Spectroscopy and Chemometrics News Weekly #7, 2021

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 6, 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 6, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 6, 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)

“Evaluation of swelling properties and drug release from mechanochemical pre-gelatinized glutinous rice starch matrix tablets by near infrared spectroscopy” LINK

” Multivariate method for prediction of fumonisins B1 and B2 and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR)” | LINK

“Non-destructive determination of fatty acid composition of in-shell and shelled almonds using handheld NIRS sensors” LINK

“Breakthrough instruments and products: Near infrared spectral sensing: Advances in portable instrumentation and implementations” LINK

“Quali-quantitative monitoring of chemocatalytic cellulose conversion into lactic acid by FT-NIR spectroscopy.” LINK

“NIR Spectroscopy Detects Chlorpyrifos-Methyl Pesticide Residue in Rough, Brown, and Milled Rice” LINK

“ANALYSIS OF TEA LEAVES WITH DIFFERENT OXIDATION STATES BY FT-NIR SPECTROSCOPY” LINK




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

“Identification of peach and apricot kernels for traditional Chinese medicines using near-infrared spectroscopy” LINK

“Breakthrough instruments and products: Near infrared spectral sensing: Advances in portable instrumentation and implementations” LINK

“Two-dimensional moisture content and size measurement of pharmaceutical granules after fluid bed drying using near-infrared chemical imaging.” LINK

“Influence of steroids on hydrogen bonds in membranes assessed by near infrared spectroscopy” LINK

“Determination of the oxidative stability of biodiesel fuels by near-infrared spectroscopy” LINK

“In Vitro Spectroscopy-Based Profiling of Urothelial Carcinoma: A Fourier Transform Infrared and Raman Imaging Study” LINK

“Novel alternative use of near-infrared spectroscopy to indirectly forecast 3D printability of purple sweet potato pastes” LINK

“Shortwave infrared hyperspectral imaging system coupled with multivariable method for TVB-N measurement in pork” LINK




Chemometrics and Machine Learning

“Discrimination between wild-grown and cultivated Gastrodia elata by near-infrared spectroscopy and chemometrics” LINK

“A Single Model to Monitor Multistep Craft Beer Manufacturing using Near Infrared Spectroscopy and Chemometrics” LINK

“Noninvasive Blood Glucose sensing by Near-Infrared Spectroscopy based on PLSR Combines SAE Deep Neural Network Approach” LINK

“Comparison of a Low-cost Prototype Optical Sensor with Three Commercial Systems in Predicting Water and Nutrient Contents of Turfgrass: Prediction performance of …” LINK

“Development of NIR-HSI and chemometrics process analytical technology for drying of beef jerky” LINK

“Soil spectroscopy with the use of chemometrics, machine learning and pre-processing techniques in soil diagnosis: Recent advances-A review” LINK

“Application of NIR handheld transmission spectroscopy and chemometrics to assess the quality of locally produced antimalarial medicines in the Democratic Republic of Congo” LINK




Facts

“An Accuracy Improvement Method Based on Multi-Source Information Fusion and Deep Learning for TSSC and Water Content Nondestructive Detection in “Luogang” Orange” LINK




Research on Spectroscopy

“Nondestructive methods for determining the firmness of apple fruit flesh” LINK




Equipment for Spectroscopy

“Development of Fluorescence Imaging Technique to Detect Fresh-Cut Food Organic Residue on Processing Equipment Surface” LINK




Process Control and NIR Sensors

“Acid number, viscosity and end-point detection in a multiphase high temperature polymerisation process using an online miniaturised MEMS Fabry-Pérot interferometer.” | LINK

“Real-Time Monitoring of Yogurt Fermentation Process by Aquaphotomics Near-Infrared Spectroscopy.” LINK




Agriculture NIR-Spectroscopy Usage

“Rapid and nondestructive determination of qualities in vacuum packaged catfish (Clarias leather) fillets during slurry ice storage” LINK

“Economic and chemometric studies to supplement food-grade soybean variety development in the Mid-Atlantic region” LINK

“Online Monitoring of Fermented Grains Parameters for Chinese Liquor Brewing Based on Near Infrared Spectroscopy” LINK




Horticulture NIR-Spectroscopy Applications

“Fast, simultaneous and contactless assessment of intact mango fruit by means of near infrared spectroscopy [J]” LINK

“The development of portable detector for apples soluble solids content based on visible and near infrared spectrum.” LINK

“Non-destructive and fast method of mapping the distribution of the soluble solids content and pH in kiwifruit using object rotation near-infrared hyperspectral imaging …” LINK




Food & Feed Industry NIR Usage

“A two-tiered system of analysis to tackle rice fraud: The Indian Basmati study” LINK





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Spectroscopy and Chemometrics News Weekly #36, 2020

NIR Calibration-Model Services

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


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

Spectroscopy and Chemometrics News Weekly 35, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 35, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 35, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near-Infrared Spectroscopy (NIRS)

“Estimating Roughage Quality with Near Infrared Reflectance (NIR) Spectroscopy and Chemometric Techniques” LINK

“Detection of Insect Damage in Green Coffee Beans Using VIS-NIR Hyperspectral Imaging” LINK

“Revisiting Water Speciation in Hydrous Alumino-Silicate glasses: A Discrepancy between Solid-state 1H NMR and NIR spectroscopy in the Determination of X-OH …” LINK

“Prediction of Organic Carbon Content of Intertidal Sediments Based on Visible-Near Infrared Spectroscopy” “可见-近红外光谱的潮间带沉积物有机碳含量的几种模型预测方法” LINK




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

“Predicting soil phosphorus and studying the effect of texture on the prediction accuracy using machine learning combined with near-infrared spectroscopy” LINK

“CIC nanoGUNE reaches new depths in infrared nanospectroscopy” LINK

“Distinguishing Hemp from Marijuana by Mid-Infrared Spectroscopy” LINK

“Glucobrassicin Enhancement Using Low Red to Far-Red Light Ratio in ‘Ruby Ball’ Cabbage and High-Throughput Glucobrassicin Estimation Using Near-Infrared …” LINK

“Near-infrared spectroscopy outperforms genomic selection for predicting sugarcane feedstock quality traits” LINK

“Estimation of critical nitrogen contents in peach orchards using visible-near infrared spectral mixture analysis” LINK

“Non-destructive and rapid measurement of sugar content in growing cane stalks for breeding programmes using visible-near infrared spectroscopy” LINK

“Quantitative Analysis of Protein and Polysaccharide in Lilium Lanzhou Based on Near Infrared Spectroscopy” LINK

“Time-stretch infrared spectroscopy” LINK

“Using near infrared reflectance spectroscopy for estimating nutritional quality of Brachiaria humidicola in breeding selections” LINK

“Quantification of phenolic acids by partial least squares Fouriertransform infrared (PLSFTIR) in extracts of medicinal plants” LINK




Chemometrics and Machine Learning

“Predicting adulteration of Palm oil with Sudan IV dye using shortwave handheld spectroscopy and comparative analysis of models” LINK

“Self-adaptive models for predicting soluble solid content of blueberries with biological variability by using near-infrared spectroscopy and chemometrics” LINK

“Rapid identification and quantitative pit mud by near infrared Spectroscopy with chemometrics” LINK

“Methane emission detection and flux quantification from exploratory hydraulic fracturing in the United Kingdom, using unmanned aerial vehicle sampling” LINK




Research on Spectroscopy

“MD dating: molecular decay (MD) in pinewood as a dating method” LINK

“Improved Dimensional Stability and Mold Resistance of Bamboo via In Situ Growth of Poly(Hydroxyethyl Methacrylate-N-Isopropyl Acrylamide)” Polymers LINK




Equipment for Spectroscopy

“Applied Sciences, Vol. 10, Pages 4896: A Novel Single-Channel Arrangement in Chirp Transform Spectrometer for High-Resolution Spectrum Detection” LINK




Agriculture NIR-Spectroscopy Usage

“Angle Distribution Measurement of Scattered Light Intensity from Needle-shaped Crystals in a Magnetic Field for Gout Diagnosis” LINK

“Use of barley silage or corn silage with dry-rolled barley, corn, or a blend of barley and corn on predicted nutrient total tract digestibility and growth performance of …” LINK

“Identification of Leaf-Scale Wheat Powdery Mildew (Blumeria graminis f. sp. Tritici) Combining Hyperspectral Imaging and an SVM Classifier” Plants LINK

“Smartphone-supported portable micro-spectroscopy/imaging system to character morphology and spectra of samples at microscale” LINK

“Novel Antioxidant Packaging Films Based on Poly(-Caprolactone) and Almond Skin Extract: Development and Effect on the Oxidative Stability of Fried Almonds” LINK

“Applied Sciences, Vol. 10, Pages 4907: Experimental Comparison of Diesel and Crude Rapeseed Oil Combustion in a Swirl Burner” LINK

“Molecules, Vol. 25, Pages 3260: Comparison of Bioactive Phenolic Compounds and Antioxidant Activities of Different Parts of Taraxacum mongolicum” LINK




Horticulture NIR-Spectroscopy Applications

“Application of a Vis-NIR Spectroscopic Technique to Measure the Total Soluble Solids Content of Intact Mangoes in Motion on a Belt Conveyor” LINK




Forestry and Wood Industry NIR Usage

“Online analysis of wood extractives” LINK




Food & Feed Industry NIR Usage

Near-Infrared Spectroscopy as a Beef Quality Tool to Predict Consumer Acceptance” Foods LINK

“Rapid Vitality Estimation and Prediction of Corn Seeds Based on Spectra and Images Using Deep Learning and Hyperspectral Imaging Techniques” LINK

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

“A simple design for the validation of a FT-NIR screening method: Application to the detection of durum wheat pasta adulteration.” LINK




Laboratory and NIR-Spectroscopy

In-line UV-Vis Spectroscopy Market Research Report 2019-2030 | Industry Report, Industry …: Success of this technology depends on the in-depth knowledge of the link between optical instrumentation design and its effect on data quality. LINK




Other

“The Detection of Substitution Adulteration of Paprika with Spent Paprika by the Application of Molecular Spectroscopy Tools” LINK

“Non-destructive Detection of Apple Maturity by Constructing Spectral Index based on Reflectance Spectrum” LINK





Spectroscopy and Chemometrics News Weekly #25, 2020

NIR Calibration-Model Services

Using cost saving NIR-Spectroscopy Analysis? You can Save even more Costs and Time! How? Read here | VIS NIR NIRS Spectroscopy LabManager Labs QualityControl CostSaving foodindustry foodproduct Spectrometer Sensor Analytics LINK

Machine Learning for NIR Spectroscopy as a Service, a Game Changer for Productivity and Accuracy/Precision! Use the free NIR-Predcitor software to combine NIRS + Lab data and send your Calibration Request. LabManager Analysis MachineLearning LINK

Spectroscopy and Chemometrics News Weekly 24, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 24, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 24, 2020 | 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 Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us.




Near-Infrared Spectroscopy (NIRS)

“Near-Infrared (NIR) Spectroscopy to Differentiate Longissimus thoracis et lumborum (LTL) Muscles of Game Species” LINK

“Estimation of Harumanis (Mangifera indica L.) Sweetness using Near-Infrared (NIR) Spectroscopy” LINK

“Handheld Near-Infrared Spectrometers: Reality and Empty Promises” miniaturization NIRS FTNIR MEMS MOEMS LVFs LINK

BESTCentreLTU research hot off the press: | In collaboration with Assoc. Prof. Kellie Tuck from , we’ve developed new near-infrared emissive electrochemiluminophores for sensing in NIR transparent biological media. LINK

“Near-Infrared Emitter for Bioanalytical Applications” NIR ECL electrochemiluminescence LINK

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

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

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

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




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

“Non-Invasive Blood Glucose Monitoring using Near-Infrared Spectroscopy based on Internet of Things using Machine Learning” LINK

“Investigating the Quality of Antimalarial Generic Medicines Using Portable Near-Infrared Spectroscopy” LINK

“Rapid quantitative detection of mineral oil contamination in vegetable oil by near-infrared spectroscopy” LINK

“THE DETERMINATION OF FATTY ACIDS IN CHEESES OF VARIABLE COMPOSITION (COW, EWE’S, AND GOAT) BY MEANS OF NEAR INFRARED SPECTROSCOPY” LINK

“Detection of melamine and sucrose as adulterants in milk powder using near-infrared spectroscopy with DD-SIMCA as one-class classifier and MCR-ALS as a means to provide pure profiles of milk and of both adulterants with forensic evidence” LINK

“Protein, weight, and oil prediction by singleseed nearinfrared spectroscopy for selection of seed quality and yield traits in pea (Pisum sativum)” LINK

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

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

“Should the Past Define the Future of Interpretation of Infrared and Raman Spectra?” LINK

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

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

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

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




Hyperspectral Imaging (HSI)

“Rapid detection of quality index of postharvest fresh tea leaves using hyperspectral imaging” LINK

“Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms” LINK

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

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

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




Chemometrics and Machine Learning

“Molecules, Vol. 25, Pages 1453: Characterization, Quantification and Quality Assessment of Avocado (Persea americana Mill.) Oils” LINK

“Comprehensive Chemometrics – Chemical and Biochemical Data Analysis Reference Work • 2nd Edition • 2020” | books Chemometrics DataAnalysis Chemical Biochemical LINK

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




Research on Spectroscopy

“Automatisierte und digitale Dokumentation der Applikation organischer Düngemittel” LINK

“Plenary Lecture Methods and Tools for Sensors Information Processing” LINK




Equipment for Spectroscopy

Using NIR scanner to assess grass in sward for composition prior to baling and wrapping for EU LIFE Farm4More project. Thanks to Dinamica Generale for providing the equipment LINK

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

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




Agriculture NIR-Spectroscopy Usage

“Hyperspectral imaging using multivariate analysis for simulation and prediction of agricultural crops in Ningxia, China” LINK

“Placing Soil Information in the Hands of Farmers” LINK

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

“Complex Food Recognition using Hyper-Spectral Imagery” LINK




Horticulture NIR-Spectroscopy Applications

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




Laboratory and NIR-Spectroscopy

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




Other

LINK



NIR-Predictor – Manual


NIR-Predictor – Manual

Predicting Spectra

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

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

Use the included data to checkout how it feels:

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

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

  3. The spectra will be

    • loaded
    • pre-processed
    • predicted and
    • reported

Note:
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”

Hint:
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.

    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

Configuration:

  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)

Usage:

  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.


Applications

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.
Histograms

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

  • 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

Note:

  • 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.

    Or

    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 Release Notes

Legend: [+] added, [*] improved, [/] bugfix, [-] removed


NIR-Predictor Download Page

V2.6.0.2 Public Release – 18.08.2021

Fixed

  • [/] “Create Properties File …” could lead in seldom cases to an IndexOutOfRangeException in SampleReplicates.analyze3ToString. The bug has not affected created data.


V2.6 Public Release – 1. June 2020

New Key Features

  • Reads and predicts .SPC spectra file format (Thermo-Scientific Galactic GRAMS)

    Support for multi spectra and single spectra .SPC files.
    Multiple multi-spectra files can be predicted in one step.

  • Spectra Plots on the prediction reports

    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.

Details

  • [+] Thermo-Scientific Galactic GRAMS SPC spectra file format support for multi spectra and single spectra files. Multiple multi-spectra files can be imported in one step.

  • [+] Spectra Plot Thumbnail on the Prediction Report

    • Spectra Plot color legend: min,median,max spectrum by predicted property or if no calibration is available by spectral intensity.
  • [+] Prediction Report Header information extended

    • because of introduced spectra plot, with
      • “Spectral Range” (x-axis) of the spectra e.g. “1000 to 2400 Nanometers [500 datapoints]”
      • “Spectral YUnit” e.g. “ABSORBANCE”
    • and fully documentation of the used system (for system validation purpose)
      • “Operating System” detailed version information about the Operating System.
  • [+] User Interface

    • A shortcut for the function “Update Applications (F4)” is also possible with a click on the “Application” text label.
    • A shortcut for the function “Update Calibrations (F5)” is also possible with a click on the “Calibrations” text label.

V2.5 Public Release – 5. May 2020

New Key Features

Details

  • [+] More Vendor Spectra File Supported: ams, Avantes, PIXELTEQ, Senorics.
  • [+] Simple Custom CSV Data Spectra File Supported.
  • [+] Properties File Creator supports now both Sample-based and Spectra-based propertyFiles templates.
  • [*] Improved parsing of JCAMP and Vendor file formats.
  • [*] Improved parsing of propertyFiles and CalibrationRequest.
  • [*] About dialog shows detailed software version.

V2.4 Public Release – September 2019

New Key Features

  • Multi spectral-formats, multi spectra-files with with multi calibrations predictions

    Automatic file format detection.

    see NIR-Predictor supported Spectral Data File Formats

  • Properties File Creator

    A tool for the NIR-User to create the propertyFile easily. It helps to create a CSV file from the measured spectraFiles with sampleNames and Properties to edit in Spreadsheet/EXCEL software.

    Sample based with automatic sample/spectra replicate/repeats detection and analysis for data cleanup for better data quality.

    Lets you enter Lab-Reference-Values in a sample-based manner, corresponding to your sample spectra for calibration. Contains clever automatic analysis mechanisms of inconsistencies in your raw-data to increase the data quality for calibration. Provides detailed analyzer information for manual data cleanup when needed. (Data Cleaning, Data Cleansing, Data Quality)

    It’s time saving and less error prone because you DON’T need to open each spectrum file separately in an editor and copy the spectral values into a table grid beside the Lab-values.

  • Create Calibration Request

    Packs created Properties files and spectra files in a compressed ZIP file for sending to the CalibrationModel.com Service. Helps with additional information about the property type you entered and if the Lab-values are enough to get calibrated.

  • Histogram Charts

    Showing the distribution of the predicted results per calibration. Color shows the out-of-calibration range results.

Details

  • [+] Menu function (F7) to “Create Calibration Request…”
  • [+] Histograms of Prediction Values per Property in the Prediction Report. Shows the distribution of the predicted results per calibration.
  • [*] Prediction Report reduced file size, ca. 25% less.
  • [*] Prediction Report – Missing ‘Date Time’ are shown as empty.
  • [*] Prediction report list the Spectra files in a compact way, same path information is shown once.
  • [+] Prediction Report supports the order/sorting of the prediction results of the spectra, which can be defined as: GivenOrder | Date_Name | Name_Date | Date_NamesWithNumbers | NamesWithNumbers or Reverse sorted.
  • [+] The last used Application is loaded on next start. Because often you need to continue on the same, if not you need to change it anyway.
  • [*] Prediction Report lists “Result Ordering” and “Outlier Symbols” settings above the table, to quick know how the table is ordered and the symbols are defined.
  • [+] Prediction Report contains an overall Outlier Statistics for multiple spectra below the header of “Prediction Value List”.
  • [+] Menu “Show latest Updates” opens the https://calibrationmodel.com/NIR-Predictor-Release-Notes/ in the browser.

V2.3 Public Release – June 2019

New Key Features

  • Native spectra file formats

    Support for many mobile NIR Spectrometers.

    See NIR-Predictor supported Spectral Data File Formats

  • Application concept

    Allows to group multiple Calibrations together for an Application.

  • Properties File Creator

    Menu “Create Properties File…” to enter Lab-Reference-Values for calibration. The file is created from a folder of spectra files, so it contains their names, dates and hashes.

Details

  • [+] Support for native file formats of many mobile and hand-held NIR Spectrometers.
  • [+] Automatic file format detection.
  • [+] Select Applications for predictions.
  • [+] Application allows to group multiple Calibrations together for a Application.
  • [+] Calibration Property Legend shows the “Folder” name of the Calibration file. That allows the user to distinguish duplicates of calibration property names. If the Calibration File is flat in the default Calibrations folder then under “Folder” stands “”.
  • [*] Calibrations are sorted in the prediction report by 1. Folder (you can structure the Calibs in subfolders as you like), 2. Property name, 3. Property Range Max.
  • [+] Menu function (F4) to “Search and load Applications” from the calibration folder, where you can arrange the calibration files in folder structure.
  • [+] Menu function (F5) to “Search and load Calibrations” from the calibration folder, where you can arrange the calibration files in folder structure and move deactivated calibs outside.
  • [+] Menu function (F6) to “Create Properties File…” to enter Lab-Reference-Values for calibration. The file is created from a folder of spectra files, so it contains their names, dates and hash.
  • [*] Ctrl+O to select spectra files to predict (same as dialog button or drag & drop files)
  • [*] File Select Dialog is only opened once to multi-select spectra files.
  • [+] Predicts multiple spectra files at once in different file-formats and different wave-ranges and wave-resolutions with all compatible calibrations.
  • [*] Prediction Report with sorted Calibration/Properties by subfolder and Property name. Allows grouping of calibrations in sub folders.
  • [*] Prediction Report results table can be copied to spreadsheet programs like Excel containing the structure.
  • [*] Instead of warning information “CalibrationIncompatibleForSpectrum” there is no predicted value, to have a compact nice readable report. And a “-” mark is set in Outlier column Out. In the legend it’s listed as “- : spectrum is incompatible to calibration”
  • [*] The property unit is not shown as [] if it is not known.
  • [*] Functions keys for menu functions, for fast access.

V2.2 Public Release – August 2018

Key Features

  • Drag & drop spectra files to be loaded, pre-processed, predicted and reported.

  • Automatic pre-processing of spectra

  • Multi spectra files with with multi calibrations prediction

Details

  • Installation: 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 run the application (.exe) file.
  • Uninstall: Make sure to backup your reports and calibrations inside your “NIR-Predictor” folder. Delete the “NIR-Predictor” folder.
  • [+] Report is stored automatically.
  • [+] Outlier statistics.
  • [+] Total predictions statistics.
  • [+] All the steps are automatic. And can be done individually to act on input changes.
  • [+] 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.
  • [+] runs on Microsoft Windows 7/8/8.1/10 (Starter, Basic, Professional) (32 bit / 64 bit).
  • [+] Minimal System Requirements: Windows 7 Starter 32Bit, 1.6 GHz, 2 GB RAM, non-Administrator account

V1.0 – V2.1 Internal Releases – 2018

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

Installation
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.

Upgrade
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.

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


Start Calibrate

See also: