Spectroscopy and Chemometrics/Machine-Learning News Weekly #25, 2022

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 24, 2022 | 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)

“Nanoarchitectonics of Glass Coatings for Near-Infrared Shielding: From Solid-State Cluster-Based Niobium Chlorides to the Shaping of Nanocomposite Films” LINK

“Differentiation of Gelsemium elegans-containing toxic honeys and non-toxic honeys by near infrared spectroscopy combine with aquaphotomics” LINK

“Identification of geographical origin and different parts of Wolfiporia cocos from Yunnan in China using PLS-DA and ResNet based on FT-NIR” LINK

“Rapid Detection of Goat Milk Mixed with Bovine Milk and Infant Goat Milk Formulas Mixed with Bovine Whey Powder by NIRS Fingerprints LINK

“Nondestructive Characterization of Citrus Fruit by near-Infrared Diffuse Reflectance Spectroscopy (NIRDRS) with Principal Component Analysis (PCA) and Fisher …” LINK

“Near-Infrared Spectroscopy to Predict Provitamin A Carotenoids Content in Maize” LINK

“… in Cytochrome C Oxidase Redox State and Hemoglobin Concentration in Rat Brain During 810 nm Irradiation Measured by Broadband Near-Infrared Spectroscopy” LINK

“A stacked regression ensemble approach for the quantitative determination of biomass feedstock compositions using near infrared spectroscopy” LINK

“Prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy” | LINK

“Country of origin label monitoring of musky and common octopuses (Eledone spp. and Octopus vulgaris) by means of a portable near-infrared spectroscopic device” LINK

“A Long Short-Term Memory Neural Network Based Simultaneous Quantitative Analysis of Multiple Tobacco Chemical Components by Near-Infrared” LINK

“Identification of human hair wigs and animal hair wigs by the method of near infrared spectroscopy modeling” LINK

“Real-time recognition of different imagined actions on the same side of a single limb based on the fNIRS correlation coefficient” | LINK

“EFFECT OF COW INDIVIDUALITY ON ACCURACY OF CALIBRATION MODELS USING NEAR-INFRARED SPECTROSCOPY FOR DETERMINING MILK …” LINK

“Agronomy : Potential of NIRS Technology for the Determination of Cannabinoid Content in Industrial Hemp (Cannabis sativa L.)” LINK

“Polymers : Near-Infrared Light-Remote Localized Drug Delivery Systems Based on Zwitterionic Polymer Nanofibers for Combination Therapy” LINK

“Identification of geographical origin and different parts of Wolfiporia cocos from Yunnan in China using PLSDA and ResNet based on FTNIR” LINK

“Highly Efficient and Stable Near-Infrared Broadband Garnet Phosphor for Multifunctional Phosphor-Converted Light-Emitting Diodes” LINK

“Comparative Determination of Phenolic Compounds in Arabidopsis thaliana Leaf Powder under Distinct Stress Conditions Using FT-IR and FT-NIR Spectroscopy” LINK

“Application of near-infrared spectroscopy/artificial neural network to quantify glycosylated norisoprenoids in Tannat grapes” LINK




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

“Excessive Increase in the Optical Band Gap of NearInfrared Semiconductor Lead (II) Sulfide via the Incorporation of Amino Acids” LINK

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

“Rapid identification of the geographic origin of Taiping Houkui green tea using near‐infrared spectroscopy combined with a variable selection method” LINK

“Near-Infrared Spectroscopy to Predict Provitamin A Carotenoids Content in Maize” LINK




Hyperspectral Imaging (HSI)

“High-Quality Self-Supervised Snapshot Hyperspectral Imaging” LINK

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

“Foods : Identification of Moldy Peanuts under Different Varieties and Moisture Content Using Hyperspectral Imaging and Data Augmentation Technologies” LINK

“Remote Sensing : Wood Decay Detection in Norway Spruce Forests Based on Airborne Hyperspectral and ALS Data” LINK

“Hyperspectral imaging coupled with CNN: A powerful approach for quantitative identification of feather meal and fish by-product meal adulterated in marine fishmeal” LINK




Chemometrics and Machine Learning

“Angle Prediction of Lipid Rich and Calcified Plaque in Computed Tomography Angiography Images” LINK

“Classification of Waste Wood Categories According to the Best Reuse Using Ft-Nir Spectroscopy and Chemometrics” LINK

“Developing a generalized vis-NIR prediction model of soil moisture content using external parameter orthogonalization to reduce the effect of soil type” LINK

“Applied Sciences : A New CO2-EOR Methods Screening Model Based on Interdependency Parameters” LINK

“Insights on the role of chemometrics and vibrational spectroscopy in fruit metabolite analysis” | LINK

“Modeling method and miniaturized wavelength strategy for near-infrared spectroscopic discriminant analysis of soy sauce brand identification” LINK




Optics for Spectroscopy

“Chemical Interface Damping in Nonstoichiometric Semiconductor Plasmonic Nanocrystals: An Effect of the Surrounding Environment” LINK




Research on Spectroscopy

“Alternative Supervised Methods” LINK

“Supervised Methods” LINK




Equipment for Spectroscopy

“As the number falls, alternatives to the Hagberg-Perten falling number method: A review” LINK




Process Control and NIR Sensors

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

“Sensors : Applications of Online UV-Vis Spectrophotometer for Drinking Water Quality Monitoring and Process Control: A Review” LINK




Agriculture NIR-Spectroscopy Usage

“Foods : Analysis of Key Chemical Components in Aqueous Extract Sediments of Panax Ginseng at Different Ages” LINK

” AA′-Stacked Trilayer Hexagonal Boron Nitride” LINK

“Biomass and Plastic Co-Pyrolysis for Syngas Production: Characterisation of Celtis Mildbraedii Sawdust as a Potential Feedstock” LINK

“Estimation of forage quality by near infrared reflectance spectroscopy in dallisgrass, Paspalum dilatatum, poir” LINK

“Detection of Stored Grain Insect” LINK

“Estimation of crude protein and amino acid contents in whole, ground and defatted ground soybeans by different types of near-infrared (NIR) reflectance spectroscopy” LINK

“Molecules : The Potential Use of Herbal Fingerprints by Means of HPLC and TLC for Characterization and Identification of Herbal Extracts and the Distinction of Latvian Native Medicinal Plants” LINK

ZEUTEC presents a new generation of the SpectraAlyzer GRAIN – the SpectraAlyzer GRAIN NEO with new features, state-of-the-art design, better performance and with the aim of bringing a new perspective to grain testing. LINK

“Genetic variation for seed storage protein composition in rapeseed (Brassica napus) and development of near‐infrared reflectance spectroscopy calibration …” LINK




Food & Feed Industry NIR Usage

“Effect of microbial community structures and metabolite profile on greenhouse gas emissions in rice varieties” LINK

“Quantitative assessment of wheat quality using near‐infrared spectroscopy: A comprehensive review” LINK




Pharma Industry NIR Usage

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




Laboratory and NIR-Spectroscopy

“Multispectral Smartphone Camera Reveals Paintings’ Inner Secrets” LINK




Other

“Preparation of One-Dimensional Polyaniline Nanotubes as Anticorrosion Coatings” LINK

“Complex Block Structures; with Focus on LShape Relations” LINK

“D–A type conjugated indandione derivatives: ultrafast broadband nonlinear absorption responses and transient dynamics” | LINK

“Spectroscopic and optical investigations on Er3+ ions doped alkali cadmium phosphate glasses for laser applications” LINK

“Carbazole Isomerism in Helical Radical Cations: Spin Delocalization and SOMO-HOMO Level Inversion in the Diradical State” LINK





Spectroscopy and Chemometrics/Machine-Learning News Weekly #20, 2022

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

Spectroscopy and Chemometrics News Weekly 19, 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 19, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 19, 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)

“Why People and AI Make Good Business Partners” | human AI relationships AI as a Service ( AIaaS ) LabManager NIRS MachineLearning LINK

“A novel aquaphotomics based approach for understanding salvianolic acid A conversion reaction with near infrared spectroscopy” LINK

“ex type determination in papaya seeds and leaves using near infrared spectroscopy combined with multivariate techniques and machine learnin” LINK

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

“Rapid authentication and composition determination of cellulose films by UV-VIS-NIR 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

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

“Sensors : LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI” LINK

“Using metaheuristic algorithms to improve the estimation of acidity in Fuji apples using NIR spectroscopy” LINK

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

“Multimodal diffuse optical system integrating DSCA-NIRS and cSFDI for measuring tissue metabolism” LINK

extruded granules extruder NIR AEE audible acoustic emission granule drying process PAT LINK

“Fast Noniterative Data Analysis Method for Frequency-Domain Near-Infrared Spectroscopy with the Microscopic Beer-Lambert Law” LINK

“Vis-NIR Hyperspectral Dimensionality Reduction for Nondestructive Identification of China Northeast Rice” | LINK

“FT-NIR Spectroscopy for the Non-Invasive Study of Binders and Multi-Layered Structures in Ancient Paintings: Artworks of the Lombard Renaissance as Case Studies” LINK

“In Vivo Measurement Strategy for Near-Infrared Noninvasive Glucose Detection and Human Body Verification” LINK

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

“Comparative study on the real-time monitoring of a fluid bed drying process of extruded granules using near-infrared spectroscopy and audible acoustic emission” LINK

“Fast detection of cotton content in silk/cotton textiles by handheld near-infrared spectroscopy: a performance comparison of four different instruments” LINK

“Evaluation of optical properties of tofu samples produced with different coagulation temperatures and times using near-infrared transmission spectroscopy” LINK

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

“Near-infrared spectroscopy to estimate the chemical element concentration in soils and sediments in a rural catchment” LINK

“Ensemble classification and regression techniques combined with portable near infrared spectroscopy for facile and rapid detection of water adulteration in bovine …” LINK

“Characterization of crude oils with a portable NIR spectrometer” CrudeOil NIRspectrometer LINK




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

“A Device for Measuring Apple Sweetness Using Near Infrared Spectroscopy” LINK

“Nearinfrared fluorophores based on heptamethine cyanine dyes: from their synthesis and photophysical properties to recent optical sensing and bioimaging applications” LINK

“Use of Attenuated Total Reflection Fourier Transform Infrared Spectroscopy and Principal Component Analysis for the Assessment of Engine Oils” | LINK

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

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

“Near-infrared spectra of aqueous glucose solutions” LINK

“Determination of storage period of harvested plums by nearinfrared spectroscopy and quality attributes” LINK




Hyperspectral Imaging (HSI)

“Rapid Detection of Different Types of Soil Nitrogen Using Near-Infrared Hyperspectral Imaging” LINK

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

“Estimating soil moisture content under grassland with hyperspectral data using radiative transfer modelling and machine learning” LINK

“Improving rice nitrogen stress diagnosis by denoising strips in hyperspectral images via deep learning” LINK

“Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach” LINK




Chemometrics and Machine Learning

“Remote Sensing : Early Detection of Dendroctonus valens Infestation with Machine Learning Algorithms Based on Hyperspectral Reflectance” LINK

“Applied microwave power estimation of black carrot powders using spectroscopy combined with chemometrics” LINK

DataScientist Job: Expectation vs. Reality [infographic] BigData DataScience Analytics AI MachineLearning ArtificialIntelligence Data DataAnalytics Python SQL Statistics DataViz Careers Jobs FeatureEngineering DataPrep DataCleaning LINK

“Agronomy : Detection of Adulterations in Fruit Juices Using Machine Learning Methods over FT-IR Spectroscopic Data” LINK

“Reflectance Based Models for Non-Destructive Prediction of Lycopene Content in Tomato Fruits” | LINK

“The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation” LINK

“Machine Learning Strategies for the Retrieval of Leaf-Chlorophyll Dynamics: Model Choice, Sequential Versus Retraining Learning, and Hyperspectral Predictors” | LINK

“In-line near-infrared analysis of milk coupled with machine learning methods for the daily prediction of blood metabolic profile in dairy cattle” LINK

“Near-infrared spectroscopy with chemometrics for identification and quantification of adulteration in high-quality stingless bee honey” LINK

“Rapid identification and quantification of intramuscular fat adulteration in lamb meat with VIS-NIR spectroscopy and chemometrics methods” LINK




Optics for Spectroscopy

“Spectrum Reconstruction with Filter-Free Photodetectors Based on Graded-Band-Gap Perovskite Quantum Dot Heterojunctions” LINK




Facts

“Sensors : Dietary Patterns Associated with Diabetes in an Older Population from Southern Italy Using an Unsupervised Learning Approach” | LINK




Research on Spectroscopy

“A Study of C= O… HO and OH… OH (Dimer, Trimer, and Oligomer) Hydrogen Bonding in a Poly (4-vinylphenol) 30%/Poly (methyl methacrylate) 70% Blend and its …” LINK

“Deeper insights into the photoluminescence properties and (photo) chemical reactivity of cadmium red (CdS1− xSex) paints in renowned twentieth century …” | LINK




Equipment for Spectroscopy

“Green Textile Materials for Surface Enhanced Raman Spectroscopy Identification of Pesticides Using a Raman Handheld Spectrometer for In-Field Detection” LINK

“Characterization of Crude Oils with a Portable Nir Spectrometer” LINK

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

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




Environment NIR-Spectroscopy Application

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

“Sensors : Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils” LINK

“Evaluation of Vis-Nir Pretreatments Combined with Pls Regression for Estimation SOC, Cec and Clay in Silty Soils from Eastern Croatia” LINK

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




Agriculture NIR-Spectroscopy Usage

“Site-specific seeding for maize production using management zone maps delineated with multi-sensors data fusion scheme” LINK

“Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine” | LINK

“A LUCASbased midinfrared soil spectral library: Its usefulness for soil survey and precision agriculture” LINK

“Identification of Microplastics in Biosolids Using Ftir and Vis-Nir Spectroscopy Enhanced by Chemometric Methods” LINK

“Agriculture : Feature Wavelength Selection Based on the Combination of Image and Spectrum for Aflatoxin B1 Concentration Classification in Single Maize Kernels” LINK




Food & Feed Industry NIR Usage

“Agronomy : Analysis of Physico-Chemical and Organoleptic Fruit Parameters Relevant for Tomato Quality” LINK




Chemical Industry NIR Usage

“Polymers : Microscopic and Structural Studies of an Antimicrobial Polymer Film Modified with a Natural Filler Based on Triterpenoids” LINK




Laboratory and NIR-Spectroscopy

“Laboratory Hyperspectral Image Acquisition System Setup and Validation” LINK




Other

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





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Spectroscopy and Chemometrics/Machine-Learning News Weekly #11, 2022

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 10, 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 10, 2022 | NIRS NIR Spectroscopy MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 10, 2022 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near-Infrared Spectroscopy (NIRS)

“Rapid Measurement of Cellulose, Hemicellulose, and Lignin Content in Sargassum horneri by Near-Infrared Spectroscopy and Characteristic Variables Selection Methods” LINK

“Freshness Identification of Oysters Based on Colorimetric Sensor Array Combined with Image Processing and Visible Near-Infrared Spectroscopy” LINK

“Impaired Brain Activity in Patients With Persistent Atrial Fibrillation Assessed by Near-infrared Spectroscopy and Its Changes After Catheter Ablation” LINK

“Evaluation of a novel wireless near-infrared spectroscopy (NIRS) device in the detection of tourniquet induced ischaemia” LINK

“Identification and Quantitative Determination of Virgin and Recycled Cashmere: a Near-Infrared Spectroscopy Study” LINK

“Effect of Different Scanning Distances on Estimation of Oil Content in Oil Palm Fruitlets using Visible Shortwave Near Infrared Spectroscopy” LINK

“Prediction for total moisture content in wood pellets by Near Infrared Spectroscopy (NIRS)” LINK

“Rapid Construction of an Optimal Model for Near-Infrared Spectroscopy (NIRS) by Particle Swarm Optimization (PSO)” | LINK

“Learning fruit class from short wave near infrared spectral features, an AI approach towards determining fruit type” LINK

“Near infrared spectroscopy as a fast and non-destructive technique for total acidity prediction of intact mango: Comparison among regression approaches” LINK

“Non-destructive determination of four tea polyphenols in fresh tea using visible and near-infrared spectroscopy” LINK

“Invited review: A comprehensive review of visible and near-infrared spectroscopy for predicting the chemical composition of cheese” |(21)01099-7/fulltext LINK

“Cr3+-Doped Broadband Near Infrared Diopside Phosphor for NIR pc-LED” | LINK

“RAPID PREDICTION OF HYDROCARBON MOLECULAR COMPOSITION OF NAPHTHA BASED ON NEAR INFRARED SPECTROSCOPY” LINK

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




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

“Nearinfrared spectroscopy for the inline classification and characterization of fruit juices for a productcustomized flash pasteurization” | LINK




Raman Spectroscopy

“A Proof-of-Principle Study of Non-invasive Identification of Peanut Genotypes and Nematode Resistance Using Raman Spectroscopy” | LINK




Hyperspectral Imaging (HSI)

“Quantitative prediction of moisture content distribution in acetylated wood using near-infrared hyperspectral imaging” | LINK

“Hyperspectral Imaging for Clinical Applications” | LINK

“Indirect quantitative analysis of soluble solid content in citrus by the leaves using hyperspectral imaging combined with machine learning” LINK

“Polysaccharide prediction in Ganoderma lucidum fruiting body by hyperspectral imaging” LINK

“Accurate prediction of soluble solid content in dried Hami jujube using SWIR hyperspectral imaging with comparative analysis of models” LINK




Spectral Imaging

“Spectral Comparison of UAV-Based Hyper and Multispectral Cameras for Precision Viticulture” LINK




Chemometrics and Machine Learning

“Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared Spectroscopy” LINK

“Screening Method for the Detection of Other Allergenic Nuts in Cashew Nuts Using Chemometrics and a Portable Near-Infrared Spectrophotometer” | LINK

“Removal of external influences from on-line vis-NIR spectra for predicting soil organic carbon using machine learning” LINK

“Silica-Carbonate of Ba(II) and Fe<sup>2+</sup>/Fe<sup>3+</sup> Complex as Study Models to Understand Prebiotic Chemistry” LINK

“Estimation of soil organic carbon using chemometrics: a comparison between mid-infrared and visible near infrared diffuse reflectance spectroscopy” LINK

“Quantitative Analysis of Methanol in Methanol Gasoline by Calibration Transfer Strategy Based on Kernel Domain Adaptive Partial Least Squares (kda-PLS)” | LINK

“Transformer Model for Functional Near-Infrared Spectroscopy Classification” LINK

“Forests : Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared Spectroscopy” LINK

“Few-Shot Open-Set Recognition of Hyperspectral Images With Outlier Calibration Network” LINK

“DISCRIMINATING BETWEEN FOREST PLANTATION GENERA USING REMOTE SENSING AND MACHINE LEARNING ALGORITHMS” LINK

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




Optics for Spectroscopy

“Ultrafast and efficient energy transfer in a one- and two-photon sensitized rhodamine-BODIPY dyad: a perspective for broadly absorbing photocages” LINK




Research on Spectroscopy

“Polymers : Multifunctional Slippery Polydimethylsiloxane/Carbon Nanotube Composite Strain Sensor with Excellent Liquid Repellence and Anti-Icing/Deicing Performance” LINK




Environment NIR-Spectroscopy Application

“Divergent responses of soil microbial functional groups to long-term high nitrogen presence in the tropical forests” LINK

“Ammonia-oxidizing bacteria and fungal denitrifier diversity are associated with N2O production in tropical soils” LINK

“In Situ Synthesis of Phenoxazine Dyes in Water: Application for “TurnOn” Fluorogenic and Chromogenic Detection of Nitric Oxide” LINK

“Remote Sensing : Autonomous Differential Absorption Laser Device for Remote Sensing of Atmospheric Greenhouse Gases” LINK

“Remote Sensing : Maize Characteristics Estimation and Classification by Spectral Data under Two Soil Phosphorus Levels” LINK




Agriculture NIR-Spectroscopy Usage

” Yield, Yield Components, and Nutritive Value of Perennial Forage Grass Grown under Supplementary Irrigation” | LINK

“Seasonality and Grazing Management Effect on Growth and Nutritional Composition of Herbage on Semi-Natural Grasslands Grazed by Dairy Cows in Southwest …” LINK

“Sensors : Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices” LINK

“Applied Sciences : Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture” LINK

“Determination of adulteration in wheat flour using multi-grained cascade forest-related models coupled with the fusion information of hyperspectral imaging” LINK

“Agronomy : Assessment of the Impact of Soil Contamination with Cadmium and Mercury on Leaf Nitrogen Content and Miscanthus Yield Applying Proximal Spectroscopy” LINK

“Agronomy : Current Skills of Students and Their Expected Future Training Needs on Precision Agriculture: Evidence from Euro-Mediterranean Higher Education Institutes” LINK




Horticulture NIR-Spectroscopy Applications

“Rapid and nondestructive prediction of firmness, soluble solids content, and pH in kiwifruit using Vis-NIR spatially resolved spectroscopy” LINK

“Spectral pattern study of citrus black rot caused by Alternaria alternata and selecting optimal wavelengths for decay detection” | LINK




Food & Feed Industry NIR Usage

“Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection” LINK

“Foods : Promoting Sustainable Lifestyle Habits: “Real Food” and Social Media in Spain” LINK

“Smartphone imaging spectrometer for egg/meat freshness monitoring” LINK




Beverage and Drink Industry NIR Usage

“The Potential of Spectroscopic Techniques in Coffee Analysis—A Review” LINK




Chemical Industry NIR Usage

“Polymers : Adsorption of Hydrolysed Polyacrylamide onto Calcium Carbonate” LINK




Pharma Industry NIR Usage

“Antibiotics : Curcumin: Biological Activities and Modern Pharmaceutical Forms” LINK




Other

“Better utilization of Lolium perenne in biorefineries based on its chemical composition” LINK

“Test-Retest Reliability of the Microvascular Oxygenation Recovery Response Subsequent to Submaximal Cycling Exercise” LINK

“Characterization of novel lunar highland and mare simulants for ISRU research applications” LINK

“Band Alignment of Ultrawide Bandgap ε-Ga2O3/h-BCN Heterojunction Epitaxially Grown by Metalorganic Chemical Vapor Deposition” LINK

“Coatings : Application of Artificial Neural Networks in Analysis of Time-Variable Optical Reflectance Spectra in Digital Light Projection Spectroscopy” LINK

“Kandungan karotenoid, antioksidan, dan kadar air dua varietas cabai rawit pada tingkat kematangan berbeda dan deteksi non-destruktif” LINK




Spectroscopy and Chemometrics News Weekly #35, 2020

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 34, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

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

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Near-Infrared Spectroscopy (NIRS)

“Modelos NIRS para as características químicas da madeira de Eucalyptus benthamii Maiden & Cambage” LINK

“Application of in situ near infra-red spectroscopy (NIRS) for monitoring biopharmaceuticals production by cell cultures” LINK

“Using the NIRS for analyzes of soil clay content” LINK

“Determination of compost maturity using near infrared spectroscopy (NIRS)” LINK

“Screening Risk Assessment of Agricultural Areas under a High Level of Anthropopressure Based on Chemical Indexes and VIS-NIR Spectroscopy” LINK

“… an algorithm for processing Red Green Blue (RGB) images for the estimation of crude protein in grasses vs Near Infrared Reflectance Spectroscopy Technology (NIRS …” LINK

“Monitoring of cheese maturation using near infrared-hyperspectral imaging (NIR-HIS)” LINK

“Selection of sugarcane clones via multivariate models using near-infrared (NIR) spectroscopy data” LINK




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

“Rapid and simultaneous analysis of multiple wine quality indicators through near-infrared spectroscopy with twice optimization for wavelength model” LINK

“Manuka honey adulteration detection based on near-infrared spectroscopy combined with aquaphotomics” LINK

” Identification of Marine Fish Taxa by Linear Discriminant Analysis of Reflection Spectra in the Near-Infrared Region” LINK

“Assessment of Intact Macadamia Nut Internal Defects Using Near-Infrared Spectroscopy” LINK

“Rational design of near-infrared platinum(ii)-acetylide conjugated polymers for photoacoustic imaging-guided synergistic phototherapy under 808 nm irradiation.” LINK

“Classification of fish species from different ecosystems using the near infrared diffuse reflectance spectra of otoliths” LINK

“Three new Amazonian species of Myrcia sect. Myrcia (Myrtaceae) based on morphology and near-infrared spectroscopy” LINK

“Rapid Online Determination of Feed Concentration in Nitroguanidine Spray Drying Process by Near Infrared Spectroscopy” LINK




Raman Spectroscopy

“Monitoring the Caustic Dissolution of Aluminum Alloy in a Radiochemical Hot Cell Using Raman Spectroscopy” LINK




Hyperspectral Imaging (HSI)

“Hyperspectral Imaging and Deep Learning for Food Safety Assessment” LINK




Chemometrics and Machine Learning

“Rapid and Nondestructive Freshness Determination of Tilapia Fillets by a Portable Near-Infrared Spectrometer Combined with Chemometrics Methods” LINK

“Non-Targeted Detection of Adulterants in Almond Powder Using Spectroscopic Techniques Combined with Chemometrics.” LINK




Environment NIR-Spectroscopy Application

“Mobile Proximal Sensing with Visible and Near Infrared Spectroscopy for Digital Soil Mapping” LINK




Agriculture NIR-Spectroscopy Usage

“Imaging Techniques for Chicken Products Detection” LINK

“Usage of visual and near-infrared spectroscopy to predict soil properties in forest stands” LINK

“NUTRIENT CONTENT OF SOYBEAN MEAL FROM DIFFERENT ORIGINS BASED ON NEAR INFRARED REFLECTANCE SPECTROSCOPY” LINK

“Robustness of visible near-infrared and mid-infrared spectroscopic models to changes in the quantity and quality of crop residues in soil” LINK

“Use of leaf hyperspectral data and different regression models to estimate photosynthetic parameters (Vcmax and Jmax) in three different row crops” LINK

“Rapid and direct detection of small microplastics in aquatic samples by a new near infrared hyperspectral imaging (NIR-HSI) method” LINK




Horticulture NIR-Spectroscopy Applications

“Prediction of Soluble Solids Content During Storage of Apples with Different Maturity Based on VIS/NIR Spectroscopy” LINK

“A new spectral pretreatment method for detecting soluble solids content of pears using Vis/NIR spectroscopy” LINK

“Research on the Performance of Juicy Peach Sugar Content Detection Model Based on Near Infrared Spectroscopy” LINK




Forestry and Wood Industry NIR Usage

“The Effect of Construction and Demolition Waste Plastic Fractions on Wood-Polymer Composite Properties” LINK




Food & Feed Industry NIR Usage

“Non-destructive Assessment of Flesh Firmness and Dietary Antioxidants of Greenhouse-grown Tomato (Solanum lycopersicum L.) at Different Fruit Maturity Stages” LINK

“Comparative analysis of rice seed viability detection based on different spectral bands” LINK

“Detection of chocolate powder adulteration with peanut using near-infrared hyperspectral imaging and Multivariate Curve Resolution” LINK





Spectroscopy and Chemometrics News Weekly #29, 2020

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 28, 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 28, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 28, 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)

“Non-invasive method to identify the type of green tea inside teabag using NIR spectroscopy, support vector machines and Bayesian optimization” LINK

“Online milk composition analysis with an on-farm near-infrared sensor” LINK

“Anonymous fecal sampling and NIRS studies of diet quality: Problem or opportunity?” LINK

“Organic and Symbiotic Fertilization of Tomato Plants Monitored by Litterbag-NIRS and Foliar-NIRS Rapid Spectroscopic Methods Running title: Litterbag-NIRS and Foliar-NIRS model in symbiotic tomato” LINK

“Determination of crude protein and metabolized energy with near infrared reflectance spectroscopy (NIRS) in ruminant mixed feeds” LINK




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

“Near Infrared Spectroscopy as an efficient tool for the Qualitative and Quantitative Determination of Sugar Adulteration in Milk” | LINK

“NEAR INFRARED SPECTROSCOPY AS A NEW FIRE SEVERITY METRIC” by Bushfire and Natural Hazards CRC LINK

“Near-infrared spectroscopy for the concurrent quality prediction and status monitoring of gasoline blending” LINK

“Application of Selective Near Infrared Spectroscopy for Qualitative and Quantitative Prediction of Water Adulteration in Milk” LINK

“Predicting Macronutrient of Baby Food using Near-infrared Spectroscopy and Deep Learning Approach” LINK

“Detection of heat treatment of honey with near infrared spectroscopy” LINK

“Use of near infrared spectroscopy in cotton seeds physiological quality evaluation” LINK

“Detection of Haemonchus contortus nematode eggs in sheep faeces using near and mid-infrared spectroscopy” LINK

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




Hyperspectral Imaging (HSI)

“Hyperspectral waveband selection algorithm based on weighted maximum relevance minimum redundancy and its stability analysis” LINK




Chemometrics and Machine Learning

“Comparative quantification of chlorophyll and polyphenol levels in grapevine leaves sampled from different geographical locations” LINK

“Screening method for determination of C18:1 trans fatty acids positional isomers in chocolate by 1H NMR and chemometrics” LINK

“Combination of efficient signal pre-processing and optimal band combination algorithm to predict soil organic matter through visible and near-infrared spectra” LINK

“A chemometric approach to the evaluation of the ageing ability of red wines” LINK

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

“A Feasible Approach to Detect Pesticides in Food Samples Using THz-FDS and Chemometrics” LINK

“Prediction of Soluble Solids Content in Green Plum by Using a Sparse Autoencoder” LINK




Process Control and NIR Sensors

“Real-time and field monitoring of the key parameters in industrial trough composting process using a handheld near infrared spectrometer” LINK




Environment NIR-Spectroscopy Application

“Detection and analysis of soil water content based on experimental reflectance spectrum data” LINK

” International Soil and Water Conservation Research” | LINK




Agriculture NIR-Spectroscopy Usage

“Detecting Low Concentrations of Nitrogen-Based Adulterants in Whey Protein Powder Using Benchtop and Handheld NIR Spectrometers and the Feasibility of Scanning through Plastic Bag.” LINK

“Assessment of the genetic diversity of sweetpotato germplasm collections for protein content” LINK

“Near-infrared spectroscopy and imaging in protein research” LINK

“Foods, Vol. 9, Pages 710: Application of ATR-FT-MIR for Tracing the Geographical Origin of Honey Produced in the Maltese Islands” LINK

“Agriculture, Vol. 10, Pages 193: Content of Polyphenolic Compounds and Antioxidant Potential of Some Bulgarian Red Grape Varieties and Red Wines, Determined by HPLC, UV, and NIR Spectroscopy” LINK

“Agronomy, Vol. 10, Pages 787: Assessing Soil Key Fertility Attributes Using a Portable X-Ray Fluorescence: A Simple Method to Overcome Matrix Effect” LINK




Food & Feed Industry NIR Usage

“Non‑destructive testing technology for raw eggs freshness: a review” LINK

“Quantification of multiple adulterants in beef protein powder by FT-NIR” LINK




Beverage and Drink Industry NIR Usage

“Beer Aroma and Quality Traits Assessment Using Artificial Intelligence” LINK




Other

“Tetrahedral Mn4+ as chromophore in sillenite-type compounds” 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





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