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

Spectroscopy and Chemometrics News Weekly #37, 2020

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 36, 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 36, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 36, 2020 | 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)

“NIR Spectroscopic Techniques for Quality and Process Control in the Meat Industry” LINK

“Estimating coefficient of linear extensibility using Vis–NIR reflectance spectral data: Comparison of model validation approaches” LINK

“NIR spectroscopy and chemometric tools to identify high content of deoxynivalenol in barley” LINK

“Combining multivariate method and spectral variable selection for soil total nitrogen estimation by Vis–NIR spectroscopy” LINK

“Multi-task deep learning of near infrared spectra for improved grain quality trait predictions” LINK

“Multi-factor Fusion Models for Soluble Solid Content Detection in Pear (Pyrus bretschneideri ‘Ya’) Using Vis/NIR Online Half-transmittance Technique” LINK

“Determining regression equations for predicting the metabolic energy values of barley-producing cultivars in Iran and comparing the results with the results of NIRS method and cultivars …” LINK

“Using Near-Infrared Reflectance Spectroscopy (NIRS) to Predict Glucobrassicin Concentrations in Cabbage and Brussels Sprout Leaf Tissue” LINK

“Near-Infrared Spectroscopy for Analyzing Changes of Pulp Color of Kiwifruit in Different Depths” LINK

“Novel NIR modeling design and assignment in process quality control of Honeysuckle flower by QbD” LINK

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

“Near-infrared spectroscopy to determine cold-flow improver concentrations in diesel fuel” LINK

“Improving spatial synchronization between X-ray and near-infrared spectra information to predict wood density profiles” LINK

“Functional principal component analysis for near-infrared spectral data: a case study on Tricholoma matsutakeis” LINK

“Midinfrared spectroscopy as a tool for realtime monitoring of ethanol absorption in glycols” LINK

“Inline characterization of dispersion uniformity evolution during a twinscrew blending extrusion based on nearinfrared spectroscopy” LINK

“Development of Fourier Transform near Infrared Spectroscopy Methods for the Rapid Quantification of Starch and Cellulose in Mozzarella and Other Italian-Type CHEESES” LINK

“Prediction of Anthocyanin Content in Three Types of Blueberry Pomace by Near-Infrared Spectroscopy” LINK

“Sweetness Detection and Grading of Peaches and Nectarines by Combining Short-and Long-Wave Fourier-Transform Near-Infrared Spectroscopy” LINK

Spectral Imaging

“Use of UAS Multispectral Imagery at Different Physiological Stages for Yield Prediction and Input Resource Optimization in Corn” Remote Sensing LINK

Chemometrics and Machine Learning

“Combination of visible reflectance and acoustic response to improve nondestructive assessment of maturity and indirect prediction of internal quality of redfleshed pomelo” LINK

“Green Analytical Methods of Antimalarial Artemether-Lumefantrine Analysis for Falsification Detection Using a LowCost Handled NIR Spectrometer with DD-SIMCA and Drug Quantification by HPLC” LINK

“Data fusion of UPLC data, NIR spectra and physicochemical parameters with chemometrics as an alternative to evaluating kombucha fermentation” LINK

“Effect of physicochemical factors and use of milk powder on milk rennet-coagulation: Process understanding by near infrared spectroscopy and chemometrics” LINK

“A Digital Approach to Model Quality and Sensory Traits of Beers Fermented under Sonication Based on Chemical Fingerprinting” LINK

“Latent Variable Graphical Modeling for High Dimensional Data Analysis” LINK

Equipment for Spectroscopy

“Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer” Sensors LINK

“Developing a soil spectral library using a low-cost NIR spectrometer for precision fertilization in Indonesia” LINK

“Compact Solid Etalon Computational Spectrometer: FY19 Optical Systems Technology Line-Supported Program” LINK

Agriculture NIR-Spectroscopy Usage

“Detection of Melamine Adulteration in Milk Powder by Using Optical Spectroscopy Technologies in the Last Decade—a Review” LINK

Horticulture NIR-Spectroscopy Applications

“Accurate non-destructive prediction of peach fruit internal quality and physiological maturity with a single scan using near infrared spectroscopy” LINK

“Recent Advances in Spectral Analysis Techniques for Non-Destructive Detection of Internal Quality in Watermelon and Muskmelon: A Review” “光谱分析在西甜瓜内部品质无损检测中的研究进展” LINK

Food & Feed Industry NIR Usage

“Detection of fraud in highquality rice by nearinfrared spectroscopy” LINK

“Detecting food fraud in extra virgin olive oil using a prototype portable hyphenated photonics sensor” LINK

“Nondestructive detection of sunset yellow in cream based on near-infrared spectroscopy and interval random forest” LINK


“The Detection of Substitution Adulteration of Paprika with Spent Paprika by the Application of Molecular Spectroscopy Tools.” LINK

“The Effect of Monomers on the Recognition Properties of Molecularly Imprinted Beads for Proto-hypericin and Proto-pseudohypericin” | FLOREA GAVRILA 1 20.pdf LINK