Spectroscopy and Chemometrics News Weekly #11, 2020


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

Simplify the process of training machine learning models for NIR spectra data with applied near-infrared spectroscopy (NIRS) knowledge. quantitative multivariate prediction equations LINK

Spectroscopy and Chemometrics News Weekly 10, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 10, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 10, 2020 | NIRS NIR Spettroscopia analisi chimica Spettrale Spettrometro IoT Sensore Attrezzatura analitica nearinfrared foodscience foodprocessing foodsafety foodproduction farming agriculture LINK

Near Infrared

“Klasifikasi Kopi Green Beans Arabika Sumatera Utara Menggunakan FT-Nirs dan Analisis Diskriminan” LINK


” Identification of common wood species in northeast China using Vis/NIR spectroscopy” LINK

“Efficient Super Broadband NIR Ca2LuZr2Al3O12:Cr3+,Yb3+ Garnet Phosphor for pc‐LED Light Source toward NIR Spectroscopy Applications” LINK

“Performance comparison of sampling designs for quality and safety control of raw materials in bulk: a simulation study based on NIR spectral data and geostatistical …” LINK

“Prediction of drug dissolution from Toremifene 80 mg tablets using NIR spectroscopy” LINK

“Nearinfrared spectroscopy (NIRS) for taxonomic entomology: A brief review” LINK

“Determination of tomato quality attributes using portable NIR-sensors” LINK

“Exploring the potential of NIR hyperspectral imaging for automated quantification of rind amount in grated Parmigiano Reggiano cheese” LINK

“Rapid detection of adulteration of minced beef using Vis/NIR reflectance spectroscopy with multivariate methods.” LINK

“Evaluation of quinclorac toxicity and alleviation by salicylic acid in rice seedlings using ground-based visible/near-infrared hyperspectral imaging.” LINK

“An optimized non-invasive glucose sensing based on scattering and absorption separating using near-infrared spectroscopy” LINK

“Identification of waxy cassava genotypes using fourier‐transform near‐infrared spectroscopy” LINK

“Kernel functions embedded in support vector machine learning models for rapid water pollution assessment via near-infrared spectroscopy” LINK

“Near-infrared-based Identification of Walnut Oil Authenticity” LINK

“Detection of flaxseed oil multiple adulteration by near-infrared spectroscopy and nonlinear one class partial least squares discriminant analysis” LINK

“Application research of sensor output digitization for compact near infrared IOT node” LINK

“Refining Transfer Set in Calibration Transfer of Near Infrared Spectra by Backward Refinement of Samples” LINK

“Highly identification of keemun black tea rank based on cognitive spectroscopy: Near infrared spectroscopy combined with feature variable selection” LINK

“Optimizing Rice Near-Infrared Models Using Fractional Order SavitzkyGolay Derivation (FOSGD) Combined with Competitive Adaptive Reweighted Sampling (CARS)” LINK

“Fourier transform near infrared spectroscopy as a tool to discriminate olive wastes: The case of monocultivar pomaces.” LINK

“Evaluation of quinclorac toxicity and alleviation by salicylic acid in rice seedlings using ground-based visible/near-infrared hyperspectral imaging” LINK

“Near Infrared Spectrometric Investigations on the behaviour of Lactate.” LINK

“Nondestructive rapid and quantitative analysis for the curing process of silicone resin by nearinfrared spectra” LINK

“An introduction to handheld infrared and Raman instrumentation” LINK


“Hyperspectral anomaly detection by local joint subspace process and support vector machine” LINK

“Assessment of matcha sensory quality using hyperspectral microscope imaging technology” LINK


“Application of Infrared Spectroscopy and Chemometrics to the Cocoa Industry for Fast Composition Analysis and Fraud Detection” LINK

“Calibration models for the nutritional quality of fresh pastures by nearinfrared reflectance spectroscopy” LINK

“Achieving robustness to temperature change of a NIRS-PLSR model for intact mango fruit dry matter content” LINK

“Application of hyperspectral imaging combined with chemometrics for the non-destructive evaluation of the quality of fruit in postharvest” LINK


“Characterization of Deep Green Infection in Tobacco Leaves Using a Hand-Held Digital Light Projection Based Near-Infrared Spectrometer and an Extreme Learning Machine Algorithm” LINK

“MEMS technology for fabricating plasmonic near-infrared spectrometers” LINK

“Sensors, Vol. 20, Pages 545: Development of Low-Cost Portable Spectrometers for Detection of Wood Defects” LINK


“Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging” LINK

“Determining mandatory nutritional parameters for Iberian meat products using a new method based on near infra-red reflectance spectroscopy and data mining” LINK


“Instrumental Procedures for the Evaluation of Juiciness in Peach and Nectarine Cultivars for Fresh Consumption” LINK

“The creation of the FT-NIR calibration for the determination of the amount of corn grain in concentrated feed” LINK

In this 9th clip from his presentation at the 2019 IFS Agronomic Conference, Wouter Saeys explains which type of NIR is best for measuring the nutrient content of manure, and why. Info on this paper is here; it’s free for Society Members to download: LINK

“Remote Sensing, Vol. 12, Pages 928: Machine Learning Algorithms to Predict Forage Nutritive Value of In Situ Perennial Ryegrass Plants Using Hyperspectral Canopy Reflectance Data” LINK

“Development of a Method To Prioritize Protein-Ligand Pairs on Beads Using Protein Conjugated to a Near-IR Dye.” LINK

“Agronomy, Vol. 10, Pages 148: Performance Evaluation of Two Commercially Available Portable Spectrometers to Non-Invasively Determine Table Grape and Peach Quality Attributes” LINK

“Investigation of a Medical Plant for Hepatic Diseases with Secoiridoids Using HPLC and FT-IR Spectroscopy for a Case of Gentiana rigescens” LINK

Food & Feed

“Comparison of sensory evaluation techniques for Hungarian wines” LINK


“Roadmap of cocoa quality and authenticity control in the industry: A review of conventional and alternative methods” LINK

NIR Calibration Service explained

Our Service is different

  • We build the optimal quantitative prediction models for your NIR analytical needs (No need for mathematical/statistical model building software usage at your site).
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  • You own your data and the calibrations. You can have access to the detailed Calibration Report with all the settings and statistics (No Black-Box Models).

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Get NIR Calibrations - Workflow
Your 4 steps to the applicable NIR calibration:
  1. Download free NIR-Predictor here
  2. Combine NIR-Spectra with Lab-Reference values, see Video for 2. and 3. (manual)
  3. Create a Calibration Request and sent it to info@CalibrationModel.com (CM)
  4. After processing you will get a link to the Web Shop to download the calibrations.

Use NIR Calibrations

Use NIR Calibrations Workflow
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In other words

Calibration Model simplifies the process of training machine learning models for NIRS data while providing an opportunity to trying different algorithms and applied near-infrared spectroscopy (NIRS) knowledge. It’s more than an AutoML platform, it’s a full service where you can download the optimal model and its describing Calibration Report that provide insights into the data preparation, feature engineering, model training, and hyperparameter tuning.

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