Spectroscopy and Chemometrics/Machine-Learning News Weekly #48, 2021

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 47, 2021 | 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 47, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 47, 2021 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

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

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

Near-Infrared Spectroscopy (NIRS)

“Integrative Computational Analysis of Muscle Near-Infrared Spectroscopy Signals: Effects of Oxygen Delivery and Blood Volume” LINK

“Near Infrared Spectroscopic Evaluation of Starch Properties of Diverse Sorghum Populations” LINK

“Non-Destructive Prediction of Paddy Seed Quality using Near Infrared Spectroscopy” LINK

“Orthogonal Signal Correction of Near-Infrared Spectra for the Prediction of Glucose in Human Tissue” LINK

“Joint calibration of soil Vis-NIR spectra across instruments, soil types & properties by attention-based spectra encoding-spectra/property decoding architecture” LINK

“Determining the Influence of Sample Preparation and Feed Form on the Predictability of the Near Infrared Reflectance Spectroscopy Technique” LINK

“Non-invasive Blood Glucose Detection Sensor System Based on Near-Infrared Spectroscopy” LINK

“Minerals : Salinity Monitoring at Saline Sites with VisibleNear-Infrared Spectral Data” LINK

“平均分布差异最小化的 NIR 标定迁移方法研究” LINK

“Effectiveness of visible – Near infrared spectroscopy coupled with simulated annealing partial least squares analysis to predict immunoglobulins G, A, and M concentration in bovine colostrum” LINK

“PSV-15 Using near infrared reflectance spectroscopy to predict lab scoured yield in Rambouillet sheep” | LINK

“Effective prediction of soil organic matter by deep SVD concatenation using FT-NIR spectroscopy” LINK

“Study on the identification of resistance of rice blast based on near infrared spectroscopy” LINK

“Predicting Organic Matter Content, Total Nitrogen and pH Value of Lime Concretion Black Soil Based on Visible and Near Infrared Spectroscopy” |(2011)43 LINK

“Application of near-infrared spectroscopy in detection of steroids adulteration in traditional Thai medicines” LINK

“Development of a new NIR-machine learning approach for simultaneous detection of diesel various properties” LINK

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

“Near infrared for white wine analysis” LINK

“Microscale Infrared Technologies for Spectral Filtering and Wireless Neural Dust” LINK

“Performance of fieldscale lab vs in situ visible/near and midinfrared spectroscopy for estimation of soil properties” LINK

Raman Spectroscopy

“Detection of SubTerahertz Raman Response and Nonlinear Optical Effects for Luminescent Yb(III) Complexes” LINK

Hyperspectral Imaging (HSI)

“Vis-NIR Hyperspectral Imaging for Online Quality Evaluation during Food Processing: A Case Study of Hot Air Drying of Purple-Speckled Cocoyam (Colocasia …” LINK

” New Approach to the Old Challenge of Free Flap Monitoring—Hyperspectral Imaging Outperforms Clinical Assessment by Earlier Detection of Perfusion Failure” LINK

“Remote Sensing : UAV-Borne Hyperspectral Imaging Remote Sensing System Based on Acousto-Optic Tunable Filter for Water Quality Monitoring” LINK

“4D line-scan hyperspectral imaging” | LINK

“Near-infrared hyperspectral imaging for polymer particle size estimation” LINK

“Foreign Object Detection from Fresh-Cut Vegetables Using Near Infrared Hyperspectral Imaging Techniques” LINK

Chemometrics and Machine Learning

“Neural-network-powered pulse reconstruction from one-dimensional interferometric cross-correlation traces. (arXiv:2111.01014v1 [physics.optics])” LINK

“Spectral reconstruction with model-based neural network for liquid crystal modulator devices” LINK

“Supporting soil and land assessment with machine learning models using the Vis-NIR spectral response” LINK

“Applications of NIR spectroscopy and chemometrics to illicit drug analysis: An example from inhalant drug screening tests” LINK

Optics for Spectroscopy

“Polymers : Composite Materials from Renewable Resources as Sustainable Corrosion Protection Coatings” LINK

“Improvement of the Physical Properties of Electro-spun Polyacrylonitrile Nano-fibers Using the Fe2O3 Nanoparticles for Wastewater Treatment” LINK

“Nanomaterials : Facile and Sensitive Detection of Nitrogen-Containing Organic Bases with Near Infrared C-Dots Derived Assays” LINK


“On the possible benefits of deep learning for spectral preprocessing” LINK

Research on Spectroscopy

“Data : A Principal Components Analysis-Based Method for the Detection of Cannabis Plants Using Representation Data by Remote Sensing” LINK

“Foods : Inner Properties Estimation of Gala Apple Using Spectral Data and Two Statistical and Artificial Intelligence Based Methods” LINK

“Effects on volatile oil and volatile compounds of Amomum tsao-ko with different pre-drying and drying methods” LINK

“Investigation of innovative analytical techniques and methodological approaches for the analysis of phyto-cannabinoids in cannabis samples” LINK

“A Novel Compression Method of Spectral Data Matrix Based on the Low-Rank Approximation and the Fast Fourier Transform of the Singular Vectors” LINK

Equipment for Spectroscopy

“Feasibility of portable NIR spectrometer for quality assurance in glue-laminated timber production” LINK

Process Control and NIR Sensors

“Applications for remote sensing by unmanned aerial vehicles in reclamation monitoring” LINK

“282 Nutritional Monitoring of Prenatally Stressed and Translocated Brahman Heifers” LINK

Environment NIR-Spectroscopy Application

“Importance of the legacy effect for assessing spatiotemporal correspondence between interannual tree-ring width and remote sensing products in the Sierra …” LINK

“Retrieving zinc concentrations in topsoil with reflectance spectroscopy at Opencast Coal Mine sites” | LINK

Agriculture NIR-Spectroscopy Usage

“Sustainability : Estimation of Heavy Metal(Loid) Contents in Agricultural Soil of the Suzi River Basin Using Optimal Spectral Indices” LINK

“Forage Grasses Steer Soil Nitrogen Processes, Microbial Populations, and Microbiome Composition in A Long-term Tropical Agriculture System” LINK

“Symmetric and asymmetric overgrowth of a Ag shell onto gold nanorods assisted by Pt pre-deposition” | LINK

“Agronomy : Applicability of Near Infrared Reflectance Spectroscopy to Predict Amylose Contents of Single-Grain Maize” LINK

“Nondestructive Detection of Weight Loss Rate, Surface Color, Vitamin C Content, and Firmness in Mini-Chinese Cabbage with Nanopackaging by Fourier Transform …” LINK

“Task Complexity and Image Clarity Facilitate Motor and Visuo-Motor Activities in Mirror Therapy in Post-stroke Patients” | LINK

“Near infrared spectrometry to evaluate the feed value of forages” LINK

“Nondestructive Prediction of Anthocyanin Content of Pigmented Soybean Seeds using Hyperspectral Near-infrared imaging” LINK

“Intravascular imaging beyond ischaemia assessment: a possible way for improving risk stratification” | LINK

Horticulture NIR-Spectroscopy Applications

“Extending the use of fiber optic equipped visible/near infrared reflectance spectrometer to measure peel color of various fruits and vegetables” LINK

“High-Throughput Plant Phenotyping for the Chilling Stress of Watermelon Plants” LINK

Food & Feed Industry NIR Usage

“Quantitative detection of benzoyl peroxide in wheat flour using line-scan short-wave infrared hyperspectral imaging” LINK

“Spectroscopy of Phenolic Antioxidants” | LINK

Pharma Industry NIR Usage

“Beyond the Patient’s Report: Self-Reported, Subjective, Objective and Estimated Walking Disability in Patients with Peripheral Artery Disease” LINK

“Greater plaque burden and cholesterol content may explain an increased incidence of non-culprit events in diabetic patients: a Lipid-Rich Plaque substudy” LINK

“Spatial covariation between solar-induced fluorescence and vegetation indices from Arctic-Boreal landscapes” LINK

“Spectroscopic characteristics of Xeloda chemodrug” LINK

Laboratory and NIR-Spectroscopy

“Particle-specific characterisation of non-hazardous, coarse-shredded mixed waste for real-time quality assurance” LINK


“Facile fabrication of PS-CHO core-shell composite microspheres via in-situ chemical deposition and their photocatalytic application on oxidative degradation …” LINK

“A trace CH<sub>4</sub> detection system based on DAS calibrated WMS technique” LINK

“Denitrification and dissimilatory nitrate reduction to ammonia in long-term lake sediment microcosms with iron (II)” LINK

“近红外光谱的通用聚苯乙烯牌号在线识别方法” LINK

“非均匀固体籽粒近红外光谱采集方法研究” LINK

“基于卷积神经网络和近红外光谱的太平猴魁茶产地鉴别分析” LINK

“鸡腿肌冻干粉蛋氨酸近红外光谱定量预测模型的建立与优化” LINK

“Anthocyanins – Definition, Benefits, Sources” LINK

“Varijabilnost parametara kvalitete zrna hrvatskih i stranih genotipova soje (Glycine max L.)” LINK

“Cultivation and Performance Analysis of Simultaneous Partial Nitrification, ANAMMOX, and Denitratation Granular Sludge” LINK


Spectroscopy and Chemometrics News Weekly #26, 2021

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 25, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

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

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

Near-Infrared Spectroscopy (NIRS)

“Thermally Stable CaLu2Mg2Si3O12:Cr3+ Phosphors for NIR LEDs” LINK

“A New Statistical Approach for fNIRS Hyperscanning to Predict Brain Activity of Preschoolers’ Using Teacher’s” LINK

“NIR: 21st-Century Innovations” LINK

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

“Suitability of the muscle O2 resaturation parameters most used for assessing reactive hyperemia: a near-infrared spectroscopy study” LINK

“Simultaneous Quantification of 14 Compounds in Achillea millefolium by GC-MS Analysis and Near-Infrared Spectroscopy Combined with Multivariate …” | LINK

“Rapid Determination of the Total Phosphorus and the Nitrate Nitrogen in Denitrifying Phosphorus Removal with iPLS and Near Infrared Spectroscopy” LINK

“Estimation of Andrographolides and Gradation of Andrographis paniculata Leaves Using Near Infrared Spectroscopy Together With Support Vector Machine” LINK

“Nondestructive Phenolic Compounds Measurement and Origin Discrimination of Peated Barley Malt using Near-infrared Hyperspectral Imagery and Machine …” LINK

” Suitability of the muscle O2 resaturation parameters most used for assessing reactive hyperemia: a near-infrared spectroscopy study” LINK

“New perspective for the in-field analysis of cannabis samples using handheld near-infrared spectroscopy: A case study focusing on the determination of Δ9 …” LINK

Hyperspectral Imaging (HSI)

“Hyperspectral Imaging Assessment of Systemic Sclerosis Using the Soft Abundance Score and Band Selection” LINK

“Comparison of Texture Feature Extraction Methods for Hyperspectral Imagery Classification” LINK

“Hyperspectral Estimation of Heavy Metal Pb Concentration in Vineyard Soil in Turpan Basin” LINK

Chemometrics and Machine Learning

“Spectroscopic Fingerprinting and Chemometrics for the Discrimination of Italian Emmer Landraces” LINK

“Determination of extractive content in Cupressus sempervirens wood through a NIRS-PLSR model and its correlation with durability” LINK

“Optimization by Means of Chemometric Tools of an Ultrasound-Assisted Method for the Extraction of Betacyanins from Red Dragon Fruit (Hylocereus polyrhizus)” LINK

“The use of vibrational spectroscopy to predict vitamin C in Kakadu plum powders” LINK

“Fast quantification of total volatile basic nitrogen (TVB-N) content in beef and pork by near-infrared spectroscopy: Comparison of SVR and PLS model” LINK

“Combination of machine learning and VIRS for predicting soil organic matter” | LINK


“Hybrid Machine Learning for Scanning Near-field Optical Spectroscopy.” LINK

Research on Spectroscopy

“Rapid Synthesis of RedEmitting Sr2Sc0.5Ga1.5O5:Eu2+ Phosphors and the Tunable Photoluminescence Via Sr/Ba Substitution” LINK

Equipment for Spectroscopy

“MEMS and MOEMS Based Visible and NearInfrared Spectrometers” LINK

Process Control and NIR Sensors

“Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland” LINK

Agriculture NIR-Spectroscopy Usage

“Usage of Airborne Hyperspectral Imaging Data for Identifying Spatial Variability of Soil Nitrogen Content” LINK

“Determination of acid value during edible oil storage using a portable” LINK

Food & Feed Industry NIR Usage

“Identifying the best rice physical form for non-destructive prediction of protein content utilising near-infrared spectroscopy to support digital phenotyping” LINK

“Bioactive Phenolic Metabolites from Adriatic Brown Algae Dictyota dichotoma and Padina pavonica (Dictyotaceae)” Foods LINK

“Spectroscopic approaches for non-destructive shell egg quality and freshness evaluation: opportunities and challenges” LINK

Medicinal Spectroscopy

“Towards Development of LED-based Time-Domain Near-IR Spectroscopy System for Delineating Breast Cancer from Adjacent Normal Tissue” LINK


“The Effect of Principal Component Analysis Parameters on Solar-Induced Chlorophyll Fluorescence Signal Extraction” LINK

“Er3+doped antimonysilica glass and fiber for broadband optical amplification” LINK

“Newly developed glass samples containing P2O5-B2O3-Bi2O3-Li2O-CdO and their performance in optical and radiation attenuation applications” LINK

“Discrimination and Quantitation of Biologically Relevant Carboxylate Anions Using A [DyePAMAM] Complex” Sensors LINK

What is a NIR spectroscopy calibration service and for which users is it suitable? In a nutshell (TL;DR)

We provide NIR spectroscopy prediction model development and optimization as a service and provide the prediction engine as a software library for integration and also our free NIR-Predictor desktop software that supports many spectral data formats for any NIR instrument sensor. Simply drag and drop spectra files into NIR-Predictor to get analysis results report.

The Problems:

  • How do you build models?
  • How many manual time consuming steps are necessary in complicated expensive software (expensive to buy, develop, use, update)?
  • How long does model development and optimization take?
  • Do you need an expert for doing so?
  • Do you have two or more experts to compare their modeling results?
  • Are your experts overworked?
  • What’s your time to market?
  • And can your models compete in prediction performance with models built on high resolution full range spectrometers data?
  • Can your models be more accurate? E.g. Optimized Calibration for Mango DM

The Solution:

We can build best performing models for you also with small data, like 60 – 100 spectra or big data. You send NIR and Lab data as a single Calibration Request (Video link below) and we return the optimized ready to use model files and delete your sent data after processing. We do not sell and not reuse our customers data and spectral libraries.

Price : Comparision

Calibration Request : Video (2 minutes)

Learn : more

Start Calibrate

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

NIR-Predictor Download

The free NIR-Predictor software
  • comes with demo data, so you can predict sample spectra with demo calibrations.
  • has no functional limitations, no nagging, no ads and needs no license-key.
  • you need no account and no registration to download and use.
  • runs on Microsoft Windows 10/8/7 (Starter, Basic, Professional) (32 bit / 64 bit).
  • no data is ever transmitted from your local machine. We don’t even collect usage data.
See more Videos

Beside the free NIR-Predictor software with Windows user interface,
the real-time Predictor Engine is also available
  • for embedded integration in application, cloud and instrument-software (ICT).
  • As a light-weigt single library file (DLL)
    with application programming interface (API),
    documentation and software development kit (SDK)
    including sample source code (C#).
  • Easy integration and deployment,
    no software license protection (no serial key, no dongle).
  • Put your spectrum as an array into the multivariate predictor,
    no specific file format needed.
  • Fast prediction speed and low latency
    because of compiled code library (direct call, no cloud API).
  • Protected prediction results with outlier detection information.
See NIR Method Development Service for Labs and NIR-Vendors (OEM, White-Label)

Software Size Date Comment
NIR-Predictor V2.6.0.2 (download)

What’s new, see Release Notes

By downloading and/or using the software
you accept the Software License Agreement (EULA)
3.7 MB 18.08.2021 public release

Minimal System Requirements
Windows 7 Starter 32Bit, 1.6 GHz, 2 GB RAM, non-Administrator account

There are no administrator rights required, unpack the zip file to a folder “NIR-Predictor” in your documents or on your desktop.
Read the ReadMe.txt and double click the NIR-Predictor.exe file.

If you have installed an older version of NIR-Predictor then unpack into a different folder named e.g. “NIR-PredictorVx.y”. All versions can run side-by-side. Copy the Calibrations in use to the new version into the “Calibration” folder. That’s all.

Make sure to backup your reports and calibrations inside your “NIR-Predictor” folder. Delete the “NIR-Predictor” folder.

Start Calibrate

See also: