Spectroscopy and Chemometrics / Machine Learning News Weekly #29, 2022

NIR Calibration-Model Services

How to Develop Near-Infrared Spectroscopy Calibrations in the 21st Century? | Chemometrics LINK

Spectroscopy and Chemometrics News Weekly 28, 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 28, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 28, 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 Determination of Cellulose and Hemicellulose Contents in Corn Stover Using Near-Infrared Spectroscopy Combined with Wavelength Selection” LINK

“A Rapid Recognition Method of Auricularia Auricula Varieties based on Near-Infrared Spectral Characteristics” LINK

“Study on robust model construction method of Multi-batch Fruit Online Sorting by near-infrared spectroscopy” LINK

“Foods : Effects of Orientations and Regions on Performance of Online Soluble Solids Content Prediction Models Based on Near-Infrared Spectroscopy for Peaches” LINK

“In Vivo Near-Infrared Noninvasive Glucose Measurement and Detection in Humans” LINK

“Monitoring freshness of crayfish (Prokaryophyllus clarkii) through the combination of near-infrared spectroscopy and chemometric method” | LINK

“Identification of Five Similar Cinnamomum Wood Species Using Portable Near-Infrared Spectroscopy” LINK

“Rapid determination of urea formaldehyde resin content in wood fiber mat using near-infrared spectroscopy” LINK

“Portable/Handheld NIR sebagai Teknologi Evaluasi Mutu Bahan Pertanian secara Non-Destruktif” LINK

“Detection of Chilling Injury Symptoms of Salak Pondoh Fruit during Cold Storage with Near Infrared Spectroscopy (NIRS)” LINK

“21st International Conference on Near Infrared Spectroscopy (NIR 2023)” LINK

“A First attempt to combine NIRS and plenoptic cameras for the assessment of grasslands functional diversity and species composition” LINK

“Establishment of NIRS Model for Oil Content in Single Seed of Oil Peony” LINK

“Vis-NIR 초분광 영상을 이용한 딸기 잿빛 곰팡이 감염 조기 검출” LINK

“Effect of Biochar on Desert Soil Wind Erosion Using Sweep Model and Vis-Nir Spectroscopy Technique” LINK

“Probing 1D convolutional neural network adapted to near-infrared spectroscopy for efficient classification of mixed fish” LINK

“Agronomic characterization of anaerobic digestates with near-infrared spectroscopy” LINK

“Rapid Detection of Green Sichuan Pepper Geographic Origin Based on Near-Infrared Spectroscopy” LINK

” Identification of multiple raisins by feature fusion combined with NIR spectroscopy” LINK

“Portable FT-NIR spectroscopic sensor for detection of chemical precursors of explosives using advanced prediction algorithms” | LINK

“Research on High-throughput Crop Authenticity Identification Method Based on Near-infrared Spectroscopy and InResSpectra model” LINK

“Differences between chemical analysis and portable near-infrared reflectance spectrometry in maize hybrids” LINK

“Organic resources from Madagascar: Dataset of chemical and near-infrared spectroscopy measurements” | LINK

“Establishment of a rapid detection model for the sensory quality and components of Yuezhou Longjing tea using near-infrared spectroscopy” LINK

“Breed authentication in Iberian pork meat analysed in situ using Near Infrared Spectroscopy” LINK

“Canopy VIS-NIR spectroscopy and self-learning artificial intelligence for a generalised model of predawn leaf water potential in Vitis vinifera” LINK

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

“Prediction of water relaxation time using near infrared spectroscopy” LINK

“Determination of ash content in silicon dioxide filled epoxy-phenolic prepreg using near infrared spectroscopy” LINK

“Online near-infrared spectroscopy for automatic polymeric material identification” Additives LINK

“Review of near infrared hyperspectral imaging applications related to wood and wood products” LINK

“Lamb-dip saturated-absorption cavity ring-down rovibrational molecular spectroscopy in the near-infrared” LINK

“Modulation of Thermally Stable Photoluminescence in Cr3+-Based Near-Infrared Phosphors” LINK

Raman Spectroscopy

“A comparative study based on serum SERS spectra in and on the coffee ring for high precision breast cancer detection” LINK

“Foods : Recent Developments in Surface-Enhanced Raman Spectroscopy and Its Application in Food Analysis: Alcoholic Beverages as an Example” LINK

“Raman spectroscopy biochemical characterisation of bladder cancer cisplatin resistance regulated by FDFT1: a review” | LINK

“Broadband Nanoscale SurfaceEnhanced Raman Spectroscopy by Multiresonant Nanolaminate Plasmonic Nanocavities on Vertical Nanopillars” LINK

Hyperspectral Imaging (HSI)

“Mid-infrared speckle reduction technique for hyperspectral imaging” LINK

“Rapid and accurate detection of starch content in mixed sorghum by hyperspectral imaging combined with data fusion technology” LINK

” A decision fusion method based on hyperspectral imaging and electronic nose techniques for moisture content prediction in frozen-thawed pork” LINK

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

“Hyperspectral imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends” LINK

“Applied Sciences : Hyperspectral Identification of Ginseng Growth Years and Spectral Importance Analysis Based on Random Forest” LINK

“Nondestructive detection of anthocyanin content in fresh leaves of purple maize using hyperspectral data” LINK

“Application of Principal Component Analysis to Hyperspectral Data for Potassium Concentration Classification on Peach leaves” LINK

Chemometrics and Machine Learning

“Aberrant brain network and eye gaze patterns during natural social interaction predict multi-domain social-cognitive behaviors in girls with fragile X syndrome” | LINK

“Agronomy : Tomato Comprehensive Quality Evaluation and Irrigation Mode Optimization with Biogas Slurry Based on the Combined Evaluation Model” LINK

“Comparison of polynomial and machine learning regression models to predict LDPE, PET, and ABS concentrations in beach sediment based on spectral …” LINK

“Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature” openaccess MachineLearning DeepLearning LINK

” NIR Validation and Calibration of Van Soest cell wall constituents (ADF, NDF, and ADL) of Available Corn Silage in Bangladesh” LINK

“Sensors : Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data” LINK

“Applied Sciences : Prediction of Soil Shear Strength Parameters Using Combined Data and Different Machine Learning Models” LINK

“Prediction and visualization of fat content in polythene-packed meat using near-infrared hyperspectral imaging and chemometrics” LINK

“Comparison of Spectroscopy-Based Methods and Chemometrics to Confirm Classification of Specialty Coffees” LINK

“Proximal sensor data fusion and auxiliary information for tropical soil property prediction: Soil texture” LINK

Environment NIR-Spectroscopy Application

“Comparison of natural and technogenic soils developed on volcanic ash by Vis-NIR spectroscopy” LINK

“Soils of the World ” LINK

“Remote Sensing : Estimation of Pb Content Using Reflectance Spectroscopy in Farmland Soil Near Metal Mines, Central China” LINK

Agriculture NIR-Spectroscopy Usage

“Agriculture : Tea Category Identification Using Wavelet Signal Reconstruction of Hyperspectral Imagery and Machine Learning” LINK

“Plants : Decontamination and Germination of Buckwheat Grains upon Treatment with Oxygen Plasma Glow and Afterglow” LINK

“Near-infrared hyperspectral imaging evaluation of Fusarium damage and DON in single wheat kernels” LINK

“Spectral Indices for Imaging Diesel and Gasoline Polluted Soils Derived from Close-Range Hyperspectral Data” LINK

“Molecular Spectroscopy Market Size is projected to reach USD 3.85 Billion by 2030, growing at a CAGR of 4.28%: Straits Research” LINK

“Remote Sensing : Hyperspectral Reflectance Proxies to Diagnose In-Field Fusarium Head Blight in Wheat with Machine Learning” LINK

“Prediction of milk protein content based on improved sparrow search algorithm and optimized back propagation neural network” | LINK

“Technological Innovations for the Management of Insect-Pests in Stored Grains” | LINK

“Current progress on innovative pest detection techniques for stored cereal grains and thereof powders” FoodSecurity LINK

“Determination of Forage Quality by Near-Infrared Reflectance Spectroscopy in Sweet Sorghum (Sorghum bicolor var. saccharatum (L.) Mohlenbr.)” LINK

“Analysis of Protein Denaturation, and Chemical Visualization, of Frozen Grass Carp Surimi Containing Soluble Soybean Polysaccharides” LINK

“Remote Sensing : Prototyping Crop Traits Retrieval Models for CHIME: Dimensionality Reduction Strategies Applied to PRISMA Data” LINK

Horticulture NIR-Spectroscopy Applications

“Development of portable nondestructive detection device for mango internal Diseases fruits NonDestructive Testing LINK

“Global model for in-field monitoring of sugar content and color of melon pulp with comparative regression approach” | LINK

Food & Feed Industry NIR Usage

“Foods : Saponification Value of Fats and Oils as Determined from 1H-NMR Data: The Case of Dairy Fats” LINK

Pharma Industry NIR Usage

“Pharmaceutical tablet compression: measuring temporal and radial concentration profiles to better assess segregation” LINK

Medicinal Spectroscopy

“Evaluation of the Effect of Del Nido and Cold Blood Cardioplegia on Renal Functions in the Surgery of Congenital Heart Diseases” LINK


“近赤外分光法を用いた脂質研究の動向と将来展望” LINK

“Minerals : Fluid Inclusion and Chemical Composition Characteristics of Emeralds from Rajasthan Area, India” LINK

“On-chip complex refractive index detection at multiple wavelengths for selective sensing” | LINK


“Hranidbena vrijednost kukuruznih silaža Sisačko-moslavačke županije” LINK

“Vibrational spectroscopic evaluation of hydrophilic or hydrophobic properties of oxide surfaces” LINK

“Controlled Synthesis, Spectral Studies, and Catalytic Activity of Silver and Gold Nanoparticles Biosynthesized Using Ficus sycomorus Leaf Extract” | LINK

Spectroscopy and Chemometrics/Machine-Learning News Weekly #16, 2022

NIR Calibration-Model Services

Custom NIR Calibration Models development for a large list of NIR spectrometers NIRS spectroscopy spectrometer chemometrics MachineLearning DigitalTransformation miniaturization mobileDevices MobileSpectrometers NIRanalysis Laboratoires LINK

Do you use a near-infrared Spectrometer with Chemometric Methods? This will save you time | NIR NIRS SWIR MIR NIT LINK

Spectroscopy and Chemometrics News Weekly 15, 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 15, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 15, 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)

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

“PhotoReduction with NIR Light of Nucleus Targeting Pt(IV) Nanoparticles for Combined TumorTargeted Chemotherapy and Photodynamic Immunotherapy” LINK

New spectra dataset released: | Vis-NIR reflectance spectra of basalts (raw pieces and powders) | ROMA database LINK

“Novel fNIRS study on homogeneous symmetric feature-based transfer learning for brain-computer interface” | LINK

“Hybrid motion artifact detection and correction approach for functional near-infrared spectroscopy measurements” LINK

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

“… of Malperfused Areas in an Irradiated Random Pattern Skin Flap Model Using Indocyanine Green Angiography and Near-Infrared Reflectance-Based Imaging and …” LINK

“Variation of properties of two contrasting Oxisols enhanced by pXRF and Vis-NIR” | LINK

“Carbon Footprint Reduction by Utilizing Real Time NIR Spectroscopy for RVP Measurement in Natural Gas Condensate Stabilizers” LINK

“Near Infrared Spectroscopy Detects Change of Tissue Hemoglobin and Water Levelsin Kawasaki Disease and Coronary Artery Lesions” LINK

“Cortical Activation of Swallowing Using fNIRS: A Proof of Concept Study with Healthy Adults” | LINK

“Sensors : A Compact Fiber-Coupled NIR/MIR Laser Absorption Instrument for the Simultaneous Measurement of Gas-Phase Temperature and CO, CO2, and H2O Concentration” LINK

“Predicting Soil Organic Carbon Mineralization Rates Using δ13C, Assessed by Near-Infrared Spectroscopy, in Depth Profiles Under Permanent Grassland Along a …” | LINK

“FGANet: fNIRS-guided Attention Network for Hybrid EEG-fNIRS Brain-Computer Interfaces” LINK

“Gasoline octane number prediction from near-infrared spectroscopy with an ANN-based model” LINK

“Non-destructive Measurements of Toona sinensis Chlorophyll and Nitrogen Content Under Drought Stress Using Near Infrared Spectroscopy” | LINK

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

“Electrochromism of Nanographenes in the NearInfrared Region” LINK

“Energies : Fast Quantitative Modelling Method for Infrared Spectrum Gas Logging Based on Adaptive Step Sliding Partial Least Squares” LINK

“Discrimination of Commercial Ibuprofen Tablets by using a Button Sample Holder and Mid-Infrared Spectroscopy” LINK

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

“Induced NearInfrared Emission and Controlled Photooxidation Based on Sulfonated Crown Ether in Water” LINK

Hyperspectral Imaging (HSI)

“Transcriptome and hyperspectral profiling allows assessment of phosphorus nutrient status in rice under field conditions” LINK

“Unsupervised Spatial-Spectral CNN-Based Feature Learning for Hyperspectral Image Classification” LINK

“Determination of viability and vigor of naturally-aged rice seeds using hyperspectral imaging with machine learning” LINK

“Diagnostical Accuracy of Hyperspectral Imaging After Free Flap Surgery” LINK

Spectral Imaging

“Remote Sensing : Genetic Programming Approach for the Detection of Mistletoe Based on UAV Multispectral Imagery in the Conservation Area of Mexico City” LINK

Chemometrics and Machine Learning

“Foods : Discriminant Analysis of Pu-Erh Tea of Different Raw Materials Based on Phytochemicals Using Chemometrics” LINK

“Algorithm of Stability-Analysis-Based Feature Selection for NIR Calibration Transfer” LINK

“Chemosensors : Nonlinear Multivariate Regression Algorithms for Improving Precision of Multisensor Potentiometry in Analysis of Spent Nuclear Fuel Reprocessing Solutions” LINK

“Blood Glucose Prediction Using Machine Learning on Jetson Nanoplatform” LINK

“Machine Learning-Powered Models for Near-Infrared Spectrometers: Prediction of Protein in Multiple Grain Cereals” LINK

“Cognitive spectroscopy for the classification of rice varieties: a comparison of machine learning and deep learning approaches in analysing long-wave near-infrared …” LINK

“Foods : Fatty Acid Profiling in Kernels Coupled with Chemometric Analyses as a Feasible Strategy for the Discrimination of Different Walnuts” LINK

“Remote Sensing : A Machine Learning Framework for the Classification of Natura 2000 Habitat Types at Large Spatial Scales Using MODIS Surface Reflectance Data” LINK

“Non-destructive detection of chilling injury in kiwifruit using a dual-laser scanning system with a principal component analysis – back propagation neural network” LINK

Research on Spectroscopy

“Applied Sciences : Pre-Disinfection of Poly-Methyl-Methacrylate (PMMA) Reduces Volatile Sulfides Compounds (VSC) Production in Experimental Biofilm In Vitro” LINK

“Remote Sensing : Study of Atmospheric Carbon Dioxide Retrieval Method Based on Normalized Sensitivity” LINK

Equipment for Spectroscopy

“Mid-IR spectroscopy with NIR grating spectrometers” LINK

Environment NIR-Spectroscopy Application

“Antibacterial Copolypeptoids with Potent Activity against Drug Resistant Bacteria and Biofilms, Excellent Stability, and Recycling Property” LINK

“Determining Water Transport Kinetics in Limestone by Dual-Wavelength Cavity Ring-Down Spectroscopy” LINK

“Verifying the predictive performance for soil organic carbon when employing field Vis-NIR spectroscopy and satellite imagery obtained using two different sampling …” LINK

Agriculture NIR-Spectroscopy Usage

“Agriculture : A 1D-SP-Net to Determine Early Drought Stress Status of Tomato (Solanum lycopersicum) with Imbalanced Vis/NIR Spectroscopy Data” LINK

“Remote Sensing : Spectral-Based Classification of Plant Species Groups and Functional Plant Parts in Managed Permanent Grassland” LINK

“Assessment of macronutrients and alpha-galactosides of texturized vegetable proteins by near infrared hyperspectral imaging” LINK

“A fuzzy multi-criteria decision-making approach for the assessment of forest health applying hyper spectral imageries: A case study from Ramsar forest, North of Iran” LINK

“Feasibility of near-infrared spectroscopic rapid detection method for the water content of vermiculite substrates in desert facility agriculture” | LINK

“Evaluation of Methods for Measuring Fusarium-Damaged Kernels of Wheat” LINK

“Effects of free-air temperature increase on grain yield and greenhouse gas emissions in a double rice cropping system” LINK

“The use of milk Fourier-transform mid-infrared spectroscopy to diagnose pregnancy and determine spectral regional associations with pregnancy in US dairy cows” LINK

“Nutrients : Analysis of the Correlation between Meal Frequency and Obesity among Chinese Adults Aged 18-59 Years in 2015” LINK

“Agriculture : N2O Emission and Nitrification/Denitrification Bacterial Communities in Upland Black Soil under Combined Effects of Early and Immediate Moisture” LINK

“Bovine fecal chemistry changes with progression of Southern Cattle Tick, Rhipicephalus (Boophilus) microplus (Acari: Ixodidae) infestation” LINK

Food & Feed Industry NIR Usage

“Effects of Irrigation Strategy and Plastic Film Mulching on Soil N 2 O Emissions and Fruit Yields of Greenhouse Tomato” LINK

Chemical Industry NIR Usage

“Polymers : π-Conjugated Polymers and Their Application in Organic and Hybrid Organic-Silicon Solar Cells” LINK

Pharma Industry NIR Usage

“Applied Sciences : Design of Two-Mode Spectroscopic Sensor for Biomedical Applications: Analysis and Measurement of Relative Intensity Noise through Control Mechanism” LINK


“How This A.I. Draws Anything You Describe [Dall-E 2]” LINK

“Asphaltene Precipitation Onsets in Relation to the Critical Dilution of Athabasca Bitumen in Paraffinic Solvents” LINK

“Design Meets Neuroscience: A Preliminary Review of Design Research Using Neuroscience Tools” LINK

“[ZITATION][C] Author Session” LINK

“Dissimilatory nitrate reduction in urban lake ecosystems: a comparison study between closed and open lakes in Chengdu, China” LINK

“[Exclusive] Elon Musk: A future worth getting excited about” LINK

Spectroscopy and Chemometrics News Weekly #25, 2021

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 24, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 24, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 24, 2021 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

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

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

Near-Infrared Spectroscopy (NIRS)


“Development of an FT-NIR Method to Predict Process Cheese Functionality” LINK

“An effective classification framework for brain-computer interface system design based on combining of fNIRS and EEG signals” LINK

“Coronary artery disease and its impact on the pulsatile brain: A functional NIRS study” LINK

“Predicting anemia using NIR spectrum of spent dialysis fluid in hemodialysis patients” | LINK

“Automated Detection of Tetranychus urticae Koch in Citrus Leaves Based on Colour and VIS/NIR Hyperspectral Imaging” LINK

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

” A novel spectral index for estimating fractional cover of non-photosynthetic vegetation using near-infrared bands of Sentinel satellite” LINK

“Spatial distribution of total polyphenols in multi-type of tea using near-infrared hyperspectral imaging” LINK

“Investigation on the Mechanisms of Mg(OH)2 Dehydration and MgO Hydration by Near-Infrared Spectroscopy” LINK

“Near Infrared Reflectance Spectroscopy Analysis to Predict Diet Composition of a Mountain Ungulate Species” LINK

“Nondestructive determination of SSC in Korla Fragrant Pear using a portable near-infrared spectroscopy system” LINK

“Applied Sciences, Vol. 11, Pages 4717: 808-Nm Near-Infrared Laser Photobiomodulation versus Switched-Off Laser Placebo in Major Aphthae Management: A Randomized Double-Blind Controlled Trial” LINK

“Titration of Inspired Oxygen in Preterm Infants with Hypoxemic Respiratory Failure Using Near Infrared Spectroscopy and Pulse Oximetry: A New Approach” LINK

“Shedding light on neuroscience: Two decades of functional nearinfrared spectroscopy applications and advances from a bibliometric perspective” LINK

Hyperspectral Imaging (HSI)

“Hyperspectral Detection and Monitoring of Salt Stress in Pomegranate Cultivars” Agronomy LINK

“Applied Sciences, Vol. 11, Pages 4588: Beef Quality Grade Classification Based on Intramuscular Fat Content Using Hyperspectral Imaging Technology” LINK

Spectral Imaging

“Artificial Intelligence Empowered Multispectral Vision Based System for Non-Contact Monitoring of Large Yellow Croaker (Larimichthys crocea) Fillets” Foods LINK

Chemometrics and Machine Learning

” Assessment of chicken breast shelf life based on bench-top and portable near-infrared spectroscopy tools coupled with chemometrics” LINK

” Prediction of the particle size and flow characteristics of powder blends for tableting by near-infrared spectroscopy and chemometrics” LINK

“Antibacterial Activity of Moroccan Zantaz Honey and the Influence of Its Physicochemical Parameters Using Chemometric Tools” AppliedSciences LINK

“Predicting pectin performance strength using nearinfrared spectroscopic data: A comparative evaluation of 1D convolutional neural network, partial least squares, and ridge regression modeling” LINK

“Sequential and orthogonalized PLS (SOPLS) regression for path analysis: Order of blocks and relations between effects” LINK

“The Impacts of Spatial Resolution, Viewing Angle, and Spectral Vegetation Indices on the Quantification of Woody Mediterranean Species Seasonality Using Remote Sensing” LINK

“Partial least squares and silver nanoparticles in spectrophotometric prediction of total hardness of water” LINK

“Genetic robust kernel sample selection for chemometric data analysis” LINK

Equipment for Spectroscopy

“Nearinfrared triggered drug delivery of Imatinib Mesylate by molybdenum disulfide nanosheets grafted copolymers as thermosensitive nanocarriers” LINK

Process Control and NIR Sensors

“IQR CUSUM charts: An efficient approach for monitoring variations in aquatic toxicity” LINK

Environment NIR-Spectroscopy Application

“Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments” LINK

“Ecometabolic mixture design-fingerprints from exploratory multi-block data analysis in Coffea arabica beans from climate changes: Elevated carbon dioxide and reduced soil water availability” LINK

Agriculture NIR-Spectroscopy Usage

“Integrating Straw Management and Seeding to Improve Seed Yield and Reduce Environmental Impacts in Soybean Production” Agronomy LINK

” Soil N 2 O flux and nitrification and denitrification gene responses to feed-induced differences in the composition of dairy cow faeces” | LINK

Food & Feed Industry NIR Usage

“Pulsed Electric Field (PEF) Processing of Chilled and Frozen-Thawed Lamb Meat Cuts: Relationships between Sensory Characteristics and Chemical Composition of Meat” Foods LINK


“Racial Differences in Hemodynamic Responses to Lower Body Negative Pressure: The Effects of Capsaicin” LINK

“苹果可溶性固形物的可见/近红外无损检测” LINK

“Quantitative vibrational spectroscopy on liquid mixtures: concentration units matter” LINK

“Enhanced light harvesting in dyesensitized solar cells enabled by TiO2:Er3+, Yb3+ upconversion phosphor particles as solar spectral converter and light scattering medium” 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

NIR-Predictor Download

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

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

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

What’s new, see Release Notes

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

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

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

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

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

Start Calibrate

See also: