Spectroscopy and Chemometrics News Weekly #23, 2020

NIR Calibration-Model Services

New Free NIR-Predictor V2.6 software is released – New : reads and predicts also *.spc spectra file format (Thermo-Scientific / Galactic GRAMS) – Spectra Plots on the Prediction Reports NIRS NIR Spectroscopy Spectrometer QualityControl Lab Laboratory Analysis LINK
Spectra Plot


Spectroscopy and Chemometrics News Weekly 22, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 22, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 22, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

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

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Near-Infrared Spectroscopy (NIRS)

“Potential of Vis-NIR spectroscopy for detection of chilling injury in kiwifruit” LINK

“The application of NIR spectroscopy in moisture determining of vegetable seeds” LINK

“Detection and quantification of active pharmaceutical ingredients as adulterants in Garcinia cambogia slimming preparations using NIR spectroscopy combined with …” LINK

“Vibrational coupling to hydration shell–Mechanism to performance enhancement of qualitative analysis in NIR spectroscopy of carbohydrates in aqueous environment” LINK

“Determination of metmyoglobin in cooked tan mutton using Vis/NIR hyperspectral imaging system” LINK

“The past, present, and prospective on UV-VIS-NIR skin photonics and spectroscopy-a wavelength guide.” LINK

“Differentiation of South African Game Meat Using Near-Infrared (NIR) Spectroscopy and Hierarchical Modelling” LINK

“Study on Detection Methods for Frying Times of Soybean Oil Based on NIRS” LINK

“Non-destructive Detection the Content of Acid Detergent Fiber in Corn Stalk Using NIRS” LINK

“Changes in chemical components with NIR spectroscopy and durability of samama wood treated with boron, methyl methacrylate and heat treatment” LINK

“Principle Component Analysis (PCA)-Classification of Arabica green bean coffee of North Sumatera Using FT–NIRS” LINK




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

“Near-infrared wavelength-selection method based on joint mutual information and weighted bootstrap sampling” LINK

“Rapid and simultaneous analysis of direct and indirect bilirubin indicators in serum through reagent-free visible-near-infrared spectroscopy combined with …” LINK

“Fast Detection Method of Antarctic Krill Meat Quality Based on Near Infrared Spectroscopy” LINK

“Sensors, Vol. 20, Pages 1472: Fusion of Mid-Wave Infrared and Long-Wave Infrared Reflectance Spectra for Quantitative Analysis of Minerals” LINK

“Determination of pectin content in orange peels by Near Infrared Hyperspectral Imaging” LINK

“Near-infrared spectroscopy as a quantitative spasticity assessment tool: A systematic review.” LINK

“Determination of nutritional parameters of bee pollen by Raman and infrared spectroscopy.” LINK

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

“Soil NPK Levels Characterization Using Near Infrared and Artificial Neural Network” LINK

” Estimation of moisture in wood chips by Near Infrared Spectroscopy” LINK

“Biosensors, Vol. 10, Pages 41: Rapid Nondestructive Detection of Water Content and Granulation in Postharvest Shatian Pomelo Using Visible/Near-Infrared Spectroscopy” LINK

“Prognostic value of near-infrared spectroscopy in hypoxic-ischaemic encephalopathy” LINK




Raman Spectroscopy

“Quantitative models for detecting the presence of lead in turmeric using Raman spectroscopy” LINK




Hyperspectral Imaging (HSI)

“Dual-camera design for hyperspectral and panchromatic imaging, using a wedge shaped liquid crystal as a spectral multiplexer” | |)/S/URI LINK

“Discriminative Reconstruction for Hyperspectral Anomaly Detection With Spectral Learning” LINK

“Classification of common recyclable garbage based on hyperspectral imaging and deep learning” LINK

“Based on hyperspectral polarization to build the quantitative remote sensing model of jujube in Southern Xinjiang” LINK

“Study on quality distribution characteristics of jujube canopy based on multi-angle hyperspectral polarization” LINK

“Germination Prediction of Sugar Beet Seeds Based on HSI and SVM-RBF” LINK




Chemometrics and Machine Learning

“ATR-MIR spectroscopy to predict commercial milk major components: A comparison between a handheld and a benchtop instrument” LINK

“Simultaneous determination of goat milk adulteration with cow milk and their fat and protein contents using NIR spectroscopy and PLS algorithms” LINK

“Optimization and comparison of models for prediction of soluble solids content in apple by online Vis/NIR transmission coupled with diameter correction method” LINK

“Building kinetic models for apple crispness to determine the optimal freshness preservation time during shelf life based on spectroscopy” LINK

“Incorporation of two-dimensional correlation analysis into discriminant analysis as a potential tool for improving discrimination accuracy: Near-infrared spectroscopic discrimination of adulterated olive oils.” LINK

“Spectroscopic techniques combined with chemometrics for fast on-site characterization of suspected illegal antimicrobials” LINK




Environment NIR-Spectroscopy Application

“Improved mapping of soil heavy metals using a Vis-NIR spectroscopy index in an agricultural area of eastern China” LINK




Agriculture NIR-Spectroscopy Usage

“The Use of Multi-temporal Spectral Information to Improve the Classification of Agricultural Crops in Landscapes” LINK

“Remote Sensing, Vol. 12, Pages 1308: Machine Learning Based On-Line Prediction of Soil Organic Carbon after Removal of Soil Moisture Effect” LINK

“Detection of Nutrition and Toxic Elements in Pakistani Pepper Powders Using Laser Induced Breakdown Spectroscopy” LINK

“Agriculture, Vol. 10, Pages 177: Prediction of Soil Oxalate Phosphorus using Visible and Near-Infrared Spectroscopy in Natural and Cultivated System Soils of Madagascar” LINK




Forestry and Wood Industry NIR Usage

“Linear Discriminant Analysis of spectral measurements for discrimination between healthy and diseased trees of Olea europaea L. artificially infected by Fomitiporia …” LINK




Food & Feed Industry NIR Usage

“Quantification of Ash and Moisture in Wheat Flour by Raman Spectroscopy” LINK

“Visualization accuracy improvement of spectral quantitative analysis for meat adulteration using Gaussian distribution of regression coefficients in hyperspectral …” LINK




Laboratory and NIR-Spectroscopy

“Laboratory-Scale Preparation and Characterization of Dried Extract of Muirapuama (Ptychopetalumolacoides Benth) by Green Analytical Techniques” LINK




Other

“Machine vision detection of pests, diseases, and weeds: A review” LINK

“On-Site Identification of the Material Composition of PV Modules with Mobile Spectroscopic Devices” LINK

“Synthesis of N-Doped ZnO Nanocomposites for Sunlight Photocatalytic Degradation of Textile Dye Pollutants” LINK





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Spectroscopy and Chemometrics News Weekly #22, 2020

NIR Calibration-Model Services

New Free NIR-Predictor V2.6 software is released – Reads and predicts *.spc spectra file format (Thermo-Scientific / Galactic GRAMS) – Spectra Plots on the Prediction Reports NIRS NIR Spectroscopy Spectrometer QualityControl Lab Laboratory Analysis LINK
Spectra Plot


Spectroscopy and Chemometrics News Weekly 21, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 21, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 21, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near-Infrared Spectroscopy (NIRS)

“NIR spectroscopy application for determination caffeine content of Arabica green bean coffee” LINK

“Review of NIR spectroscopy methods for nondestructive quality analysis of oilseeds and edible oils” LINK

“Omega-3 and Omega-6 Determination in Nile Tilapia’s Fillet Based on MicroNIR Spectroscopy and Multivariate Calibration” LINK

“Determination of metmyoglobin in cooked tan mutton using Vis/NIR hyperspectral imaging system” LINK

“Prediction of water content in Lintong green bean coffee using FT-NIRS and PLS method” LINK

Discrimination of legal and illegal Cannabis spp. according to European legislation using near infrared spectroscopy and chemometrics. LINK

“A system using in situ NIRS sensors for the detection of product failing to meet quality standards and the prediction of optimal postharvest shelf-life in the case of oranges kept in cold storage” LINK

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

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




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

“Rapid and Non-destructive Detecting Frying Times of Peanut Oil Based on Near Infrared Reflectance Spectroscopy” LINK

“Different Supervised and unsupervised classification approaches based on Visible/Near infrared spectral analysis for discrimination of microbial contaminated lettuce …” LINK

“Nondestructive determination of lignin content in Korla fragrant pear based on near-infrared spectroscopy” LINK

“Monitoring the Progress and Healing Status of Burn Wounds Using Infrared Spectroscopy” LINK

“Detection of melamine and sucrose as adulterants in milk powder using near-infrared spectroscopy with DD-SIMCA as one-class classifier and MCR-ALS … forensic evidence” LINK

“Differentiating Between Malignant Mesothelioma and Other Pleural Lesions Using Fourier Transform Infrared Spectroscopy” LINK

“Confirmation of brand identification in infant formulas by near-infrared spectroscopy fingerprints” LINK

“Near-infrared spectroscopy of the placenta for monitoring fetal oxygenation during labour.” LINK

“Impact of H2O on atmospheric CH4 measurement in near-infrared absorption spectroscopy.” LINK

“Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds” LINK

“Protein, weight, and oil prediction by single-seed near-infrared spectroscopy for selection of seed quality and yield traits in pea (Pisum sativum).” phenotyping LINK

“Multiple-depth Modeling of Soil Organic Carbon using Visible–Near Infrared Spectroscopy” LINK

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

“Simultaneous determination of antioxidant properties and total phenolic content of Siraitia grosvenorii by near infrared spectroscopy” LINK

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

“Estimating δ15N and δ13C in Barley and Pea Mixtures Using Near-Infrared Spectroscopy with Genetic Algorithm Based Partial Least Squares Regression” LINK

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

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

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




Raman Spectroscopy

“Raman Technology for Today’s Spectroscopists” LINK

“Diagnosis of Citrus Greening using Raman Spectroscopy-Based Pattern Recognition” LINK




Hyperspectral Imaging (HSI)

“Classification of Hyperspectral Endocrine Tissue Images Using Support Vector Machines.” LINK

“Using dual-channel CNN to classify hyperspectral image based on spatial-spectral information” LINK

“Diagnosis of Late Blight of Potato Leaves Based on Deep Learning Hyperspectral Images” LINK

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

“Applied Sciences, Vol. 10, Pages 2259: Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves” LINK

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

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




Chemometrics and Machine Learning

“Rapid detection of saffron (Crocus sativus L.) Adulterated with lotus stamens and corn stigmas by near-infrared spectroscopy and chemometrics” LINK

“Simultaneous quantification of active constituents and antioxidant capability of green tea using NIR spectroscopy coupled with swarm intelligence algorithm” LINK

“Comparison of CNN Algorithms on Hyperspectral Image Classification in Agricultural Lands” LINK

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

“Identification of Tea Diseases Based on Spectral Reflectance and Machine Learning” LINK

“Machine learning estimators for the quantity and quality of grass swards used for silage production using drone-based imaging spectrometry and …” LINK




Research on Spectroscopy

“Lanthanide complexes with N-(2, 6-dimethylphenyl) oxamate: Synthesis, characterisation and cytotoxicity” LINK

“Automatisierte und digitale Dokumentation der Applikation organischer Düngemittel” LINK




Equipment for Spectroscopy

“Evaluation of Depth Measurement Method Based on Spectral Characteristics Using Hyperspectrometer” LINK

“Monitoring wine fermentation deviations using an ATR-MIR spectrometer and MSPC charts” LINK




Process Control and NIR Sensors

“Process analytical technology tools for process control of roller compaction in solid pharmaceuticals manufacturing.” LINK




Agriculture NIR-Spectroscopy Usage

“The effect of bubble formation within carbonated drinks on the brewage foamability, bubble dynamics and sensory perception by consumers” LINK

“Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis” LINK

“Remote Sensing, Vol. 12, Pages 1256: Crop Separability from Individual and Combined Airborne Imaging Spectroscopy and UAV Multispectral Data” LINK

“Portable IoT NIR Spectrometer for Detecting Undesirable Substances in Forages of Dairy Farms” LINK

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

“Automatisierte und digitale Dokumentation der Applikation organischer Düngemittel” LINK




Horticulture NIR-Spectroscopy Applications

” Nondestructive determining the soluble solids content of citrus using near infrared transmittance technology combined with the variable selection algorithm” LINK




Food & Feed Industry NIR Usage

“Statistical Analysis of Protein Content in Wheat Germplasm Based on Near-infrared Reflectance Spectroscopy” LINK

“Prediction of infertile chicken eggs before hatching by the Naïve-Bayes method combined with visible near infrared transmission spectroscopy” LINK




Other

“Microsoft lays off journalists to replace them with AI” LINK





NIR-Predictor Release Notes

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


V2.6 Public Release – 1. June 2020

New Key Features

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

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

  • Spectra Plots on the prediction reports

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

Details

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

  • [+] Spectra Plot Thumbnail on the Prediction Report

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

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

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

V2.5 Public Release – 5. May 2020

New Key Features

Details

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

V2.4 Public Release – September 2019

New Key Features

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

    Automatic file format detection.

    see NIR-Predictor supported Spectral Data File Formats

  • Properties File Creator

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

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

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

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

  • Create Calibration Request

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

  • Histogram Charts

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

Details

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

V2.3 Public Release – June 2019

New Key Features

  • Native spectra file formats

    Support for many mobile NIR Spectrometers.

    See NIR-Predictor supported Spectral Data File Formats

  • Application concept

    Allows to group multiple Calibrations together for an Application.

  • Properties File Creator

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

Details

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

V2.2 Public Release – August 2018

Key Features

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

  • Automatic pre-processing of spectra

  • Multi spectra files with with multi calibrations prediction

Details

  • Installation: There are no administrator rights required, unpack the zip file to a folder “NIR-Predictor” in your documents or on your desktop. Read the ReadMe.txt and run the application (.exe) file.
  • Uninstall: Make sure to backup your reports and calibrations inside your “NIR-Predictor” folder. Delete the “NIR-Predictor” folder.
  • [+] Report is stored automatically.
  • [+] Outlier statistics.
  • [+] Total predictions statistics.
  • [+] All the steps are automatic. And can be done individually to act on input changes.
  • [+] comes with demo data, so you can predict sample spectra with demo calibrations.
  • [+] has no functional limitations, no nagging, no ads and needs no license-key.
  • [+] runs on Microsoft Windows 7/8/8.1/10 (Starter, Basic, Professional) (32 bit / 64 bit).
  • [+] Minimal System Requirements: Windows 7 Starter 32Bit, 1.6 GHz, 2 GB RAM, non-Administrator account

V1.0 – V2.1 Internal Releases – 2018