Spectroscopy and Chemometrics/Machine Learning News Weekly #36, 2021

NIR Calibration-Model Services

With CM Service you can have customized optimized NIR calibrations developed without subscription. | NIRS NIR Spectroscopy ModelDevelopment MachineLearning Chemometrics LINK

Spectroscopy and Chemometrics News Weekly 35, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

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

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Near-Infrared Spectroscopy (NIRS)

“NIRS-ICA: A MATLAB Toolbox for Independent Component Analysis Applied in fNIRS Studies” | LINK

“NIRS and manure composition (NIMACO)” LINK

“Shining a light on cultural neuroscience: Recommendations on the use of fNIRS to study how sociocultural contexts shape the brain” LINK

“Evaluation of Grape Juice Quality by Near Infrared Spectroscopy and Chemometrics” “การ ประเมิน คุณภาพ น้ำ องุ่น ด้วย เนีย ร์ อินฟราเรด สเปก โทร ส โก ปี และ เค โมเม ท ริก ซ์” LINK

“Development of a soil fertility index using on-line Vis-NIR spectroscopy” LINK

“PDA@Ti3C2Tx as a novel carrier for pesticide delivery and its application in plant protection: NIRresponsive controlled release and sustained antipest activity” LINK

“NIR surface enhanced Raman spectra of biological hemes: Solvation and plasmonic metal effects” LINK

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

“Near Infrared Feature Waveband Selection for Fishmeal Quality Assessment by Frequency Adaptive Binary Differential Evolution” LINK

“Polymers : Prediction of Residual Curing Capacity of Melamine-Formaldehyde Resins at an Early Stage of Synthesis by In-Line FTIR Spectroscopy” LINK

“Infrared spectroscopy and forensic entomology: Can this union work? A literature review” | LINK

“Near-Infrared Spectroscopy for Ganoderma Boninense Detection: An Outlook” | LINK

“Investigation of domestic hen Egg quality in terms of Fertilization during storage using Near Infrared Spectroscopy” LINK

“Application of near-infrared spectroscopy in detecting residual crystallinity in carbamazepine-Soluplus® solid dispersions prepared with solvent casting and hot-melt …” LINK

“From at-line analysis to on-site control in the Iberian pig industry using Near Infrared Spectroscopy sensors” LINK

“Determination of α-guaiene and azulene chemical content in patchouli aromatic oil (Pogostemon cablin Benth.) from Indonesia by Near-infrared spectroscopy” LINK

Hyperspectral Imaging (HSI)

“Hyperspectral Image Stitching via Optimal Seamline Detection” LINK

“Optimization of a saccharin molecularly imprinted solid-phase extraction procedure and evaluation by MIR hyperspectral imaging for analysis of diet tea by HPLC” LINK

“Hyperspectral detection and monitoring of salt stress in pomegranate cultivars” LINK

“Supervoxel-Based Intrinsic Scene Properties From Hyperspectral Images and LiDAR” LINK

” A combination method of stacked autoencoder and 3D deep residual network for hyperspectral image classification” LINK

Chemometrics and Machine Learning

“Multi-product Calibration Model for Soluble Solids and Water Content Quantification in Cucurbitaceae family, using Visible/Near-Infrared Spectroscopy” LINK

“Deep chemometrics: Validation and transfer of a global deep nearinfrared fruit model to use it on a new portable instrument” LINK

“Healthcare : Multimodal Early Alzheimers Detection, a Genetic Algorithm Approach with Support Vector Machines” LINK

“Ability of near infrared spectroscopy and chemometrics to measure the phytic acid content in maize flour” LINK

“One-class classification of special agroforestry Brazilian coffee using NIR spectrometry and chemometric tools” LINK

“Remote Sensing : Comparison of Modelling Strategies to Estimate Phenotypic Values from an Unmanned Aerial Vehicle with Spectral and Temporal Vegetation Indexes” LINK


“Spectral Anomaly Detection Based on Dictionary Learning for Sea Surfaces” LINK

Research on Spectroscopy

“A Review on Meat Quality Evaluation Methods Based on Non-Destructive Computer Vision and Artificial Intelligence Technologies” | LINK

“Electrochemical and Spectroscopic Studies on Triarylamine‐Polychlorotriphenylmethyl Dyads with Particularly Strong Triarylamine Donors” LINK

Equipment for Spectroscopy

“Polymers : Polyethylene/Polyamide Blends Made of Waste with Compatibilizer: Processing, Morphology, Rheological and Thermo-Mechanical Behavior” LINK

Future topics in Spectroscopy

According to Precision Business Insights the global molecular spectroscopy market size valued USD 5.6 billion in 2020 and expected to reach USD 10.4 billion by 2027, at a CAGR of 7.23% during forecast period 2021 to 2027. LINK

Process Control and NIR Sensors

“Monitoring of high-load dose formulations based on co-processed and non co-processed excipients” LINK

“Sensors : Development of Gas Sensor Array for Methane Reforming Process Monitoring” LINK

“Process analytical technology as a tool to optimize and accelerate pharmaceutical process development” LINK

“Recent developments in vibrational spectral analyses for dynamically assessing and monitoring food dehydration processes” LINK

Environment NIR-Spectroscopy Application

“Effects of Biochar Addition Under Different Water Management Conditions on N 2 O Emission From Paddy Soils in Northern Hainan” LINK

“Remote Sensing : Using Synthetic Remote Sensing Indicators to Monitor the Land Degradation in a Salinized Area” LINK

Agriculture NIR-Spectroscopy Usage

“Development of Attenuated Total Reflectance Mid-Infrared (ATR-MIR) and Near-Infrared (NIR) Spectroscopy for the Determination of Resistant Starch Content in Wheat Grains” LINK

“Extraction of spatial-spectral homogeneous patches and fractional abundances for field-scale agriculture monitoring using airborne hyperspectral images” LINK

“Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts” LINK

“Agronomy : Characterization of Oilseed Crop Noug (Guizotia abyssinica) Using Agro-Morphological Traits” LINK

“Tradeoffs in the Spatial and Spectral Resolution of Airborne Hyperspectral Imaging Systems: A Crop Identification Case Study” LINK

“Agriculture : Quantitative Evaluation of Color, Firmness, and Soluble Solid Content of Korla Fragrant Pears via IRIV and LS-SVM” LINK

“Agronomy : Agro-Morphological and Molecular Variability among Algerian Faba Bean (Vicia faba L.) Accessions” LINK

“Effects of Dielectric Properties and Microstructures on Microwave-Vacuum Drying of Mushroom (Agaricus bisporus) Caps and Stipes Evaluated by Non-destructive …” LINK

“Liming improves the stability of soil microbial community structures against the application of digestate made from dairy wastes” LINK

“Animals : Identifying Health Status in Grazing Dairy Cows from Milk Mid-Infrared Spectroscopy by Using Machine Learning Methods” LINK

“Agronomy : Effect of Ascophyllum nodosum Alga Application on Microgreens, Yield, and Yield Components in Oats Avena sativa L” LINK

Horticulture NIR-Spectroscopy Applications

“… of the Soluble Solid Content and Acidity by Prediction Models for Different Colored Tomato Fruits using a Small Device for Visible and Near-infrared Spectroscopy …” LINK

Food & Feed Industry NIR Usage

“Foods : Study on the Lamb Meat Consumer Behavior in Brazil” LINK

“Foods : Analytical Rheology of Honey: A State-of-the-Art Review” LINK

“Nondestructive identification of barley seeds variety using near‐infrared hyperspectral imaging coupled with convolutional neural network” LINK

“Foods : Interaction of Bioactive Mono-Terpenes with Egg Yolk on Ice Cream Physicochemical Properties” LINK


“Influence of modified biochar supported Fe-Cu/polyvinylpyrrolidone on nitrate removal and high selectivity towards nitrogen in constructed wetlands” LINK

“The Story of 5d Metallocorroles: From Metal-Ligand Misfits to New Building Blocks for Cancer Phototherapeutics” LINK

“Effect of Surface Chemical Modification on the Self Assembly of Metal Nanoparticles” LINK


“On the signal contribution function with respect to different norms” LINK

“Forests : Bioclimatic Suitability of Actual and Potential Cultivation Areas for Jacaranda mimosifolia in Chinese Cities” LINK

“Effects of Vanadium on the Structural and Optical Properties of Borate Glasses Containing Er<sup>3+</sup> and Silver Nanoparticles” LINK


Spectroscopy and Chemometrics News Weekly #13, 2021

NIR Calibration-Model Services

NIRS Analytical Laboratory Method Development : Reduce Workload and Response Time | MethodDevelopment modeling LINK

Spectroscopy and Chemometrics News Weekly 12, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Application Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Service Software Sensors QA QC Testing Quality LINK

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

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

Near-Infrared Spectroscopy (NIRS)

“Differentiation between Fresh and Thawed Cephalopods Using NIR Spectroscopy and Multivariate Data Analysis. Foods 2021, 10, 528” LINK

“Ethanol-soluble carbohydrates of cool-season grasses: prediction of concentration by near-infrared reflectance spectroscopy (NIRS) and evaluation of effects of …” LINK

“An Evaluation of Different NIR-Spectral Pre-Treatments to Derive the Soil Parameters C and N of a Humus-Clay-Rich Soil” LINK

“Prediction of Physicochemical Properties in Honeys with Portable Near-Infrared (microNIR) Spectroscopy Combined with Multivariate Data Processing” LINK

“Comparison between single and mixed-species NIRS databases’ accuracy of dairy fiber feed value detection” LINK

“Using autoencoders to compress soil VNIR–SWIR spectra for more robust prediction of soil properties” LINK

“Prediction of some quality properties of rice and its flour by near-infrared spectroscopy (NIRS) analysis.” ricequality Amylose viscosity LINK

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

“Nitrogen Management Based on Visible/Near Infrared Spectroscopy in Pear Orchards” Remote Sensing LINK

“Applications of near infrared spectroscopy for fish and fish products quality: a review” LINK

“Near Infrared Spectroscopy as a PAT Tool for Monitoring and Control of Protein and Excipient Concentration in Ultrafiltration of Highly Concentrated Antibody Formulations” LINK

“Determination of soluble solid content in market tomatoes using Near-infrared Spectroscopy” LINK

“Discriminating Coalho cheese by origin through near and middle infrared spectroscopy and analytical measures” LINK

“Current and future research directions in computer-aided near-infrared spectroscopy: A perspective” LINK

“Estimation of the Relative Abundance of Quartz to Clay Minerals Using the Visible–Near-Infrared–Shortwave- Infrared Spectral Region” LINK

“Estimating the Lactate Threshold Using Wireless Near-Infrared Spectroscopy and Threshold Detection Analyses” LINK

“Smart SelfAssembly Amphiphilic CyclopeptideDye for NearInfrared WindowII Imaging” LINK

“Application of Long-Wave Near Infrared Hyperspectral Imaging for Determination of Moisture Content of Single Maize Seed” LINK

“Near Infrared Spectroscopy as a PAT Tool for Monitoring and Control of Protein and Excipient Concentration in Ultrafiltration of Highly Concentrated Antibody …” LINK

” Achieving the potential multifunctional near-infrared materials Ca 3 In 2− x Ga x Ge 3 O 12: Cr 3+ using a solid state method” LINK

“ATR-FTIR Microspectroscopy Brings a Novel Insight Into the Study of Cell Wall Chemistry at the Cellular Level” LINK

“Development and performance tests of an on-the-go detector of soil total nitrogen concentration based on near-infrared spectroscopy” LINK

“Mid-Infrared Scattering in -Al2O3 Catalytic Powders” LINK

“Rapid tannin profiling of tree fodders using untargeted mid-infrared spectroscopy and partial least squares regression” LINK

“Intelligent evaluation of taste constituents and polyphenols-to-amino acids ratio in matcha tea powder using near infrared spectroscopy” LINK

Raman Spectroscopy

“In vivo diagnosis of skin cancer with a portable Raman spectroscopic device” LINK

Hyperspectral Imaging (HSI)

” A chemometric view of hyperspectral images” LINK

Chemometrics and Machine Learning

” A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in …” LINK

“Soil exchangeable cations estimation using Vis-NIR spectroscopy in different depths: Effects of multiple calibration models and spiking” LINK

“Prediction of Tea Theanine Content using Near-Infrared Spectroscopy and Flower Pollination Algorithm” LINK

“Predicting Oil Content In Ripe Macaw Fruits (Acrocomia Aculeata) From Unripe Ones By Near Infrared Spectroscopy And Pls Regression” LINK

“A Model Based on Clusters of Similar Color and NIR to Estimate Oil Content of Single Olives” LINK

“Quick Determination and Discrimination of Commercial Hand Sanitisers Using Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy and Chemometrics” LINK

“A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in mango fruit” LINK

“Comparative study between Partial Least Squares and Rational function Ridge Regression models for the prediction of moisture content of woodchip samples using a handheld spectrophotometer” LINK

“Classification of Lingwu long jujube internal bruise over time based on visible near-infrared hyperspectral imaging combined with partial least squares-discriminant …” LINK

“Nondestructive qualitative and quantitative analysis of Yaobitong capsule using near-infrared spectroscopy in tandem with chemometrics” LINK

“Near infrared reflectance spectroscopy: classification and rapid prediction of patchouli oil content” LINK

“Chemometric classification of geothermal and non-geothermal ethanol leaf extract of seurapoh (Chromolaena odorata Linn) using infrared spectroscopy” LINK

Process Control and NIR Sensors

“In-Line Technologies for the Analysis of Important Milk Parameters during the Milking Process: A Review” LINK

Environment NIR-Spectroscopy Application

“Remote Sensing, Vol. 13, Pages 1105: Water Conservation Estimation Based on Time Series NDVI in the Yellow River Basin” LINK

“A novel framework to estimate soil mineralogy using soil spectroscopy” LINK

Agriculture NIR-Spectroscopy Usage

“Pentosan polysulfate maculopathy: Prevalence, spectrum of disease, and choroidal imaging analysis based on prospective screening: Pentosan maculopathy: disease spectrum & choroidal analysis” LINK

“An Alternative Approach to Evaluate the Quality of Protein-Based Raw Materials for Dry Pet Food. Animals 2021, 11, 458” LINK

“The use of NIR sensor technology for soil test-based decision making in agriculture” LINK

“Estimation of Starch Hydrolysis in Sweet Potato (Beni haruka) Based on Storage Period Using Nondestructive Near-Infrared Spectrometry. Agriculture 2021, 11, 135” LINK

“Handheld vs. Benchtop NearInfrared Spectrometers – How Do They Compare for Analyzing Forage Nutritive Value?” LINK

“Foods, Vol. 10, Pages 612: Preliminary Insights in Sensory Profile of Sweet Cherries” LINK

“Comparing CalReg performance with other multivariate methods for estimating selected soil properties from Moroccan agricultural regions using NIR spectroscopy” LINK

“Potential of Multivariate Statistical Technique Based on the Effective Spectra Bands to Estimate the Plant Water Content of Wheat Under Different Irrigation Regimes” LINK

“Agriculture, Vol. 11, Pages 239: In-Line Technologies for the Analysis of Important Milk Parameters during the Milking Process: A Review” LINK

“Foods, Vol. 10, Pages 496: Fatty Acid Composition from Olive Oils of Portuguese Centenarian Trees Is Highly Dependent on Olive Cultivar and Crop Year” LINK

“Automated in-field leaf-level hyperspectral imaging of corn plants using a Cartesian robotic platform” LINK

“A novel compact intrinsic safety full range Methane microprobe sensor using “trans-world” processing method based on near- infrared spectroscopy” LINK

“Organic carbon in agricultural and agroforestry soils: Effect of different management practices” LINK

“Machine Learning-Based Approach to Predict Insect-Herbivory-Damage and Insect-Type Attack in Maize Plants Using Hyperspectral Data” LINK

” Spectral reflectance of marine macroplastics in the VNIR and SWIR measured in a controlled environment” LINK

Forestry and Wood Industry NIR Usage

“Chemometric development using portable molecular vibrational spectrometers for rapid evaluation of AVC (Valsa mali Miyabe et Yamada) infection of apple trees” LINK

Food & Feed Industry NIR Usage

“Quantitative Analysis of Colony Number in Mouldy Wheat based on Near Infrared Spectroscopy combined with Colorimetric Sensor” LINK

Pharma Industry NIR Usage

” Integration of transcriptomes analysis with spectral signature of total RNA for generation of affordable remote sensing of Hepatocellular carcinoma in serum …” LINK

Laboratory and NIR-Spectroscopy

” Prediction of meat quality traits in the abattoir using portable near-infrared spectrometers: heritability of predicted traits and genetic correlations with laboratory …” LINK


“Ultrasonic-assisted catalytic transfer hydrogenation for upgrading pyrolysis-oil” LINK

“Quantitation of volatile aldehydes using chemoselective response dyes combined with multivariable data analysis” LINK

“Evaluation and optimization on the reflection and durability of reflective coatings for cool pavement” LINK

“Polyvinyl chloride: chemical modification and investigation of structural and thermal properties” LINK


Spectroscopy and Chemometrics News Weekly #48, 2020

NIR Calibration-Model Services

Do you use Molecular Spectroscopy with Multivariate Regression Models? That will save you development time LINK

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

Spectroscopy and Chemometrics News Weekly 47, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

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

Near-Infrared Spectroscopy (NIRS)

“Influence of forage particle size and residual moisture on near infrared reflectance spectroscopy (NIRS) calibration accuracy for macro-mineral determination” LINK

“Rapid detection of adulteration in Dendrobium Huoshanense using NIR spectroscopy coupled with chemometric methods” LINK

“Using near-infrared spectroscopy to discriminate closely related species: A case study of neotropical ferns” LINK

“Temperature-dependent, VIS-NIR reflectance spectroscopy of sodium sulfates” LINK

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

“Using near-infrared spectroscopy to determine intramuscular fat and fatty acids of beef applying different prediction approach.” LINK

“Estimating the sensory qualities of tomatoes using visible and near-infrared spectroscopy and interpretation based on gas chromatography–mass …” LINK

“Near Infrared Spectroscopy-Based Evaluation of Patellar Tendon and Knee Ligaments” LINK

“Predictive capacity of some wood properties by near-infrared spectroscopy” LINK

“Predicting Marian Plum Fruit Quality without Environmental Condition Impact by Handheld Visible–Near-Infrared Spectroscopy” LINK

“Near infrared reflectance spectroscopy to quantify Perkinsus marinus infecting Crassostrea virginica” LINK

“Application of genetic algorithm and multivariate methods in detection and measurement of milk‐surfactant adulteration by attenuated total reflection and near‐infrared spectroscopy” LINK

“Classifying Thermal Degradation of Polylactic Acid by Using Machine Learning Algorithms Trained on Fourier Transform Infrared Spectroscopy Data” LINK

“Classification Option for Korean Traditional Paper Based on Type of Raw Materials, Using Near-infrared Spectroscopy and Multivariate Statistical Methods” LINK

“Rapid and simultaneous quality analysis of the three active components in Lonicerae Japonicae Flos by near-infrared spectroscopy” LINK

“Determination of Adenosine and Cordycepin Concentrations in Cordyceps militaris Fruiting Bodies Using Near-Infrared Spectroscopy” LINK

“Application of near-infrared hyperspectral imaging for variety identification of coated maize kernels with deep learning” LINK

Hyperspectral Imaging (HSI)

Konica Minolta to acquire Specim, the leading global supplier of hyperspectral imaging.   “Konica Minolta shares our vision and values and will greatly support our business through improved sell-through,” said Tapio Kallonen, CEO of Specim LINK

“Research on Citrus grandis Granulation Determination Based on Hyperspectral Imaging through Deep Learning” LINK

Wavelength Selection Method Based on Partial Least Square from Hyperspectral Unmanned Aerial Vehicle Orthomosaic of Irrigated Olive Orchards” LINK

Chemometrics and Machine Learning

“Fractional order modeling and recognition of nitrogen content level of rubber tree foliage” LINK

“Development of multi-product calibration models of various root and tuber powders by fourier transform near infra-red (FT-NIR) spectroscopy for the quantification of …” LINK

“Detection and Quantification of Tomato Paste Adulteration Using Conventional and Rapid Analytical Methods” LINK

“Prediction of Acidity Level of Avomango (Gadung Klonal 21) Using Local Polynomial Estimator” LINK

“A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra” LINK

“Spectrometric Classification of Bamboo Shoot Species by Comparison of Different Machine Learning Methods” LINK

Supporting the Sensory Panel to Grade Virgin Olive Oils: An In-House-Validated Screening Tool by Volatile Fingerprinting and Chemometrics” LINK

Process Control and NIR Sensors

“A Process Analytical Concept for In-Line FTIR Monitoring of Polysiloxane Formation” Polymers LINK

Agriculture NIR-Spectroscopy Usage

“Vis–NIR spectroscopy: from leaf dry mass production estimate to the prediction of macro-and micronutrients in soybean crops” LINK

“Comparison of benchtop and handheld near‐infrared spectroscopy devices to determine forage nutritive value” LINK

Food & Feed Industry NIR Usage

“Rapid Authentication of 100% Italian Durum Wheat Pasta by FT-NIR Spectroscopy Combined with Chemometric Tools” LINK

“Identification of Corn Seeds with Different Freezing Damage Degree Based on Hyperspectral Reflectance Imaging and Deep Learning Method” LINK

“Near-and mid-infrared determination of some quality parameters of cheese manufactured from the mixture of different milk species” LINK

“Portable NIR spectrometer for quick identification of fat bloom in chocolates.” LINK

Laboratory and NIR-Spectroscopy

“A Novel Spectral Matching Approach for Pigment: Spectral Subsection Identification Considering Ion Absorption Characteristics” LINK


“Development of a novel quantitative function between spectral value and metmyoglobin content in Tan mutton” LINK

” 基于稀疏网络的可见光/近红外反射光谱土壤有机质含量估算” LINK

“基于可见-近红外光谱的茄子叶绿素荧光参数 Fv/Fm 预测方法” LINK

“A Miniaturized and Fast System for Thin Film Thickness Measurement” LINK

“Molecular spectroscopy with optical frequency combs” LINK

NIR-Predictor – Manual

NIR-Predictor – Manual

Predicting Spectra

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

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

Use the included data to checkout how it feels:

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

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

  3. The spectra will be

    • loaded
    • pre-processed
    • predicted and
    • reported

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

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

Creating your own Calibrations

How it works – step by step

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

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

  2. Now you need to combine these data.

    NIR-spectra + Lab-references
    -> PropertiesBySamples

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

    The NIR-Predictor provides tooling for that:

    Menu > Create Properties File... (F6)

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

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

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

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

    Menu > Create Calibration Request... (F7)

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

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

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

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

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

Configure the Calibrations for prediction usage


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

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

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

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

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


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

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

  3. the NIR-Predictor is now ready to predict

  4. to switch the application, goto 6.


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

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

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

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

The use-all case

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

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

Prediction Result Report

Histograms of Prediction Values per Property

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

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

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

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

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

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

Spectra Plot Thumbnail on the Prediction Report

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

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

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

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

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

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

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

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


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

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

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

Spectra Plot

Outlier Detection

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

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

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

Outlier (Out) Symbol Description

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

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

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

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

The technical names in literature correspond to:

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

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

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

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

Some hints to avoid these Outliers:

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

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

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

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

Result Ordering

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

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

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

*) sorted : ascending sort

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

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

  • Rev_Date_Name
  • Rev_Name_Date
  • Rev_Date_NamesWithNumbers
  • Rev_NamesWithNumbers_Date

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

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

Archiving Reports

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

Enter lab values to NIR spectra

Entering the laboratory reference values for NIR calibrations

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

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

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

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

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

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

How it works

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

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

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

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

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

Create Properties File


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

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

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

The file header line contains :

Sample Replicates Names Prop1 Prop2 Prop3 DateFirst DateLast Hashes

Where Name and Date describes the spectrum.

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

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

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

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

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

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

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

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

Enter the Lab Reference Concentrations to the spectra/sample.

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

Hints: Data handling:

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

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

  • How to add more spectra files?

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


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

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

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

Create Calibration Request

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

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

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

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

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

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

When all is fine

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

The ZIP file contains:

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

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

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

Program Settings

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

Further References