NIR Analysis in Laboratory and Laboratories – aka NIR Labs and NIR testing


Do you have a NIR spectrometer in your Lab?

How many other analytics you do in the Lab could be done faster and cheaper with NIR?

Is this possible and precise enough?

Try, we have the solution for you!
You have the NIR, scan the samples, you have the lab values and the spectra, we calibrate for you!

To see if the application is possible and how precise it can be due to knowledge based intensive model optimizations.

We do the NIR feasibility study with data for you. Fixed prices

NIR has huge application potentials and it’s a Green analytical method, that’s fast and easy to use. And has today the possibility to scale out with inexpensive mobile NIR spectrometers.

Bring the Lab to the sample. To avoid sample transport and get immediate results for decision at the place or in the process.

Just try the NIR application, use the NIR daily, collect data in parallel, we develop, optimize and maintain the calibration models for you.

How do you think?

Start Calibrate


What is possible today with NIR?
The number of different Applications exploded in the last 2-3 years!
See NIR research papers news daily on @CalibModel or the 7-day summariesNIR News Weekly” here.

Spectroscopy and Chemometrics News Weekly #30, 2020

NIR Calibration-Model Services

Development of quantitative Multivariate Prediction Models for Near Infrared Spectrometers | NIRS HSI LINK

Spectroscopy and Chemometrics News Weekly 29, 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 29, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 29, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

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




Near-Infrared Spectroscopy (NIRS)

“Non-Destructive Determination of Quality Traits of Cashew Apples (Anacardium Occidentale, L.) Using a Portable near Infrared Spectrophotometer” LINK

“Non-destructive classification and prediction of aflatoxin-B1 concentration in maize kernels using Vis–NIR (400–1000 nm) hyperspectral imaging” LINK

“Determination of glucose content with a concentration within the physiological range by FT-NIR spectroscopy in a trans-reflectance mode” LINK

“Evaluating taste-related attributes of black tea by micro-NIRS” LINK




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

“Rapid authentication of Pseudostellaria heterophylla (Taizishen) from different regions by nearinfrared spectroscopy combined with chemometric methods” LINK

“Agronomy, Vol. 10, Pages 828: Estimating Sensory Properties with Near-Infrared Spectroscopy: A Tool for Quality Control and Breeding of Calçots (Allium cepa L.)” LINK

“Spectral observation of agarwood by infrared spectroscopy: The differences of infected and normal Aquilaria microcarpa” LINK

“Quantitative near infrared spectroscopic analysis of Tricholoma matsutake based on information extraction using the elastic net” LINK

“Visible-near infrared spectroscopy for detection of blood in sheep faeces” LINK

” … dans le proche infrarouge et techniques de chimiométrie Detection of addition of barley to coffee using near infrared spectroscopy and chemometric techniques” LINK

“Forests, Vol. 11, Pages 644: A Comparison of the Loading Direction for Bending Strength with Different Wood Measurement Surfaces Using Near-Infrared Spectroscopy” LINK

“Rapid assessment of soil condition in Kenya through development of near infrared spectral indicatators” LINK




Chemometrics and Machine Learning

“Determination of apple varieties by near infrared reflectance spectroscopy coupled with improved possibilistic Gath–Geva clustering algorithm” LINK

“Two-Dimensional Correlation Spectroscopy: The Power of Power Spectra” LINK

“Simple and fast spectrophotometric method based on chemometrics for the measurement of multicomponent adsorption kinetics” LINK

“Real time detection of amphetamine in oral fluids by MicroNIR/Chemometrics.” LINK

“In‐vitro digestion of the bioactives originating from the Lamiaceae family herbal teas: A kinetic and PLS modeling study” LINK

“Models for predicting the within-tree and regional variation of tracheid length and width for plantation loblolly pine” LINK




Research on Spectroscopy

“Study on rapid quality analysis method of Shengxuebao Mixture” LINK

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

Altersbestimmung von Holz mittels FTIR-Spektroskopie: Durch die Zusammenarbeit von Holz-, Materialwissenschaftler*innen und Statistikern konnte nach über 70 Jahren eine dritte Datierungsmethode neben der Jahrringanalyse und der Radiokarbonmethode im… LINK




Equipment for Spectroscopy

“Quality assessment of instant green tea using portable NIR spectrometer.” LINK




Process Control and NIR Sensors

“From powder to tablets: Investigation of residence time distributions in a continuous manufacturing process train as basis for continuous process verification” LINK

“Non-destructive, non-invasive, in-line real-time phase-based reflectance for quality monitoring of fruit” LINK




Agriculture NIR-Spectroscopy Usage

“Estimating soil organic carbon density in Northern China’s agro-pastoral ecotone using vis-NIR spectroscopy” LINK

“Retrieval of aboveground crop nitrogen content with a hybrid machine learning method” LINK

“Prediction of Soil Oxalate Phosphorus using Visible and Near-Infrared Spectroscopy in Natural and Cultivated System Soils of Madagascar” LINK

“Sensors, Vol. 20, Pages 3208: Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants” LINK

“The application of R language in the selection of characteristic bands for the prediction of protein content in milk powder by Near Infrared Spectroscopy” LINK

“Onsite nutritional diagnosis of tea plants using micro near-infrared spectrometer coupled with chemometrics” LINK




Horticulture NIR-Spectroscopy Applications

“Improving the accuracy of near-infrared (NIR) spectroscopy method to predict the oil content of oil palm fresh fruits” LINK




Laboratory and NIR-Spectroscopy

“Non-destructive determination of apple quality parameters of variety’red jonaprince’using near infrared spectroscopy.” LINK

“Laboratory Methods for Evaluating Forage Quality” LINK




Other

“Automatic Walnut Sorting System Based on Adaptive Fuzzy Control” LINK

“Industrial gas chromatographs” LINK





Spectroscopy and Chemometrics News Weekly #29, 2020

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 28, 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 28, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 28, 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)

“Non-invasive method to identify the type of green tea inside teabag using NIR spectroscopy, support vector machines and Bayesian optimization” LINK

“Online milk composition analysis with an on-farm near-infrared sensor” LINK

“Anonymous fecal sampling and NIRS studies of diet quality: Problem or opportunity?” LINK

“Organic and Symbiotic Fertilization of Tomato Plants Monitored by Litterbag-NIRS and Foliar-NIRS Rapid Spectroscopic Methods Running title: Litterbag-NIRS and Foliar-NIRS model in symbiotic tomato” LINK

“Determination of crude protein and metabolized energy with near infrared reflectance spectroscopy (NIRS) in ruminant mixed feeds” LINK




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

“Near Infrared Spectroscopy as an efficient tool for the Qualitative and Quantitative Determination of Sugar Adulteration in Milk” | LINK

“NEAR INFRARED SPECTROSCOPY AS A NEW FIRE SEVERITY METRIC” by Bushfire and Natural Hazards CRC LINK

“Near-infrared spectroscopy for the concurrent quality prediction and status monitoring of gasoline blending” LINK

“Application of Selective Near Infrared Spectroscopy for Qualitative and Quantitative Prediction of Water Adulteration in Milk” LINK

“Predicting Macronutrient of Baby Food using Near-infrared Spectroscopy and Deep Learning Approach” LINK

“Detection of heat treatment of honey with near infrared spectroscopy” LINK

“Use of near infrared spectroscopy in cotton seeds physiological quality evaluation” LINK

“Detection of Haemonchus contortus nematode eggs in sheep faeces using near and mid-infrared spectroscopy” LINK

“Feasibility of using near-infrared measurements to detect changes in water quality” LINK




Hyperspectral Imaging (HSI)

“Hyperspectral waveband selection algorithm based on weighted maximum relevance minimum redundancy and its stability analysis” LINK




Chemometrics and Machine Learning

“Comparative quantification of chlorophyll and polyphenol levels in grapevine leaves sampled from different geographical locations” LINK

“Screening method for determination of C18:1 trans fatty acids positional isomers in chocolate by 1H NMR and chemometrics” LINK

“Combination of efficient signal pre-processing and optimal band combination algorithm to predict soil organic matter through visible and near-infrared spectra” LINK

“A chemometric approach to the evaluation of the ageing ability of red wines” LINK

“Determination of Loline Alkaloids and Mycelial Biomass in Endophyte-Infected Schedonorus Pratensis by Near-Infrared Spectroscopy and Chemometrics” LINK

“A Feasible Approach to Detect Pesticides in Food Samples Using THz-FDS and Chemometrics” LINK

“Prediction of Soluble Solids Content in Green Plum by Using a Sparse Autoencoder” LINK




Process Control and NIR Sensors

“Real-time and field monitoring of the key parameters in industrial trough composting process using a handheld near infrared spectrometer” LINK




Environment NIR-Spectroscopy Application

“Detection and analysis of soil water content based on experimental reflectance spectrum data” LINK

” International Soil and Water Conservation Research” | LINK




Agriculture NIR-Spectroscopy Usage

“Detecting Low Concentrations of Nitrogen-Based Adulterants in Whey Protein Powder Using Benchtop and Handheld NIR Spectrometers and the Feasibility of Scanning through Plastic Bag.” LINK

“Assessment of the genetic diversity of sweetpotato germplasm collections for protein content” LINK

“Near-infrared spectroscopy and imaging in protein research” LINK

“Foods, Vol. 9, Pages 710: Application of ATR-FT-MIR for Tracing the Geographical Origin of Honey Produced in the Maltese Islands” LINK

“Agriculture, Vol. 10, Pages 193: Content of Polyphenolic Compounds and Antioxidant Potential of Some Bulgarian Red Grape Varieties and Red Wines, Determined by HPLC, UV, and NIR Spectroscopy” LINK

“Agronomy, Vol. 10, Pages 787: Assessing Soil Key Fertility Attributes Using a Portable X-Ray Fluorescence: A Simple Method to Overcome Matrix Effect” LINK




Food & Feed Industry NIR Usage

“Non‑destructive testing technology for raw eggs freshness: a review” LINK

“Quantification of multiple adulterants in beef protein powder by FT-NIR” LINK




Beverage and Drink Industry NIR Usage

“Beer Aroma and Quality Traits Assessment Using Artificial Intelligence” LINK




Other

“Tetrahedral Mn4+ as chromophore in sillenite-type compounds” LINK




Spectroscopy and Chemometrics News Weekly #8, 2020

CalibrationModel.com

Knowledge-Based Variable Selection and Model Selection for near infrared spectroscopy NIRS LINK

Stop wasting too much time for NIRS Chemometrics Method development | foodanalyticaltechnologies analytic qualitycontrol foodindustry beverageindustry materialsensing LINK

Spectroscopy and Chemometrics News Weekly 7, 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 7, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 7, 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)

“Determination of Glucose by NIR Spectroscopy Under Magnetic Field” LINK

“Sensors, Vol. 20, Pages 230: The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods when using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods” LINK

“Quantum mechanical modeling of NIR spectra of thymol” LINK

“Using a handheld near-infrared spectroscopy (NIRS) scanner to predict meat quality” LINK

“NIR spectroscopy in simulation–a new way for augmenting near-infrared phytoanalysis” LINK

“Using visible-near-infrared spectroscopy to classify lichens at a Neotropical Dry Forest” LINK

“Near infrared spectroscopy as a rapid method for detecting paprika powder adulteration with corn flour” LINK

“Application of deep learning and near infrared spectroscopy in cereal analysis” LINK

“Using near infrared spectroscopy to determine the scots pine place of growth” LINK

“Chagas disease vectors identification using visible and near-infrared spectroscopy” LINK

“Ensemble of extreme learning machines for multivariate calibration of near-infrared spectroscopy” LINK

“Quantification of Silymarin in Silybum marianum with near-infrared spectroscopy: a comparison of benchtop vs. handheld devices” LINK

“N-way partial least squares combined with new self-construction strategy—A promising approach of using near infrared spectral data for quantitative determination of …” LINK

“Identification of rice flour types with near-infrared spectroscopy associated with PLS-DA and SVM methods” LINK

” Multivariate Classification of Prunus Dulcis Varieties using Leaves of Nursery Plants and Near Infrared Spectroscopy” LINK




Hyperspectral

“Frost damage to maize in northeast India: assessment and estimated loss of yield by hyperspectral proximal remote sensing” LINK

“Identification of authenticity, quality and origin of saffron using hyperspectral imaging and multivariate spectral analysis” LINK




Chemometrics

“Early detection of chilling injury in green bell peppers by hyperspectral imaging and chemometrics” LINK

“Evaluating photosynthetic pigment contents of maize using UVE-PLS based on continuous wavelet transform” LINK

“Near-infrared spectroscopy coupled with chemometrics algorithms for the quantitative determination of the germinability of Clostridium perfringens in four different …” LINK

“Analysis of residual moisture in a freeze-dried sample drug using a multivariate fitting regression model” LINK

“Spectroscopy based novel spectral indices, PCA-and PLSR-coupled machine learning models for salinity stress phenotyping of rice” LINK

“Standardisation of near infrared hyperspectral imaging for quantification and classification of DON contaminated wheat samples” LINK

“Vibrational spectroscopy and chemometric data analysis: the principle components of rapid quality control of herbal medicines” LINK

“A Model for Yellow Tea Polyphenols Content Estimation Based on Multi-Feature Fusion” LINK




Process Control

“Review of near-infrared spectroscopy as a process analytical technology for real-time product monitoring in dairy processing” LINK




Environment

“POTENTIAL OF SENSOR-BASED SORTING IN ENHANCED LANDFILL MINING” LINK

“Characterization of the salt marsh soils and visible-near-infrared spectroscopy along a chronosequence of Spartina alterniflora invasion in a coastal wetland of …” LINK




Agriculture

“Remote Sensing, Vol. 12, Pages 126: Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy” LINK

“Novel implementation of laser ablation tomography as an alternative technique to assess grain quality and internal insect development in stored products” LINK

“Comparative Study of Two Different Strategies for Determination of Soluble Solids Content of Apples From Multiple Geographical Regions by Using FT-NIR Spectroscopy” LINK




Food & Feed

“Adulteration of Olive Oil” LINK




Laboratory

“Laboratory Raman and VNIR spectroscopic studies of jarosite and other secondary mineral mixtures relevant to Mars” LINK




Other

“Combining analytical tools to identify adulteration: some practical examples” LINK

“… questioned whether the growth and sustainability of AI technology will lead to the need for two copyright systems — one to address human creation and one to address machine creation.” LINK





Spectroscopy and Chemometrics News Weekly #6, 2020

CalibrationModel.com

Spectroscopy and Chemometrics News Weekly 5, 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 5, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 5, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near Infrared Spectroscopy

“Reduction of repeatability error for Analysis of variance-Simultaneous Component Analysis (REP-ASCA): Application to NIR spectroscopy on coffee sample” LINK

“Permafrost soil complexity evaluated by laboratory imaging Vis–NIR spectroscopy” LINK

“Total nitrogen in rice paddy field independently predicted from soil carbon using Near Infrared Reflectance Spectroscopy (NIRS)” LINK

” Visible-near Infrared (VIS-NIR) Spectroscopy as a Rapid Measurement Tool to Assess the Effect of Tillage on Oil Contaminated Sites” LINK

“Detection of aflatoxin B1 on corn kernel surfaces using visible-near infrared spectra” LINK

“The model updating based on near infrared spectroscopy for the sex identification of silkworm pupae from different varieties by a semi-supervised learning with pre-labeling method” LINK

“Quantification of Carbon Nanotube Doses in Adherent Cell Culture Assays Using UV-VIS-NIR Spectroscopy.” LINK

“A new application of NIR spectroscopy to describe and predict purees quality from the non-destructive apple measurements.” LINK

“Analysis of temperature influence on physical properties of aqueous extracts of winter savoury (Satureja montana L.) with UV-VIS and NIR spectroscopy” LINK

“Mixed Fuzzy Maximum Entropy Clustering Analysis of FT-NIR Spectra of Tea” LINK

“Determination of Chinese Honey Adulterated with Syrups by Near Infrared Spectroscopy Combined with Chemometrics” LINK

“Surface Analysis of Various Oxide Materials by using NIR Spectroscopy—Is Silica Surface Really Hydrophilic?—” LINK

“Grape Seeds: Chromatographic Profile of Fatty Acids and Phenolic Compounds and Qualitative Analysis by FTIR-ATR Spectroscopy” Foods LINK

“Continuous statistical modelling in characterisation of complex hydrocolloid mixtures using near infrared spectroscopy” LINK

“Probing Sucrose Contents in Everyday Drinks Using Miniaturized Near-Infrared Spectroscopy Scanners” LINK

” RAPID DETECTION OF FORMALIN IN MILK BY FOURIER-TRANSFORM NEAR-INFRARED SPECTROSCOPY” LINK

“Determination of sodium alginate in algae by near-infrared spectroscopy” LINK

“Near-infrared spectroscopy and hidden graphics applied in printing security documents in the offset technique” LINK

“Differentiation between normal and white striped turkey breasts by visible/near infrared spectroscopy and multivariate data analysis” LINK

“Rapid Determination of Holocellulose and Lignin in Wood by Near Infrared Spectroscopy and Kernel Extreme Learning Machine” LINK




Hyperspectral Imaging

“Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery” LINK




Chemometrics / Machine Learning

“Data Preprocessing and Homogeneity: The Influence on Robustness and Modeling by PLS Via NIR of Fish Burgers” LINK

“Identification of Rice Varieties and Transgenic Characteristics Based on Near-Infrared Diffuse Reflectance Spectroscopy and Chemometrics” LINK

“Rapid detection model of Bacillus subtilis in solidstate fermentation of rapeseed meal” LINK

” PREDICTION OF SOIL PROPERTIES WITH SPECTRORADIOMETRIC MEASUREMENTS” LINK




Optics

“The Jacopo Tintoretto “Wedding Feast at Cana”: A non-invasive and multi-technique analytical approach for studying painting materials” LINK




Environment

“The effect of soil moisture on the accuracy of the spectroscopy method in estimating the amount of soil organic matter” LINK




Agriculture

“Determination of The Effect of Technological Procedures Applied in Feed Factories on Mixed Feed Nutrition and Forming Quality Critical Points” LINK

” HARVEST TIMING DETERMINATION IN GRASS SEED CROPS BY PORTABLE NIR SPECTROSCOPY” LINK

“Spectroscopic diagnosis of zinc contaminated soils based on competitive adaptive reweighted sampling algorithm and an improved support vector machine” LINK

” High-protein rice in high-yielding background, cv. Naveen” LINK




Food & Feed

“Development and implementation of novel sensory evaluation procedures of consumer acceptability towards chocolate based on emotions and biometric responses” 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