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 #42, 2021

NIR Calibration-Model Services

Spectroscopy and Chemometrics/Machine-Learning News Weekly 41, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensor QA QC Testing Quality LINK

Spektroskopie und Chemometrie/Machine-Learning Neuigkeiten Wöchentlich 41, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT sensors Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria/Machine-Learning Weekly News 41, 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

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Near-Infrared Spectroscopy (NIRS)

“Development and Submission of Near Infrared Analytical Procedures Guidance for Industry” FDA LINK

“Identification prediction moisture content of Thai coconut sugar (Cocos nucifera L.) using FT-NIR spectroscopy” LINK

“Blood identification of NIR spectroscopy based on BP neural network combined with particle swarm optimization” LINK

“A preliminary study on the utilisation of near infrared spectroscopy to predict age and in vivo human metabolism” LINK

“Near-infrared guidance finalized for small molecule testing, with biologics to come” RAPS LINK

“Wavelength Selection Method for Near Infrared Spectroscopy Based on Iteratively Retains Informative Variables and Successive Projections Algorithm” LINK

“Near-infrared spectroscopy calibration strategies to predict multiple nutritional parameters of pasture species from different” LINK

” Comparison of metabolites and variety authentication of Amomum tsao-ko and Amomum paratsao-ko using GC-MS and NIR spectroscopy” LINK

“Hyperfine-Resolved Near-Infrared Spectra of H(2)(17)O” LINK

“Geographical Differentiation of Hom Mali Rice Cultivated in Different Regions of Thailand Using FTIR-ATR and NIR Spectroscopy” LINK

“Seasonal Snowpack Classification Based on Physical Properties Using Near-Infrared Proximal Hyperspectral Data” | LINK

“NIR-based sensing system for non-Invasive detection of Hemoglobin for point-of-care applications” LINK

“A promising inorganic YFeO3 pigments with high near-infrared reflectance and infrared emission” LINK

“Classification of Softwoods using Wood Extract Information and Near Infrared Spectroscopy.” LINK

“Rapid determination and origin identification of total polysaccharides contents in Schisandra chinensis by near-infrared spectroscopy” LINK

“Near-Infrared Spectroscopy Technology in Food” | LINK

“Postharvest ripeness assessment of ‘Hass’ avocado based on development of a new ripening index and Vis-NIR spectroscopy” LINK

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

“Utility of near‐infrared spectroscopy to detect the extent of lipid core plaque leading to periprocedural myocardial infarction” LINK

“Scaling up Sagebrush Chemistry with Near-Infrared Spectroscopy and Uas-Acquired Hyperspectral Imagery” LINK

Hyperspectral Imaging (HSI)

“Spatially Resolved Spectroscopic Characterization of Nanostructured Films by Hyperspectral Dark-Field Microscopy” LINK

“Visual attention-driven framework to incorporate spatial-spectral features for hyperspectral anomaly detection” LINK

Spectral Imaging

“Spectral Super-Resolution of Multispectral Images Using Spatial-Spectral Residual Attention Network” LINK

Chemometrics and Machine Learning

“Feasibility of a chromameter and chemometric techniques to discriminate pure and mixed organic and conventional red pepper powders: A pilot study” LINK

“Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage” LINK

“Applied Sciences : A Novel Principal Component Analysis Integrating Long Short-Term Memory Network and Its Application in Productivity Prediction of Cutter Suction Dredgers” LINK

“Plants : Morpho-Physiological Classification of Italian Tomato Cultivars (Solanum lycopersicum L.) According to Drought Tolerance during Vegetative and Reproductive Growth” LINK

“Automatic food and beverage authentication and adulteration detection by classification hybrid fusion” LINK

“Remote Sensing : Improving Accuracy of Herbage Yield Predictions in Perennial Ryegrass with UAV-Based Structural and Spectral Data Fusion and Machine Learning” LINK

“Estimating Fat Components of Potato Chips Using Visible and Near-Infrared Spectroscopy and a Compositional Calibration Model” LINK

“Wavelengths selection based on genetic algorithm (GA) and successive projections algorithms (SPA) combine with PLS regression for determination the soluble …” LINK

“Comparison of PLS and SVM models for soil organic matter and particle size using vis-NIR spectral libraries” LINK

“Verified the rapid evaluation of the edible safety of wild porcini mushrooms, using deep learning and PLS‐DA” LINK

Optics for Spectroscopy

“Scientists Teach AI Cameras to See Depth in Photos Better” AI Camera Depth LINK


“Deep learning accelerates super-resolution microscopy by up to ten times” | DeepLearning microscopy LINK

“Statistical Learning to Operationalize a Domain Agnostic Data Quality Scoring. (arXiv:2108.08905v1 [cs.LG])” LINK

Research on Spectroscopy

“Foods : Instrumentation for Routine Analysis of Acrylamide in French Fries: Assessing Limitations for Adoption” LINK

Process Control and NIR Sensors

“NIR spectroscopy for monitoring of the critical manufacturing steps and quality attributes of paliperidone prolonged release tablets” LINK

Environment NIR-Spectroscopy Application

“Spatial Differentiation Analysis of Water Quality in Dianchi Lake Based on GF-5 NDVI Characteristic Optimization” LINK

“Remote Sensing : Using Sentinel-2 for Simplifying Soil Sampling and Mapping: Two Case Studies in Umbria, Italy” LINK

“Sensors : Ground-Based Hyperspectral Remote Sensing for Estimating Water Stress in Tomato Growth in Sandy Loam and Silty Loam Soils” LINK

“Potential of high-spectral resolution for field phenotyping in plant breeding: Application to maize under water stress” LINK

Agriculture NIR-Spectroscopy Usage

“Study the Genetic Diversity in Protein, Zinc and Iron in Germplasm Pools of Desi Type Chickpeas as Implicated in Quality Breeding” LINK

“Additives and soy detection in powder rice beverage by vibrational spectroscopy as an alternative method for quality and safety control” LINK

“Remote Sensing : Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel” LINK

“Fodder biomass, nutritive value, and grain yield of dual‐purpose Pearl Millet, Sorghum and Maize cultivars across different agro‐ecologies in Burkina Faso” LINK

“Agronomy : Effects of the Foliar Application of Potassium Fertilizer on the Grain Protein and Dough Quality of Wheat” LINK

“Remote Sensing : Sentinel-2 and Landsat-8 Multi-Temporal Series to Estimate Topsoil Properties on Croplands” LINK

“Sensors : Assessment of Grain Harvest Moisture Content Using Machine Learning on Smartphone Images for Optimal Harvest Timing” LINK

“Using UAV image data to monitor the effects of different nitrogen application rates on tea quality” LINK

“A single calibration of near-infrared spectroscopy to determine the quality of forage for multiple species” LINK

Forestry and Wood Industry NIR Usage

“Teakwood Chemistry and Natural Durability” LINK

Food & Feed Industry NIR Usage

“Foods : Temporal Sensory Perceptions of Sugar-Reduced 3D Printed Chocolates” LINK

“Foods : Rapid Nondestructive Simultaneous Detection for Physicochemical Properties of Different Types of Sheep Meat Cut Using Portable Vis/NIR Reflectance Spectroscopy System” LINK

“Foods : Real-Time Gauging of the Gelling Maturity of Duck Eggs Pickled in Strong Alkaline Solutions” LINK

Chemical Industry NIR Usage

“Polymers : Drug Amorphous Solid Dispersions Based on Poly(vinyl Alcohol): Evaluating the Effect of Poly(propylene Succinate) as Plasticizer” LINK

Pharma Industry NIR Usage

” Effects of acetazolamide and furosemide on ventilation and cerebral blood volume in normocapnic and hypercapnic COPD patients” LINK


“Advances, challenges and perspectives of quantum chemical approaches in molecular spectroscopy of the condensed phase” LINK

“Calidad composicional y sensorial de la carne bovina y su determinación mediante infrarrojo cercano” LINK

“Bioinspired StimuliResponsive Hydrogel with Reversible Switching and Fluorescence Behavior Served as LightControlled Soft Actuators” LINK

“Neural Efficiency in Athletes: A Systematic Review” LINK

“Tailored Chiral Copper Selenide Nanochannels for Ultrasensitive Enantioselective Recognition and Detection” LINK

“In vivo diffuse reflectance spectroscopic analysis of fatty liver with inflammation in mice” LINK

“Métodos de análise da composição química e valor nutricional de alimentos para ruminantes” LINK

“Dissociation between exercise intensity thresholds: mechanistic insights from supine exercise” 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

Professional Development of NIR‑Spectroscopic Chemometric Calibration Models as a Service

In a nutshell (TL;DR) : spectroscopy calibration service and for which users is it suitable?

From your NIR + Lab data, we develop optimal NIR-Calibrations for you.
  • For any NIR spectrometer.
  • You don’t need a Chemometric or Math software!
It’s Your Data and Your Calibration.
  • You can anonymize your NIR + Lab data before sending.
  • We delete your NIR + Lab data after processing.
  • Only you get download access to your Calibrations.
Download the Calibrations.
  • You can see the Calibrations performance statistics.
  • You can try the Calibrations before you buy.
  • Fix cost per Calibration development and use. Paid on download.
Use the free NIR-Predictor Software tooling to
  • Check which of your NIR Spectral Data Formats is supported.
  • Combine your NIR + Lab data and create your Calibration Request.
  • Use your Calibrations to create Analysis Reports from new NIR data of measured samples.

For all NIR Spectrometers.

Use our included free NIR-Predictor software to create results!
Now new with native File Format support of mobile NIR instruments!

With the NIR-Predictor software,
you can use your NIRS calibration files locally and offline.

That means you can predict as much NIR data as you want,
at full speed without waiting at no extra cost
(it’s NOT a cloud prediction where you pay per result).

The NIR-Predictor shows which samples should be sent to the laboratory for reference analysis in order to complete the data for the next calibration.
This minimizes the laboratory effort and further costs. This is based on the fact that sample spectra that are foreign to the calibration are marked as outliers in the prediction report generated by the NIR-Predictor. This way, these samples can be analyzed in the laboratory and used to enhance the NIR + Lab data.

You don’t need a Chemometric Software.

NIR Calibration Service explained

See detailed Price List

Start Calibrate


It Enables





Our Knowhow

Why you can Trust us

  • Try before you buy with : free NIR-Predictor software included
  • Off-line predictions with NIR-Predictor, your data will not go into the cloud.
  • Data Privacy :
    General Data Protection Regulation (GDPR)
    Datenschutz-Grundverordnung (DSGVO)
  • We delete your data after processing : Terms of Service
  • Optionally data can be anonymized : Anonymizer Software
  • Swiss Quality Service and Software made in Switzerland
What our service provides is also known as:
  • NIR chemometric analytical method development
  • NIR chemometric analysis method development
  • NIR Spectrometric analytical method development
  • NIR Spectroscopic analytical method development
  • NIR spectrometry analytical method development
  • NIR spectrometry analysis method development
  • NIR Spectroscopic Analysis Methods Development
  • NIR spectral analysis methods development
  • NIR Spectrometry Analysis Methods Development
  • NIR Spectroscopy Analysis Methods Creation
  • Development of chemometric analysis methods in the NIR range
  • Development of chemometric analysis methods in the NIRS range
  • NIR Spectrometric analytical method development
  • NIR Spectrometric Analysis Method Development
  • NIRS Spectroscopic Analysis Method Development
  • NIR Development of spectroscopic analysis methods
  • Development of analytical methods of NIR spectrometry
  • NIR spectrometry analysis method development