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 #40, 2020

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 39, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK




Near-Infrared Spectroscopy (NIRS)

“Development of a Near Infrared Reflectance Spectroscopy (NIRS) Platform for Rapid Wheat Quality Analysis” LINK

Near infrared spectroscopy (NIRS), however, is relatively cheap (once you have the machine), and non-destructive. In this article, we demonstrate that adequate calibrations can be obtained for total terpene content and some specific terpenoids for pines, spruces and thuja LINK

“Special Issue on Brain Machine/Computer Interface and its Application” fNIRS LINK

“Performance of near-infrared (NIR) spectroscopy in pork shoulder as a predictor for pork belly softness” LINK

“Omega-3 and Omega-6 Determination in Nile Tilapia’s Fillet Based on MicroNIR Spectroscopy and Multivariate Calibration” LINK

“Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt) Seed Viability with Multivariate Data Analysis” LINK

“Untargeted classification for paprika powder authentication using visible–Near infrared spectroscopy (VIS-NIRS)” LINK

“Monitoring the composition, authenticity and quality dynamics of commercially available Nigerian fat-filled milk powders under inclement conditions using NIRS, chemometrics, packaging and …” LINK

“Predicting total petroleum hydrocarbons in field soils with Vis–NIR models developed on laboratory‐constructed samples” LINK

“Modeling mass loss of biomass by NIRspectrometry during the torrefaction process” LINK




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

“Rapid determination of the chemical compositions of peanut seed (Arachis hypogaea.) using portable Near-Infrared Spectroscopy” LINK

“Simultaneous detection of trace adulterants in food based on multi-molecular infrared (MM-IR) spectroscopy” LINK

“Monitoring the quality of ethanol-based hand sanitizers by low-cost near-infrared spectroscopy” LINK

“Prediction of neutral detergent fiber content in corn stover using near-infrared spectroscopy technique” LINK

“Applied Sciences, Vol. 10, Pages 5801: Potential Use of Near-Infrared Spectroscopy to Predict Fatty Acid Profile of Meat from Different European Autochthonous Pig Breeds” LINK

“Rapid and non-destructive seed viability prediction using near-infrared hyperspectral imaging coupled with a deep learning approach” LINK

“Multivariate classification for the direct determination of cup profile in coffee blends via handheld near-infrared spectroscopy” LINK

“QUANTITATIVE CHARACTERIZATION OF SUSTAINED RELEASE TABLETS WITH DICLOFENAC SODIUM BY MEANS OF NEAR-INFRARED SPECTROSCOPY AND …” LINK




Raman Spectroscopy

“Raman spectroscopy and machine-learning for edible oils evaluation” LINK




Hyperspectral Imaging (HSI)

“Application of hyperspectral imaging for detecting and visualizing leaf lard adulteration in minced pork” LINK

“Detection of Shape Characteristics of Kiwifruit Based on Hyperspectral Imaging Technology” LINK




Chemometrics and Machine Learning

“A rapid food chain approach for authenticity screening: the development, validation and transferability of a chemometric model using two handheld near infrared spectroscopy …” LINK




Equipment for Spectroscopy

“Principles and applications of miniaturized nearinfrared (NIR) spectrometers” LINK

“A comparison of different optical instruments and machine learning techniques to identify sprouting activity in potatoes during storage” | LINK




Environment NIR-Spectroscopy Application

“Water-based measured-value fuzzification improves the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy.” LINK




Agriculture NIR-Spectroscopy Usage

“In situ effective snow grain size mapping using a compact hyperspectral imager” LINK

“Estimation of soil nitrogen in agricultural regions by VNIR reflectance spectroscopy” LINK




Food & Feed Industry NIR Usage

“Rapid screening of DON contamination in whole wheat meals by Vis/NIR spectroscopy and computer vision coupling technology” LINK

“Quantitative Analysis of Perennial Buckwheat Leaves Protein and GABA Using Near Infrared Spectroscopy” LINK




Laboratory and NIR-Spectroscopy

“Near-infrared laboratory measurements of feldspathic rocks as a reference for hyperspectral Martian remote sensing data interpretation.” LINK




Other

“Determinación de la calidad de carne bovina y la aceptación por parte del consumidor mediante el uso de pruebas con base en infrarrojo cercano” LINK

“Validación de un algoritmo de procesamiento de imágenes Red Green Blue (RGB), para la estimación de proteína cruda en gramíneas vs la tecnología de …” LINK

“IonQ claims it has built the most powerful quantum computer yet” QuantumComputing LINK

“D-Wave’s 5,000-qubit quantum computing platform handles 1 million variables” LINK

“The Sample, the Spectra and the Maths-The Critical Pillars in the Development of Robust and Sound Applications of Vibrational Spectroscopy.” LINK





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