Spectroscopy and Chemometrics/Machine-Learning News Weekly #50, 2021

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 49, 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 49, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 49, 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)

“The Role of Lipid Core Burden Index Measured by Near-Infrared Spectroscopy in Predicting Slow TIMI Flow After Coronary Intervention” LINK

“Rapid quality evaluation of Plantaginis Semen by near infrared spectroscopy combined with chemometrics” LINK

“Characterization of sun-injury and prediction of sunscald on ‘Packham’s Triumph’pears using Vis-NIR spectroscopy” LINK

“Nondestructive Detection of Internal Flavor in ‘Shatian’Pomelo Fruit Based on Visible/Near Infrared Spectroscopy” LINK

“Remote Sensing : A NIRS-Aided Methodology to Elucidate the Nutrition of the Endangered Mountain Gazelle (Gazella gazella) Using Samples of Rumen Contents from Roadkills” LINK

” The concentrations of immunoglobulins in bovine colostrum determined by the gold standard method are genetically correlated with their near-infrared …” LINK

“Detection of Lipids by Near-infrared Spectroscopy in Calcified Coronary Plaques Containing Nodular Calcification: Insights from Multimodality Imaging and …” LINK

“A Novel Method for Rapid Particle Size Analysis of Ibuprofen Using Near-infrared Spectroscopy” | LINK

“Geographical identification of Italian extra virgin olive oil by the combination of near infrared and Raman spectroscopy: A feasibility study” LINK

“Empirical Modelling of Household Oils in UV-Vis-NIR Spectrum through Developed Low-Cost Spectroscopy Setup (LCSS)” LINK

“Synthesis of NIR-II Absorbing Gelatin Stabilized Gold Nanorods and Its Photothermal Therapy Application against Fibroblast Histiocytoma Cells” LINK

“Non-destructive prediction of the hotness of fresh pepper with a single scan using portable near infrared spectroscopy and a variable selection strategy” | capsaicin dihydrocapsaicin food Agricultural Biotechnology hot Spices analysislab LINK


“Foods : Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques” LINK

“The concentrations of immunoglobulins in bovine colostrum determined by the gold standard method are genetically correlated with their near-infrared prediction” LINK

“Apple chips moisture analysis made easy with near-infrared spectroscopy” LINK

“Nondestructive Analysis of Internal Quality in Pears with a Self-Made Near-Infrared Spectrum Detector Combined with Multivariate Data Processing” LINK

“The performance of surface enhanced Raman scattering and spatial resolution with triangular plate dimer from ultra-ultraviolet to near-infrared range” LINK

“… of coronary atherosclerotic features in response to achieving LDL-C< 55 mg/dl between non-diabetic and diabetic patients: insights from the REASSURE-NIRS …” LINK

“A measurement method in near infrared spectroscopy for reference correction with the homologous optical beams” LINK

“Quantitative Analysis of Multi-optical Length NIR Spectroscopy Based on Quaternion Parallel Feature Extraction Method” LINK

“Detección de medicamentos falsificados por espectroscopia en el infrarrojo cercano (NIR)” LINK

“Adulteration Detection in Goat Dairy Beverage Through NIR Spectroscopy and DD-SIMCA” | LINK

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

“Grana Cheese monitoring by low cost pocket size near infrared sensor” LINK

“Molecular device turns infrared into visible light – A new way to detect infrared light by changing its frequency to that of visible light” LINK

“Can current hyperspectral infrared sounders capture the small scale atmospheric water vapor spatial variations?” LINK

“Distinguishing between different types of multilayered PETbased backsheets of PV modules with nearinfrared spectroscopy” | LINK

“Nearinfrared spectroscopy aids ecological restoration by classifying variation of taxonomy and phenology of a native shrub” LINK

Hyperspectral Imaging (HSI)

“Classification of pulse flours using near-infrared hyperspectral imaging” LINK

“Chemometric strategies for near infrared hyperspectral imaging analysis: classification of cotton seed genotypes” LINK

“Nondestructive nitrogen content estimation in tomato plant leaves by Vis-NIR hyperspectral imaging and regression data models” LINK

“Rancidity and moisture estimation in shelled almond kernels using NIR hyperspectral imaging and chemometric analysis” LINK

“Detection of different chemical binders in coatings using hyperspectral imaging” | LINK

Spectral Imaging

“In vivo sensing of cutaneous edema: a comparative study of diffuse reflectance, Raman spectroscopy, and multispectral imaging” LINK

Chemometrics and Machine Learning

“Determination of the total viable count of Chinese meat dishes by near‐infrared spectroscopy: A predictive model” LINK

“Exploring the use of Near-infrared spectroscopy as a tool to predict quality attributes in prickly pear (Rosa roxburghii Tratt) with chemometrics variable strategy” LINK

“Determination of the total viable count of Chinese meat dishes by nearinfrared spectroscopy: A predictive model” LINK

“Wavelet-Based Neurovascular Coupling Can Predict Brain Abnormalities In Neonatal Encephalopathy” LINK

“Multivariate analysis of food fraud: A review of NIR based instruments in tandem with chemometrics” | foodfraud analysis instruments LINK

“Top November Stories: Why Machine Learning Engineers are Replacing Data Scientists; 19 Data Science Project Ideas for Beginners” | MachineLearning DataScientists LINK

“Optimizing the quantitative analysis of solid biomass fuel properties using laser induced breakdown spectroscopy (LIBS) coupled with a kernel partial least squares (KPLS) model” LINK

“Chemometric strategies for near infrared hyperspectral imaging analysis: classification of cotton seed genotypes.” LINK

“Spectroscopy and imaging technologies coupled with machine learning for the assessment of the microbiological spoilage associated to ready-to-eat leafy vegetables” LINK


“Fodder development in Sub‐Saharan Africa: An Introduction” LINK

“Applied Sciences : Automatic Asbestos Control Using Deep Learning Based Computer Vision System” LINK

Research on Spectroscopy

“A Methodological Literature Review on Non-Invasive Blood Group Detection” LINK

“Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in COVID-19 patients admitted to intermediate …” | LINK

Equipment for Spectroscopy

“Green Synthesis of Ag2S Quantum Dots as Sensing Probe: An Optical Sensor for the Detection of Cholesterol” LINK

“Applied Sciences : Real-Time Quantification of Crude Protein and Neutral Detergent Fibre in Pastures under Montado Ecosystem Using the Portable NIR Spectrometer” LINK

Environment NIR-Spectroscopy Application

“Remote Sensing : Automated Training Data Generation from Spectral Indexes for Mapping Surface Water Extent with Sentinel-2 Satellite Imagery at 10 m and 20 m Resolutions” LINK

Agriculture NIR-Spectroscopy Usage

“Investigation on aging resistance of ternary compound carbon nitride (CN-Bi-Tr-DC) modified asphalt” LINK

“Agriculture : Estimation of Soil Nutrient Content Using Hyperspectral Data” LINK

“Prediction of protein and amino acid composition and digestibility in individual feedstuffs and mixed diets for pigs using near-infrared spectroscopy” LINK

“Applications of computer vision in the field of agriculture” LINK

“Genome-wide association study and population structure analysis of seed-bound amino acids and total protein in watermelon” LINK

Horticulture NIR-Spectroscopy Applications

“Applied Sciences : Studies of the Variability of Sugars, Vitamin C, and Chlorophylls in Differently Fermented Organic Leaves of Willowherb” LINK

Food & Feed Industry NIR Usage

“Foods : Development of High-Protein Vegetable Creams by Using Single-Cell Ingredients from Some Microalgae Species” LINK

“Transfer-learning-based approach for leaf chlorophyll content estimation of winter wheat from hyperspectral data” LINK

“Foods : Two Statistical Tools for Assessing Functionality and Protein Characteristics of Different Fava Bean (Vicia faba L.) Ingredients” LINK

Medicinal Spectroscopy

“A Tissue Section-Based Near-Infrared Spectroscopical Analysis of Salivary Gland Tumors” LINK


“Indium-tin oxide regulated band gap of nitrogen-doped titanium oxide thin films for visible light photocatalyst” | LINK

“Discriminação de espécies manejadas na Amazônia central: um princípio para a rastreabilidade da madeira por meio da espectroscopia no infravermelho próximo” LINK

“How charge trapping affects the conductivity of electrochemically doped poly (3-hexylthiophene) films” LINK

“Dimensionality reduction, regularization, and generalization in overparameterized regressions.” LINK


“Additive genetic variation in Pinus radiata bark chemistry and the chemical traits associated with variation in mammalian bark stripping” | LINK

“Structural, optical and dispersion studies on Cu2NiSn (SSe) 4 nanocrystals thin films” LINK

“Linking genetics and chemistry to minimise bark stripping in Pinus radiata” LINK


Spectroscopy and Chemometrics/Machine-Learning News Weekly #40, 2021

NIR Calibration-Model Services

Improve Accuracy of fast Nondestructive NIR Analytics by Optimal Calibration | Food Feed FoodSafety ag Lab LINK

Increase Your Profit with optimized NIR Accuracy Process Protein Oil plastic colors paints milk soy Soybean LINK

Spectroscopy and Chemometrics News Weekly 39, 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 39, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 39, 2021 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

Near-Infrared Spectroscopy (NIRS)

“Convenient use of near-infrared spectroscopy to indirectly predict the antioxidant activitiy of edible rose (Rose chinensis Jacq “Crimsin Glory” HT) petals during …” LINK

“Near-infrared emission from spatially indirect excitons in type II ZnTe/CdSe/(Zn,Mg)Te core/double-shell nanowires” LINK

“Rapid Detection of Fatty Acids in Edible Oils Using Vis-NIR Reflectance Spectroscopy with Multivariate Methods” LINK

“An Improved Residual Network for Pork Freshness Detection using Near-Infrared Spectroscopy” LINK

“Methylglyoxal Adducts Levels in Blood Measured on Dried Spot by Portable Near-Infrared Spectroscopy” LINK

“Application of Near-Infrared Spectroscopy to statistical control in freeze-drying processes” LINK

“TeaNet: Deep learning on Near-Infrared Spectroscopy (NIR) data for the assurance of tea quality” LINK

“Detecting Residual Awareness in Patients With Prolonged Disorders of Consciousness: An fNIRS Study” | LINK

“Dry Matter Estimation of Standing Corn with Near-infrared Reflectance Spectroscopy” LINK

“Conditional-GAN Based Data Augmentation for Deep Learning Task Classifier Improvement Using fNIRS Data” | LINK

“Application of Various Algorithms for Spectral Variable Selection in NIRS Modeling of Red Ginseng Extraction” LINK

“Vis-NIR hyperspectral imaging along with Gaussian process regression to monitor quality attributes of apple slices during drying” LINK

“Potential of Near Infrared Spectroscopy as a Rapid Method to Discriminate OTA and Non-OTA-Producing Mould Species in a Dry-Cured Ham Model System” LINK

“Pendugaan Tingkat Fermentasi Kakao Secara Non-Destruktif dengan NIRS” LINK

“Penentuan Tingkat Kekerasan dan Kemanisan Buah Naga Merah (Hylocereus polyrhizus) Secara Nondestruktif Menggunakan Near Infrared Spectroscopy (NIRS)” LINK

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


“A novel strategy of “pick the best of the best” for the nondestructive identification of Poria cocos based on near-infrared spectroscopy” LINK

“The Neural Processing of Vocal Emotion After Hearing Reconstruction in Prelingual Deaf Children: A Functional Near-Infrared Spectroscopy Brain Imaging Study” LINK

“Antinutrient to mineral molar ratios of raw common beans and their rapid prediction using near-infrared spectroscopy” LINK

“Effect of spectral pretreatment on qualitative identification of adulterated bovine colostrum by near-infrared spectroscopy” LINK

“A Rotational-Linear Sample Probing Device to Improve the Performance of Compact Near-Infrared Spectrophotometers” LINK

“Machine Learning Calibration for Near TS Infrared Spectroscopy Data: A Visual kkS Programming Approach” LINK

Hyperspectral Imaging (HSI)

“Selecting informative bands for partial least squares regressions improves their goodness-of-fits to estimate leaf photosynthetic parameters from hyperspectral data” LINK

“Hyperspectral detection of salted sea cucumber adulteration using different spectral preprocessing techniques and SVM method” LINK

“An automated approach for fringe frequency estimation and removal in infrared spectroscopy and hyperspectral imaging of biological samples” LINK

“New evidence from hyperspectral imaging analysis on the effect of photobiomodulation therapy on normal skin oxygenation” LINK

Chemometrics and Machine Learning

“The Use of Chemometrics for Classification of Sidaguri (<i>Sida rhombifolia</i>) Based on FTIR Spectra and Antiradical Activities” LINK

“Massive spectral data analysis for plant breeding using parSketch-PLSDA method: Discrimination of sunflower genotypes” LINK

“Htype indices with applications in chemometrics I: hmultiple similarity index” LINK

“Near-Infrared Spectroscopy and Machine Learning-Based Classification and Calibration Methods in Detection and Measurement of Anionic Surfactant in Milk” LINK

“Remote Sensing : Validation of FY-3D MERSI-2 Precipitable Water Vapor (PWV) Datasets Using Ground-Based PWV Data from AERONET” LINK

“Verified the rapid evaluation of the edible safety of wild porcini mushrooms, using deep learning and PLS-DA” LINK

“Foods : Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality” LINK

“Estimating the Forage Neutral Detergent Fiber Content of Alpine Grassland in the Tibetan Plateau Using Hyperspectral Data and Machine Learning Algorithms” LINK

“Optical spectroscopy methods for the characterization of sol-gel materials” LINK

“Sensors : Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors” LINK

“Agronomy : How Different Cooking Methods Affect the Phenolic Composition of Sweet Potato for Human Consumption (Ipomea batata (L.) Lam)” LINK

Equipment for Spectroscopy

“Miniaturized VIS-NIR Spectrometers Based on Narrowband and Tunable Transmission Cavity Organic Photodetectors with Ultrahigh Specific Detectivity above 10(14) Jones” LINK

In the market for a palm spectrometer, bandpass filter, or microscopy stage? Those and more are featured in the October Product Showcase out today. | photonics optics LINK


Process Control and NIR Sensors

“Multi-modal diffuse optical spectroscopy for high-speed monitoring and wide-area mapping of tissue optical properties and hemodynamics” LINK

“Process analytical technique (PAT) miniaturization for monoclonal antibody aggregate detection in continuous downstream processing” LINK

“Development of a Robust Control Strategy for Fixed-Dose Combination Bilayer Tablets with Integrated Quality by Design, Statistical, and Process Analytical …” LINK

Environment NIR-Spectroscopy Application

“Recent Advances in Plasmonic Photocatalysis Based on TiO2 and Noble Metal Nanoparticles for Energy Conversion, Environmental Remediation, and Organic Synthesis” LINK

“Mapping liquid water content in snow: An intercomparison of mixed-phase optical property models using hyperspectral imaging and in situ measurements” LINK

“Estimating Atterberg limits of soils from reflectance spectroscopy and pedotransfer functions” LINK

Agriculture NIR-Spectroscopy Usage

“Seeing the wood for the trees: hyperspectral imaging for high throughput QTL detection in raspberry, a perennial crop species” LINK

“Agronomy : Effect of Different Edaphic Crop Conditions on the Free Amino Acid Profile of PH-16 Dry Cacao Beans” LINK

Horticulture NIR-Spectroscopy Applications

“Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches” LINK

“Predicting soluble solids content in “Fuji” apples of different ripening stages based on multiple information fusion” LINK

Food & Feed Industry NIR Usage

“Effects of cultivars and fertilization levels on the quality of brown and polished rice” LINK

“Foods : Physicochemical and Functional Properties of Snack Bars Enriched with Tilapia (Oreochromis niloticus) by-Product Powders” LINK

“Using data science to combat poverty” | BASE foodwaste LINK

“Foods : HPLC Fingerprints for the Characterization of Walnuts and the Detection of Fraudulent Incidents” LINK

“A novel approach to identify the spectral bands that predict moisture content in canola and wheat” LINK

Medicinal Spectroscopy

“Investigating spectroscopic measurement of sublingual veins and tissue to estimate central venous oxygen saturation” LINK

“Detection of the Communication Site by Indocyanine Green Adsorbed to Human Serum Albumin Fluorescence During Surgery for a Pleuroperitoneal …” LINK



“Efecto del ambiente ruminal y la fuente de fibra sobre la dinámica de desaparición de la materia orgánica y sus componentes en bovinos en confinamiento.” LINK

“催熟对采后菠萝品质的影响与光谱识别” LINK

Spectroscopy and Chemometrics News Weekly #21, 2021

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 20, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Near-Infrared Spectroscopy (NIRS)

“Implementation of Non Destructive FTNIR Method for Quick Estimation of Peanut Quality Based on FFA and Peroxide Value” LINK

“Fast Detection of Cumin and Fennel Using NIR Spectroscopy Combined with Deep Learning Algorithms” LINK

“Prediction of bioactive compounds in barley by near-infrared reflectance spectroscopy (NIRS)” LINK

“Classification by bitterness of intact almonds analysed in bulk using NIR spectroscopy” LINK

” Feature discovery in NIR spectroscopy based Rocha pear classification” LINK

” The effect of muscle type and ageing on Near Infrared (NIR) Spectroscopy classification of game meat species using a portable instrument” LINK

“Application of benchtop NIR spectroscopy coupled with multivariate analysis for rapid prediction of antioxidant properties of walnut (Juglans regia)” LINK

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

“Performance of near infrared spectroscopy of a solid cattle and poultry manure database depends on the sample preparation and regression method used” LINK

“Measurements of High Oleic Purity in Peanut Lots Using Rapid, Single Kernel NearInfrared Reflectance Spectroscopy” LINK

“Predicting Calcium and Phosphorus Concentrations in Imported Hay by near Infrared Reflectance Spectroscopy” LINK

“Assessment of calibration methods for nitrogen estimation in wet and dry soil samples with different wavelength ranges using near-infrared spectroscopy” LINK

“Near-infrared spectroscopy: Alternative method for assessment of stable carbon isotopes in various soil profiles in Chile” LINK

“Near-Infrared Spectroscopy in Neurocritical Care: a Review of Recent Updates” LINK

“Near infrared methodology for growth monitoring of spinach plants in the field” LINK

“The Penetration Analysis of Airborne Ku-Band Radar versus Satellite Infrared Lidar Based on the Height and Energy Percentiles in the Boreal Forest” LINK

Chemometrics and Machine Learning

“A near infrared spectroscopy calibration for the prediction of fresh grass quality on Irish pastures” LINK

“Determination of petroleum hydrocarbon contamination in soil using VNIR DRS and PLSR modeling” LINK

“Combining Thermal and RGB Imaging Indices with Multivariate and Data-Driven Modeling to Estimate the Growth, Water Status, and Yield of Potato under Different Drip Irrigation Regimes” LINK

“Near infrared spectroscopy coupled chemometric algorithms for prediction of the antioxidant activity of peanut seed (Arachis hypogaea)” LINK

“A probabilistic model for missing traffic volume reconstruction based on data fusion.” LINK

“Evaluating validation strategies on the performance of soil property prediction from regional to continental spectral data” LINK

“A sample selection method specific to unknown test samples for calibration and validation sets based on spectra similarity” LINK

“Comparison of the predictive ability of NIR calibration models developed to predict nutritional parameters in total mixed rations by using reference data expressed “as …” LINK

“An Automated Data Fusion-Based Gear Faults Classification Framework in Rotating Machines” LINK

Optics for Spectroscopy

“Glassy Carbon Electrode Modified with C/Au Nanostructured Materials for Simultaneous Determination of Hydroquinone and Catechol in Water Matrices” Chemosensors LINK


“Machine Learning Approaches for Inferring Liver Diseases and Detecting Blood Donors from Medical Diagnosis. (arXiv:2104.12055v1 [stat.ML])” LINK

Equipment for Spectroscopy

“Physicochemical Analysis and Adulteration Detection in Malaysia Stingless Bee Honey Using a Handheld Near‐Infrared Spectrometer” LINK

Environment NIR-Spectroscopy Application

“Association of Physicochemical Characteristics, Aggregate Indices, Major Ions, and Trace Elements in Developing Groundwater Quality Index (GWQI) in Agricultural Area” LINK

“Mapping Smallholder Maize Farms Using Multi-Temporal Sentinel-1 Data in Support of the Sustainable Development Goals” LINK

Agriculture NIR-Spectroscopy Usage

“Discrimination of soils managed with different sources of fertilization and plant species in organic and conventional farming through nearinfrared spectroscopy and chemometrics” LINK

“Sensors, Vol. 21, Pages 3038: Addressing the Selectivity of Enzyme Biosensors: Solutions and Perspectives” LINK

” Wheat and triticale whole grain near infrared hyperspectral imaging for protein, moisture and kernel hardness quantification” LINK

“Fostering soil sustainability and food safety in urban agricultural areas of Naples, Italy” LINK

“Protein vibrations and their localization behaviour. A numerical scaling analysis” LINK

Horticulture NIR-Spectroscopy Applications

” Effects of The Odors of Japanese Citrus Iyokan (Citrus Iyo) and Yuzu (Citrus Junos) on Human Mood and Physiology” LINK


Forestry and Wood Industry NIR Usage

“Density, extractives and decay resistance variabilities within branch wood from four agroforestry hardwood species” LINK

Food & Feed Industry NIR Usage

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

Pharma Industry NIR Usage

“Influence of ESGC Indicators on Financial Performance of Listed Pharmaceutical Companies” LINK

Medicinal Spectroscopy

“Hybrid Spectral-IRDx: Near-IR and Ultrasound Attenuation System for Differentiating Breast Cancer from Adjacent Normal Tissue” LINK


“Formation of phosphonate coatings for improved chemical stability of upconverting nanoparticles under physiological conditions” LINK

“不同贮藏期水蜜桃硬度及糖度的检测研究” LINK

“基于野外可见近红外光谱和水分影响校正算法的土壤剖面有机碳预测” LINK

“Manipulation of up-conversion emission in NaYF4 core@shell nanoparticles doped by Er3+, Tm3+, or Yb3+ ions by excitation wavelength-three ions-plenty of possibilities” LINK

“Covalent modification of franckeite with maleimides: connecting molecules and van der Waals heterostructures” 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