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

NIR Calibration-Model Services

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

Spectroscopy and Chemometrics News Weekly 19, 2022 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 19, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 19, 2022 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

Near-Infrared Spectroscopy (NIRS)

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

“Near-infrared spectra of aqueous glucose solutions” LINK

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

Hyperspectral Imaging (HSI)

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

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

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

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

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

Chemometrics and Machine Learning

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

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

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

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

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

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

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

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

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

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

Optics for Spectroscopy

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


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

Research on Spectroscopy

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

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

Equipment for Spectroscopy

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

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

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

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

Environment NIR-Spectroscopy Application

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

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

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

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

Agriculture NIR-Spectroscopy Usage

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

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

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

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

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

Food & Feed Industry NIR Usage

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

Chemical Industry NIR Usage

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

Laboratory and NIR-Spectroscopy

“Laboratory Hyperspectral Image Acquisition System Setup and Validation” LINK


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


Spectroscopy and Chemometrics/Machine-Learning News Weekly #14, 2022

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 13, 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 13, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 13, 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)

“NIR Spectroscopy and Aquaphotomics Approach to Identify Soil Characteristics as a Function of the Sampling Depth” LINK

“Improving the multi-class classification of Alzheimer’s disease with machine learning-based techniques: An EEG-fNIRS hybridization study” LINK

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


“Near infrared‐based process analytical technology module for estimating gelatinization optimal point” LINK

“Near Infrared Technology in Agricultural Sustainability: Rapid Prediction of Nitrogen Content from Organic Fertilizer” LINK

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

“Rapid On-site Identification of Geographical Origin and Storage Age of Tangerine Peel by Near-infrared Spectroscopy” LINK

“Empirical mode decomposition of near-infrared spectroscopy signals for predicting oil content in palm fruits” LINK

“Miniaturization in NIR Spectroscopy Reshapes Chemical Analysis” LINK

“Unscrambling spectral interference and matrix effects in Vitis vinifera Vis-NIR spectroscopy: Towards analytical grade ‘in vivo’sugars and acids quantification” LINK

“Research Progress of Bionic Materials Simulating Vegetation Visible-Near Infrared Reflectance Spectra” LINK

“Latent Variable Machine Learning Methods Applied for NIR Quantitative Analysis of Coffee” LINK

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

“Vis-NIR Spectroscopy and Machine Learning Methods for the Discrimination of Transgenic Brassica napus L. and Their Hybrids with B. juncea” LINK

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

“LIONirs: flexible Matlab toolbox for fNIRS data analysis” LINK

Raman Spectroscopy

“Raman Spectroscopic Detection and Quantification of Macro- and Microhematuria in Human Urine” LINK

Hyperspectral Imaging (HSI)

“Prediction of total carotenoids, color and moisture content of carrot slices during hot air drying using noninvasive hyperspectral imaging technique” LINK

“Growth simulation of Pseudomonas fluorescens in pork using hyperspectral imaging” LINK

“Estimation of Leaf Water Content of Different Leaves from Different Species Using Hyperspectral Reflectance Data” LINK

Chemometrics and Machine Learning

“Automation : Predictive Performance of Mobile Vis-NIR Spectroscopy for Mapping Key Fertility Attributes in Tropical Soils through Local Models Using PLS and ANN” LINK

“Prediction of South American Leaf Blight and Disease-Induced Photosynthetic Changes in Rubber Tree, Using Machine Learning Techniques on Leaf Hyperspectral …” LINK

“Proximal spectroscopy sensing for sugarcane quality prediction and spatial variability mapping” LINK

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

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

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

“Modelling soil water retention and water‐holding capacity with visible-near infrared spectra and machine learning” LINK

“Machine learning for atherosclerotic tissue component classification in combined near-infrared spectroscopy intravascular ultrasound imaging: Validation against …” LINK

“Modelling soil water retention and waterholding capacity with visiblenear infrared spectra and machine learning” LINK

“Cancers : Novel Non-Invasive Quantification and Imaging of Eumelanin and DHICA Subunit in Skin Lesions by Raman Spectroscopy and MCR Algorithm: Improving Dysplastic Nevi Diagnosis” LINK


“Evaluation of portable vibrational spectroscopy for identifying plasticizers in dairy tubing” LINK

Optics for Spectroscopy

“Chemosensors : Carbocyanine-Based Fluorescent and Colorimetric Sensor Array for the Discrimination of Medicinal Compounds” LINK

“Platinum(II)Acetylide Conjugated Polymer Containing AzaBODIPY Moieties for Panchromatic Photodetectors” LINK

Equipment for Spectroscopy

“Polymers : Role of Macrodiols in the Synthesis and Thermo-Mechanical Behavior of Anti-Tack Water Borne Polyurethane Dispersions” LINK

Process Control and NIR Sensors

“A Perfect Pair: Stabilized Black Phosphorous Nanosheets Engineering with Antimicrobial Peptides for Robust Multidrug Resistant Bacteria Eradication” LINK

Environment NIR-Spectroscopy Application

“Long-Term Liming Reduces the Emission and Temperature Sensitivity of N2o Via Altering Denitrification Functional Gene Ratio in Acidic Soil” LINK

“Environmental metabolomics approaches to identify and enhance secondary compounds in medicinal plants for bio-based plant protection” LINK

“Soil moisture determines nitrous oxide emission and uptake” LINK

“Remote Sensing : Evaluating the Capability of Satellite Hyperspectral Imager, the ZY1-02D, for Topsoil Nitrogen Content Estimation and Mapping of Farmlands in Black Soil Area, China” LINK

“Remote Sensing : Extending the GOSAILT Model to Simulate Sparse Woodland Bi-Directional Reflectance with Soil Reflectance Anisotropy Consideration” LINK

Agriculture NIR-Spectroscopy Usage

“Evaluation of Near-Infrared Reflectance and Transflectance Sensing System for Predicting Manure Nutrients” LINK

“Ecoenzymatic stoichiometry reflects the regulation of microbial carbon and nitrogen limitation on soil nitrogen cycling potential in arid agriculture ecosystems” | LINK

“Remote Sensing : Phenotypic Traits Estimation and Preliminary Yield Assessment in Different Phenophases of Wheat Breeding Experiment Based on UAV Multispectral Images” LINK

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

“Agriculture : Non-Destructive Detection of pH Value of Kiwifruit Based on Hyperspectral Fluorescence Imaging Technology” LINK

“Agronomy : Optical Spectrometry to Determine Nutrient Concentrations and other Physicochemical Parameters in Liquid Organic Manures: A Review” LINK

“Agriculture : The Effect of Tytanit on Fibre Fraction Content in Medicago x varia T. Martyn and Trifolium pratense L. Cell Walls” LINK

Horticulture NIR-Spectroscopy Applications

“Redefining the impact of preharvest factors on peach fruit quality development and metabolism: A review” LINK

“Accurate nondestructive prediction of soluble solids content in citrus by nearinfrared diffuse reflectance spectroscopy with characteristic variable selection” LINK

Forestry and Wood Industry NIR Usage

“Spectrometric Prediction of Nitrogen Content in Different Tissues of Slash Pine Trees” 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

“Mini shortwave Spectroscopic Techniques and Multivariate Statistical Analysis as a Tool for Testing intact Cocoa beans at farmgate for Quality Control in Ghana” LINK

“Foods : Gluten Conformation at Different Temperatures and Additive Treatments” LINK

Pharma Industry NIR Usage

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

“Biomedicines : Pathophysiological Response to SARS-CoV-2 Infection Detected by Infrared Spectroscopy Enables Rapid and Robust Saliva Screening for COVID-19” LINK


“Unraveling the potential of phenomic selection within and among diverse breeding material of maize (Zea mays L.)” LINK

“Applied Sciences : Impact of Pheidole fallax (Hymenoptera: Formicidae) as an Ecosystem Engineer in Rehabilitated Coal Mine Areas” LINK

“The Spectral Mixture Residual: A Source of LowVariance Information to Enhance the Explainability and Accuracy of Surface Biology and Geology Retrievals” LINK

“Glycosylated MoS2 Sheets for Capturing and Deactivating E. coli Bacteria: Combined Effects of Multivalent Binding and Sheet Size” LINK

Spectroscopy and Chemometrics News Weekly #27, 2021

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 26, 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

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

Near-Infrared Spectroscopy (NIRS)

“Modelling of the optical signature of oil slicks at sea for the analysis of multi-and hyperspectral VNIR-SWIR images” LINK

“Sensors : A Guide to Parent-Child fNIRS Hyperscanning Data Processing and Analysis” LINK

“Evaluation of Pozzolanic Activity of Metakaolin Chemometric Method According to the UV-VIS-NIR Spectroscopy” | LINK

“Determining the end-date of long-ripening cheese maturation using NIR hyperspectral image modelling: A feasibility study” LINK

“NIRResponsive Ti3C2 MXene Colloidal Solution for Curing Purulent Subcutaneous Infection through the Nanothermal Blade Effect” LINK

“On-line Powerplant Control using Near-InfraRed Spectroscopy: OPtiC-NIRS” LINK

“Maturity determination at harvest and spatial assessment of moisture content in okra using Vis-NIR hyperspectral imaging” LINK

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

“Analysis methods for measuring passive auditory fNIRS responses generated by a block-design paradigm” LINK

“Boosting the generalization ability of Vis-NIR-spectroscopy-based regression models through dimension reduction and transfer learning” LINK

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

“Nondestructive determination of GABA in germinated brown rice with near infrared spectroscopy based on wavelet transform denoising” LINK

“Continuous downstream processing of milled electrospun fibers to tablets monitored by near-infrared and Raman spectroscopy” LINK

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

“Fourier transform infrared spectroscopic analysis of organic archaeological materials: background paper” LINK

“Near infrared technique as a tool for the rapid assessment of waste wood quality for energy applications” LINK

“Research Note: Nondestructive detection of super grade chick embryos or hatchlings using near-infrared spectroscopy” LINK

“Fraud Detection in Batches of Sweet Almonds by Portable Near-Infrared Spectral Devices” LINK

“Contaminant detection in pistachio nuts by different classification methods applied to short-wave infrared hyperspectral images” LINK

“Prediction of fatty acid and mineral composition of lentils using near infrared spectroscopy” LINK

“Intact Macadamia Nut Quality Assessment Using Near-Infrared Spectroscopy and Multivariate Analysis” LINK

“Identification of Armeniacae Semen Amarum and Persicae Semen from different origins based on near infrared hyperspectral imaging technology” LINK

“Applicability of Raman and Near-infrared Spectroscopy in the Monitoring of freeze-drying injectable ibuprofen” LINK

“Monitoring Breast Reconstruction Flaps Using Near-Infrared Spectroscopy Tissue Oximetry” | LINK

“Prediction of Peking duck intra-muscle fat content by near-infrared spectroscopy” LINK

Hyperspectral Imaging (HSI)

“Combination of spectral and image information from hyperspectral imaging for the prediction and visualization of the total volatile basic nitrogen content in cooked …” | LINK

“A Spectrum Extension Approach for Radiometric Calibration of the Advanced Hyperspectral Imager Aboard the Gaofen-5 Satellite” LINK

“Affinity between Bitumen and Aggregate in Hot Mix Asphalt by Hyperspectral Imaging and Digital Picture Analysis” “Coatings LINK

Chemometrics and Machine Learning

“Remote Sensing : Sentinel-1 and 2 Time-Series for Vegetation Mapping Using Random Forest Classification: A Case Study of Northern Croatia” LINK

“Calibration Maintenance Application of Near-infrared Spectrometric Model in Food Analysis” LINK

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

“Nutritive quality prediction of peaches during storage” LINK

Research on Spectroscopy

“Efficient Maize Tassel-Detection Method using UAV based Remote Sensing” LINK

“A Mini Review on Characteristics and Analytical Methods of Otilonium Bromide” LINK

“Design of an Innovative Methodology for Cerebrospinal Fluid Analysis: Preliminary Results” Sensors LINK

“Effect of Na doping on microstructures, optical and electrical properties of ZnO thin films grown by sol-gel method” LINK

Process Control and NIR Sensors

“Conjugated Organic Photothermal Films for Spatiotemporal Thermal Engineering” LINK

“Extended neuromonitoring in aortic arch surgery” | LINK

Agriculture NIR-Spectroscopy Usage

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

“The Application of Near Infrared Reflectance Spectroscopy in Nutrient Ingredient Determination of Silage Maize” LINK

“Identification of soybean varieties based on hyperspectral imaging technology and onedimensional convolutional neural network” LINK

“Minerals, Vol. 11, Pages 576: Flow Properties Analysis and Identification of a Fly Ash-Waste Rock Mixed Backfilling Slurry” LINK

“Applied Sciences, Vol. 11, Pages 5023: Drying Stress and Strain of Wood: A Review” LINK

“Spectral analysis of total phosphorus in soils based on its diagnostic reflectance spectra” LINK

Horticulture NIR-Spectroscopy Applications

“Concurrent starch accumulation in stump and high fruit production in coffee (Coffea arabica)” LINK

Food & Feed Industry NIR Usage

“Foods : Potential Contribution of Climate Change to the Protein Haze of White Wines from the French Southwest Region” LINK

” Foods : Is the Consumer Ready for Innovative Fruit Wines? Perception and Acceptability of Young Consumers” LINK

Pharma Industry NIR Usage

“[ZITATION][C] Bilateral acute macular neuroretinopathy following COVID‐19 infection” LINK

Medicinal Spectroscopy

“Utility of Ultrasonographic Assessment of Distal Femoral Arterial Flow during Minimally Invasive Valve Surgery” LINK

“Revealing Inflammatory Indications Induced by Titanium Al-loy Wear Debris in Periprosthetic Tissue by Label-Free Correla-tive High-Resolution Ion, Electron …” LINK

Laboratory and NIR-Spectroscopy

“Use of non-destructive instrumental techniques to evaluate effects of gamma irradiation, quality and sensory attributes in orange juice” LINK


“Crossing the threshold: new insights on exercise thresholds and acute recovery from high-intensity exercise” LINK

“The polymeric glyco-linker controls the signal outputs for plasmonic gold nanorod biosensors due to biocorona formation” LINK

“近红外光谱传感物联网研究与应用进展” LINK

“Charge Transport in and Electroluminescence from sp<sup>3</sup>-Functionalized Carbon Nanotube Networks” LINK

“Unprecedented Thermal Stability of Plasmonic Titanium Nitride Films up to 1400 °C” LINK

“CopperBased Plasmonic Catalysis: Recent Advances and Future Perspectives” 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