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 #11, 2020


How to Develop Near-Infrared Spectroscopy Calibrations in the 21st Century? | Chemometrics Analytische Chemie LINK

Simplify the process of training machine learning models for NIR spectra data with applied near-infrared spectroscopy (NIRS) knowledge. quantitative multivariate prediction equations LINK

Spectroscopy and Chemometrics News Weekly 10, 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 10, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 10, 2020 | NIRS NIR Spettroscopia analisi chimica Spettrale Spettrometro IoT Sensore Attrezzatura analitica nearinfrared foodscience foodprocessing foodsafety foodproduction farming agriculture LINK

Near Infrared

“Klasifikasi Kopi Green Beans Arabika Sumatera Utara Menggunakan FT-Nirs dan Analisis Diskriminan” LINK


” Identification of common wood species in northeast China using Vis/NIR spectroscopy” LINK

“Efficient Super Broadband NIR Ca2LuZr2Al3O12:Cr3+,Yb3+ Garnet Phosphor for pc‐LED Light Source toward NIR Spectroscopy Applications” LINK

“Performance comparison of sampling designs for quality and safety control of raw materials in bulk: a simulation study based on NIR spectral data and geostatistical …” LINK

“Prediction of drug dissolution from Toremifene 80 mg tablets using NIR spectroscopy” LINK

“Nearinfrared spectroscopy (NIRS) for taxonomic entomology: A brief review” LINK

“Determination of tomato quality attributes using portable NIR-sensors” LINK

“Exploring the potential of NIR hyperspectral imaging for automated quantification of rind amount in grated Parmigiano Reggiano cheese” LINK

“Rapid detection of adulteration of minced beef using Vis/NIR reflectance spectroscopy with multivariate methods.” LINK

“Evaluation of quinclorac toxicity and alleviation by salicylic acid in rice seedlings using ground-based visible/near-infrared hyperspectral imaging.” LINK

“An optimized non-invasive glucose sensing based on scattering and absorption separating using near-infrared spectroscopy” LINK

“Identification of waxy cassava genotypes using fourier‐transform near‐infrared spectroscopy” LINK

“Kernel functions embedded in support vector machine learning models for rapid water pollution assessment via near-infrared spectroscopy” LINK

“Near-infrared-based Identification of Walnut Oil Authenticity” LINK

“Detection of flaxseed oil multiple adulteration by near-infrared spectroscopy and nonlinear one class partial least squares discriminant analysis” LINK

“Application research of sensor output digitization for compact near infrared IOT node” LINK

“Refining Transfer Set in Calibration Transfer of Near Infrared Spectra by Backward Refinement of Samples” LINK

“Highly identification of keemun black tea rank based on cognitive spectroscopy: Near infrared spectroscopy combined with feature variable selection” LINK

“Optimizing Rice Near-Infrared Models Using Fractional Order SavitzkyGolay Derivation (FOSGD) Combined with Competitive Adaptive Reweighted Sampling (CARS)” LINK

“Fourier transform near infrared spectroscopy as a tool to discriminate olive wastes: The case of monocultivar pomaces.” LINK

“Evaluation of quinclorac toxicity and alleviation by salicylic acid in rice seedlings using ground-based visible/near-infrared hyperspectral imaging” LINK

“Near Infrared Spectrometric Investigations on the behaviour of Lactate.” LINK

“Nondestructive rapid and quantitative analysis for the curing process of silicone resin by nearinfrared spectra” LINK

“An introduction to handheld infrared and Raman instrumentation” LINK


“Hyperspectral anomaly detection by local joint subspace process and support vector machine” LINK

“Assessment of matcha sensory quality using hyperspectral microscope imaging technology” LINK


“Application of Infrared Spectroscopy and Chemometrics to the Cocoa Industry for Fast Composition Analysis and Fraud Detection” LINK

“Calibration models for the nutritional quality of fresh pastures by nearinfrared reflectance spectroscopy” LINK

“Achieving robustness to temperature change of a NIRS-PLSR model for intact mango fruit dry matter content” LINK

“Application of hyperspectral imaging combined with chemometrics for the non-destructive evaluation of the quality of fruit in postharvest” LINK


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

“MEMS technology for fabricating plasmonic near-infrared spectrometers” LINK

“Sensors, Vol. 20, Pages 545: Development of Low-Cost Portable Spectrometers for Detection of Wood Defects” LINK


“Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging” LINK

“Determining mandatory nutritional parameters for Iberian meat products using a new method based on near infra-red reflectance spectroscopy and data mining” LINK


“Instrumental Procedures for the Evaluation of Juiciness in Peach and Nectarine Cultivars for Fresh Consumption” LINK

“The creation of the FT-NIR calibration for the determination of the amount of corn grain in concentrated feed” LINK

In this 9th clip from his presentation at the 2019 IFS Agronomic Conference, Wouter Saeys explains which type of NIR is best for measuring the nutrient content of manure, and why. Info on this paper is here; it’s free for Society Members to download: LINK

“Remote Sensing, Vol. 12, Pages 928: Machine Learning Algorithms to Predict Forage Nutritive Value of In Situ Perennial Ryegrass Plants Using Hyperspectral Canopy Reflectance Data” LINK

“Development of a Method To Prioritize Protein-Ligand Pairs on Beads Using Protein Conjugated to a Near-IR Dye.” LINK

“Agronomy, Vol. 10, Pages 148: Performance Evaluation of Two Commercially Available Portable Spectrometers to Non-Invasively Determine Table Grape and Peach Quality Attributes” LINK

“Investigation of a Medical Plant for Hepatic Diseases with Secoiridoids Using HPLC and FT-IR Spectroscopy for a Case of Gentiana rigescens” LINK

Food & Feed

“Comparison of sensory evaluation techniques for Hungarian wines” LINK


“Roadmap of cocoa quality and authenticity control in the industry: A review of conventional and alternative methods” LINK

Cost comparison / Price comparison of Chemometrics / Machine Learning / Data Science for NIR-Spectroscopy

Reduce Operating Costs and Total Cost of Ownership (TCO) of NIR-Spectroscopy (NIRS) in the Digitalization Age.
NIR-Spectroscopy (NIRS) - Reduce cost, Increase revenue
Reduce Cost by automated NIR development.
Increase Revenue by higher accuracy NIR results.

CalibrationModel.com (CM) versus Others

Costs are not everything, there are other important factors listed in the table.

CM fix € pricing (approx.) Others € Price Range (approx.)
Chemometric Package not‑needed
€3500 – €6500 per user
Chemometric Predictor
€1500 – €2500 per NIR device
Chemometric Training not‑needed
€1500 – €2500 per user
Chemometrician* Salary not‑needed
1 years Salary / year
(+ risk of Employee Turnover)
Powerful Computer (many Processors, lot of RAM for big data) not‑needed
€1500 – €4500 per computer
Development and Usage
Development of a Calibration
€80 – €150 / hour
of Chemometrician* using a Chemometric Software (click and wait) and applying it’s knowledge
Usage of a Calibration
€60 / year
Total €178 in first year
€60 in second year
initial (min €8000 , max €15500)
+ 2 * (2 – 4)(hour to cost same! as CM service) * (€80 – €150) Chemometrician* work
no initial cost
very high initial costs
no personnel cost
high personnel* costs
constant CM services
risk of Employee Turnover
global knowledge
risk of only use personal knowledge
easy to calculate fix cost on demand
difficult to calculate variable cost on demand plus Chemometrician* Recruitment needed
Results :
calibration prediction performance
always reproducible highly optimized
only as good as your Chemometrician* daily condition
better prediction performance, due to best-of 10’000x calibrations
small size of experiments, non-optimal calibrations

See also: pricing

Start Calibrate

*) Personnel / Chemometrician / Data Scientist / Data Analyst / Machine Learning Engineer : We are not against it, we are one of them a long time ago, but the way the work is done is changing (see below).

2019 Digitalization and the Future of Work: Macroeconomic Consequences
2019 The Digitalization of the American Workforce
2017 Digitalization and the American workforce , full-report

Spectroscopy and Chemometrics News Weekly #46, 2019


Use Calibration Model for your customized NIR Applications. Start Optimizing now! | NIRS Spectroscopy QAQC quality LINK

Pro Tip: NIR Calibration is the Key to Accurate NIRS Measurement LINK

Spectroscopy and Chemometrics News Weekly 45, 2019 | NIRS NIR Spectroscopy Chemometrics Spectrometer Spectrometric Analytical Analysis Lab Labs Laboratories Laboratory Sensors QA QC Testing Quality Checking MachineLearning ML Software LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 45, 2019 | NIRS NIR Spektroskopie Spektrometer Sensor Nahinfrarot Analytik Lab Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse laboratoryequipment ML Software LINK

Spettroscopia e Chemiometria Weekly News 45, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem prediction models LINK

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

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


“Comparison of Multivariate Regression Models Based on Water- and Carbohydrate-Related Spectral Regions in the Near-Infrared for Aqueous Solutions of Glucose.” LINK


“Robust Fourier transformed infrared spectroscopy coupled with multivariate methods for detection and quantification of urea adulteration in fresh milk samples” LINK

“Comparative Analysis of Non-Destructive Prediction Model of Soluble Solids Content for Malus micromalus Makino Based on Near-Infrared Spectroscopy” LINK

“Determination of SSC in pears by establishing the multi-cultivar models based on visible-NIR spectroscopy” LINK

“Chemometrics-assisted calibration transfer strategy for determination of three agrochemicals in environmental samples: Solving signal variation and maintaining second-order advantage” LINK

“Semi-Supervised Learning in Multivariate Calibration” LINK

“Assessing heavy metal concentrations in earth-cumulic-orthic-anthrosols soils using Vis-NIR spectroscopy transform coupled with chemometrics.” LINK

Near Infrared

“以近紅外光技術量測葡萄之花青素含量” “Evaluation of Anthocyanin Contents in Grapes Using NIR Spectroscopy” LINK


“Improvement of proximate data and calorific value assessment of bamboo through near infrared wood chips acquisition” LINK

“Effect of peeling and point of spectral recording on sucrose determination in sugar beet root using near infrared spectroscopy” sugarbeet LINK


“Raman Spectral Analysis for Non-invasive Detection of External and Internal Parameters of Fake Eggs” LINK


“Investigation into crystal size effect on sodium chloride uptake and water activity of pork meat using hyperspectral imaging” LINK

“Detection of the Chemical Agents Based on Hyperspectral Data Analysis” LINK


“Performance of optimized hyperspectral reflectance indices and partial least squares regression for estimating the chlorophyll fluorescence and grain yield of wheat …” LINK

“Prediction of Soil Nutrient Contents Using Visible and Near-Infrared Reflectance Spectroscopy” LINK

“Detection of viability of soybean seed based on fluorescence hyperspectra and CARS-SVM-AdaBoost model” LINK


“Laser irradiation behavior of plasma-sprayed tantalum oxide coatings” LINK

“On the Way to Efficient Analytical Measurements: The Future of Robot-Based Measurements” LINK

“Identification of Cannabis sativa L. (hemp) Retailers by Means of Multivariate Analysis of Cannabinoids” LINK

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

Spectroscopy and Chemometrics News Weekly #37, 2019


The new Version V2.4 of the free NIR-Predictor supports multiple native file formats of miniature, mobile and desktop spectrometers get your spectra analyzed as easy as Drag’n’Drop. LINK

Spectroscopy and Chemometrics News Weekly 36, 2019 | NIRS NIR Spectroscopy Chemometrics Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC Testing Quality Checking LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 36, 2019 | NIRS NIR Spektroskopie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse Qualitätslabor LINK

Spettroscopia e Chemiometria Weekly News 36, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem predictionmodel LINK


“Identification of Passion Fruit Oil Adulteration by Chemometric Analysis of FTIR Spectra” LINK

“Investigating the use of visible and near infrared spectroscopy to predict sensory and texture attributes of beef M.longissimus thoracis et lumborum.” LINK

“Optimized prediction of sugar content in ‘Snow’ pear using near-infrared diffuse reflectance spectroscopy combined with chemometrics” LINK

“FT-NIR spectroscopy and multivariate classification strategies for the postharvest quality of green-fleshed kiwifruit varieties” FTNIR LINK

“An Approach to Rapid Determination of Tween-80 for the Quality Control of Traditional Chinese Medicine Injection by Partial Least Squares Regression in Near-Infrared Spectral Modeling” LINK

“Assessing macro-element content in vine leaves and grape berries of vitis vinifera by using near-infrared spectroscopy and chemometrics” LINK

“Investigating the use of visible and near infrared spectroscopy to predict sensory and texture attributes of beef M. longissimus thoracis et lumborum” LINK

“Rapid classification of commercial Cheddar cheeses from different brands using PLSDA, LDA and SPA-LDA models built by hyperspectral data” LINK

Near Infrared

“A Comparison of Sparse Partial Least Squares and Elastic Net in Wavelength Selection on NIR Spectroscopy Data.” LINK

“Reliability of NIRS portable device for measuring intercostal muscles oxygenation during exercise” LINK

“Ability of near-infrared spectroscopy for non-destructive detection of internal insect infestation in fruits: Meta-analysis of spectral ranges and optical measurement modes.” LINK

“Analysis of the Acid Detergent Fibre Content in Turnip Greens and Turnip Tops (Brassica rapa L. Subsp. rapa) by Means of Near-Infrared Reflectance.” LINK

“Lipid-Core Plaque Assessed by Near-Infrared Spectroscopy and Procedure Related Microvascular Injury.” LINK

“Analysis of the Acid Detergent Fibre Content in Turnip Greens and Turnip Tops (Brassica rapa L. Subsp. rapa) by Means of Near-Infrared Reflectance” Foods LINK

“Online monitoring of multiple component parameters during ethanol fermentation by near-infrared spectroscopy.” LINK


“Identifying Freshness of Spinach Leaves Stored at Different Temperatures Using Hyperspectral Imaging.” LINK

“Identifying Freshness of Spinach Leaves Stored at Different Temperatures Using Hyperspectral Imaging” Foods LINK


“Investigations into the use of handheld near-infrared spectrometer and novel semi-automated data analysis for the determination of protein content in different cultivars of Panicum miliaceumL.” LINK


“Use of near-infrared spectroscopy for the rapid evaluation of soybean [Glycine max (L.) Merri.] water soluble protein content.” LINK

Food & Feed

“Rapid visible-near infrared (Vis-NIR) spectroscopic detection and quantification of unripe banana flour adulteration with wheat flour” LINK


Spectroscopy and Chemometrics News Weekly #34, 2019


Develop customized NIR applications and freeing up hours of spectroscopy analysts time. chemometric software LINK

Spectroscopy and Chemometrics News Weekly 33, 2019 | NIRS NIR Spectrometer Analytical Chemistry Chemical Analysis Lab Labs Laboratories QAQC Testing Quality LabManager LabManagers laboratory digitalization labdata laboratorydata LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 33, 2019 | NIRS NIR FTNIR Spektroskopie Chemometrie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysemethode Laborleiter Laboranalyse Qualitätskontrolle LINK

Spettroscopia e Chemiometria Weekly News 33, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità prediction controllo qualità LINK


“A Spectral Fitting Algorithm to Retrieve the Fluorescence Spectrum from Canopy Radiance” Remote Sensing RemoteSensing LINK

“A hyperspectral GA-PLSR model for prediction of pine wilt disease” LINK

“Hyperspectral Anomaly Detection via Convolutional Neural Network and Low Rank With Density-Based Clustering” LINK

“Use of near-infrared hyperspectral (NIR-HS) imaging to visualize and model the maturity of long-ripening hard cheeses” LINK

“Identification of lactic acid bacteria Enterococcus and Lactococcus by near-infrared spectroscopy and multivariate classification.” LINK

“A practical convolutional neural network model for discriminating Raman spectra of human and animal blood” LINK

“Non-destructive prediction of texture of frozen/thaw raw beef by Raman spectroscopy” LINK

“Incorporating brand variability into classification of edible oils by Raman spectroscopy” LINK

“Three-way data splits (training, test and validation) for model selection and performance estimation” LINK

“Importance of spatial predictor variable selection in machine learning applications — Moving from data reproduction to spatial prediction.” LINK

“Tracing the dune activation of Badain Jaran Desert and Tengger Desert by using near infrared spectroscopy and chemometrics” LINK

Near Infrared

“On-The-Go VIS + SW – NIR Spectroscopy as a Reliable Monitoring Tool for Grape Composition within the Vineyard.” LINK

“Improved Functional Near Infrared Spectroscopy Enables Enhanced Brain Imaging” fNIR FDNIR LINK

“Estabilishing A Calibration For Neutral Detergent Fiber (NDF) Value by Using Near Infrared Spectroscopy (NIR) in Corn Grain” LINK

“Using Near Infrared Spectroscopy and Machine Learning to diagnose Systemic Sclerosis.” LINK

“Strategies for the efficient estimation of soil organic carbon at the field scale with vis-NIR spectroscopy: Spectral libraries and spiking vs. local calibrations” LINK

“Sensomics-from conventional to functional NIR spectroscopy-shining light over the aroma and taste of foods” LINK


“Assessment of Spinal Cord Ischemia With Near-Infrared Spectroscopy: Myth or Reality?” LINK

“Identification of antibiotic mycelia residues in cottonseed meal using Fourier transform near-infrared microspectroscopic imaging.” LINK

“Application of near-infrared spectroscopy for frozen-thawed characterization of cuttlefish (Sepia officinalis)” Aquaphotomics LINK

” Identification of Tilletia foetida, Ustilago tritici, and Urocystis tritici Based on Near-Infrared Spectroscopy” LINK

“Assessment of meat freshness and spoilage detection utilizing visible to near-infrared spectroscopy” LINK


“Estimating the severity of apple mosaic disease with hyperspectral images” LINK

“Spectral filter design based on in-field hyperspectral imaging and machine learning for mango ripeness estimation” LINK


“Feasibility Study of the Use of Handheld NIR Spectrometer for Simultaneous Authentication and Quantification of Quality Parameters in Intact Pineapple Fruits” LINK


“Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy” FTNIR FTMIR LINK


“Near-infrared spectroscopy analysis-a useful tool to detect apple proliferation diseased trees?”LINK

“Evaluation of near infrared spectroscopy to non-destructively measure growth strain in trees” LINK


“Spectral Screening Based on Comprehensive Similarity and Support Vector Machine” LINK

“Aquaphotomics-From Innovative Knowledge to Integrative Platform in Science and Technology.” LINK

NIR-Predictor – Manual

NIR-Predictor – Manual

Predicting Spectra

It’s easy to use with NIR-Predictor,
just drag & drop your data for getting the prediction results.

It supports an automatic file format detection.
So you don’t need to specify the instrument type and settings! See the list of supported formats and NIR Vendors: NIR-Predictor supported Spectral Data File Formats

Use the included data to checkout how it feels:

  1. Open the demo Spectra folder by using the Menu > Open Demo Spectra or press F8.
    There are files with spectra from different Vendors.

  2. Drag & drop a spectra file onto the NIR-Predictor window (or press Ctrl+O as for ’Open some files).

  3. The spectra will be

    • loaded
    • pre-processed
    • predicted and
    • reported

All the steps are fully automatic.
All calibrations that are compatible with the spectra, will produce prediction results in one go.
To select specific calibrations choose the Application. Where the ” ” empty means use all the calibrations.
To define a Application read more in chapter “Applications”

To get access to Statistics of Predictions and Reports use the Menu > Show more/less (Ctrl+M) or you can simply resize the window. Here you can also re-do the Analyze step manually with changed inputs (e.g. Result Ordering).

Creating your own Calibrations

How it works – step by step

  1. You have measured your samples with you NIR-Instrument Software.
    And got the Lab-values of these samples.

    -> measured NIR-spectra
    -> Lab-references analytics

  2. Now you need to combine these data.

    NIR-spectra + Lab-references
    -> PropertiesBySamples

    Note: If you combined these data already in your NIR software used,
    and you can export it as a JCAMP-DX file then use
    Menu > Create Request File .req ... (F2)
    and read the “Help.html” and NIR-Predictor JCAMP.
    Else proceed as below.

    The NIR-Predictor provides tooling for that:

    Menu > Create Properties File... (F6)

    Select the folder with your NIR spectra measured for an application.
    NIR-Predictor creates a customized Properties file template for that data to enter the Lab values.

    Note: You don’t need to specify your instrument or vendor or an application. It’s all done automatically. And also the sample spectra are detected and grouped automatically!

  3. Use your favorite editor or spreadsheet program to enter and copy&paste
    the Lab-references Values into the columns “Prop1”, “Prop2” etc. and save the file.

  4. A final check of your entered data is done by NIR-Predictor,
    to make sure your data ist complete and all is fine.

    Menu > Create Calibration Request... (F7)

    Select the folder with the filled file.
    A CalibrationRequest.zip is created with the necessary data
    if enougth diverse Lab values are entered.

  5. Email the CalibrationRequest.zip file
    to info@CalibrationModel.com to develop the calibrations.

  6. When your calibrations are ready, you will receive an email with a link
    to the CalibrationModel WebShop where
    you can purchase and download the calibration files,
    that work with our free NIR-Predictor software without internet access.

    Note: Your sent NIR data is deleted after processing.
    We do not collect your NIR data!

Note: Further details can be found under “Create Properties File” and “Create Calibration Request”.

Configure the Calibrations for prediction usage


  1. in NIR-Predictor : Menu > Open Calibrations (F9)

  2. an explorer window is opened where the calibrations are located

  3. create a folder for your application, choose a name

  4. copy the calibration file(s) (*.cm) into that folder

  5. in NIR-Predictor : Menu > Search and load Applications (F4)


  1. in NIR-Predictor : open the Application drop down list, and select your application by name

  2. if all is fine, the calibration file is valid and not expired, it shows : Calibration “1 valid calibation”

  3. the NIR-Predictor is now ready to predict

  4. to switch the application, goto 6.


The Application concept allows to group multiple Calibrations together for an Application. By selecting an Application before prediction, only the Calibrations belonging to the Application will be used for Prediction. In the Demo Data this is used to have multiple spectrometer as Application. This can be used easily as e.g. as Application “Meat Products” containing Fat and Moisture Calibration.

To create an Application, create a folder with the Application’s name inside the Calibrations folder, and move/copy all the Calibrations files to this Application folder. To remove a Calibration from the Application, remove the Calibration file from the Application folder.

After creating an new Application folder, press menu Search and load Applications (F4) to update the NIR-Predictor dialog where the Application can be selected via the dropdown list. You don’t need to close the NIR-Predictor.

After moving Calibration files around, press menu Search and load Calibrations (F5) to update the NIR-Predictor dialog.

The use-all case

In the NIR-Predictor dialog where the Application can be selected via the dropdown list, the empty "" name means that all (yes all) valid Calibrations will be used for prediction.

Note: The Prediction Report will contain only results from spectral compatible Calibrations with the given spectra. That allows to automatically handle the multi vendor NIR instrument usage.

Prediction Result Report

Histograms of Prediction Values per Property

Shows the distribution of the predicted results per calibration. The histogram range contains the range of the calibrated property and includes the predicted results.

The histogram bar (bin) color is defined as follow:

  • blue : all predictions inside calibration range.
  • red : all predictions outside calibration range.
  • orange : some overlaps with calibration range.
    So not all spectra in a orange bin are outside calibration range.

Note: Predicted values are always shown in Histogram table and Prediction Value List table, even if the spectrum does not fit into model (spectrum different to model, aka Residual Outlier) shown as Out = X.

Note: Old browsers like Microsoft Internet Explorer 11 don’t support the grafics for Histogram charts. Use an current browser like Firefox or Chrome or Edge.

Note: If your browser opens the report too slow, try to deactivate some browser plugins, because they can filter what you look at and some add-ons are really slow.

Spectra Plot Thumbnail on the Prediction Report

Visualizes the min,median,max spectrum of the spectra dropped as files on the NIR-Predictor. This gives a minimal and good spectral overview of the predicted property results.

  • Spectra Plot color legend: min,median,max spectrum by predicted property or if no calibration is available by spectral intensity.

  • The min,median,max is determined from the predicted properties or if not available from the intensity of the spectra.

  • Beside the histogram of the predicted properties, where the distribution can be seen, the spectra shown are the ones from min,median,max predicted property.

  • This gives a minimal and good spectral overview of the predicted property results.

  • The “Spectral Range” and number of datapoints is shown in the Prediction Report Header below the listed spectra files.

  • To zoom the spectra plot a little, zoom the report in the browser (hold ctrl + mouse wheel, or pinch on touch screen).

  • The spectra plots and histograms are stored with the report and can be archived.


  • Note that the spectra are shown in the raw values that are loaded, they are not shown pre-processed as the calibration model uses them to make the prediction.

  • Note that the median property spectrum is the median from the predicted property pobulation and not the “median” of the calibration property range.

  • Note that in the multi calibration prediction case, the spectra are selected for each property based on the related predicted property values and so the spectra plots shows typical different spectra.

Spectra Plot

Outlier Detection

To safeguard the prediction results, outliers are automatically checked for each individual prediction. This is based on limits that are determined when creating the calibration with the base data. Thus, a strange spectral measurement can be detected and signaled as an outlier even without base data only by means of the calibration and the NIR predictor. A prediction result with outlier warning is to be distrusted. How the various outlier tests are interpreted and how to avoid them in practice is described here.

The spectrum is an outlier to the model, if the spectrum is not similar with the spectra and lab-values the model is built with.

This legend is shown on each NIR-Predictor prediction report below the results:

Outlier (Out) Symbol Description

  • “X” : spectrum does not fit into model (spectrum different to model)
  • “O” : spectrum is wide outside model center (spectrum similar to model but far away)
  • “=” : prediction is outside upper or lower range of model (property outside model range)
  • “-” : spectrum is incompatible to calibration

Note: A prediction result with outlier warning is to be distrusted.

There are 3 outlier cases (X, O, =) and the incompatible data case “-”.

  • The bad case is “X”
  • the medium case is “O”
  • and the soft case is “=”.

The technical names in literature correspond to:

  • “X” : Spectral Residual Outlier
  • “O” : Leverage Outlier
  • “=” : Property Range Outlier

These 3 outlier cases can appear in combinations, like “XO=” or “XO” or “O=” or “X=”. The more outlier marker are shown the more likely the spectrum is an Outlier.

The default setting in NIR-Predictor Menu > “Report with Simplified Outlier Symbols”

  • is ON, that will show only the worst case instead of all combinations to have a simplified minimal information.
  • if OFF, that will show the combinations (e.g. “XO=” or “XO” or “O=” or “X=”), which is more informative for analyzing problem cases.

Some hints to avoid these Outliers:

  • “X” : spectrum does not fit into model (spectrum different to model)
    Check if the spectrum is noise only, or has no proper signal. That can happen when measured past the sample or measured into the air or at a different substance. If you have multiple NIR instruments of the same type, use spectra measured with different instruments for the calibration.

  • “O” : spectrum is wide outside model center (spectrum similar to model but far away) Sample temperature has an effect on NIR spectra shape, use spectra measured at different (typical use) temperatures (sample temperature, instrument temperature).

  • “=” : prediction is outside upper or lower range of model (property outside model range)
    Use more spectra for the calibration in the Lab value region where your special interest is. If the predicted value is only a little bit out of the calibration range, it can be Ok. Add these spectra to the calibration spectra (with the Lab values), to extend the prediction range of the calibration.

  • “-” : spectrum is incompatible to calibration
    The spectra (from the NIR instrument) has a different wavelength range or a different resolution than the spectra used for calibration. Check Instrument settings (wavelength range, resolution)

Result Ordering

To change the ordering, a drop-down-box is located below the Analyze button. If there is an analysis from the current session, and the Result Ordering is changed, the data is re-Analyzed and reported with the new Result Ordering setting. That allows to compare the different orderings. The Result Ordering is listed in the Prediction Report above the Prediction Value List and stored in the settings.

The order/sorting of the prediction results of the spectra can be defined:

  • GivenOrder (default) the given order of the spectra from file select dialog or drag&drop

*) sorted : ascending sort

  • Date_Name sorted by Date (if any) and then by Name
  • Name_Date sorted by Name and then by Date
  • Date_NamesWithNumbers sorted by Date (if any) and then by Name with number logic
  • NamesWithNumbers_Date sorted by Name with number logic (e.g. “ABC1” is before “ABC002” ) and then by Date

*) as above but sorted Rev : reverse sort = descending sort

  • Rev_Date_Name
  • Rev_Name_Date
  • Rev_Date_NamesWithNumbers
  • Rev_NamesWithNumbers_Date

E.g. with reverse sort by Rev_Date_Name, the newest spectra appear on top.

Depending on how many calibrations are used the result table is getting broader. To print the report (e.g. to Adobe PDF, FreePDF or Microsoft XPS), sometimes the landscape format is shorter in number of pages or in portrait a scale of 80% fits nicely. Or try another internet browser (Mozilla Firefox, Google Chrome, Microsoft Edge, …) to print the report and set the browser as your default browser so it will be opened by default.

Archiving Reports

Each report is contained in one file only, including the grafics. To save storage space the report file folder can be compressed to a zip file (.zip, .7z).

Enter lab values to NIR spectra

Entering the laboratory reference values for NIR calibrations

We have developed specialized tools into NIR-Predictor to combine the NIR and Lab data is a sample-based safe manner.

The main target is to improve Data Quality during the step of combining of the Lab data and the NIR data, because to model a good reliable calibration the data that build the base needs to be of high quality.

It also simplifies to enter the lab values manually to the corresponding NIR data, because of automatically grouping repeated NIR measurements of the same sample, so the lab values can be entered sample based and not by spectrum.

It helps to avoid false reference data, because of the broken relation of NIR spectra and reference values, data entry on the wrong position in the table.

And Helps to detect errors of duplicated or multiple copies of spectra files, and checks for inconsistencies in Date-Time and Sample-Naming. It also checks for missing values.

That all increases the Data Quality for the next step of Calibration Development, and makes data entry a less time consuming and less risky work.

How it works

  1. Menu > Create Properties File... (F6) select the folder with your NIR spectra measured for an application. NIR-Predictor creates a Properties file template for that data : PropertiesBySamples.csv.txt

  2. Use your favorite editor or spreadsheet program to enter and copy&paste the Lab Values into the columns and save the file.

  3. Menu > Create Calibration Request... (F7) select the folder with the filled file for a last check and a Calibration Request file is created with the needed data as a single zip file.

  4. Email the Calibration Request file to info@CalibrationModel.com to develop the calibrations.

Ok that is it, the NIR-Predictor guides you through the steps needed. And if you need to know more details, the Chapter “Create Properties File” is for you.

Create Properties File


  • If you have (exported) JCAMP-DX files containing the Lab-Values, you don’t need to do this step.
    You can send the JCAMP file with your Request (.req) file directly to the calibration service at info@CalibrationModel.com.
  • If your JCAMP-DX files does NOT contain Lab-Values, this is a way to go.

For calibrating the spectra to the lab-values you need to assign the lab-values to the spectra. The easiest way is to have a table where each spectrum (row) is linked to multiple lab-values (columns). This function Create Properties File build such a table for the selected spectra folder automatically!

This table is stored in the file PropertiesBySamples.csv.txt. This can be created for any spectra folder you like. The file extension is .csv.txt to make it easy to edit in a text editor and also in a spreadsheet (excel). The columns are standard TAB separated.

The file header line contains :

Sample Replicates Names Prop1 Prop2 Prop3 DateFirst DateLast Hashes

Where Name and Date describes the spectrum.

Prop1, Prop2, Prop3 are the place to enter the Lab Reference Concentrations properties corresponding to each spectrum. It can be extended to Prop4, Prop5, … etc. Of course you can enter real word names like “Fat (%)” instead of “Prop1”. It’s recommended to put the measurement unit beside the name.

Replicates is the number of replicated or repeated spectra of a sample that is grouped together in the Sample based property file. Sample name and the DateFirst / DateLast between the sample spectra are measured.

Date format is ISO-8601. Missing Dates are 0002-02-02T00:00:00.0000000.

If the file PropertiesBySamples.csv.txt already exist in the selected folder, the user will be notified (it will not be overwritten, because the file may contain user entered Lab-values). The Lab Reference Concentrations values are initialized to 0 (zero) and needed to be changed.

Note: 0 is not interpreted as missing value! If you have a 0 concentration value, put in 0 or 0.0 .

The entry of properties is as easy as possible, because it’s organized by Sample (and not by Spectra), so it’s like your Lab-Value Table that is sample based. The sample rows are sorted in a special way by Sample name. Sorting by Date or alphabetically by Sample can done easily in a spreadsheet program.

Note: when coping lab values to the samples make sure they correspond, so that there are no gaps and the sorting is the same.

The Spectra (rows) are initially sorted by name (and date) to have the replicates/repeats together. You can sort for your convenience in a spreadsheet program.

Enter the Lab Reference Concentrations to the spectra/sample.

Enter the Lab-Values in spreadsheet (e.g. Excel) or a text editor (e.g. Notepad++). If done, use the next menu Create Calibration Request.

Hints: Data handling:

  • The NIR-Predictor creates the PropertiesBySamples.csv.txt once, after that the user is responsible for its content. That means NIR-Predictor does not change this file anymore.

  • You can remove entire rows (spectra) in the property file. You don’t need to remove the spectra files. The NIR-Predictor is aware of the relation, the PropertiesBySamples.csv.txt defines what will be calibrated.

  • How to add more spectra files?

    The additional spectra can be handled in a separate folder, create the property file and copy the spectra to the other folder and copy/merge the property files together in your editor or spreadsheet.


    Copy the spectra into the folder, rename the PropertiesBySamples.csv.txt to e.g. “PropertiesBySamples-Part1.csv.txt” and use Create Properties File to create a new PropertiesBySamples.csv.txt with all the spectra. You can copy/merge the content of the Properties files together in your editor or spreadsheet.

  • What happens with possible duplicate rows? It does no harm to the Calibration because we do an exact checking and data cleaning in the calibration process.

  • What happens to duplicate spectra names? The spectra names are not relevant for the calibration process. The spectra names are helpful to assign the lab-values to the corresponding spectrum entry. That’s why the table is initially sorted by name. The spectra names can be edited by the user.

Create Calibration Request

The menu function Create Calibration Request packs a created Properties file (see “Create Properties File”) and it’s linked spectra files in a compressed ZIP file for sending to the CalibrationModel.com Service.

Please note that the number of measured quantitative samples need to be at least 60 . That means you need at least 60 different spectra (not counting the replicate/repeated measurements).

It shows additional property information about the data you have entered, like – the property type (Quantitative) – it’s range (min – max) and – the number of unique values and – if the Lab-values are enough diverse to get calibrated.

First select the folder with the PropertiesBySamples.csv.txt and measured spectra files of samples you have Lab-values. The data is checked and you get notified what is missing or might be wrong. If something needs to be changed, edit the PropertiesBySamples.csv.txt and do Create Calibration Request again. Your last selected folder is remembered, so you can press return in the folder selection dialog.

Hint: The keyboard shortcuts for redoing it after you edited some entries is : F7 Return – that allows you to get the property information quickly.

Hint: If you open the PropertiesBySamples.csv.txt in a spreadsheet program, you can create Histogram plots of the entered Lab-values, to see in which range are to less samples measurements.

When all is fine

When all is fine the “CalibrationRequest.zip” file is created for that data.

The ZIP file contains:

  • your PropertiesBySamples.csv.txt
  • your personal REQuest file for your computer system, that looks like
    e.g. “337dcdc06b2d6dfb0b5c4bba578642312edf2ae84d909281624d7e26283e8b07 WIN-GB0PB48GSK4.req”
  • the spectra data files

Note: If the CalibrationRequest.zip file is already created and you change the PropertiesBySamples.csv.txt make sure to delete the old CalibrationRequest.zip file first! In the dialog it states if it was successfully created or NOT because it already exist. So you are always on the safe side.

Note: CalibrationRequest.zip file name contains the property names to know what would be calibrated and at the end an identification number for referencing the file. E.g. “CalibrationRequest ‘Prop1’ – ‘Prop2’ h31T3wOH.zip”

Program Settings

  • The users program settings are stored in UserSettings.json
  • The program counters are stored in GlobalCounters.json

Further References

Spectroscopy and Chemometrics News Weekly #33, 2019


SAFE COST IN MAINTAINING NIR-SPECTROSCOPY METHODS | NIRSpectroscopy NIRS Spectroscopy DigitalTransformation Analysis Lab Laboratory Application Quantitative Analysis Methods Measurements Analytical Parameters Spectrometer Quality Accuracy LINK

Spectroscopy and Chemometrics News Weekly 32, 2019 | NIRS NIR Spectroscopy Chemometrics Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC Testing Quality Checking LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 32, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LaborNIR LINK

Spettroscopia e Chemiometria Weekly News 32, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem prediction NIRmodels LINK

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


“Predicting the dry matter intake of grazing dairy cows using infrared reflectance spectroscopy analysis.” LINK

“Simultaneous determination of food colorants in liquid samples by UVVisible spectroscopy and multivariate data analysis using a reduced calibration matrix” LINK

“Coupling MicroNIR / Chemometrics for the on-site detection of cannabinoids in hemp flours” LINK

“Calibration and Characterization of Hyperspectral Imaging Systems Used for Natural Scene Imagery” LINK

“Analysis of wood thermal degradation using 2D correlation of near infrared and visible-light spectroscopy” LINK

“Rapid method for the quantification and identification of emerging compounds in wastewater based in nir spectroscopy and chemometrics” LINK

“Predicting the dry matter intake of grazing dairy cows using infrared reflectance spectroscopy analysis” |(19)30642-3/fulltext LINK

Near Infrared

“Application of Artificial Neural Networks (ANN) Coupled with Near-InfraRed (NIR) Spectroscopy for Detection of Adulteration in Honey” LINK

“Statistical Analysis of Amylose and Protein Content in Landrace Rice Germplasm Collected from East Asian Countries Based on Near-Infrared Reflectance Spectroscopy (NIRS)” LINK

“Identification of wheat kernels by fusion of RGB, SWIR, and VNIR samples.” LINK

“Purity analysis of multi-grain rice seeds with non-destructive visible and near-infrared spectroscopy” LINK

“Development of a methodology to analyze leaves from Prunus dulcis varieties using near infrared spectroscopy.” LINK

“Analysis of hydration water around human serum albumin using near-infrared spectroscopy” LINK

“Real-time Biomass Characterization in Energy Conversion Processes using Near Infrared Spectroscopy-A Machine Learning Approach” LINK

“Support vector machine regression on selected wavelength regions for quantitative analysis of caffeine in tea leaves by near infrared spectroscopy” LINK

“Near Infrared Reflectance Spectroscopy to analyze texture related characteristics of sous vide pork loin.” LINK

“Estimation of the Alcoholic Degree in Beers through Near Infrared Spectrometry Using Machine Learning” LINK

“The quantitative detection of botanical impurities contained in seed cotton with near infrared spectroscopy method” LINK


New post: Raman spectroscopy may make thyroid cancer diagnosis less invasive | Raman spectroscopy thyroid cancer LINK


“A multi-pixel diffuse correlation spectroscopy system based on a single photon avalanche diode array.” LINK


“The acute influence of sucrose consumption with and without vitamin C co-ingestion on microvascular reactivity in healthy young adults” vitaminC LINK

“Identification and characterization of a fast-neutron-induced mutant with elevated seed protein content in soybean” LINK

“Quantitative analysis and hyperspectral remote sensing of the nitrogen nutrition index in winter wheat” LINK

Food & Feed

“Multidimensional scaling assisted Fourier-transform Infrared spectroscopic analysis of fruit wine samples: Introducing a novel analytical approach” LINK

Spectroscopy and Chemometrics News Weekly #32, 2019


Spectroscopy and Chemometrics News Weekly 31, 2019 | NIRS NIR Spectroscopy Chemometrics Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC Testing Quality Checking LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 31, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysemethode Nahinfrarotspektroskopie Laboranalyse Qualitätslabor LINK

Spettroscopia e Chemiometria Weekly News 31, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore analitica Laboratorio analisi prova qualità Analysesystem prediction models Calibrazione LINK


“Near infrared spectroscopic investigation of lipid oxidation in model solid food systems” LINK

Rapid quantification of the adulteration of fresh coconut water by dilution and sugars using Raman spectroscopy and chemometrics, published in Food Chemistry, is now OpenAccess via LINK

“Recent Progress in Rapid Analyses of Vitamins, Phenolic, and Volatile Compounds in Foods Using Vibrational Spectroscopy Combined with Chemometrics: a Review” LINK

” Beef Tenderness Prediction by a Combination of Statistical Methods: Chemometrics and Supervised Learning to Manage Integrative Farm-To-Meat Continuum Data” Foods LINK

“P-Wave VisibleShortwaveNear-Infrared (Vis-SW-NIR) Detection System for the Prediction of Soluble Solids Content and Firmness on Wax Apples” LINK

“Model development for soluble solids and lycopene contents of cherry tomato at different temperatures using near-infrared spectroscopy” LINK

Near Infrared

“Preliminary Assessment of Visible, Near-Infrared, and Short-Wavelength-Infrared Spectroscopy with a Portable Instrument for the Detection of Staphylococcus aureus Biofilms on Surfaces.” LINK

” Inline monitoring of powder blend homogeneity in continuous drug manufacture using near infrared spectroscopy” LINK

“Nondestructive real-time assessment of sausage quality based on visible-near infrared spectrographic technique” LINK

“Lipid oxidation degree of pork meat during frozen storage investigated by near-infrared hyperspectral imaging: Effect of ice crystal growth and distribution” LINK

“High prevalence of cholesterol-rich atherosclerotic lesions in ancient mummies: A near-infrared spectroscopy study” LINK

“Application of Infrared Spectroscopy for Functional Compounds Evaluation in Olive Oil: A Current Snapshot” LINK

“Towards online Near-Infrared spectroscopy to optimise food product mixing” LINK


“Estimation of chlorophyll content in intertidal mangrove leaves with different thicknesses using hyperspectral data” LINK

“Snapshot Multispectral and Hyperspectral Data Processing for Estimating Food Quality Parameters” LINK


“Field Spectroscopy: A Non-Destructive Technique for Estimating Water Status in Vineyards” LINK

“Optical detection of contamination event in water distribution system using online Bayesian method with UVVis spectrometry” LINK


“GrassQ-A holistic precision grass measurement and analysis system to optimize pasture based livestock production” LINK

“Monitoring of Nitrogen and Grain Protein Content in Winter Wheat Based on Sentinel-2A Data” Remote Sensing LINK

“Identification and characterization of a fast-neutron-induced mutant with elevated seed protein content in soybean.” LINK

“Sensors, Vol. 19, Pages 3147: NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Study the Residues of Different Concentrations of Omethoate on Wheat Grain Surface” LINK

Food & Feed

“Multi-target Prediction of wheat flour quality parameters with near infrared spectroscopy” LINK


” Spectroscopic determination of leaf chlorophyll content and color for genetic selection on Sassafras tzumu” LINK

Record-breaking new analytical method for fingerprinting petroleum and other complex mixtures LINK

A record-breaking 244,779 molecular compositions within a sample of petroleum have been assigned using a powerful method of analysing and ‘fingerprinting’ chemical mixtures developed by at . Read more: LINK

“Optical properties of living corals determined with diffuse reflectance spectroscopy” LINK