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

NIR Analysis in Laboratory and Laboratories – aka NIR Labs and NIR testing

Do you have a NIR spectrometer in your Lab?

How many other analytics you do in the Lab could be done faster and cheaper with NIR?

Is this possible and precise enough?

Try, we have the solution for you!
You have the NIR, scan the samples, you have the lab values and the spectra, we calibrate for you!

To see if the application is possible and how precise it can be due to knowledge based intensive model optimizations.

We do the NIR feasibility study with data for you. Fixed prices

NIR has huge application potentials and it’s a Green analytical method, that’s fast and easy to use. And has today the possibility to scale out with inexpensive mobile NIR spectrometers.

Bring the Lab to the sample. To avoid sample transport and get immediate results for decision at the place or in the process.

Just try the NIR application, use the NIR daily, collect data in parallel, we develop, optimize and maintain the calibration models for you.

How do you think?

Start Calibrate

What is possible today with NIR?
The number of different Applications exploded in the last 2-3 years!
See NIR research papers news daily on @CalibModel or the 7-day summariesNIR News Weekly” here.

Spectroscopy and Chemometrics News Weekly #32, 2020

NIR Calibration-Model Services

Increase Your Profit with optimized NIR Accuracy Laboratory QC QA Food Feed Aquaculture petfood grain milk LINK

NIR User? Get better results faster … here is how | Food Science QC Lab Laboratory Manager chemist LabWork Chemie analytik LINK

Spectroscopy and Chemometrics News Weekly 31, 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 31, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 31, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

Near-Infrared Spectroscopy (NIRS)

“A Performance Comparison of Low-Cost Near-Infrared (NIR) Spectrometers to a Conventional Laboratory Spectrometer for Rapid Biomass Compositional Analysis” LINK

“Lifting wavelet transform for Vis-NIR spectral data optimization to predict wood density.” LINK

New paper ‘Comparison of Raman and Near-Infrared Chemical Mapping for the Analysis of Pharmaceutical Tablets’ in from Hannah Carruthers () PhD work with Don Clark, Fiona Clarke LINK

“Peach variety detection using VIS-NIR spectroscopy and deep learning” LINK

“Rapid Screening of Phenolic Compounds from Wild Lycium ruthenicum Murr. Using Portable near-Infrared (NIR) Spectroscopy Coupled Multivariate Analysis” LINK

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

“Accurate Prediction of Sensory Attributes of Cheese Using Near-Infrared Spectroscopy Based on Artificial Neural Network” Sensors LINK

“Determination of Ethanol in Gel Hand Sanitizers Using Mid and Near Infrared Spectroscopy” LINK

“Dynamic monitoring of fatty acid value in rice storage based on a portable near-infrared spectroscopy system” LINK

“Establishment of an Accurate Starch Content Analysis System for Fresh Cassava Roots Using Short-Wavelength Near Infrared Spectroscopy” LINK

“Photochemical upconversion of near-infrared light from below the silicon bandgap” LINK

” Rapid determination of adulteration in virgin and copra coconut oil using Fourier transform near infrared spectroscopy” LINK

“Non-destructive determination of fat and moisture contents in salmon (Salmo salar) fillets using near-infrared hyperspectral imaging coupled with spectral and textural …” LINK

“Penentuan Kandungan Kimia Utama Minyak Nilam (Pogostemon cablin Benth.) Menggunakan Portable Near Infrared Spectroscopy” LINK

“Evaluation of an autoencoder as a feature extraction tool for near-infrared spectroscopic discriminant analysis” LINK

Chemometrics and Machine Learning

“High-precision identification of the edible oil actual storage periods by FT-NIR spectroscopy combined with chemometrics methods” LINK

“Nearinfrared multivariate model transfer for quantification of different hydrogen bonding species in aqueous systems” LINK

“Rapid determination of polysaccharides and antioxidant activity of Poria cocos using near-infrared spectroscopy combined with chemometrics” LINK

“Quantifying Soluble Sugar in Super Sweet Corn Using Near-Infrared Spectroscopy Combined with Chemometrics” LINK

“… parameter optimization for discriminant model development: A case study of differentiating Pinellia ternata from Pinellia pedatisecta with near infrared spectroscopy” LINK

Optics for Spectroscopy

“Effective of adhesives in textiles on quantitative chemical analysis of fibers.” LINK

Research on Spectroscopy

“Assessment of Supercritical CO2 Extraction as a Method for Plastic Waste Decontamination” LINK

Equipment for Spectroscopy

“A Visible and Near-Infrared Light Activatable Diazo-Coumarin Probe for Fluorogenic Protein Labeling in Living Cells” LINK

Environment NIR-Spectroscopy Application

“Alterations of plastics spectra in MIR and the potential impacts on identification towards recycling” LINK

“Carbonates and organic matter in soils characterized by reflected energy from 350–25000 nm wavelength” LINK

Agriculture NIR-Spectroscopy Usage

“Remote Sensing, Vol. 12, Pages 2019: Study on Spectral Response and Estimation of Grassland Plants Dust Retention Based on Hyperspectral Data” LINK

“Application of FT-NIR spectroscopy and NIR hyperspectral imaging to predict nitrogen and organic carbon contents in mine soils” LINK

Forestry and Wood Industry NIR Usage

“The Effect of Construction and Demolition Waste Plastic Fractions on Wood-Polymer Composite Properties.” LINK

“Genetic improvement of the chemical composition of Scots pine (Pinus sylvestris L.) juvenile wood for bioenergy production” LINK

Food & Feed Industry NIR Usage

“Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network” LINK

“Compositional method for measuring the nutritional label components of industrial pastries and biscuits based on Vis/NIR spectroscopy” PUFA TUFA LINK

Beverage and Drink Industry NIR Usage

“Reliable Discrimination of Green Coffee Beans Species: A Comparison of UV-Vis-Based Determination of Caffeine and Chlorogenic Acid with Non-Targeted Near …” LINK

Laboratory and NIR-Spectroscopy

“Machine vision estimates the polyester content in recyclable waste textiles” LINK


“A spectral analysis of common boreal ground lichen species” LINK

“A 3D-polyphenylalanine network inside porous alumina: Synthesis and characterization of an inorganic–organic composite membrane” LINK

“近赤外分光法と多変量解析を用いた建築用材の識別とその汎化性能向上” LINK

“Implication de la vasopressine dans l’hypoperfusion tissulaire au cours du choc cardiogénique compliquant l’infarctus du myocarde” LINK

“Spectral Characteristics and Application of Synthetic Hydrothermal Red Beryl” LINK

“Structural and optical properties of copper oxide (CuO) nanocoatings as selective solar absorber” LINK