Spectroscopy and Chemometrics/Machine-Learning News Weekly #24, 2022

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 23, 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)

“Real-Time Defect Inspection of Green Coffee Beans Using NIR Snapshot Hyperspectral Imaging” 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

“Fruit variability impacts puree quality: assessment on individually processed apples using the visible and near infrared spectroscopy” LINK

“Visible-NIR Spectral Characteristics and Grade Inversion Model of Skarn-type Iron Ore” LINK

“Digital Detection of Olive Oil Rancidity Levels and Aroma Profiles Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Machine Learning Modelling” LINK

“Qualitative classification of Dendrobium huoshanense (Feng dou) using fast non-destructive hand-held near infrared spectroscopy” LINK

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


“Near-Infrared Transflectance Spectroscopy Discriminates Solutions Containing Two Commercial Formulations of Botulinum Toxin Type A Diluted at Recommended …” LINK

“… of the column chromatographic process of Phellodendri Chinensis Cortex Part I: End-point determination based on near-infrared spectroscopy combined with machine …” | LINK

“NIR-Based Intelligent Sensing of Product Yield Stress for High-Value Bioresorbable Polymer Processing” LINK

“Measurement of water-holding capacity in fermented milk using near-infrared spectroscopy combined with chemometric methods” LINK

“Fighting Fake Medicines” by | fake medicines pills falsified medicines fakemedicines fakedrugs fakepills illicit drugs digitaltechnology NIRS LINK

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

“Rapid detection of adulteration of glutinous rice as raw material of Shaoxing Huangjiu (Chinese Rice Wine) by near infrared spectroscopy combined with chemometrics” LINK

“On-site illicit-drug detection with an integrated near-infrared spectral sensor: A proof of concept” LINK

“Effects of degraded speech processing and binaural unmasking investigated using functional near-infrared spectroscopy (fNIRS)” | LINK

“Qualitative analysis of post-consumer and post-industrial waste via near-infrared, visual and induction identification with experimental sensor-based sorting setup” | LINK

“Blended fabric with integrated neural network based on attention mechanism qualitative identification method of near infrared spectroscopy” LINK

“Deep learning-based motion artifact removal in functional 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

“Rapid Nondestructive Detection Enabled by an UltraBroadband NIR pcLED” LINK

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

“Determination of Dangshan Pear Pollen Vitality Using Near-Infrared Spectroscopy” LINK

“Green tea grades identification via Fourier transform nearinfrared spectroscopy and weighted global fuzzy uncorrelated discriminant transform” LINK

“Physicochemical Properties and Detection of Glucose Syrup Adulterated Kelulut (Heterotrigona Itama) Honey Using Near‐Infrared Spectroscopy” LINK

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

“Oral lichen planus identification by mid-infrared spectroscopy of oral biofluids: a case-control study” LINK

Hyperspectral Imaging (HSI)

“Plant Species Classification Based on Hyperspectral Imaging via a Lightweight Convolutional Neural Network Model” | LINK

“Sensors : Differentiation of Livestock Internal Organs Using Visible and Short-Wave Infrared Hyperspectral Imaging Sensors” LINK

“Prediction of peanut seed vigor based on hyperspectral images” LINK

“Agriculture : Green Banana Maturity Classification and Quality Evaluation Using Hyperspectral Imaging” LINK

“Detecting aggressive papillary thyroid carcinoma using hyperspectral imaging and radiomic features” | LINK

Chemometrics and Machine Learning

“Modeling Method and Miniaturized Wavelength Strategy for Near-infrared Spectroscopic Discriminant Analysis of Soy Sauce Brand Identification” LINK

“Improvement of NIR Prediction Ability by Dual Model Optimization in Fusion of NSIA and SA Methods” LINK

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

“Foods : Rapid Detection of Carbendazim Residue in Apple Using Surface-Enhanced Raman Scattering and Coupled Chemometric Algorithm” LINK

“Biosensors : Benchmarking Spectroscopic Techniques Combined with Machine Learning to Study Oak Barrels for Wine Ageing” LINK

“Agronomy : Estimation of Winter Wheat Residue Coverage Based on GF-1 Imagery and Machine Learning Algorithm” LINK

“Construction and application of a qualitative and quantitative analysis system of three boscalid polymorphs based on solid-state analytical methods and chemometric tools” LINK

“Discrimination and source correspondence of black gel inks using Raman spectroscopy and chemometric analysis with UMAP and PLS-DA” LINK

Optics for Spectroscopy

“Recent Advances in Luminescent Downconversion: New Materials, Techniques, and Applications in Solar Cells” LINK


“Ordered structures in liquid water: Is cold water a genuine liquid?” LINK

Environment NIR-Spectroscopy Application

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

“Genetic dissection of seed characteristics in field pennycress via genome-wide association mapping studies” | LINK

“Remote Sensing : Retrieval of Chlorophyll-a Concentrations of Class II Water Bodies of Inland Lakes and Reservoirs Based on ZY1-02D Satellite Hyperspectral Data” LINK

“Boron Imidazolate FrameworkDerived Porous Carbon Nanospheres for DualMode BioimagingGuided Photothermal/Sonodynamic Synergistic Cancer Therapy” LINK

Agriculture NIR-Spectroscopy Usage

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

“Performance of open-path lasers and Fourier transform infrared spectroscopic systems in agriculture emissions research” LINK

“Plants : Sucrose Synthase and Fructokinase Are Required for Proper Meristematic and Vascular Development” LINK

“Genetic variation for seed storage protein composition in rapeseed (Brassica napus) and development of nearinfrared reflectance spectroscopy calibration equations” LINK

“Synthesis, Characterization and Antibacterial Activity of Ag‐TiO2‐Fe Composite Thin Films” LINK

Horticulture NIR-Spectroscopy Applications

“Foods : Consensual Regression of Soluble Solids Content in Peach by Near Infrared Spectrocopy” LINK

Forestry and Wood Industry NIR Usage

“Molecules : Chemical Diversity and Potential Target Network of Woody Peony Flower Essential Oil from Eleven Representative Cultivars (Paeonia × suffruticosa Andr.)” LINK

Food & Feed Industry NIR Usage

“Discrimination of centre composition in panned chocolate goods using nearinfrared spectroscopy” LINK

“Response of N2O emissions to N fertilizer reduction combined with biochar application in a rain-fed winter wheat ecosystem” LINK

“Quantitative detection of talcum powder in wheat flour based on near-infrared spectroscopy and hybrid feature selection” LINK

“Foods : Characterization and Discrimination of Italian Olive (Olea europaea sativa) Cultivars by Production Area Using Different Analytical Methods Combined with Chemometric Analysis” LINK

Pharma Industry NIR Usage

“Detection of low numbers of bacterial cells in pharmaceutical drug product using Raman Spectroscopy and PLS-DA multivariate analysis” LINK


“On honey authentication and adulterant detection techniques” LINK

“Intensifying upconverted ultraviolet emission towards efficient reactive oxygen species generation” LINK

“Optical frequency comb Fourier transform cavity ring-down spectroscopy” LINK

“Novel Approaches and Cognitive Neuroscience Perspectives on False Memory and Deception” | LINK

“Investigation of structural and optical properties of lithium lead bismuth silicate glasses” | LINK


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


“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


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


“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


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


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

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

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

“Taming salophen in rare earth metallocene chemistry” LINK

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

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

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

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

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

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

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

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

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

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

Spectroscopy and Chemometrics/Machine-Learning News Weekly #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


“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


“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


“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/Machine-Learning News Weekly #45, 2021

NIR Calibration-Model Services

Get the Spectroscopy and Chemometrics / Machine-Learning News Weekly in real time on Twitter Twitter @ CalibModel and follow us.

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

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

Near-Infrared Spectroscopy (NIRS)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Raman Spectroscopy

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

Hyperspectral Imaging (HSI)

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

“Hyperspectral camera development on an unmanned aerial vehicle” LINK

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

Spectral Imaging

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

Chemometrics and Machine Learning

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

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


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

Equipment for Spectroscopy

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

Environment NIR-Spectroscopy Application

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

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

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

Agriculture NIR-Spectroscopy Usage

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

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

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

Horticulture NIR-Spectroscopy Applications

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

Food & Feed Industry NIR Usage

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


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

Spectroscopy and Chemometrics News Weekly #21, 2021

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 20, 2021 | 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)

“Implementation of Non Destructive FTNIR Method for Quick Estimation of Peanut Quality Based on FFA and Peroxide Value” LINK

“Fast Detection of Cumin and Fennel Using NIR Spectroscopy Combined with Deep Learning Algorithms” LINK

“Prediction of bioactive compounds in barley by near-infrared reflectance spectroscopy (NIRS)” LINK

“Classification by bitterness of intact almonds analysed in bulk using NIR spectroscopy” LINK

” Feature discovery in NIR spectroscopy based Rocha pear classification” LINK

” The effect of muscle type and ageing on Near Infrared (NIR) Spectroscopy classification of game meat species using a portable instrument” LINK

“Application of benchtop NIR spectroscopy coupled with multivariate analysis for rapid prediction of antioxidant properties of walnut (Juglans regia)” LINK

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

“Performance of near infrared spectroscopy of a solid cattle and poultry manure database depends on the sample preparation and regression method used” LINK

“Measurements of High Oleic Purity in Peanut Lots Using Rapid, Single Kernel NearInfrared Reflectance Spectroscopy” LINK

“Predicting Calcium and Phosphorus Concentrations in Imported Hay by near Infrared Reflectance Spectroscopy” LINK

“Assessment of calibration methods for nitrogen estimation in wet and dry soil samples with different wavelength ranges using near-infrared spectroscopy” LINK

“Near-infrared spectroscopy: Alternative method for assessment of stable carbon isotopes in various soil profiles in Chile” LINK

“Near-Infrared Spectroscopy in Neurocritical Care: a Review of Recent Updates” LINK

“Near infrared methodology for growth monitoring of spinach plants in the field” LINK

“The Penetration Analysis of Airborne Ku-Band Radar versus Satellite Infrared Lidar Based on the Height and Energy Percentiles in the Boreal Forest” LINK

Chemometrics and Machine Learning

“A near infrared spectroscopy calibration for the prediction of fresh grass quality on Irish pastures” LINK

“Determination of petroleum hydrocarbon contamination in soil using VNIR DRS and PLSR modeling” LINK

“Combining Thermal and RGB Imaging Indices with Multivariate and Data-Driven Modeling to Estimate the Growth, Water Status, and Yield of Potato under Different Drip Irrigation Regimes” LINK

“Near infrared spectroscopy coupled chemometric algorithms for prediction of the antioxidant activity of peanut seed (Arachis hypogaea)” LINK

“A probabilistic model for missing traffic volume reconstruction based on data fusion.” LINK

“Evaluating validation strategies on the performance of soil property prediction from regional to continental spectral data” LINK

“A sample selection method specific to unknown test samples for calibration and validation sets based on spectra similarity” LINK

“Comparison of the predictive ability of NIR calibration models developed to predict nutritional parameters in total mixed rations by using reference data expressed “as …” LINK

“An Automated Data Fusion-Based Gear Faults Classification Framework in Rotating Machines” LINK

Optics for Spectroscopy

“Glassy Carbon Electrode Modified with C/Au Nanostructured Materials for Simultaneous Determination of Hydroquinone and Catechol in Water Matrices” Chemosensors LINK


“Machine Learning Approaches for Inferring Liver Diseases and Detecting Blood Donors from Medical Diagnosis. (arXiv:2104.12055v1 [stat.ML])” LINK

Equipment for Spectroscopy

“Physicochemical Analysis and Adulteration Detection in Malaysia Stingless Bee Honey Using a Handheld Near‐Infrared Spectrometer” LINK

Environment NIR-Spectroscopy Application

“Association of Physicochemical Characteristics, Aggregate Indices, Major Ions, and Trace Elements in Developing Groundwater Quality Index (GWQI) in Agricultural Area” LINK

“Mapping Smallholder Maize Farms Using Multi-Temporal Sentinel-1 Data in Support of the Sustainable Development Goals” LINK

Agriculture NIR-Spectroscopy Usage

“Discrimination of soils managed with different sources of fertilization and plant species in organic and conventional farming through nearinfrared spectroscopy and chemometrics” LINK

“Sensors, Vol. 21, Pages 3038: Addressing the Selectivity of Enzyme Biosensors: Solutions and Perspectives” LINK

” Wheat and triticale whole grain near infrared hyperspectral imaging for protein, moisture and kernel hardness quantification” LINK

“Fostering soil sustainability and food safety in urban agricultural areas of Naples, Italy” LINK

“Protein vibrations and their localization behaviour. A numerical scaling analysis” LINK

Horticulture NIR-Spectroscopy Applications

” Effects of The Odors of Japanese Citrus Iyokan (Citrus Iyo) and Yuzu (Citrus Junos) on Human Mood and Physiology” LINK


Forestry and Wood Industry NIR Usage

“Density, extractives and decay resistance variabilities within branch wood from four agroforestry hardwood species” LINK

Food & Feed Industry NIR Usage

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

Pharma Industry NIR Usage

“Influence of ESGC Indicators on Financial Performance of Listed Pharmaceutical Companies” LINK

Medicinal Spectroscopy

“Hybrid Spectral-IRDx: Near-IR and Ultrasound Attenuation System for Differentiating Breast Cancer from Adjacent Normal Tissue” LINK


“Formation of phosphonate coatings for improved chemical stability of upconverting nanoparticles under physiological conditions” LINK

“不同贮藏期水蜜桃硬度及糖度的检测研究” LINK

“基于野外可见近红外光谱和水分影响校正算法的土壤剖面有机碳预测” LINK

“Manipulation of up-conversion emission in NaYF4 core@shell nanoparticles doped by Er3+, Tm3+, or Yb3+ ions by excitation wavelength-three ions-plenty of possibilities” LINK

“Covalent modification of franckeite with maleimides: connecting molecules and van der Waals heterostructures” 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

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”

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.

    -> 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


  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)


  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.


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.

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 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


  • 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.


    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