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 #21, 2020

This week’s NIR news Weekly is sponsored by Your-Company-Name-Here – NIR-spectrometers. Check out their product page … link

Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us.

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 20, 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 20, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 20, 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)

“New applications of visnir spectroscopy for the prediction of soil properties” LINK

“Simultaneous determination of quality parameters in yerba mate (Ilex paraguariensis) samples by application of near-infrared (NIR) spectroscopy and partial least …” LINK

“Fault detection with moving window PCA using NIRS spectra for the monitoring of anaerobic digestion process” LINK

“Control of ascorbic acid in fortified powdered soft drinks using near-infrared spectroscopy (NIRS) and multivariate analysis.” LINK

“Fiber Content Determination of Linen/Viscose Blends Using NIR Spectroscopy” LINK

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

“ripening stages monitoring of Lamuyo pepper using a new‐generation near‐infrared spectroscopy sensor” LINK

“Modeling bending strength of oil-heat-treated wood by near-infrared spectroscopy” LINK

“Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interface Using Regularized Linear Discriminant Analysis Ensemble Classifier Based on Bootstrap Aggregating.” LINK

“Continuously measurement of the dry matter content using near-infrared spectroscopy” LINK

“A Rapid and Nondestructive Approach for the Classification of Different-Age Citri Reticulatae Pericarpium Using Portable Near Infrared Spectroscopy.” LINK

“Rapid identification of Lilium species and polysaccharide contents based on near infrared spectroscopy and weighted partial least square method.” LINK

“Near-infrared spectroscopy as a new method for post-harvest monitoring of white truffles” LINK

“Functional Classification of Feed Items in Pampa Grassland, Based on Their Near-Infrared Spectrum” LINK

“A Rapid and Nondestructive Approach for the Classification of Different-Age Citri Reticulatae Pericarpium Using Portable Near Infrared Spectroscopy” LINK

“Rapid Prediction of Apparent Amylose, Total Starch, and Crude Protein by Near‐Infrared Reflectance Spectroscopy for Foxtail Millet (Setaria italica)” LINK

“A novel CC-tSNE-SVR model for rapid determination of diesel fuel quality by near infrared spectroscopy” LINK

Raman Spectroscopy

“Differentiating cancer cells using Raman spectroscopy (Conference Presentation)” LINK

Hyperspectral Imaging (HSI)

“A deep learning based regression method on hyperspectral data for rapid prediction of cadmium residue in lettuce leaves” LINK

“Detection of fish fillet substitution and mislabeling using multimode hyperspectral imaging techniques” LINK

“Deep learning applied to hyperspectral endoscopy for online spectral classification” DOI:10.1038/s41598-020-60574-6 LINK

“Estimating leaf mercury content in Phragmites australis based on leaf hyperspectral reflectance” LINK

“A hyperspectral microscope based on a birefringent ultrastable common-path interferometer (Conference Presentation)” LINK

Chemometrics and Machine Learning

“Identification of invisible biological traces in forensic evidences by hyperspectral NIR imaging combined with chemometrics” LINK

“Sample selection, calibration and validation of models developed from a large dataset of near infrared spectra of tree leaves” Eucalyptus forage quality LINK

“Detection and Assessment of Nitrogen Effect on Cold Tolerance for Tea by Hyperspectral Reflectance with PLSR, PCR, and LM Models” LINK

Equipment for Spectroscopy

“Determination of soluble solids content in Prunus avium by Vis/NIR equipment using linear and non-linear regression methods” LINK

“Characterization of Deep Green Infection in Tobacco Leaves Using a Hand-Held Digital Light Projection Based Near-Infrared Spectrometer and an Extreme Learning …” LINK

Environment NIR-Spectroscopy Application

“An Evaluation of Citizen Science Smartphone Apps for Inland Water Quality Assessment” LINK

Agriculture NIR-Spectroscopy Usage

“Robustness of visible/near and midinfrared spectroscopic models to changes in the quantity and quality of crop residues in soil” LINK

“Complex Food Recognition using Hyper-Spectral Imagery” LINK

“Development of a compact multimodal imaging system for rapid characterisation of intrinsic optical properties of freshly excised tissue (Conference Presentation)” LINK

Horticulture NIR-Spectroscopy Applications

” The Effect of Spent Mushroom Substrate and Cow Slurry on Sugar Content and Digestibility of Alfalfa Grass Mixtures” LINK

Laboratory and NIR-Spectroscopy

“The influence analysis of reflectance anisotropy of canopy on the prediction accuracy of Cu stress based on laboratory multi-directional measurement” LINK

“UV Irradiation and Near Infrared Characterization of Laboratory Mars Soil Analog Samples: the case of Phthalic Acid, Adenosine 5′-Monophosphate, L-Glutamic Acid …” molecular biosignatures; spectroscopy; lifedetection LINK

Spectroscopy and Chemometrics News Weekly #13, 2020


We have updated the free NIR-Predictor-Software Spectral Data format support list for many mobile and benchtop NIR Spectroscopy Sensors. | Used in QualityControl for Food Fruits Milk Meat LINK

CalibrationModel.com has changed the pricing structure and NIRS-Calibration licensing options (including new perpetual and unlimited systems). NIR Spectroscopy Chemometric AutoML Calibration Development Service milk meet food qualitycontrol LINK

“NIR-Spectroscopy Cost & Price Comparison of Chemometrics, MachineLearning and DataScience for NIRS Application Development” | HomeOffice Laboratory Spectroscopy TimeSaving BetterResults TCO LINK

Do you want better NIRS prediction results? Use your Near-Infrared Analysis data and do recalibration adjustment LINK

Spectroscopy and Chemometrics News Weekly 12, 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 12, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot FoodTech Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie LINK

Spettroscopia e Chemiometria Weekly News 12, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

Near Infrared

The journal Sensors (ISSN 1424-8220) is currently running a Special Issue  “Using Vis-NIR Spectroscopy for Predicting Quality Compounds in Foods” LINK

“Application of NIRS in Nutrient Composition Evaluation of Lathyrus sativus” LINK

“Forecasting the potential of apple fruitlet drop by in-situ Vis-NIR spectroscopy” LINK

“Near infrared spectroscopy (NIRS) data analysis for a rapid and simultaneous prediction of feed nutritive parameters” LINK

“Evaluation by NIRS technology of curing process of ham with low sodium content” LINK

“Optical transmission spectra study in visible and near-infrared spectral range for identification of rough transparent plastics in aquatic environments.” LINK

“Sensors, Vol. 20, Pages 874: Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors” LINK

“Terahertz Time of Flight Spectroscopy as a Coating Thickness Reference Method for Partial Least Squares Near Infrared Spectroscopy Models.” LINK

“Quantitative detection of apple watercore and soluble solids content by near infrared transmittance spectroscopy” LINK

“Glucose Monitoring in Cell Culture with Online Ultrasound-Assisted Near-Infrared Spectroscopy.” LINK

“Development of nearinfrared online grading device for long jujube” LINK

“Nearinfrared reflectance spectroscopy based online moisture measurement in copra” LINK

“Remote Sensing, Vol. 12, Pages 469: Repaid Identification and Prediction of CadmiumLead Cross-Stress of Different Stress Levels in Rice Canopy Based on Visible and Near-Infrared Spectroscopy” LINK

“In vivo relationship between near-infrared spectroscopy-detected lipid-rich plaques and morphological plaque characteristics by optical coherence tomography and …” LINK

“The Kinetic Model of the Peel Brittleness of Stored Cucumis Melons Based on Visible/Near-Infrared Spectroscopy” LINK

Hyperspectral Imaging

“Applied Sciences, Vol. 10, Pages 1173: Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods” LINK

“Remote Sensing, Vol. 12, Pages 537: Detecting the Sources of Methane Emission from Oil Shale Mining and Processing Using Airborne Hyperspectral Data” LINK

“Comparative analysis of mineral mapping for hyperspectral and multispectral imagery” LINK


“Comparison of the performance of partial least squares and support vector regressions for predicting fatty acids/fatty acid classes in marine oil dietary supplements using vibrational spectroscopic data.” LINK

“Prediction of water contents in biscuits using near infrared hyperspectral imaging spectroscopy and chemometrics” LINK

“Vis-NIR Hyperspectral Imaging for the Classification of Bacterial Foodborne Pathogens based on pixel-wise analysis and a novel CARS-PSO-SVM model” LINK

“Using the random forest model and validated MODIS with the field spectrometer measurement promote the accuracy of estimating aboveground biomass and …” LINK


“A spatially resolved transmittance spectroscopy system for detecting internal rots in onions” LINK


“Use of a handheld near infrared spectrometer and partial least squares regression to quantify metanil yellow adulteration in turmeric powder” LINK


“Remote Sensing, Vol. 12, Pages 574: Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles” LINK

“Evaluation of analytical and statistical approaches for predicting in vitro nitrogen solubility and in vivo pre-caecal crude protein digestibility of cereal grains in growing pigs.” LINK

“Detecting Frost Stress in Wheat: A Controlled Environment Hyperspectral Study on Wheat Plant Components and Implications for Multispectral Field Sensing” LINK

“Sensors, Vol. 20, Pages 867: Predicting Forage Quality of Warm-Season Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques” LINK

On their page, they offer a “Purchase Instant Access”. Or contact the author. LINK

“Detection of Invisible Damage of Kiwi Fruit Based on Hyperspectral Technique” LINK

“Nondestructive detection of storage time of strawberries using visible/near-infrared hyperspectral imaging” LINK

“Scaling up of NIRS facility in Mali for analysis of biomass quality for GLDC crops Final Technical Report” LINK

Food & Feed

“Comparison of various pharmaceutical properties of clobetasol propionate cream formulations – considering stability of mixture with moisturizer.” LINK

“Foods, Vol. 9, Pages 154: Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging” LINK


What Lab Manager need to know about NIRSpectroscopy total cost of ownership (TCO) and DataScience. LabManagers LabManager FoodQuality Automate QualityControl foodtech foodtechnologies Laboratory LINK


NIR Method Development Service for Labs and NIR-Vendors (OEM)

CalibrationModel.com ia a perfect match for
    – NIR Vendors    , selling NIR            , with limited capacity for NIR method development
    – Labs                , using NIR            , with limited capacity for NIR method development
    – small Labs        , starting with NIR , with no or less Chemometric knowledge

The Triple to success :
faster better analytics
    LAB Reference Analytics + NIR Spectroscopy + ChemoMetrics
    LAB + NIR +
    => use CM as a Service : CalibrationModel

NIR Method Development : Before / After
    – The
need of a chemometric software ($$)
    – The
need of expert training courses (time,$$)
    – The
need of manual expert work (time,$$$)
    – The
freedom without a chemometric software
    – The
freedom without being an expert
    – The
freedom of using a Service ($)
work smart, not hard
See Cost Comparision

    Cloud Service
        DATA ->
CalibrationModel -> CALIB
                    fix cost, pay per CALIB development and usage

    Local Usage (no internet connection)
        DATA -> CALIB +
Predictor -> RESULT
                                included, no extra cost

    DATA = exported
Spectra and (Lab-)reference values as JCAMP-DX or other data formats
    CALIB = single quantitative property

Sending DATA
    DATA is sent by email, 2-3 days later, receive email with link to
      WebShop to purchase CALIB with PayPal/CreditCard
    DATA is
deleted after processing (Terms of Service TOS)
    optional: JCAMP
Anonymizer (removes sensitive information) before sending DATA

As Middleman you can
hide/cover the Service (white-label)
    Customer <————————> CalibrationModel
    Customer <–>
Middleman <–> CalibrationModel
                        NIR Company
                        NIR Sales, Consultancy

Riskless Predictor OEM integration (white label) (in NIR-Vendors Instrument Software)
    Predictor is included at
no extra cost (for software licenses and development kit (SDK))
    Predictor as a
hidden second engine (second Heart)
    Windows .NET, easy programming interface (API)

DATA owner -> CALIB owner ==> use as your Pre-CALIB
    CALIB is licensed to owner and so copy protected
    The owner can Re-License a CALIB to others
    owner can
re-sell CALIBs in its own WebShop with own prices

    DATA + DATA -> CALIB    same easy workflow as    DATA -> CALIB
    optimize from scratch, benefit from complete optimization possibilities
learn more
NIR-Predictor Software