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

Digitization in the field of NIR spectroscopy (smart sensors)

Digitalization is advancing, also in NIR spectroscopy, which enables trainable miniature smart sensors e.g. for analyses in the food&feed, chemical and pharmaceutical sectors.

The calibration is the core of a NIR spectroscopy sensor, it enables the numerous applications and should therefore not be the weakest link in the measurement chain.

The development of calibrations that turn NIR spectrometers into smart sensors is done manually by experts (NIR specialist, chemometrician, data scientist) with so-called chemometrics software.

This is very time-consuming (time to market) and the result is person-dependent and thus suboptimal, because each expert has his own preferred way of proceeding. In addition, the calibrations have to be maintained, as new data has been collected in the meantime, which can be used to extend and improve the calibrations.

This is where our automated service comes in, combining the knowledge and good practices of NIR spectroscopy and chemometrics collected in one software and using machine learning to generate optimal calibrations.

Based on this, we have developed a complete technology platform (Time to Market) that covers the entire process from sending NIR + Lab data, to NIR Calibration as a Service, from online purchase of calibrations, to NIR Predictor software that directly evaluates newly measured NIR data locally and generates result reports.

Besides the free desktop version with user interface, the NIR Predictor can also be integrated (OEM). This can be integrated in parallel as a complement to your current Predictor, allowing the user to choose how they want to calibrate. And give them the advantage in NIR feasibility studies and NIR spectrometer evaluations to quickly provide the customer with a solid and accurate calibration that will make their NIR system deliver better results.

Advantages for your NIR users (internal or external)
  • no initial costs (no chemometrics software license required),
  • calculable operating costs (fixed amount instead of time and hourly rate) (calibration development, calibration maintenance)
  • easy to use (no chemometrics and software training),
  • quicker to use (no calibration development work) and
  • better calibrations (precision, accuracy, robustness, …)

Our chargeable service is based on the calibration development and the annual calibration use. Calibration development and calibration use can also be carried out separately (manufacturer / user).

For you as a spectrometer manufacturer, this means that you can deliver your system pre-calibrated for certain applications without incurring software license costs. And without your application specialists having to provide additional calibration services.

The unique advantages of our calibration service together with the free NIR Predictor are:
  • no software license costs (chemometrics software, predictor software, OEM integration)
  • no chemometrics know-how necessary
  • no time needed to develop optimal NIR calibrations.

If interested in using/evaluating the service :

About CalibrationModel.com : Time and knowledge intensive creation and optimization of chemometric evaluation methods for spectrometers as a service to enable more accurate analysis and measurement results.

see also

Paradigm Change in NIR

Five Mistakes to avoid on Digitalization in NIR

NIR – Total cost of ownership (TCO)

OEM / White Label Software

White Paper

Start Calibrate – NIR Quick Guide

More Details

Quick Start NIR Workflow: step by step

1. Check if your NIR-Device Data Format is directly supported (anyway you can convert to JCAMP) : NIR-Predictor supported Spectral Data File Formats

2. Download the free NIR-Predictor Software that contains demo data so you can play with it to see if it is the way you want analyse your NIR spectra (no registration needed) : NIR-Predictor Download

3. With your “NIR device” measurement software:

  • measure samples with NIR, that gets you spectra files,
  • label them with a proper sample name, so you know which is which,
  • and determine quantitative reference values by Laboratory reference method.
  • at least 60 samples with different contents is needed for a minimal calibration.
  • NIR-Predictor helps to create a template file (.csv) to enter the Lab values.

4. Creating your own Calibrations with NIR-Predictor to combine your NIR and Lab data for a calibration request : watch Video read Manual