NIR-Predictor – Frequently Asked Questions (FAQ)

NIR-Predictor – FAQ

Please also refer to the NIR-Predictor – Manual and check the Hints and Notes.

Do you have a calibration file for XY ?

We create custom calibrations out of your NIR + Lab measurements of XY.
We do not sell off-the-shelf calibrations.

I have downloaded the software but can’t see it?

Please note, that the download time will be very short, because of the small file size.
Check your browser’s download folder. The download is the file “”

Why a .zip and no installer (Setup.exe) ?

Because a .zip deploy keeps it simple for all:

  • easy : no Administrator rights needed to install, delete it to uninstall
  • harmless : no system changes during setup
  • transparency : you see what you get

Is there a command line (CLI) version of NIR-Predictor ?

If you want to customize it in all details, our OEM API for NIR-instrument-software (White-Label) integration gives you full access. If you are an NIR-Vendor (or similar) please contact us via email

The free NIR-Predictor does not create a model, what is wrong ?

As the name says, the NIR-Predictor just predicts NIR data with a model. To create a model you need to send your data to the CalibrationModel service, after development process you get an email with a link to the calibration where it can be purchased and downloaded.

Why does the creation of the PropertyFile.txt take so long with hundrets of spectra files?

It normally takes only 1-10 seconds not minutes.
Make sure that the spectra data files are stored locally on your main drive
and not on a cloud-drive or network storage or slow USB thumb drive or SD-Card.

Is there a way to use converted ASCII spectrum data to be used in NIR-Predictor?

Yes, this is the simplest ASCII CSV file format the NIR-Predictor supports.
And there are other formats supported.

Can you convert old calibration data from vendor A to be integrated to our new vendor B NIRS calibration data in our instrument?

No, we don’t do model or spectra conversion / transformation (aka model transfer).
We build optimized models with wavelength compatible data.

Does NIR-Predictor contain any malware, spyware or adware?

No, NIR-Predictor does not contain any malware, spyware or adware.

How to copy the prediction results from the table in the browser?

Copy selected columns from the table.
By holding down the Ctrl key, rectangular areas in the table can be selected with the mouse and copied to the clipboard with Ctrl+C and then copied to a spreadsheet program with Ctrl+V.

Will an expired calibration still work?

No. Until you extend the usage time.
The expired calibration file will be moved to the CalibrationExpired folder on the next start of NIR-Predictor or “Search and load Calibrations” menu function.

Selected Calibration files from the folder CalibrationExpired can be send to with your Request file (.req) files for extending their usage time.

There is the possibility to get a perpetual usage, which means their is no expiration (valid until 2050).

That way you can get the time extended calibration back, that behaves exactly as the one before with extended usage time.

What does the calibration Expiration date mean exactly on the Prediction Report?

The Expiration date, it is the final day when the calibration will be valid (similar to credit cards).

What does the (number) in brackets after the [range] mean?

During the creation of a Calibration Request the NIR-Predictor shows a message containing
” ‘Prop1’ / “Quantitative [1.40 – 2.90] (154) ”
here 154 is the number of unique values in the property range.

How long does it take to create of the property file and calibration request file from 500 spectra files?

Use a local folder on your computer (not a network drive) for your spectra files then the property file and the calibration request file is created in around 1-3 seconds. (measured on a system with SSD drive, Intel i7, 2.4 GHz)

I tried to create the calibration request and got the error message: The number of Property Values of all spectra are different?

Use the generated PropertiesBySpectra (Note: Spectra not Sample) template file.
Do not reformat the generated template file, just fill in the Property values and save as Text CSV (*.csv) file (not as Excel file “.xlsx”).

Are you able to create the calibration if we only have 20 spectra?

To create a reliable quantitative calibration you need measured spectra of at least 48..60 (more is better) different samples with different Lab-values in the required measuring range!
The NIR-Predictor will check for that automatically.

Same sample measured multiple times (replicate measurements), how to enter the Lab-value only once in Property file?

Use the created PropertiesBySample template (not the PropertiesBySpectra).

Different samples with exact the same Lab-value, how to enter the Lab-Values?

Entering the Lab-values into the generated PropertiesBySpectra template the NIR-Predictor detects them as the same Sample and as a result we have too less different values to build a calibration. But in my case these are different samples with exact the same Lab-value, how to do?

You have to cheat a little bit to make NIR-Predictor do not detect same sample as measured multiple times. This is because most NIR users do replicate measurements on the same sample and NIR-Predictor looks for that. In PropertiesBySpectra modify the values a little bit to make them different, e.g.

Is the free NIR-Predictor software you provide for anyone to download and use?

Yes, if downloaded directly from our homepage by the user.
See also Software License Agreement

What is an Outlier?

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.

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.
See also manual chapter Outliers.

If something is wrong, please tell us

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 is created with the necessary data
    if enougth diverse Lab values are entered.

  5. Email the file
    to 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”.


The Application concept allows to group multiple Calibrations together for a 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

What is an Outlier? What are Outliers?

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

There are 3 outlier cases (X, O, =) and the incompatible data case “-“.
The bad case is “X”
the medium case is “O”
and 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 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
  • 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 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 “” 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 file is already created and you change the PropertiesBySamples.csv.txt make sure to delete the old 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: 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’”

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 #25, 2019

Spectroscopy and Chemometrics News Weekly 24, 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 24, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 24, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem prediction models LINK


“Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning” LINK

“Multivariate Discriminant Analysis of Single Seed Near Infrared Spectra for Sorting Dead-Filled and Viable Seeds of Three Pine Species: Does One Model Fit All Species?” forests LINK

“Development of near infrared spectroscopic methods to predict and understand dissolution of solid oral dosage forms” LINK

” Replication Data for: Towards a global arctic-alpine model for Near-infrared reflectance spectroscopy (NIRS) predictions of foliar nitrogen, phosphorus and …” LINK

” Genetic parameters for cow-specific digestibility predicted by near infrared reflectance spectroscopy” LINK

“Classification of Glycyrrhiza Seeds by Near Infrared Hyperspectral Imaging Technology” LINK

“Comprehensive comparison of multiple quantitative near-infrared spectroscopy models for Aspergillus flavus contamination detection in peanut.” LINK

“Non-Destructive Classification of Fruits Based on Vis-nir Spectroscopy and Principal Component Analysis” LINK

Near Infrared

“Assessment of applied microwave power of intermittent microwave-dried carrot powders from Colour and NIRS” LINK

“Soil Quality Analysis Using Modern Statistics and NIR spectroscopy Procedure” LINK

“Near-infrared diffusereflectance spectroscopy for discriminating fruit and vegetable products preserved in glass containers” LINK


“A review of the application of near-infrared spectroscopy to rare traditional Chinese medicine” LINK

“Near-infrared spectroscopy as a tool for in vivo analysis of human muscles” LINK

“Surface Functionality and Water Adsorption Studies of a-Aluminium (III) Oxide Nanoparticles by near Infrared Spectroscopy” LINK

“A comparative study of mango solar drying methods by visible and near-infrared spectroscopy coupled with ANOVA-simultaneous component analysis (ASCA)” LINK

“High throughput phenotyping of Camelina sativa seeds for crude protein, total oil, and fatty acids profile by near infrared spectroscopy” LINK

“Near-infrared spectroscopic study of molecular interaction in ethanol-water mixtures” LINK


“Potential of hyperspectral imaging for nondestructive determination of chlorogenic acid content in Flos Lonicerae” LINK

“Thickness estimation of crude oil slicks by hyperspectral data based on partial least square regression method” LINK

“Development of a polarized hyperspectral imaging system for investigation of absorption and scattering properties” LINK

“Hyperspectral Imaging Retrieval Using MODIS Satellite Sensors Applied to Volcanic Ash Clouds Monitoring” LINK


“Sensor Fusion and Machine Learning for Soil Characterization from Farm to National Scale” LINK


“Extension of the Measurable Wavelength Range for a Near-Infrared Spectrometer Using a Plasmonic Au Grating on a Si Substrate.” LINK


“Application of PROSPECT for estimating Total Petroleum Hydrocarbons in contaminated soils from leaf optical properties” LINK


“Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada” LINK

“Saving Old Bones: a non-destructive method for bone collagen prescreening” LINK

“Nondestructive On-site Detection of Soybean Contents Based on An Electrothermal MEMS Fourier Transform Spectrometer” LINK

“Remote Sensing Extraction of Crop Disaster Information Based on Support Vector Machine” LINK

“Remote Sensing, Vol. 11, Pages 1331: Evaluation of Leaf N Concentration in Winter Wheat Based on Discrete Wavelet Transform Analysis” LINK

“Predicting coefficient of linear extensibility and Atterberg limits of fine-grained soils using vis-NIR spectra” LINK


“Effect of external compression on femoral retrograde shear and microvascular oxygenation in exercise trained and recreationally active young men” LINK


Spectroscopy and Chemometrics News Weekly #1, 2019

Develop customized NIRS applications and freeing up hours of spectroscopy analysts time | spectroscopist chemist laboratory LINK

Increase Your Profit with optimized NIR Accuracy Beverage Processing Dairy LINK

Neue Möglichkeiten in der Entwicklung von Applikationen für die NIR-Analytik | Labor NIRS Analytik LaborAnalytik LINK

New : FREE NIR Predictor Software, drag&drop spectral data to plug&play calibrations, for any NIRS Analysis sensor type. | Chemometrics Prediction LINK

Service für professionelle Entwicklung von Nah-Infrarot Spektroskopie Kalibrations Methoden | NIRS Qualität Testen LINK

Spectroscopy and Chemometrics News Weekly 52, 2018 | NIRS Spectroscopy Chemometrics analysis Spectral Spectrometer Sensors Spectrometry LINK


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


“Nocturnal Hypoglycemic Alarm Based on Near-Infrared Spectroscopy: In Vivo Studies with a Rat Animal Model.” LINK

“Hydrolysis kinetics of silane coupling agents studied by near-infrared spectroscopy plus partial least squares model” LINK

“Utilizing visible and near infrared spectroscopy based on multi-class support vector machines classification to characterize olive oil adulteration” LINK

“Analysis of Near-Infrared (NIR) Spectroscopy for Chlorophyll Prediction in Oil Palm Leaves” LINK

“基于卷积神经网络的烟叶近红外光谱分类建模方法研究” “The Study of Classification Modeling Method for Near Infrared Spectroscopy of Tobacco Leaves Based on Convolution Neural Network” LINK

Near Infrared

“Determination of the geographical origin of green coffee beans using NIR spectroscopy and multivariate data analysis” LINK

“Acton Optics & Coatings Develops New UV-NIR Neutral Density Filters That Offer Unmatched Broadband Performance” LINK

“The Influence of Packaging on Cosmetic Emulsion during Storage Assessed by FT-NIR Spectroscopy and Color Measurements” – Society of Cosmetic Chemists LINK

The Influence of Packaging on Cosmetic Emulsion during Storage Assessed by FT-NIR Spectroscopy and Color Measurements – Society of Cosmetic Chemists LINK

“Measurement of pesticide residues in peppers by near-infrared reflectance spectroscopy” NIRS LINK

“Near infrared reflectance spectroscopy of pasticceria foodstuff as protein content predicting method” NIRS NIR LINK

“Application of portable micro near infrared spectroscopy to the screening of extractable polyphenols in grape skins: A complex challenge.” vineyard NIR LINK

“Qualitative Identification of Pesticide Residues in Pakchoi Based on Near Infrared Spectroscopy” NIRS LINK

“稻谷有害霉菌侵染的近红外光谱快速检测” “Rapid Detection of Harmful Mold Infection in Rice by Near Infrared Spectroscopy” LINK

“Quantitative Characterization of Arnicae flos by RP-HPLC-UV and NIR Spectroscopy.” LINK


“Fast detection of cocoa shell in cocoa powders by Near Infrared Spectroscopy and multivariate analysis” LINK

“SDAE-BP Based Octane Number Soft Sensor Using Near-infrared Spectroscopy in Gasoline Blending Process” LINK

“Differentiating between bottled water from different sources using near-infrared spectroscopy” LINK

“Nondestructive Detection of Pesticide Residue on Banana Surface Based on Near Infrared Spectroscopy” LINK

“Raman spectroscopy of a near infrared absorbing proteorhodopsin: Similarities to the bacteriorhodopsin O photointermediate.” LINK

“Determination of Total Polysaccharides and Total Flavonoids in Chrysanthemum morifolium Using Near-Infrared Hyperspectral Imaging and Multivariate Analysis.” LINK


“Surface Chemistry of Oil-Filled Organic Nanoparticle Coated Papers Analyzed Using Micro-Raman Mapping” LINK


“Spectroscopy and Spectral Imaging Techniques for Non-destructive Food Microbial Assessment” LINK

“Evaluating Soybean Cultivars for Low- and High-Temperature Tolerance During the Seedling Growth Stage” Agronomy NIRS LINK


to acquire Celgene to create a leading innovative biopharma company LINK


“The nutritive value of hay from the family farms of northwestern Croatia” LINK

“A comparison study of five different methods to measure carotenoids in biofortified yellow cassava (Manihot esculenta)” LINK

“大気中光電子収量分光分析による有機薄膜半導体のエネルギー準位の測定” LINK

Spectroscopy and Chemometrics News Weekly #49, 2018

NIR Machine Learning Software as a Service, a Game Changer for NIR Productivity and NIR Accuracy and NIR Precision! ( NIRS Spectroscopy AI MLaaS AutoML) LINK

Spectroscopy and Chemometrics News Weekly 48, 2018 | NIRS Spectroscopy Chemometrics analysis Spectral Spectrometer Sensors Spectrometry LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 48, 2018 | NIRS Spektroskopie Chemometrie analyse Spektral Spektrometer Sensor LINK

Spettroscopia e Chemiometria Weekly News 48, 2018 | NIRS Spettroscopia Chemiometria analisi Spettrale Spettrometro Sensore LINK


“Using deep learning to predict soil properties from regional spectral data” LINK

“Determination of Pesticide in Banana by Infrared Spectroscopy Using Partial Least Square-Discriminant Analysis” LINK

“Quantification of Different Forms of Iron from Intact Soil Cores of Paddy Fields with Vis-NIR Spectroscopy” LINK

“A Vis-NIR spectral library to predict clay in Australian cotton growing soil” LINK

Near Infrared

“Water spectral pattern as a marker for studying apple sensory texture” aquaphotomics crispness juiciness mealiness NIR LINK

“Near-Infrared (NIR) Spectrometry as a Fast and Reliable Tool for Fat and Moisture Analyses in Olives” LINK

“A New Statistical Approach to Describe the Quality of Extra Virgin Olive Oils Using Near Infrared Spectroscopy (NIR) and Traditional Analytical Parameters” LINK

“Olive oil nutritional labeling by using Vis/NIR spectroscopy and compositional statistical methods” LINK

“Sparse NIR Optimization method (SNIRO) to quantify analyte composition with visible (VIS)/near infrared (NIR) spectroscopy (350nm-2500nm)” LINK

“Monitoring the growth and maturation of apple fruit on the tree with handheld Vis/NIR devices” LINK


“Direct Determination of Ni2+-Capacity of IMAC Materials Using Near-Infrared Spectroscopy” LINK

“Quantitative analysis of quality for marian plum (Bouea burmanica Griff.) by transmittance near infrared spectroscopy” LINK

“Variable selection for the determination of total polar materials in fried oils by near infrared spectroscopy” LINK

“Biodiesel Synthesis Monitoring using Near Infrared Spectroscopy” LINK

“Non-destructive chemical analysis of water and chlorine content in cement paste using near-infrared spectroscopy” LINK


“Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging” LINK


“Interval Multiple-output Soft sensors Development with Capacity Control for Wastewater Treatment Applications: A Comparative Study” LINK


“Nondestructive measurements of postharvest changes in lamb’s lettuce” LINK

Spectroscopy and Chemometrics News Weekly #41, 2018

NIR Machine Learning as a Service, a Game Changer for Productivity and Accuracy/Precision! ( NIRS Spectroscopy AI MLaaS ) LINK

Near Infrared (NIR) Analysis Software, work smart with all NIR Spectrometers for quantitative sensing & detection. | AnalyticalChemistry LabManger Chemical Analysis Equipment ChemicalAnalysis Analytical Instruments Laboratory LabEquipment LINK

New : FREE NIR Predictor Software, drag&drop spectral data to plug&play calibrations, for any NIRS Analysis sensor type. | Chemometrics Prediction LINK

Spectroscopy and Chemometrics News Weekly 40, 2018 | NIRS Spectroscopy Chemometrics analysis Spectral Spectrometer Sensors LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 40, 2018 | NIRS Spektroskopie Chemometrie analyse Spektral Spektrometer Sensor LINK

Spettroscopia e Chemiometria Weekly News 40, 2018 | NIRS Spettroscopia Chemiometria analisi Spettrale Spettrometro Sensore LINK


“Classification of different animal fibers by near infrared spectroscopy and chemometric models” LINK

“Discrimination of organic and conventional rice by chemometric analysis of NIR spectra: a pilot study” LINK

“Discrimination between conventional and omega-3 fatty acids enriched eggs by FT-Raman spectroscopy and chemometric tools” omega3 LINK

“Quantification of mineral composition of Brazilian bee pollen by near infrared spectroscopy and PLS regression.” LINK

“Sex determination of silkworm pupae using VIS-NIR hyperspectral imaging combined with chemometrics.” LINK

Near Infrared

“Spinning-disc confocal microscopy in the second near-infrared window (NIR-II)” Fluorescence LINK

“Measuring the brain’s fast optical signal could speed up Brain–computer interfaces (BCI) response” NIRS LINK

“Bioprofiling for the quality control of Egyptian propolis using an integrated NIR-HPTLC-image analysis strategy.” LINK

“lab for the pocket” hertzstueck NIRS LINK

“NIR gas phase spectroscopy – Pressure broadening effects” LINK

“Near-infrared Band Used for Permanent, Wireless Self-charging System – R & D Magazine” LINK

“Non-Destructive NIR Spectral Imaging Assessment of Bone Water: Comparison to MRI Measurements” – NIRS vs. Magnetic Resonance Imaging LINK


“Near-infrared spectroscopy could improve flu vaccine manufacturing” LINK

“Detection of Alone Stress and Combined Stress by CU and NI in Wheat Using Visible to Near-Infrared Spectroscopy” LINK

“A Novel Method for Classifying Driver Cognitive Distraction under Naturalistic Conditions with Information from Near-Infrared Spectroscopy” LINK


“Quality evaluation of fried soybean oil base on near infrared spectroscopy” LINK

Curious about new developments in various fields of spectroscopy and their application in plant sciences? Register now for the International Plant Spectroscopy Conference (IPSC) organised by our colleagues in Berlin, March 24-28th, 2019: LINK

“Application of Vibrational Spectroscopy and Imaging to Point-of-Care Medicine: A Review” LINK

Food & Feed

“From NIR spectra to singular wavelengths for the estimation of the oil and water contents in olive fruits” LINK


“New, noninvasive blood glucose test as effective as finger prick test – Clinical Innovation + Technology” Raman spectroscopy LINK


so, spent some time down the hardware rabbit hole. the core sensors, | is available in a number of combos, on a number of boards; e.g. | LINK

Spectroscopy and Chemometrics News Weekly #34, 2018

New : FREE NIR Predictor Software, drag&drop spectral data to plug&play calibrations, for any NIRS Analysis sensor type. | Chemometrics Prediction LINK

Spectroscopy and Chemometrics News Weekly 33, 2018 | NIRS Spectroscopy Chemometrics analysis Spectral Spectrometer Sensors LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 33, 2018 | NIRS Spektroskopie Chemometrie analyse Spektral Spektrometer Sensor LINK

Spettroscopia e Chemiometria Weekly News 33, 2018 | NIRS Spettroscopia Chemiometria analisi Spettrale Spettrometro Sensore LINK

We updated the Near Infrared (NIR) Spectrometer Directory of Suppliers / Manufacturers / Vendors. The list includes now also mobile miniature NIR spectrometer sensors. | NIRS NIR FTNIR NIT NearInfrared MEMS Spectral Sensor IoT LINK


“Enhancing near infrared spectroscopy models to identify omega-3 fish oils used in the nutraceutical industry by means of calibration range extension” omega3 LINK

“Near infrared spectroscopy coupled with chemometric algorithms for predicting chemical components in black goji berries (Lycium ruthenicum Murr.)” LINK

“Towards innovation in paper dating: a MicroNIR analytical platform and chemometrics” nondestructive diagnostic forensic LINK

“Least-squares support vector machine and successive projection algorithm for quantitative analysis of cotton-polyester textile by near infrared spectroscopy” LINK

“Direct calibration transfer to principal components via canonical correlation analysis” NIRS corn tobacco LINK

“Collaborative representation based classifier with partial least squares regression for the classification of spectral data” LINK

Near Infrared

“Rapid and non-destructive discrimination of special-grade flat green tea using Near-infrared spectroscopy.” LINK

“HOW DID SCIENTISTS DISCOVER WATER ON THE SURFACE OF THE MOON? …. used near-infrared reflectance spectroscopy (NIRS) to find surface water at the moon’s polar regions. …. electromagnetic spectrum, from about 700 to 2,500 manometers.” LINK


“DSC, FTIR and Raman Spectroscopy Coupled with Multivariate Analysis in a Study of Co-Crystals of Pharmaceutical Interest” LINK


“Calibration transfer of near infrared spectrometers for the assessment of plasma ethanol precipitation process” LINK


“ILS: An R package for statistical analysis in Interlaboratory Studies” | outliers ANOVA LINK


“Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination” LINK

Spectroscopy and Chemometrics News Weekly #33, 2018

Near Infrared

“Accurate and rapid detection of soil and fertilizer properties based on visible/near-infrared spectroscopy.” LINK

“Authenticity Detection of Black Rice by Near-Infrared Spectroscopy and Support Vector Data Description.” NIRS LINK

“MicroNIR™ PAT-W for Blend Endpoint Analysis in a High Dosage Product” LINK


“Which regression method to use? Making informed decisions in “data-rich/knowledge poor” scenarios – The Predictive Analytics Comparison framework (PAC)” LINK

“Determination of salvianolic acid B and borneol in compound Danshen tablet by near-infrared spectroscopy and establishment of dependency model” LINK

“Error propagation of partial least squares for parameters optimization in NIR modeling.” LINK

“Rapid quantification of the adulteration of fresh coconut water by dilution and sugars using Raman spectroscopy and chemometrics” LINK

“Predicting pork freshness using multi-index statistical information fusion method based on near infrared spectroscopy.” LINK

“Validation of short wave near infrared calibration models for the quality and ripening of ‘Newhall’ orange on tree across years and orchards” fruits SWNIRS LINK

“Fault detection based on time series modeling and multivariate statistical process control.” LINK

Near Infrared (NIR) Analysis Software, work smart with all NIR Spectrometers for quantitative sensing & detection. | AnalyticalChemistry LabManger Chemical Analysis Equipment ChemicalAnalysis Analytical Instruments Laboratory LabEquipment LINK

New : FREE NIR Predictor Software, drag&drop spectral data to plug&play calibrations, for any NIRS Analysis sensor type. | Chemometrics Prediction LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 32, 2018 | NIRS Spektroskopie Chemometrie analyse Spektral Spektrometer Sensor LINK

Spettroscopia e Chemiometria Weekly News 32, 2018 | NIRS Spettroscopia Chemiometria analisi Spettrale Spettrometro Sensore LINK

The non-destructive technique such as Near Infrared Spectroscopy NIRS along with Chemometrics can predict quality parameters of measurements by using the free NIR-Predictor Software. QualityControl QualityAssurance foodsafety productinspection LINK


“Estimating soil heavy metals concentration at large scale using visible and near-infrared reflectance spectroscopy.” LINK

“Overall uncertainty measurement for near infrared analysis of cryptotanshinone in tanshinone extract.” LINK


“Hyperspectral imaging reveals wound problems” LINK


“Sensoren machen guten Wein – Mit Hilfe von Sensoren können Winzer Informationen zu Reife, Qualität, Ertragsaussichten und Krankheitsrisiken ihrer Reben erhalten.” LINK


“Fourier transform infrared spectrometer based on an electrothermal MEMS mirror.” LINK


“Discrimination of Milks with a Multisensor System Based on Layer-by-Layer Films” LINK


“Watch out, birders: Artificial intelligence has learned to spot birds from their songs” LINK

Spectroscopy and Chemometrics News Weekly #31, 2018


How to Configure the Number of Layers and Nodes in NeuralNetworks: BigData DataScience AI MachineLearning DeepLearning Algorithms by Source for graphic: | abdsc (2018.08.02) LINK

“Visible-Near-Infrared Spectroscopy can predict Mass Transport of Dissolved Chemicals through Intact Soil.” (2018.08.02) LINK

“Classification and compositional characterization of different varieties of cocoa beans by near infrared spectroscopy and multivariate statistical analyses.” (2018.08.02) LINK

“Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning.” (2018.08.02) LINK

“Rapid Prediction of Low (<1%) trans Fat Content in Edible Oils and Fast Food Lipid Extracts by Infrared Spectroscopy and Partial Least Squares Regression” (2018.07.31) LINK

“Evaluating the performance of a consumer scale SCiO™ molecular sensor to predict quality of horticultural products” (2018.07.30) LINK

“Estimation of Total Phenols, Flavanols and Extractability of Phenolic Compounds in Grape Seeds Using Vibrational Spectroscopy and Chemometric Tools” (2018.07.26) LINK

Near Infrared

“FT-NIR, MicroNIR and LED-MicroNIR for Detection of Adulteration in Palm Oil via PLS and LDA” FTNIR NIRS (2018.08.03) LINK

“Long-Length Fiber Optic Near-Infrared (NIR) Spectroscopy Probes for On-Line Quality Control of Processed Land Animal Proteins” (2018.08.03) LINK

“Near-infrared spectroscopy for rapid and simultaneous determination of five main active components in rhubarb of different geographical origins and processing.” (2018.08.02) LINK

“Marktech Optoelectronics Introduces Silicon Avalanche Photodiodes for Low-Level Light and Short Pulse Detection” UV NIR NIRS SWIR (2018.08.02) LINK

“Innovative Technology Promises Fast, Cost-Efficient Age Data for Fisheries Management” FTNIR (2018.07.31) LINK

“Rapid qualitative and quantitative analysis of methamphetamine, ketamine, heroin, and cocaine by near-infrared spectroscopy.” (2018.07.31) LINK

We (led by ) have been independently assessing thew value of consumer scale NIR devices for horticultural quality assessment. Here is our published work assessing (2018.07.30) LINK


“Fault Detection Based on Near-Infrared Spectra for the Oil Desalting Process” (2018.08.05) LINK

“Common Infrared Optical Materials and Coatings: A Guide to Properties, Performance and Applications” (2018.08.04) LINK


SpectraBase – FREE, fast text access to hundreds of thousands of NMR, IR, Raman, UV-Vis, and mass spectra! spectroscopy (2018.08.02) LINK


“Three-Dimensional Reconstruction of Soybean Canopies Using Multisource Imaging for Phenotyping Analysis” (2018.08.03) LINK

“Smartphone Spectroscopy Promises a Data-Rich Future – An upsurge of portable, consumer-facing devices at the intersection of smartphone computing and spectroscopy is now leveraging integration. ” (2018.08.02) LINK

Innovative Technology Promises Fast, Cost-Efficient Age Data for Fisheries Management (2018.07.31) LINK

“Smartphone-Based Food Diagnostic Technologies: A Review” (2018.07.30) LINK


“Detection, Purity Analysis, and Quality Assurance of Adulterated Peanut (Arachis Hypogaea) Oils” (2018.08.02) LINK


“Potential of near infrared spectroscopy and pattern recognition for rapid discrimination and quantification of Gleditsia sinensis thorn powder with adulterants.” (2018.08.02) LINK


A micro-spectrometer fit for a smartphone: Could the power to measure things like CO2, food freshness, and blood sugar levels soon be in the palm of our hands? |rld/magazine/article/323/micro-spectrometer-opens-door-to-a-wealth-of-new-smartphone-functions? health safety medicine spectroscopy (2018.02.25) LINK

“Near-infrared spectroscopy detects age-related differences in skeletal muscle oxidative function: promising implications for geroscience.” (2018.02.08) LINK


69% of decision makers say industrial analytics will be crucial for business in 2020. | IoT IIoT MT LINK

Free Chemometric NIR Predictor Software! Simple plug&play calibrations, drag&drop spectral data, for any NIRS sensor device. Easy to use software for off-line and real-time prediction without limits. offline realtime (2018.08.04) LINK

Automated NIRS spectroscopy chemometrics method development with MachineLearning for spectrometer Spectral IoT sensor SmartSensor SmartSensors (2018.07.25) LINK

Automatic NIR Spectroscopy Calibration-Development as a Service. Applicable with free NIR-Predictor software or via OEM API. | NIRSpectroscopy NearInfrared NIRanalysis spectrometers DataAnalytics Regression Spectral Sensors QualityControl Lab (2018.07.26) LINK

Increase Your Profit with optimized NIRS Accuracy with Calibration as a Service (CaaS) and the new free NIR-Predictor software. | foodsafety Feed Lab QC QA testing (2018.08.03) LINK

New : FREE NIR Predictor Software, drag&drop spectral data to plug&play calibrations, for any NIRS Analysis sensor type. | Chemometrics Prediction (2018.07.24) LINK

Spectroscopy and Chemometrics News (KW 11-30 2018) | NIRS Spectroscopy Chemometrics analysis Spectral Spectrometer Sensors (2018.07.25) LINK

Spektroskopie und Chemometrie Neuigkeiten (KW 11-30 2018) | NIRS Spektroskopie Chemometrie analyse Spektral Spektrometer Sensor (2018.07.25) LINK

Spettroscopia e Chemiometria Weekly (KW 11-30 2018) | NIRS Spettroscopia Chemiometria analisi Spettrale Spettrometro Sensore (2018.07.25) LINK

光谱学和化学计量学新闻(KW#11-#30 2018) | 近红外光谱化学计量学分析光谱仪传感器 (2018.07.26) LINK

分光法とケモメトリックスニュース(KW#11-#30 2018) | 赤外分光法・ケモメトリックスの分光センサーの近く (2018.07.26) LINK

Procedures for NIR calibration – Creation of NIRS spectroscopy calibration curves

Do you know the effect that you prefer to try out their favorite data pretreatments in combination and often try the same wavelength selections based spectra of the visualized?

You try as six to ten combinations until one of them selects his favorite calibration model, to then continue to optimize. Since then suddenly fall to outliers, because it goes in depth, so is familiar with the data, we know now the spectra of numbers of outliers and is familiar with the extreme values.

Now, the focus is on the major components (principal components, Latent Variables, factors) and makes sure not to over-fit and under-fit not to. The whole takes a few hours and finally one is content with the model found.

So what would happen if you all in the beginning tried variants found outliers removed and re-evaluated and compared? The results would be better than that of the previous model choice? One does not try out? Because it is cumbersome and takes hours again?

We have developed a software which simplifies this so that also the number of model variations can be increased as desired. The variants generation is automated with an intelligent control system, as well as the optimization and comparing the models and finally the final selection of the best calibration model.

Our software includes all the usual known data pretreatment methods (data pre-processing) and can combine them useful. Since many Preteatments are directly dependent on the wavelength selection, such as the normalization the determined within a wavelength range of the scaling factors to normalize the spectra so that pretreatments with the wavelength ranges may be combined. So a variety of settings sensible model comes together that are all calculated and optimized. For the automatic selection of the relevant wavelength ranges, different methods are used, which are based on the spectral intensities. Thus, for example, regions with total absorption is not used, and often interfering water bands removed or retained.

Over all the calculated model variations as a summary outlier analysis can be made. Are there any new outliers (hidden outlier) discovered, all previous models can be automatically recalculated, optimized and compared without these outliers.

From this great number of calculated models with the statistical quality reviews (prediction performance) the optimum calibration can now be selected. For this purpose, not simply sorting by the prediction error (prediction error, SEP RMSEP) or the coefficient of determination (coefficient of determination r2), but by several statistical and test values are used jointly toward the final assessment of optimal calibration.

Thus we have created a platform that allows the highly automated work what a man can never do with a commercial software.

We therefore offer the largest number of matched to your application problem modeling calculations and choose the best calibration for you!

This means that our results are faster, more accurate, robust and objective basis (person independent) and quite easy for you to apply.

You have the full control of the models supplied by us, because we provide a clearly structured and detailed blueprint of the complete calibration, with all settings and parameters, with all necessary statistical characteristics and graphics.

Using this blueprint, you can adjust the quantitative calibration model itself in the software you use, understand and compare. You have everything under control form model creation, model validation and model refinement.

Your privacy is very important to us. The NIR data that you briefly provide us for the custom calibration development will remain of course your property. Your NIR data will be deleted after the job with us.

Start Calibrate

Interested, then do not hesitate to contact us.