Spectroscopy and Chemometrics News Weekly #25, 2020

NIR Calibration-Model Services

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

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

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

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

Spettroscopia e Chemiometria Weekly News 24, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

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

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




Near-Infrared Spectroscopy (NIRS)

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

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

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

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

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

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

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

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

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




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

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

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

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

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

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

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

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

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

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

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

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

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

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




Hyperspectral Imaging (HSI)

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

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

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

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

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




Chemometrics and Machine Learning

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

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

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




Research on Spectroscopy

“Automatisierte und digitale Dokumentation der Applikation organischer Düngemittel” LINK

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




Equipment for Spectroscopy

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

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

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




Agriculture NIR-Spectroscopy Usage

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

“Placing Soil Information in the Hands of Farmers” LINK

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

“Complex Food Recognition using Hyper-Spectral Imagery” LINK




Horticulture NIR-Spectroscopy Applications

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




Laboratory and NIR-Spectroscopy

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




Other

LINK



Spectroscopy and Chemometrics News Weekly #16, 2020

NIR Calibration-Model Services

Feed your favorite spectral Food Scanner sensor with customized specialized analysis models by downloading the free NIR-Predictor software. Collect Laboratory data and build your calibrations! | spectroscopy analyzer food dairy quality IoT LINK

Ho to improve your Near Infra Red (NIR) Analyzer Precision Accuracy Performance | nearIR Lab NIRS Light LINK

How to improve your NIRS analysis, get the free White Paper | infrared QAQC foodquality ingredients constituents LINK

Improve Accuracy of fast Nondestructive NIRS Measurements by Optimal Calibration | Feed Lab prediction Sensor LINK

Increase Your Profit with optimized NIR Accuracy Chocolate Bakery Tea Meat LINK

Spectroscopy and Chemometrics News Weekly 15, 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 15, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 15, 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)

“Authentication of Iberian pork official quality categories using a portable near infrared spectroscopy (NIRS) instrument” meat pig LINK

“NIR SPECTROSCOPY METHOD FOR FATTY ACID CONTENT OF OILSEEDS” LINK

“Proximate composition determination in goat cheese whey by near infrared spectroscopy (NIRS).” LINK

“Soil Organic Carbon Prediction by Vis-NIR Spectroscopy: Case Study the Kur-Aras Plain, Azerbaijan” LINK

“Near infrared spectroscopy (NIRS) data analysis for a rapid and simultaneous prediction of feed nutritive parameters.” LINK




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

“Identification of human brown/beige adipose tissue using near-infrared time-resolved spectroscopy” LINK

“Visible near infrared reflectance molecular chemical imaging of human ex vivo carcinomas and murine in vivo carcinomas” LINK

“Data analysis on near infrared spectroscopy as a part of technology adoption for cocoa farmer in Aceh Province, Indonesia.” LINK

“Sensor Development for Soil-Property Detection Using Near Infrared Spectroscopy” LINK

“Classifying maize kernels naturally infected by fungi using near-infrared hyperspectral imaging” LINK

“Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance” LINK




Hyperspectral Imaging (HSI)

“Novel Deep-Learning-Based Spatial-Spectral Feature Extraction For Hyperspectral Remote Sensing Applications” LINK

“Estimation of grapevine predawn leaf water potential based on hyperspectral reflectance data in Douro wine region” LINK




Chemometrics and Machine Learning

“Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks.” LINK

“Analysis of the Influence of Substrate Formulations on the Bioactive Chemical Profile of Lingzhi or Reishi Medicinal Mushroom, Ganoderma lucidum (Agaricomycetes) by Conventional and Chemometrics Methods.” LINK

“Use of precision farming practices and crop modelling for enhancing water and phosphorus efficiency” LINK

“A chemometric strategy to evaluate the comparability of PLS models obtained from quartz cuvettes and disposable glass vials in the determination of extra virgin olive …” LINK




Research on Spectroscopy

Classical Least Squares Method for Quantitative Spectral Analysis with Python LINK

“Method for Quantitative Broadband Diffuse Optical Spectroscopy of Tumor-Like Inclusions” LINK




Process Control and NIR Sensors

“Monitoring composting process of olive oil solid waste using FT-NIR spectroscopy” LINK

“Monitoring of CO2 Absorption Solvent in Natural Gas Process Using Fourier Transform Near-Infrared Spectrometry.” LINK




Agriculture NIR-Spectroscopy Usage

“Development of sugarcane and trash identification system in sugar production using hyperspectral imaging” LINK

“Smart Agriculture: The Age of Drones in Agriculture” LINK

“Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data” LINK

“Hyperspectral imaging in assessing the condition of plants: strengths and weaknesses” LINK

“Detection and identification of fungal growth on freeze-dried Agaricus bisporus using spectrum and olfactory sensor.” LINK

“Effect of strain and nutritional density of the diet on the water-protein ratio, fat and collagen levels in the breast and legs of broilers slaughtered at different …” LINK

“Detecting Bruise Damage and Level of Severity IN APPLES USING A CONTACTLESS NIR SPECTROMETER” LINK

“Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images” LINK

“Non-destructive estimation of winter wheat leaf moisture content using near-ground hyperspectral imaging technology” LINK




Food & Feed and Beverage Industry NIR Usage

“Chemometric tools for food fraud detection: the role of target class in non-targeted analysis” LINK

“Utilization of text mining as a big data analysis tool for food science and nutrition” LINK





Spectroscopy and Chemometrics News Weekly #14, 2020

CalibrationModel.com

NIR User? Get better results faster | Food Science QC Lab Laboratory Manager chemist LabWork Chemie analytik LINK

NIR-Predictor Software supports spectral file formats out of the box from: and others – Mobile spectroscopy NIRS portable Analyzers H2020 LINK

Timesaving Calibration Modeling Method: for near-infrared NIR Spectroscopy (NIRS) Multivariate Quantitative Prediction. food quality laboratory LINK

Spectroscopy and Chemometrics News Weekly 13, 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 13, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse analytic LINK

Spettroscopia e Chemiometria Weekly News 13, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

We have updated the free NIR-Predictor-Software Spectral Data format support list for many mobile and benchtop NIR Spectroscopy Sensors. | Used in QualityControl for Food Fruits Milk Meat LINK



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

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


Near Infrared Spectroscopy (NIRS)

“Aplicação da espectroscopia de reflectância no infravermelho próximo (NIRS) na determinação do potencial bioquímico de metano–Revisão” LINK

“Prediction of soil organic matter and clay contents by near-infrared spectroscopy-NIRS” LINK

“Fast detection and quantification of pork meat in other meats by reflectance FT-NIR spectroscopy and multivariate analysis” LINK

“Improved GA-SVM Algorithm and Its Application of NIR Spectroscopy in Orange Growing Location Identification” LINK

“Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors.” Tobacco LINK

“Data analysis on near infrared spectroscopy as a part of technology adoption for cocoa farmer in Aceh Province, Indonesia” LINK

“Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors” LINK

“Identification of a Glass Substrate to Study Cells Using Fourier Transform Infrared Spectroscopy: Are We Closer to Spectral Pathology?” LINK

“Raman-Infrared spectral fusion combined with partial least squares (PLS) for quantitative analysis of polycyclic aromatic hydrocarbons in soil” LINK

“Identification metliod of ginger-processed Pinelliaternata based on infrared spectroscopy data fusion.” LINK

“Terahertz Time of Flight Spectroscopy as a Coating Thickness Reference Method for Partial Least Squares Near Infrared Spectroscopy Models” LINK

“Quantitative detection of apple watercore and soluble solids content by near infrared transmittance spectroscopy” LINK




Hyperspectral

“Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging.” LINK

“Hyperspectral monitoring of maize leaves under copper stress at different growth stages” LINK

“Classification of small-scale hyperspectral images with multi-source deep transfer learning” LINK




Chemometrics

“Detection of fat content in peanut kernels based on chemometrics and hyperspectral imaging technology” LINK

“Hyperspectral Imaging Feature Selection Using Regression Tree Algorithm: Prediction of Carotenoid Content Velvet Apple Leaf” LINK

“Modelos de calibración para la cuantificación nutricional de praderas frescas mediante espectroscopía de infrarojo cercano” LINK

“Performance Evaluation of Chemometric Prediction Models—Key Components of Wheat Grain” LINK




Equipment

“Rapid Nondestructive Analysis of Intact Canola Seeds Using a Handheld NearInfrared Spectrometer” LINK

“Confirmatory non-invasive and non-destructive differentiation between hemp and cannabis using a handheld Raman spectrometer” LINK




Process Control

“Monitoring of CO2 Absorption Solvent in Natural Gas Process Using Fourier Transform Near-Infrared Spectrometry” LINK




Environment

“Comparing laboratory and airborne hyperspectral data for the estimation and mapping of topsoil organic carbon: Feature selection coupled with random forest” LINK




Agriculture

“Predicting Forage Quality of Warm-Season Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques.” LINK

“Les défis de la technologie de l’aliment en nutrition volaille: pertinence et enjeux pour répondre aux attentes industrielles et sociétales” LINK

“CHANGES IN THE CONTENT OF STRUCTURAL CARBOHYDRATES AND LIGNIN IN THE BIOMASS OF Lolium multiflorum (Lam.) AFTER APPLYING SLURRY …” LINK

“Rapid Analysis of Alcohol Content During the Green Jujube Wine Fermentation by FT-NIR” LINK

“Spectral Analysis and Deconvolution of the Amide I Band of Proteins Presenting with High-Frequency Noise and Baseline Shifts” LINK




Petro

“Spectroscopic evidence of special intermolecular interaction in iodomethane-ethanol mixtures: the cooperative effect of halogen bonding, hydrogen bonding, and …” LINK




Pharma

“Defocused Spatially Offset Raman Spectroscopy in Media of Different Optical Properties for Biomedical Applications Using a Commercial Spatially Offset Raman Spectroscopy Device” LINK




Medicinal

“A single oral dose of beetroot-based gel does not improve muscle oxygenation parameters, but speeds up handgrip isometric strength recovery in recreational combat …” LINK




Other

“Spectral differentiation of oak wilt from foliar fungal disease and drought is correlated with physiological changes” LINK

“Wearing a headset containing both electroencephalography (EEG) and near-infrared spectroscopy (NIRS) sensors, the user simply imagines moving either his right hand, left hand, tongue or feet – and ASIMO makes a corresponding movement. ” BrainInterface LINK

KnowItAll Software / Spectral Libraries & ChemWindow are now part of Wiley Science Solutions. See press release: LINK

“The uses of near infra-red spectroscopy in postharvest decision support: A review” LINK





Spectroscopy and Chemometrics News Weekly #39, 2019

CalibrationModel.com

Get NIR results effectively and smart with one software, the free NIR-Predictor V2.4
– now includes clever tooling to combine NIR and Lab data with minimal effort.

Spectroscopy and Chemometrics News Weekly 38, 2019 | NIRS NIR Spectroscopy Chemometrics Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC Testing Quality Checking LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 38, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse Qualitätslabor FTNIR LINK

Spettroscopia e Chemiometria Weekly News 38, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem predictionmodel LINK




Near Infrared (NIR)

Get NIR results effectively and smart with one software, it includes clever tooling to combine NIR and Lab data with minimal effort. LINK

“Raw Material Variability and Its Impact on the Online Adaptive Control of Cohesive Powder Blend Homogeneity Using NIR Spectroscopy” LINK

“Pain Analysis, in Premature Infants, Using Near Infrared Spectroscopy (NIRS)” LINK

“Fast determination of oxides content in cement raw meal using NIR spectroscopy combined with synergy interval partial least square and different preprocessing …” LINK

“Near Infrared Reflectance (NIR) Spectroscopy assessment for Reproductive status detection and discrimination in Plethodontid females” LINK

“Detección temprana y discriminación de enfermedades fúngicas en plantas usando espectroscopía in situ” NIRS LINK

” Dataset on equine cartilage near infrared spectra, composition, and functional properties” LINK

“On-line monitoring of multiple component parameters during ethanol fermentation by near-infrared spectroscopy” LINK

“The potential of portable near infrared spectroscopy for assuring quality and authenticity in the food chain, using Iberian hams as an example” LINK

“Near-infrared spectroscopy to assess typhaneoside and isorhamnetin-3-O-glucoside in different processed products of pollen typhae” LINK




Chemometrics and NIR

“Near infrared spectroscopy as a tool for predicting growth habit and gender of Araucaria angustifolia” LINK

“A Systematic Chemometric Approach to Identify the Geographical Origin of Olive Oils” LINK

“Accuracy and biases in predicting the chemical and physical traits of many types of cheeses using different visible and near-infrared spectroscopic techniques and …” LINK

“An Approach to Rapid Determination of Tween-80 for the Quality Control of Traditional Chinese Medicine Injection by Partial Least Squares Regression in Near-Infrared Spectral Modeling” LINK

“Classification of Pathogenic Bacteria Using Near-Infrared Diffuse Reflectance Spectroscopy” LINK




Hyperspectral Imaging (HSI)

” Hyperspectral Imaging (HSI) in anatomic left liver resection” LINK




Environment

“Evaluating low-cost portable near infrared sensors for rapid analysis of soils from South Eastern Australia” LINK




Agriculture and NIR

“Vis-Nir Reflectance Spectroscopy for Assessment of Soil Organic Carbon in a Rice-Wheat Field of Ludhiana District of Punjab” LINK

“The use of near infrared spectroscopy for the prediction of gaseous and particulate emissions from agricultural feedstock pellets” LINK

“Comparison of methods to estimate crude protein and digestible organic matter content of diets ingested by free-ranging sheep” LINK




Pharma and NIR

“QbD Innovation Through Advances in PAT, Data Analysis Methodologies, and Material Characterization” LINK




Other

“Non-Destructive Evaluation Techniques and What They Tell Us about Wood Property Variation” LINK





NIR-Predictor – Manual


NIR-Predictor – Manual

Predicting Spectra

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

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

Use the included data to checkout how it feels:

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

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

  3. The spectra will be

    • loaded
    • pre-processed
    • predicted and
    • reported

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

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


Creating your own Calibrations

How it works – step by step

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

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

  2. Now you need to combine these data.

    NIR-spectra + Lab-references
    -> PropertiesBySamples

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

    The NIR-Predictor provides tooling for that:

    Menu > Create Properties File... (F6)

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

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

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

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

    Menu > Create Calibration Request... (F7)

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

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

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

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

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


Configure the Calibrations for prediction usage

Configuration:

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

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

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

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

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

Usage:

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

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

  3. the NIR-Predictor is now ready to predict

  4. to switch the application, goto 6.


Applications

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

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

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

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

The use-all case

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

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


Prediction Result Report

Histograms of Prediction Values per Property

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

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

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

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

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

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

Spectra Plot Thumbnail on the Prediction Report

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

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

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

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

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

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

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

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

Note

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

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

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

Spectra Plot

Outlier Detection

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

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

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

Outlier (Out) Symbol Description

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

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

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

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

The technical names in literature correspond to:

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

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

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

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

Some hints to avoid these Outliers:

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

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

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

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

Result Ordering

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

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

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

*) sorted : ascending sort

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

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

  • Rev_Date_Name
  • Rev_Name_Date
  • Rev_Date_NamesWithNumbers
  • Rev_NamesWithNumbers_Date

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

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

Archiving Reports

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


Enter lab values to NIR spectra

Entering the laboratory reference values for NIR calibrations

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

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

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

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

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

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

How it works

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

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

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

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

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

Create Properties File

Note:

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

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

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

The file header line contains :

Sample Replicates Names Prop1 Prop2 Prop3 DateFirst DateLast Hashes

Where Name and Date describes the spectrum.

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

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

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

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

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

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

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

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

Enter the Lab Reference Concentrations to the spectra/sample.

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

Hints: Data handling:

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

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

  • How to add more spectra files?

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

    Or

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

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

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


Create Calibration Request

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

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

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

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

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

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

When all is fine

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

The ZIP file contains:

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

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

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


Program Settings

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

Further References

Spectroscopy and Chemometrics News Weekly #26, 2019

CalibrationModel.com

NIR Method Development Service for Labs and NIR-Vendors (OEM) LINK

Spectroscopy and Chemometrics News Weekly 25, 2019 | NIRS NIR Spectroscopy Chemometrics Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC Testing Quality Checking LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 25, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Sensoren Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Qualitätslabor LINK

Spettroscopia e Chemiometria Weekly News 25, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem prediction models LINK




Chemometrics

“X-ray fluorescence and visible near infrared sensor fusion for predicting soil chromium content” LINK

“Staling of white wheat bread crumb and effect of maltogenic a-amylases. Part 2: Monitoring the staling process by using near infrared spectroscopy and chemometrics” LINK

“Rapid and Nondestructive Quantification of Trimethylamine by FT-NIR Coupled with Chemometric Techniques” Fish quality LINK

“Prediction of yerba mate caffeine content using near infrared spectroscopy” LINK

“Journal Highlight: A new flow cell and chemometric protocol for implementing inline Raman spectroscopy in chromatography” LINK

Teaching Machine Learning at the moment and a student asks whether “PCA” stands for “Pretty Cool Algorithm” after I apparently used that phrase… That should really have been deliberate (it wasn’t). I will never use “Principal Component Analysis” again. PrettyCoolAlgorithm LINK

“A screening method based on Visible-NIR spectroscopy for the identification and quantification of different adulterants in high-quality honey.” LINK

“Chemometric studies of the effects of milk fat replacement with different proportions of vegetable oils in the formulation of fat-filled milk powders: Implications for quality assurance.” LINK

“Comparison of Bayesian and partial least squares regression methods for mid-infrared prediction of cheese-making properties in Montbéliarde cows” LINK

“NIR model transfer of alkali-soluble polysaccharides in Poria cocos with piecewise direct standardization” LINK

“Comparison of three different classification methods performance for the determination of biofuel quality by means of NIR spectroscopy” LINK

“Application of hierarchical classification models and reliability estimation by bootstrapping, for authentication and discrimination of wine vinegars by UV-vis spectroscopy” LINK

“Geographical origin traceability of Cabernet Sauvignon wines based on Infrared fingerprint technology combined with chemometrics.” LINK

“Determination of Adulteration Content in Extra Virgin Olive Oil Using FT-NIR Spectroscopy Combined with the BOSSPLS Algorithm” LINK




Near Infrared

“NIR-based Sudan I to IV and Para-Red food adulterants screening.” Paprika adulteration LINK

“Nondestructive detection of rape leaf chlorophyll level based on Vis-NIR spectroscopy.” LINK

“High-throughput analysis of leaf physiological and chemical traits with VIS–NIR–SWIR spectroscopy: a case study with a maize diversity panel” | New phenomics paper from Ge, Schnable, Sigmon and Yang labs of & LINK

High-throughput analysis of leaf physiological and chemical traits with VIS–NIR–SWIR spectroscopy: a case study with a maize diversity panel LINK

“Estimating dry matter and fat content in blocks of Swiss cheese during production using on-line near infrared spectroscopy” LINK

“Temperature-dependent near-infrared spectroscopy for studying the interactions in protein aqueous solutions” LINK

” 滑皮金桔糖度的近红外光谱无损检测技术.” “Non-destructive testing technology of sugar content in Huapikumquat by near infrared spectroscopy” LINK

“Grading and Sorting of Grape Berries Using Visible-Near Infrared Spectroscopy on the Basis of Multiple Inner Quality Parameters” LINK

“Modified silver nanoparticles enhanced single drop micro extraction of tartrazine in food samples coupled with diffuse reflectance Fourier transform infrared spectroscopic analysis” LINK

“Multicolor lanthanide-doped CaS and SrS near-infrared stimulated luminescent nanoparticles with bright emission: application in broad-spectrum lighting, information coding, and bio-imaging.” LINK

“The use of mid-infrared spectra to map genes affecting milk composition” |(19)30485-0/fulltext?rss=yes LINK




Raman

“Semi-Automated Heavy-Mineral Analysis by Raman Spectroscopy” Minerals LINK




Hyperspectral

“Discrimination of astringent and deastringed hard Rojo Brillante persimmon fruit using a sensory threshold by means of hyperspectral imaging” LINK

“Remote Sensing, Vol. 11, Pages 1485: Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction” LINK




Agriculture

“Applied Sciences, Vol. 9, Pages 2472: Comparison of Raman and Mid-Infrared Spectroscopy for Real-Time Monitoring of Yeast Fermentations: A Proof-of-Concept for Multi-Channel Photometric Sensors” LINK

“Agronomy, Vol. 9, Pages 293: Field Spectroscopy to Determine Nutritive Value Parameters of Individual Ryegrass Plants” LINK




Pharma

“Quantification of Inkjet-Printed Pharmaceuticals on Porous Substrates Using Raman Spectroscopy and Near-Infrared Spectroscopy” LINK




Laboratory

“Adapted-Consumer-Technology Approach to Making Near-Infrared-Reflectography Visualization of Paintings and Murals Accessible to a Wider Audience” – Journal of Chemical Education LINK





Spectroscopy and Chemometrics News Weekly #25, 2019

CalibrationModel.com

Spectroscopy and Chemometrics News Weekly 24, 2019 | NIRS NIR Spectroscopy Chemometrics Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC Testing Quality Checking LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 24, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 24, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem prediction models LINK




Chemometrics

“Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning” LINK

“Multivariate Discriminant Analysis of Single Seed Near Infrared Spectra for Sorting Dead-Filled and Viable Seeds of Three Pine Species: Does One Model Fit All Species?” forests LINK

“Development of near infrared spectroscopic methods to predict and understand dissolution of solid oral dosage forms” LINK

” Replication Data for: Towards a global arctic-alpine model for Near-infrared reflectance spectroscopy (NIRS) predictions of foliar nitrogen, phosphorus and …” LINK

” Genetic parameters for cow-specific digestibility predicted by near infrared reflectance spectroscopy” LINK

“Classification of Glycyrrhiza Seeds by Near Infrared Hyperspectral Imaging Technology” LINK

“Comprehensive comparison of multiple quantitative near-infrared spectroscopy models for Aspergillus flavus contamination detection in peanut.” LINK

“Non-Destructive Classification of Fruits Based on Vis-nir Spectroscopy and Principal Component Analysis” LINK




Near Infrared

“Assessment of applied microwave power of intermittent microwave-dried carrot powders from Colour and NIRS” LINK

“Soil Quality Analysis Using Modern Statistics and NIR spectroscopy Procedure” LINK

“Near-infrared diffusereflectance spectroscopy for discriminating fruit and vegetable products preserved in glass containers” LINK




Infrared

“A review of the application of near-infrared spectroscopy to rare traditional Chinese medicine” LINK

“Near-infrared spectroscopy as a tool for in vivo analysis of human muscles” LINK

“Surface Functionality and Water Adsorption Studies of a-Aluminium (III) Oxide Nanoparticles by near Infrared Spectroscopy” LINK

“A comparative study of mango solar drying methods by visible and near-infrared spectroscopy coupled with ANOVA-simultaneous component analysis (ASCA)” LINK

“High throughput phenotyping of Camelina sativa seeds for crude protein, total oil, and fatty acids profile by near infrared spectroscopy” LINK

“Near-infrared spectroscopic study of molecular interaction in ethanol-water mixtures” LINK




Hyperspectral

“Potential of hyperspectral imaging for nondestructive determination of chlorogenic acid content in Flos Lonicerae” LINK

“Thickness estimation of crude oil slicks by hyperspectral data based on partial least square regression method” LINK

“Development of a polarized hyperspectral imaging system for investigation of absorption and scattering properties” LINK

“Hyperspectral Imaging Retrieval Using MODIS Satellite Sensors Applied to Volcanic Ash Clouds Monitoring” LINK




Facts

“Sensor Fusion and Machine Learning for Soil Characterization from Farm to National Scale” LINK




Equipment

“Extension of the Measurable Wavelength Range for a Near-Infrared Spectrometer Using a Plasmonic Au Grating on a Si Substrate.” LINK




Environment

“Application of PROSPECT for estimating Total Petroleum Hydrocarbons in contaminated soils from leaf optical properties” LINK




Agriculture

“Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada” LINK

“Saving Old Bones: a non-destructive method for bone collagen prescreening” LINK

“Nondestructive On-site Detection of Soybean Contents Based on An Electrothermal MEMS Fourier Transform Spectrometer” LINK

“Remote Sensing Extraction of Crop Disaster Information Based on Support Vector Machine” LINK

“Remote Sensing, Vol. 11, Pages 1331: Evaluation of Leaf N Concentration in Winter Wheat Based on Discrete Wavelet Transform Analysis” LINK

“Predicting coefficient of linear extensibility and Atterberg limits of fine-grained soils using vis-NIR spectra” LINK




Other

“Effect of external compression on femoral retrograde shear and microvascular oxygenation in exercise trained and recreationally active young men” LINK





.

Spectroscopy and Chemometrics News Weekly #20, 2019

CalibrationModel.com

What Lab Managers and QC Laboratories need to know about NIR Spectroscopy (NIRS) Calibration LINK

“Automated Analytical Method Development for NIRS. Software/Service Solution for Automation of Machine Learning for the NIR-Spectroscopy Domain.” LINK

Do you use a near-infrared Spectrometer with Chemometric Methods? This will save you time NIR NIRS SWIR FTNIR LINK

Neue Möglichkeiten in der Entwicklung von Applikationen für die NIR-Analytik | Labor NIRS Analytik LaborAnalytik LINK

Rapid development of robust quantitative methods by near-infrared spectroscopy for NIR NIRS LINK

Spectroscopy and Chemometrics News Weekly 19, 2019 | NIRS NIR Spectroscopy Chemometrics Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC Testing Quality Checking LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 19, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 19, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem prediction models LINK



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




Chemometrics

” Prediction of Intermuscular Fat of lamb topside in-situ using Near Infrared Spectroscopy” LINK

“Comparison of Unsupervised Algorithms for Vineyard Canopy Segmentation from UAV Multispectral Images” RemoteSensing LINK

“复烤片烟常规化学成分的傅里叶变换近红外光谱法的模型转移” “Model Transfer of Routine Chemical Components in Redried Lamina on Fourier Transform Near Infrared Spectroscopy” LINK

“血浆醇沉过程中近红外光谱在线蛋白含量监测及定量模型转移研究” “On-line protein content monitoring and quantitative model transfer in near-infrared spectroscopy during plasma alcohol deposition” LINK

“A Sparse Classification Based on a Linear Regression Method for Spectral Recognition” LINK

“Rapid Recognition of Geoherbalism and Authenticity of a Chinese Herb by Data Fusion of Near-Infrared Spectroscopy (NIR) and Mid-Infrared (MIR) Spectroscopy Combined with Chemometrics” LINK

“Rock lithological classification by hyperspectral, range 3D and color images” LINK

“Modeling of oil near-infrared spectroscopy based on similarity and transfer learning algorithm” LINK

“Recent advances in modeling vibrational spectra of food adulterants Theoretical simulation of IR and NIR bands of melamine” LINK

“Fine root lignin content is well predictable with near-infrared spectroscopy” LINK

“Study on identification of different producing areas of Gastrodia elata using multivariable selection and two-dimensional correlation spectroscopy of near infrared spectroscopy” LINK

“Discrimination of Trichosanthis Fructus from Different Geographical Origins Using Near Infrared Spectroscopy Coupled with Chemometric Techniques” LINK




Near Infrared

“FT-NIR spectroscopy coupled with multivariate analysis for detection of starch adulteration in turmeric powder.” LINK

“< 전시-P-67> 근적외선 분광법에 의한 Fenton oxidation-열수처리 고형바이오매스 성분분석” “Analysis of chemical component of pretreated biomass by Fenton oxidation-hydrothermal treatment using near infrared spectroscopy” LINK

“Rapid and non-destructive analysis for the identification of multi-grain rice seeds with near-infrared spectroscopy.” LINK

“Near infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy” LINK

“Near-Infrared Light Emitting Diode Based Non-Invasive Glucose Detection System” LINK

“Rapid bacteria selection using Aquaphotomics and near infrared spectroscopy” LINK

“Characterisation of organic colourants in ukiyo-e prints by Fourier transform near infrared fibre optics reflectance spectroscopy” LINK




Optics

“Deep Learning Reveals Underlying Physics of Light-matter Interactions in Nanophotonic Devices.” LINK




Environment

“Determining the significance of individual factors for orthogonal designs” LINK

“Exploring the Influence of Spatial Resolution on the Digital Mapping of Soil Organic Carbon by Airborne Hyperspectral VNIR Imaging” RemoteSensing LINK




Agriculture

“Determination of calcium and magnesium in the Solanaceae plant by near infrared spectroscopy combined with interval combination optimization algorithm” LINK

“The Effect of Omega-3 and Omega-6 Polyunsaturated Fatty Acids on the Production of Cyclooxygenase and Lipoxygenase Metabolites by Human Umbilical Vein Endothelial Cells” Nutrients Omega3 Omega6 LINK

“Assessing the potential of two customized fiber-optic probes for on-site analysis of bulk feed grains” LINK




Food & Feed

“Detection of Additives and Chemical Contaminants in Turmeric Powder Using FT-IR Spectroscopy” Foods LINK




Other

“Review of New Spectroscopic Instrumentation 2019” LINK

“Development of an optical biosensor for the detection of Trypanosoma evansi and Plasmodium berghei.” LINK





.

Spectroscopy and Chemometrics News Weekly #19, 2019

CalibrationModel.com

Spectroscopy and Chemometrics News Weekly 18, 2019 | NIRS NIR Spectroscopy Chemometrics Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC Testing Quality Checking LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 18, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse Labor LINK

Spettroscopia e Chemiometria Weekly News 18, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem prediction models LINK

This week’s NIR news Weekly is sponsored by YourCompanyNameHere – BestNIRinstruments. Check out their product page … link




Chemometrics

“Correlation between the composition of green Arabica coffee beans and the sensory quality of coffee brews” LINK

“High accuracy deternimation of Angelica dahurica origin based on near infrared spectroscopy and random forest pruning algorithm” LINK

“Rapid analysis of the Tanreqing injection by near-infrared spectroscopy combined with least squares support vector machine and Gaussian process modeling techniques.” LINK

“Classification and Identification of Pesticide Residues in Cucumber Based on Near Infrared Spectroscopy” LINK

“Analytical strategies based on near infrared spectroscopy and multivariate calibration for rapid quantification of florfenicol at low-concentrations in medicated-feed pellets” LINK

“Lighting the Ivory Track: Are Near Infrared and Chemometrics Up to the Job? A Proof of Concept” LINK

“NIR spectroscopy-multivariate analysis for discrimination and bioactive compounds prediction of different Citrus species peels” | /> LINK

“Fast and Non-Destructive Prediction of Moisture Content and Chologenic Acid of Intact Coffee Beans Using Near Infrared Reflectance Spectroscopy” LINK

“Characterization of Edible Oils Using NIR Spectroscopy and Chemometric Methods” LINK

“A diffuse reflectance NIR spectroscopy method for determine drug concentration in pharmaceutical production tablets based on a PLS calibration model from manufactured tablets” LINK




Near Infrared (NIR) Spectroscopy

“Estimating forest soil organic carbon content using vis-NIR spectroscopy: Implications for large-scale soil carbon spectroscopic assessment” LINK

“Multivariate data analysis of near-infrared spectra of cultivation medium powders for mammalian cells” LINK

“Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy” LINK

“REC-NIR-BLACK carbon-black free masterbatch provides a second life for black plastic packaging by allowing scanning by near-infrared technology for automated sorting at recovery facilities.” LINK

“Near-infrared spectroscopy for food quality evaluation” LINK

“Near infrared spectroscopy as an alternative method for rapid determination of the solidification point of 2,4,6-trinitrotoluene in production” TNT LINK

“Spectral noise-to-signal ratio priority method with application for visible and near-infrared analysis of whole blood viscosity” LINK




Spectral Imaging

“Multispectral Approach for Identifying Invasive Plant Species Based on Flowering Phenology Characteristics” Remote Sensing LINK




Equipment

“Comparative performance of bench and portable near infrared spectrometers for measuring wood samples of two Eucalyptus species (E. pellita and E. benthamii)” LINK

“Non-destructive determination of silymarin in Silybum marianum extracts with a handheld near infrared spectrometer” LINK




Food & Feed

“A rapid classification of wheat flour protein content using artificial neural network model based on bioelectrical properties” LINK




Laboratory

“Non-destructive Methods to Determine Ripening Quality of Intact Muskmelon” LINK





.

Spectroscopy and Chemometrics News Weekly #18, 2019

CalibrationModel.com

Spectroscopy and Chemometrics News Weekly 17, 2019 | NIRS NIR Spectroscopy Chemometrics Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC Testing Quality Checking LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 17, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 17, 2019 | NIRS Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità LINK

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

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




Chemometrics

“Comparison and rapid prediction of lignocellulose and organic elements of a wide variety of rice straw based on near infrared spectroscopy” LINK

“Rapid Determination of Green Tea Origins by Near-Infrared Spectroscopy and Multi-Wavelength Statistical Discriminant Analysis” LINK

“Automated Chinese medicinal plants classification based on machine learning using leaf morpho-colorimetry, fractal dimension and visible/near infrared spectroscopy” LINK

“Monitoring model for predicting maize moisture content at the grain-filling stage using near-infrared spectroscopy and a small sample size” LINK

“Near-infrared-based models for lignin syringyl/guaiacyl ratio of Eucalyptus benthamii and E. pellita using a streamlined thioacidolysis procedure as the reference method” LINK

“Arthroscopic Near-Infrared Spectroscopic Prediction of Human Meniscus Properties” LINK

“变量优选补正算法的鲜枣可溶性固形物检测模型传递方法研究” 变量优选补正算法的鲜枣可溶性固形物检测模型传递方法研究 Model Transfer Method of Fresh Jujube Soluble Solids Detection Using Variables Optimization and Correction Algorithms LINK

New software article published in | No excuse for not using realistic noise when evaluating proposed algorithms anymore! chemometrics machinelearning statistics LINK

“Fine root lignin content is well predictable with near-infrared spectroscopy.” LINK




Near Infrared

“Abundance and composition of kaolinite on Mars: Information from NIR spectra of rocks from acid-alteration environments, Riotinto, SE Spain” LINK

“Distinct Difference in Sensitivity of NIR vs. IR Bands of Melamine to Inter-Molecular Interactions with Impact on Analytical Spectroscopy Explained by Anharmonic Quantum Mechanical Study” LINK

” Association Between Near Infrared Spectroskopy (NIRS) and Normobaric and Hyperbaric Oxygen Treatment in Acute Carbon Monoxide Poisoning” LINK

“Sensors, Vol. 19, Pages 2076: Towards Integrated Mid-Infrared Gas Sensors” LINK

“Detection of Acrylamide in Food Using Near Infrared Spectroscopy” LINK

“Determination of natural rubber and resin content of guayule fresh biomass by near infrared spectroscopy” LINK

“Automated Preprocessing of Near Infrared Spectroscopic Data” LINK

“Evaluation of a low-cost portable near-infrared spectrophotometer for in situ cocaine profiling.” LINK

“Application of a Fourier transformnear infrared reflectance spectroscopy method for the rapid proximate analysis of the greenshell mussel (Perna canaliculus) and king (Chinook) salmon (Oncorhynchus tshawytscha)” LINK

” Assessment of the chemical change in heat treated pine wood by near infrared spectroscopy” LINK

“Characterisation of organic colourants in ukiyo-e prints by Fourier transform near infrared fibre optics reflectance spectroscopy” LINK

” Plastic Solid Waste identification system based on Near Infrared Spectroscopy in combination with support vector machine” LINK

“Development and application of Fourier transform infrared spectroscopy for detection of milk adulteration in practice” FTIR LINK

“Near-Infrared Light Emitting Diode Based Non-Invasive Glucose Detection System.” LINK

“Determination of cellulose, hemicellulose and lignin content using near-infrared spectroscopy in flax fiber” LINK




Raman

“Raman spectroscopic discrimination of normal and cancerous lung tissues.” LINK




Spectral Imaging

“A new low-cost portable multispectral optical device for precise plant status assessment” LINK

“Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV” Remote Sensing LINK




Equipment

“On site monitoring of Grana Padano cheese production using portable spectrometers” LINK




Agriculture

“Assessing the potential of two customized fiber-optic probes for on-site analysis of bulk feed grains” LINK

“A Cost Effective and Field Deployable System for Soil Macronutrient Analysis Based on Near-Infrared Reflectance Spectroscopy” LINK




Food & Feed

“Freshness Evaluation in Chub Mackerel (Scomber japonicus) Using Near-Infrared Spectroscopy Determination of the Cadaverine Content.” Fish Fisheries FoodSafety FoodProtection LINK




Pharma

“An update on contribution of hot-melt extrusion technology to novel drug delivery in the 21st century: part I” LINK




Other

” Evaluación de dos atributos de calidad críticos en la producción de formas farmacéuticas sólidas utilizando la espectroscopia de infrarrojo cercano” LINK