Quantitative detection of apple watercore and soluble solids content by near infrared transmittance spectroscopy” LINK




Hyperspectral

“Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging.” LINK

“Hyperspectral monitoring of maize leaves under copper stress at different growth stages” LINK

“Classification of small-scale hyperspectral images with multi-source deep transfer learning” LINK




Chemometrics

“Detection of fat content in peanut kernels based on chemometrics and hyperspectral imaging technology” LINK

“Hyperspectral Imaging Feature Selection Using Regression Tree Algorithm: Prediction of Carotenoid Content Velvet Apple Leaf” LINK

“Modelos de calibración para la cuantificación nutricional de praderas frescas mediante espectroscopía de infrarojo cercano” LINK

“Performance Evaluation of Chemometric Prediction Models—Key Components of Wheat Grain” LINK




Equipment

“Rapid Nondestructive Analysis of Intact Canola Seeds Using a Handheld NearInfrared Spectrometer” LINK

“Confirmatory non-invasive and non-destructive differentiation between hemp and cannabis using a handheld Raman spectrometer” LINK




Process Control

“Monitoring of CO2 Absorption Solvent in Natural Gas Process Using Fourier Transform Near-Infrared Spectrometry” LINK




Environment

“Comparing laboratory and airborne hyperspectral data for the estimation and mapping of topsoil organic carbon: Feature selection coupled with random forest” LINK




Agriculture

“Predicting Forage Quality of Warm-Season Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques.” LINK

“Les défis de la technologie de l’aliment en nutrition volaille: pertinence et enjeux pour répondre aux attentes industrielles et sociétales” LINK

“CHANGES IN THE CONTENT OF STRUCTURAL CARBOHYDRATES AND LIGNIN IN THE BIOMASS OF Lolium multiflorum (Lam.) AFTER APPLYING SLURRY …” LINK

“Rapid Analysis of Alcohol Content During the Green Jujube Wine Fermentation by FT-NIR” LINK

“Spectral Analysis and Deconvolution of the Amide I Band of Proteins Presenting with High-Frequency Noise and Baseline Shifts” LINK




Petro

“Spectroscopic evidence of special intermolecular interaction in iodomethane-ethanol mixtures: the cooperative effect of halogen bonding, hydrogen bonding, and …” LINK




Pharma

“Defocused Spatially Offset Raman Spectroscopy in Media of Different Optical Properties for Biomedical Applications Using a Commercial Spatially Offset Raman Spectroscopy Device” LINK




Medicinal

“A single oral dose of beetroot-based gel does not improve muscle oxygenation parameters, but speeds up handgrip isometric strength recovery in recreational combat …” LINK




Other

“Spectral differentiation of oak wilt from foliar fungal disease and drought is correlated with physiological changes” LINK

“Wearing a headset containing both electroencephalography (EEG) and near-infrared spectroscopy (NIRS) sensors, the user simply imagines moving either his right hand, left hand, tongue or feet – and ASIMO makes a corresponding movement. ” BrainInterface LINK

KnowItAll Software / Spectral Libraries & ChemWindow are now part of Wiley Science Solutions. See press release: LINK

“The uses of near infra-red spectroscopy in postharvest decision support: A review” LINK





Spectroscopy and Chemometrics News Weekly #8, 2020

CalibrationModel.com

Knowledge-Based Variable Selection and Model Selection for near infrared spectroscopy NIRS LINK

Stop wasting too much time for NIRS Chemometrics Method development | foodanalyticaltechnologies analytic qualitycontrol foodindustry beverageindustry materialsensing LINK

Spectroscopy and Chemometrics News Weekly 7, 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 7, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 7, 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)

“Determination of Glucose by NIR Spectroscopy Under Magnetic Field” LINK

“Sensors, Vol. 20, Pages 230: The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods when using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods” LINK

“Quantum mechanical modeling of NIR spectra of thymol” LINK

“Using a handheld near-infrared spectroscopy (NIRS) scanner to predict meat quality” LINK

“NIR spectroscopy in simulation–a new way for augmenting near-infrared phytoanalysis” LINK

“Using visible-near-infrared spectroscopy to classify lichens at a Neotropical Dry Forest” LINK

“Near infrared spectroscopy as a rapid method for detecting paprika powder adulteration with corn flour” LINK

“Application of deep learning and near infrared spectroscopy in cereal analysis” LINK

“Using near infrared spectroscopy to determine the scots pine place of growth” LINK

“Chagas disease vectors identification using visible and near-infrared spectroscopy” LINK

“Ensemble of extreme learning machines for multivariate calibration of near-infrared spectroscopy” LINK

“Quantification of Silymarin in Silybum marianum with near-infrared spectroscopy: a comparison of benchtop vs. handheld devices” LINK

“N-way partial least squares combined with new self-construction strategy—A promising approach of using near infrared spectral data for quantitative determination of …” LINK

“Identification of rice flour types with near-infrared spectroscopy associated with PLS-DA and SVM methods” LINK

” Multivariate Classification of Prunus Dulcis Varieties using Leaves of Nursery Plants and Near Infrared Spectroscopy” LINK




Hyperspectral

“Frost damage to maize in northeast India: assessment and estimated loss of yield by hyperspectral proximal remote sensing” LINK

“Identification of authenticity, quality and origin of saffron using hyperspectral imaging and multivariate spectral analysis” LINK




Chemometrics

“Early detection of chilling injury in green bell peppers by hyperspectral imaging and chemometrics” LINK

“Evaluating photosynthetic pigment contents of maize using UVE-PLS based on continuous wavelet transform” LINK

“Near-infrared spectroscopy coupled with chemometrics algorithms for the quantitative determination of the germinability of Clostridium perfringens in four different …” LINK

“Analysis of residual moisture in a freeze-dried sample drug using a multivariate fitting regression model” LINK

“Spectroscopy based novel spectral indices, PCA-and PLSR-coupled machine learning models for salinity stress phenotyping of rice” LINK

“Standardisation of near infrared hyperspectral imaging for quantification and classification of DON contaminated wheat samples” LINK

“Vibrational spectroscopy and chemometric data analysis: the principle components of rapid quality control of herbal medicines” LINK

“A Model for Yellow Tea Polyphenols Content Estimation Based on Multi-Feature Fusion” LINK




Process Control

“Review of near-infrared spectroscopy as a process analytical technology for real-time product monitoring in dairy processing” LINK




Environment

“POTENTIAL OF SENSOR-BASED SORTING IN ENHANCED LANDFILL MINING” LINK

“Characterization of the salt marsh soils and visible-near-infrared spectroscopy along a chronosequence of Spartina alterniflora invasion in a coastal wetland of …” LINK




Agriculture

“Remote Sensing, Vol. 12, Pages 126: Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy” LINK

“Novel implementation of laser ablation tomography as an alternative technique to assess grain quality and internal insect development in stored products” LINK

“Comparative Study of Two Different Strategies for Determination of Soluble Solids Content of Apples From Multiple Geographical Regions by Using FT-NIR Spectroscopy” LINK




Food & Feed

“Adulteration of Olive Oil” LINK




Laboratory

“Laboratory Raman and VNIR spectroscopic studies of jarosite and other secondary mineral mixtures relevant to Mars” LINK




Other

“Combining analytical tools to identify adulteration: some practical examples” LINK

“… questioned whether the growth and sustainability of AI technology will lead to the need for two copyright systems — one to address human creation and one to address machine creation.” LINK





Spectroscopy and Chemometrics News Weekly #39, 2019

CalibrationModel.com

Get NIR results effectively and smart with one software, the free NIR-Predictor V2.4
– now includes clever tooling to combine NIR and Lab data with minimal effort.

Spectroscopy and Chemometrics News Weekly 38, 2019 | NIRS NIR Spectroscopy Chemometrics Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC Testing Quality Checking LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 38, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse Qualitätslabor FTNIR LINK

Spettroscopia e Chemiometria Weekly News 38, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem predictionmodel LINK




Near Infrared (NIR)

Get NIR results effectively and smart with one software, it includes clever tooling to combine NIR and Lab data with minimal effort. LINK

“Raw Material Variability and Its Impact on the Online Adaptive Control of Cohesive Powder Blend Homogeneity Using NIR Spectroscopy” LINK

“Pain Analysis, in Premature Infants, Using Near Infrared Spectroscopy (NIRS)” LINK

“Fast determination of oxides content in cement raw meal using NIR spectroscopy combined with synergy interval partial least square and different preprocessing …” LINK

“Near Infrared Reflectance (NIR) Spectroscopy assessment for Reproductive status detection and discrimination in Plethodontid females” LINK

“Detección temprana y discriminación de enfermedades fúngicas en plantas usando espectroscopía in situ” NIRS LINK

” Dataset on equine cartilage near infrared spectra, composition, and functional properties” LINK

“On-line monitoring of multiple component parameters during ethanol fermentation by near-infrared spectroscopy” LINK

“The potential of portable near infrared spectroscopy for assuring quality and authenticity in the food chain, using Iberian hams as an example” LINK

“Near-infrared spectroscopy to assess typhaneoside and isorhamnetin-3-O-glucoside in different processed products of pollen typhae” LINK




Chemometrics and NIR

“Near infrared spectroscopy as a tool for predicting growth habit and gender of Araucaria angustifolia” LINK

“A Systematic Chemometric Approach to Identify the Geographical Origin of Olive Oils” LINK

“Accuracy and biases in predicting the chemical and physical traits of many types of cheeses using different visible and near-infrared spectroscopic techniques and …” LINK

“An Approach to Rapid Determination of Tween-80 for the Quality Control of Traditional Chinese Medicine Injection by Partial Least Squares Regression in Near-Infrared Spectral Modeling” LINK

“Classification of Pathogenic Bacteria Using Near-Infrared Diffuse Reflectance Spectroscopy” LINK




Hyperspectral Imaging (HSI)

” Hyperspectral Imaging (HSI) in anatomic left liver resection” LINK




Environment

“Evaluating low-cost portable near infrared sensors for rapid analysis of soils from South Eastern Australia” LINK




Agriculture and NIR

“Vis-Nir Reflectance Spectroscopy for Assessment of Soil Organic Carbon in a Rice-Wheat Field of Ludhiana District of Punjab” LINK

“The use of near infrared spectroscopy for the prediction of gaseous and particulate emissions from agricultural feedstock pellets” LINK

“Comparison of methods to estimate crude protein and digestible organic matter content of diets ingested by free-ranging sheep” LINK




Pharma and NIR

“QbD Innovation Through Advances in PAT, Data Analysis Methodologies, and Material Characterization” LINK




Other

“Non-Destructive Evaluation Techniques and What They Tell Us about Wood Property Variation” 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

Spectroscopy and Chemometrics News Weekly #29, 2019

CalibrationModel.com

Spectroscopy and Chemometrics News Weekly 28, 2019 | NIRS NIR Spectroscopy Chemometrics Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC Testing Quality Checking LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 28, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Qualitätslabor LINK

Spettroscopia e Chemiometria Weekly News 28, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem prediction models LINK

This week’s NIR news Weekly is sponsored by YourCompanyNameHere – BestNIRinstruments. Check out their product page … link




Chemometrics

Distinguishing donor age groups via Raman spectroscopic analysis of whole blood & chemometrics! research by Kyle C. Doty & Igor K. Lednev : | LINK

“Variable selection by double competitive adaptive reweighted sampling for calibration transfer of near infrared spectra” LINK

“Intuitive Visualization of Outlier Detection Methods” LINK

“Comparison of three different classification methods performance for the determination of biofuel quality by means of NIR spectroscopy” LINK

” Predicting the quality of ryegrass using hyperspectral imaging” LINK

“A Clustering-Based Partial Least Squares Method for Improving the Freshness Prediction Model of Crucian Carps Fillets by Hyperspectral Image Technology” LINK

” Hyperspectral Uncertainty Quantification by Optimization” LINK




Near Infrared

“Hardwood Species Classification with Hyperspectral Microscopic Images” LINK

” Vibrational Spectroscopy and Chemometrics in Forensic Chemistry: Critical Review, Current Trends and Challenges” LINK

“Combining Fourier Transform MidInfrared Spectroscopy with Chemometric Methods to Detect Adulterations in Milk Powder.” LINK

“Comparison of Cation Exchange Capacity Estimated from Vis-NIR Spectral Reflectance Data and a Pedotransfer Function” LINK



“Modeling of Fatty Acid Methyl Esters, Monoglycerides, Triglycerides and Diglycerides in Rapeseed Oil Biodiesel by Near Infrared Spectroscopy” LINK

“Predicting the contents of polysaccharides and its monosugars in Dendrobium huoshanense by partial least squares regression model using attenuated total reflectance Fourier transform infrared spectroscopy” LINK

“Fast determination of oxides content in cement raw meal using NIR spectroscopy with SPXY algorithm” LINK

“Application of near infrared spectroscopy and chemometrics for the analysis of nutraceuticals in South Africa” LINK


Infrared

“Determination of total sugar content in Siraitia grosvenorii by near infrared diffuse reflectance spectroscopy with wavelength selection techniques” LINK

“How to Measure Coating Thickness of Tablets: Method Comparison of Optical Coherence Tomography, Near-infrared Spectroscopy and Weight-, Height- and Diameter Gain.” LINK




Hyperspectral

“Spectral difference analysis and identification of different maturity blueberry fruit based on hyperspectral imaging using spectral index” LINK




Optics

Focus on photonics, spectroscopy, and spectrometry LINK




Equipment

“Sensors, Vol. 19, Pages 2995: Effect of the Architecture of Fiber-Optic Probes Designed for Soluble Solid Content Prediction in Intact Sugar Beet Slices” LINK

“Miniaturized analytical platform for cocaine detection in oral fluids by MicroNIR/Chemometrics.” LINK

“Handheld near-infrared spectrometer for on-line monitoring of biodiesel production in a continuous process” LINK




Environment

“Optimized Multivariate Analysis for the Discrimination of Cucumber Green Mosaic Mottle Virus-Infected Watermelon Seeds Based on Spectral Imaging” LINK




Agriculture

“Non-destructive screening method for detecting the presence of insects in sorghum grains using near infrared spectroscopy and discriminant analysis” LINK

” NIR hyperspectral imaging with multivariate analysis for measurement of oil and protein” LINK




Pharma

“Development and Validation of In-line Near-Infrared Spectroscopy Based Analytical Method for Commercial Production of a Botanical Drug Product” LINK

In-line monitoring of powder blend homogeneity in continuous drug manufacture using near infrared spectroscopy (with PDF) pharmaceutical binder excipients LINK




Laboratory

“The essential role of omni-capable research laboratories in advancing analytical spectroscopy” LINK




Other

“How to Build Disruptive DataScience Teams: 10 Best Practices” LINK