Spectroscopy and Chemometrics News Weekly #14, 2021

NIR Calibration-Model Services

How to create Near-Infrared Spectroscopy Equations today? | Application Prozess Chemie NIR Engineer Lab NIR ag LINK

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

Spectroscopy and Chemometrics News Weekly 13, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 13, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 13, 2021 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

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

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




Near-Infrared Spectroscopy (NIRS)

Green Coffee Lab Intern – NIR Methods Nestlé SA Company LINK

Handheld near infrared spectroscopy has again proved a powerful tool in detecting food fraud, this time in oregano samples, according to research published in the journal Food Chemistry LINK

“Infrared Spectroscopy—Mid-infrared, Near-infrared, and Far-infrared/Terahertz Spectroscopy” NIR MIR THz FIR LINK

“SOIL NIR-SPECTROSCOPY AND OBJECT-BASED LANDSURFACE SEGMENTATION FOR FLUVIAL TERRACE LEVEL DIFFERENTIATION” LINK

“Rheo-optical near-infrared (NIR) analysis of binary amorphous polymer blend consisting of polyvinyl chloride (PVC) and polymethyl methacrylate (PMMA)” LINK

“Application of portable NIR spectroscopy for classifying and quantifying cocoa bean quality parameters” LINK

Tip: Here are all NIR Spectroscopy tweets collected from last week , without hash tags for better readability. LINK

“A comprehensive Vis-NIRS equation for rapid quantification of seed glucosinolate content and composition across diverse Brassica oilseed chemotypes” LINK

“Evaluating basic density calibrations based on NIR spectra recorded on the three wood faces and subject to different mathematical treatments” LINK




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

“Near infrared reflectance spectroscopy and molecular tools to evaluate land use impact on soil quality. A case study in a tropical ecosystem (altitude plains, Lao PDR)” LINK

“Non-destructive Detection of Heavy Metals in Vegetable Oil Based on Nano-Chemoselective Response Dye Combined with Near- infrared Spectroscopy” LINK

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

“Nitrogen Management Based on Visible/Near Infrared Spectroscopy in Pear Orchards. Remote Sens. 2021, 13, 927” LINK

“Predicting grapevine canopy nitrogen status using proximal sensors and near‐infrared reflectance spectroscopy” LINK

“Model Development for Fat Mass Assessment Using Near-Infrared Reflectance in South African Infants and Young Children Aged 3–24 Months” LINK

“Identification of Multi-Class Drugs Based on Near Infrared Spectroscopy and Bidirectional Generative Adversarial Networks” | LINK

“Identification of mahogany sliced veneer using handheld near-infrared spectroscopy device and multivariate data analysis” LINK

“Multivariate approach for the authentication of vanilla using infrared and Raman spectroscopy” LINK

“Rapid determination of active components in Ginkgo biloba leaves by near infrared spectroscopy combined with genetic algorithm joint extreme learning machine” LINK

“Deconvolution of hemodynamic responses along the cortical surface using personalized functional near infrared spectroscopy” | LINK

“Predicting Water Stress in Wild Blueberry Fields Using Airborne Visible and Near Infrared Imaging Spectroscopy” LINK

“Quantum chemical design of nearinfrared sensitive fused ring electron acceptors containing selenophene as bridge for highperformance organic solar cells” LINK

“Fast Quantification of Air Pollutants by Mid-Infrared Hyperspectral Imaging and Principal Component Analysis” LINK

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

“BODIPYPtPorphyrins Polyads for Efficient NearInfrared LightEmitting Electrochemical Cells” LINK

“Robust prediction performance of inner quality attributes in intact cocoa beans using near infrared spectroscopy and multivariate analysis” LINK

” Robust prediction performance of inner quality attributes in intact cocoa beans using near infrared spectroscopy and multivariate analysis” LINK

“Understanding the effect of urea on the phase transition of poly(N-isopropylacrylamide) in aqueous solution by temperature- dependent near-infrared spectroscopy.” LINK

“Visible/Short-wave near-infrared hyperspectral analysis of lipid concentration and fatty acid unsaturation of Scenedesmus obliquus in situ” LINK

“Non-invasive detection of medicines and edible products by direct measurement through vials using near-infrared spectroscopy: A review” LINK

“EXPRESS: Device-Independent Discrimination of Falsified Amoxicillin Capsules Using Heterogeneous Near-Infrared Spectroscopic Devices for Training and Testing …” LINK




Hyperspectral Imaging (HSI)

“Depth feature extraction for mineral mixed spectrum analysis based hyperspectral data” LINK

“Fusion of spectrum and image features to identify Glycyrrhizae Radix et Rhizoma from different origins based on hyperspectral imaging technology” LINK




Chemometrics and Machine Learning

“Principal component regression that minimizes the sum of the squares of the relative errors: Application in multivariate calibration models” LINK

“Assessing the potential of visible–near-infrared spectroscopy method and PLSR and SVMR regressions in modeling organic carbon and total neutralizing value of soil” LINK

“Development and Verification of Prediction Model of Moisture Content of Para Rubber Timber using Portable Near Infrared Spectrometer” LINK

“Laserinduced breakdown spectroscopy and chemometric analysis of black toners for forensic applications” LINK

“Reduction of the Number of Samples for Cost-Effective Hyperspectral Grape Quality Predictive Models” | LINK

“Monitoring two different drying methods of Kakadu plum puree by combining infrared and chemometrics analysis” LINK

“What’s in this drink? Classification and adulterant detection in Irish Whiskey samples using Near Infrared Spectroscopy combined with Chemometrics.” LINK

“Robustness of hyperspectral imaging and PLSR model predictions of intramuscular fat in lamb M. longissimus lumborum across several flocks and years” LINK




Research on Spectroscopy

“Spectroscopic and Molecular Methods to Differentiate Gender in Immature Date Palm (Phoenix dactylifera L.)” LINK

“Comparison between red, green and blue images and near-infrared spectroscopy methods in the neutral detergent fiber (NDF) analysis.” LINK

“A Novel End-To-End Feature Selection and Diagnosis Method for Rotating Machinery” Sensors LINK




Equipment for Spectroscopy

“Prototype near-infrared (NIR) reflectance spectrometer for the analysis of maize flour” LINK

“Balanced-detection interferometric cavity-assisted photothermal spectroscopy employing an all-fiber-coupled probe laser configuration” LINK




Process Control and NIR Sensors

“Monitoring, by HPLC, NIR, and color measurement, of phytonutrients in tomato juice subjected to thermal processing and high hydrostatic pressure.” LINK




Environment NIR-Spectroscopy Application

“Mid-Infrared Spectroscopy Supports Identification of the Origin of Organic Matter in Soils” LINK

“Environmental covariates improve the spectral predictions of organic carbon in subtropical soils in southern Brazil” LINK

“Transfer learning strategy for plasticpollution detection in soil: Calibration transfer from high-throughput HSI system to NIR sensor” LINK




Agriculture NIR-Spectroscopy Usage

“Agronomy, Vol. 11, Pages 575: Early Detection of Excess Nitrogen Consumption in Cucumber Plants Using Hyperspectral Imaging Based on Hybrid Neural Networks and the Imperialist Competitive Algorithm” LINK

“Application of near infrared spectroscopy for determination of relationship between crop year, maturity group, and location on carbohydrate composition in soybeans” LINK

“Metabolites, Vol. 11, Pages 179: Untargeted Metabolomics Analysis by UHPLC-MS/MS of Soybean Plant in a Compatible Response to Phakopsora pachyrhizi Infection” LINK

“Staling of white wheat bread crumb and effect of maltogenic -amylases. Part 3: Spatial evolution of bread staling with time by near infrared hyperspectral imaging” LINK

“Genome-Wide Identification of QTLs for Grain Protein Content Based on Genotyping-by-Resequencing and Verification of qGPC1-1 in Rice” LINK

“Detection of Oil Palm Disease in Plantations in Krabi Province, Thailand with High Spatial Resolution Satellite Imagery” Agriculture LINK

“Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat” RemoteSensing LINK

“… Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery. Remote Sens …” LINK

“Agronomy, Vol. 11, Pages 433: Soil Properties Prediction for Precision Agriculture Using Visible and Near-Infrared Spectroscopy: A Systematic Review and Meta-Analysis” LINK

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




Horticulture NIR-Spectroscopy Applications

“Growth, respiration and physicochemical changes during the maturation of cacao fruits” LINK




Food & Feed Industry NIR Usage

“EXPRESS: Assessment of Bulk Composition of Heterogeneous Food Matrices Using Raman Spectroscopy” LINK




Pharma Industry NIR Usage

“Rapid Nondestructive Postharvest Potato Freshness and Cultivar Discrimination Assessment” LINK




Medicinal Spectroscopy

“Noninvasive Monitoring of Glucose Using Near-Infrared Reflection Spectroscopy of SkinConstraints and Effective Novel Strategy in Multivariate Calibration” Biosensors LINK




Other

“The attempt to classify the botanical origin of honey using VIS spectroscopy” LINK

“Integration of Spectral Reflectance Indices and Adaptive Neuro-Fuzzy Inference System for Assessing the Growth Performance and Yield of Potato under Different …” LINK

“Mode-resolved dual-comb spectroscopy using error correction based on single optical intermedium” LINK

” Ultrasonic-Assisted Catalytic Transfer Hydrogenation for Upgrading Pyrolysis-oil” LINK

Unwritten Rule 1 : “Samples never return.” LINK

“古建木构件化学组分近红外光谱分析” LINK

“Hydrocarbon pollutant impact on spectral and biophysical properties of willow and grass species” LINK

“One pot hydrothermal synthesis and characterization of Cu2ZnSn(S,Se)4 nanocrystalline thin films: Photovoltaic performance” LINK

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





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Spectroscopy and Chemometrics News Weekly #20, 2020

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.


NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 19, 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 19, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 19, 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)

” RAPID EVALUATION OF DRY WHITE KIDNEY BEANS COOKING CHARACTERISTICS BY NEAR-INFRARED (NIR) SPECTROSCOPY” LINK

“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

“Comparison of the Potential Abilities of Three Spectroscopy Methods: Near-Infrared, Mid-Infrared, and Molecular Fluorescence, to Predict Carotenoid, Vitamin and Fatty Acid Contents in Cow Milk ” LINK




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

” Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy” LINK

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

“Non-destructive assessment of moisture content and modulus of rupture of sawn timber Hevea wood using near infrared spectroscopy technique” LINK

“Accurate prediction of glucose concentration and identification of major contributing features from hardly distinguishable near-infrared spectroscopy” LINK

“Determination of in situ ruminal degradation of phytate phosphorus from single and compound feeds in dairy cows using chemical analysis and near-infrared spectroscopy” LINK

” RAPID, NONDESTRUCTIVE AND SIMULTANEOUS PREDICTIONS OF SOIL CONTENT IN WULING MOUNTAIN AREA USING NEAR INFRARED …” LINK

” Multiblock PLS-DA on fecal and plasma visible-near-infrared spectra for discriminating young bulls according to their efficiency. Preliminary results” LINK

“Developing deep learning based regression approaches for determination of chemical compositions in dry black goji berries (Lycium ruthenicum Murr.) using near-infrared hyperspectral …” LINK

“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

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

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




Raman Spectroscopy

“Surfaceenhanced Raman spectroscopy for onsite analysis: A review of recent developments” LINK




Hyperspectral Imaging (HSI)

“Comparative Study on Hyperspectral and Satellite Image for the Estimation of Chlorophyll a Concentration on Coastal Areas” LINK

“Performance of Fluorescence and Diffuse Reflectance Hyperspectral Imaging for Characterization of Lutefisk: A Traditional Norwegian Fish Dish” LINK

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




Spectral Imaging

“Functional Imaging of the Ocular Fundus Using an 8-Band Retinal Multispectral Imaging System” LINK

“Multispectral imaging for predicting the water status in mushroom during hotair dehydration” LINK




Chemometrics and Machine Learning

“Development of spectral signatures and classification using hyperspectral face recognition” LINK

The Google Cloud Developer’s Cheat Sheet. BigData Analytics DataScience AI MachineLearning CyberSecurity IoT IIoT Python RStats TensorFlow Java JavaScript ReactJS CloudComputing Serverless Linux Programming Coding 100DaysofCode LINK

“Vibrational spectroscopy and chemometrics for quantifying key bioactive components of various plum cultivars grown in New Zealand” LINK

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

“Chemometrics as a Green Analytical Tool” LINK




Facts

“Systematic review of deep learning techniques in plant disease detection” LINK

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




Environment NIR-Spectroscopy Application

“Remote Sensing, Vol. 12, Pages 931: Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters” LINK

“Estimation of total nitrogen and organic carbon contents in mine soils with NIR reflectance spectroscopy and various chemometric methods” LINK

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




Agriculture NIR-Spectroscopy Usage

“Remote Sensing, Vol. 12, Pages 940: Editorial for the Special Issue Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”” LINK

“Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm.” LINK

“The development of models to predict the nutritional value of feedstuffs and feed mixture using NIRS” LINK

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

“Agriculture, Vol. 10, Pages 164: Chemical Variation and Implications on Repellency Activity of Tephrosia vogelii (Hook f.) Essential Oils Against Sitophilus zeamais Motschulsky” LINK

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




Horticulture NIR-Spectroscopy Applications

“Recent advances in imaging techniques for bruise detection in fruits and vegetables” LINK




Food & Feed Industry NIR Usage

“Beef Nutritional Quality Testing and Food Packaging” LINK

“Foods, Vol. 9, Pages 619: Evaluation of the Physicochemical and Sensory Characteristics of Different Fig Cultivars for the Fresh Fruit Market” LINK




Laboratory and NIR-Spectroscopy

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




Other

“Sony releases industrial SWIR sensors with 5m pixels” LINK





NIR-Predictor – Frequently Asked Questions (FAQ)


NIR-Predictor – FAQ

Please also refer to the NIR-Predictor – Manual and check the Hints and Notes.


How to Configure / Load / Import / Activate / Setup / Use the Calibrations (*.cm) in NIR-Predictor?

Chapter “Configure the Calibrations for prediction usage” – NIR-Predictor – Manual


Do you have a calibration file for XY ?

We create custom calibrations out of your NIR + Lab measurements of XY.
We do not sell off-the-shelf calibrations.


I have downloaded the software but can’t see it?

Please note, that the download time will be very short, because of the small file size.
Check your browser’s download folder. The download is the file “NIR-PredictorVx.y.zip”


Why a .zip and no installer (Setup.exe) ?

Because a .zip deploy keeps it simple for all:

  • easy : no Administrator rights needed to install, delete it to uninstall
  • harmless : no system changes during setup
  • transparency : you see what you get

Is there a command line (CLI) version of NIR-Predictor ?

If you want to customize it in all details, our OEM API for NIR-instrument-software (White-Label) integration gives you full access. If you are an NIR-Vendor (or similar) please contact us via email info@CalibrationModel.com


The free NIR-Predictor does not create a model, what is wrong ?

As the name says, the NIR-Predictor just predicts NIR data with a model. To create a model you need to send your data to the CalibrationModel service, after development process you get an email with a link to the calibration where it can be purchased and downloaded.


Why does the creation of the PropertyFile.txt take so long with hundrets of spectra files?

It normally takes only 1-10 seconds not minutes.
Make sure that the spectra data files are stored locally on your main drive
and not on a cloud-drive or network storage or slow USB thumb drive or SD-Card.


Is there a way to use converted ASCII spectrum data to be used in NIR-Predictor?

Yes, this is the simplest ASCII CSV file format the NIR-Predictor supports.
And there are other formats supported.


Can you convert old calibration data from vendor A to be integrated to our new vendor B NIRS calibration data in our instrument?

No, we don’t do model or spectra conversion / transformation (aka model transfer).
We build optimized models with wavelength compatible data.


Does NIR-Predictor contain any malware, spyware or adware?

No, NIR-Predictor does not contain any malware, spyware or adware.


How to copy the prediction results from the table in the browser?

Copy selected columns from the table.
By holding down the Ctrl key, rectangular areas in the table can be selected with the mouse and copied to the clipboard with Ctrl+C and then copied to a spreadsheet program with Ctrl+V.


Will an expired calibration still work?

No. Until you extend the usage time.
The expired calibration file will be moved to the CalibrationExpired folder on the next start of NIR-Predictor or “Search and load Calibrations” menu function.

Selected Calibration files from the folder CalibrationExpired can be send to info@CalibrationModel.com with your Request file (.req) files for extending their usage time.

There is the possibility to get a perpetual usage, which means their is no expiration (valid until 2050).

That way you can get the time extended calibration back, that behaves exactly as the one before with extended usage time.


What does the calibration Expiration date mean exactly on the Prediction Report?

The Expiration date, it is the final day when the calibration will be valid (similar to credit cards).


What does the (number) in brackets after the [range] mean?

During the creation of a Calibration Request the NIR-Predictor shows a message containing
” ‘Prop1’ / “Quantitative [1.40 – 2.90] (154) ”
here 154 is the number of unique values in the property range.


How long does it take to create of the property file and calibration request file from 500 spectra files?

Use a local folder on your computer (not a network drive) for your spectra files then the property file and the calibration request file is created in around 1-3 seconds. (measured on a system with SSD drive, Intel i7, 2.4 GHz)


I tried to create the calibration request and got the error message: The number of Property Values of all spectra are different?

Use the generated PropertiesBySpectra (Note: Spectra not Sample) template file.
Do not reformat the generated template file, just fill in the Property values and save as Text CSV (*.csv) file (not as Excel file “.xlsx”).


Are you able to create the calibration if we only have 20 spectra?

To create a reliable quantitative calibration you need measured spectra of at least 48..60 (more is better) different samples with different Lab-values in the required measuring range!
The NIR-Predictor will check for that automatically.


Same sample measured multiple times (replicate measurements), how to enter the Lab-value only once in Property file?

Use the created PropertiesBySample template (not the PropertiesBySpectra).


Different samples with exact the same Lab-value, how to enter the Lab-Values?

Entering the Lab-values into the generated PropertiesBySpectra template the NIR-Predictor detects them as the same Sample and as a result we have too less different values to build a calibration. But in my case these are different samples with exact the same Lab-value, how to do?

You have to cheat a little bit to make NIR-Predictor do not detect same sample as measured multiple times. This is because most NIR users do replicate measurements on the same sample and NIR-Predictor looks for that. In PropertiesBySpectra modify the values a little bit to make them different, e.g.
0.18
0.18001
0.18002


Is the free NIR-Predictor software you provide for anyone to download and use?

Yes, if downloaded directly from our homepage by the user.
See also Software License Agreement


What is an Outlier?

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.

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.
See also manual chapter Outliers.


If something is wrong, please tell us

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 #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





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Spectroscopy and Chemometrics News Weekly #32, 2018

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


Chemometrics

“Quantification of active ingredients in Potentilla tormentilla by Raman and infrared spectroscopy.” LINK

“Selecting the Best Machine Learning Algorithm for Your Regression Problem” LINK

New Release via OSA_Optica: Machine Learning Technique Reconstructs Images Passing through a Multimode Fiber. “The new method showed remarkable robustness against environmental instabilities even over long fibers.” Read More: LINK

“Building Reliable Machine Learning Models with Cross-validation” LINK

“Assessing the capability of Fourier transform infrared spectroscopy in tandem with chemometric analysis for predicting poultry meat spoilage” LINK

“Rapid identification and quantification of Panax notoginseng with its adulterants by near infrared spectroscopy combined with chemometrics.” Adulteration LINK

“Quantitative analysis modeling of infrared spectroscopy based on ensemble convolutional neural networks” LINK

“Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification” LINK

“Rapid detection of three quality parameters and classification of wine based on Vis-NIR spectroscopy with wavelength selection by ACO and CARS algorithms.” LINK




Near Infrared

“Single-trial NIRS data classification for brain-computer interfaces using graph signal processing.” LINK

“Non-destructive prediction of protein content in wheat using NIRS.” Nondestructive LINK

“Fast authentication of tea tree oil through spectroscopy.” NIRS AnalyticalChemistry LINK

“Real time release testing of tablet content and content uniformity.” | Pharma dosage QualityByDesign QbD NIRS LINK

“Physical Barrier Type Abuse-Deterrent Formulations: Monitoring Sintering-Induced Microstructural Changes in Polyethylene Oxide Placebo Tablets by Near Infrared Spectroscopy (NIRS).” LINK

“Rapid Authentication and Quality Evaluation of Cinnamomum verum Powder Using Near-Infrared Spectroscopy and Multivariate Analyses.” LINK




Infrared

“New study first to demonstrate a chip-scale broadband optical system that can sense molecules in the mid-infrared” LINK




Raman

“An improved method based on a new wavelet transform for overlapped peak detection on spectrum obtained by portable Raman system” LINK




Facts

“A small team of student AI coders beats Google’s machine-learning code” LINK

“Machine Learning Technique Reconstructs Images Passing through a Multimode Fiber” LINK




DataFormats

“Are your spectroscopic data FAIR?” – FAIR stands for Findable, Accessible, Interoperable, Reusable. – IUPAC JCAMP-DX 6.0 is coming…. – spectroscopy LINK




Process Control

The key concept of sampling errors – the Theory of Sampling (TOS) – is applied in key industrial sectors (mining, minerals, cement and metals processing). LINK




Agriculture

“Canopy Hyperspectral Sensing of Paddy Fields at the Booting Stage and PLS Regression can Assess Grain Yield” LINK

“Agricultural Applications of Spectroscopy – The basic principle of spectroscopy involves dissecting the light of a specific object into various wavelengths that represent different physical properties of the object, some of which include temperature..” LINK




Laboratory

“ILS: An R package for statistical analysis in Interlaboratory Studies” LINK




CalibrationModel.com

Near Infrared (NIR) Analysis Software, work smart with all NIR Spectrometers for quantitative sensing & detection. | AnalyticalChemistry LabManger Chemical Analysis Equipment ChemicalAnalysis Analytical Instruments Laboratory LabEquipment LINK

New : FREE NIR Predictor Software, drag&drop spectral data to plug&play calibrations, for any NIRS Analysis sensor type. | Chemometrics Prediction LINK




Spectroscopy and Chemometrics News (KW #11-#30 2018)

Chemometrics

“Identifying and filtering out outliers in spatial datasets” (2018.07.25) LINK

“Phenomic selection: a low-cost and high-throughput method based on indirect predictions. Proof of concept on wheat and poplar.” (2018.07.25) LINK

“An improved variable selection method for support vector regression in NIR spectral modeling” (2018.07.25) LINK

“Near-Infrared Spectroscopy and Chemometrics for the Routine Detection of Bilberry Extract Adulteration and Quantitative Determination of the Anthocyanins” (2018.07.25) LINK

“Modern practical convolutional neural networks for multivariate regression: Applications to NIR calibration” (2018.07.25) LINK

“Automated NIR-Spectroscopy chemometrics-method development with machine-learning for spectrometers, spectral IoT-sensor/smart-sensors” – read without Hashtags (2018.07.25) LINK

An interesting explanation of “Automated Machine Learning vs Automated Data Science” | Automated MachineLearning DataScience (2018.07.03) LINK

“Evaluation of Quality Parameters of Apple Juices Using Near-Infrared Spectroscopy and Chemometrics” (2018.07.02) LINK

“Chemometric approaches for document dating: Handling paper variability” (2018.06.27) LINK

“Development and comparison of regression models for the determination of quality parameters in margarine spread samples using NIR spectroscopy” (2018.06.27) LINK

“The non-destructive technique such as Near Infrared Spectroscopy (NIRS) along with Chemometrics has been successful in predicting the quality parameters but not well established for on-vine/in-orchard fruit quality measurement.” (2018.06.27) LINK

“Quantification of leghaemoglobin content in pea nodules based on near infrared hyperspectral imaging spectroscopy and chemometrics” (2018.06.15) LINK

“Exploring the Applicability of Quantitative Models Based on NIR Reflectance Spectroscopy of Plant Samples” | tobacco (2018.06.15) LINK

“Interval lasso regression based Extreme learning machine for nonlinear multivariate calibration of near infrared spectroscopic datasets” (2018.06.05) LINK

“Soft and Robust Identification of Body Fluid Using Fourier Transform Infrared Spectroscopy and Chemometric Strategies for Forensic Analysis” (2018.06.05) LINK

“Temporal decomposition sampling and chemical characterization of eucalyptus harvest residues using NIR spectroscopy and chemometric methods” (2018.06.05) LINK

: The emission spectrum of each element is a unique identifier — like the DNA of the element — and the spectral analysis of a light source is essentially a Principal Component Analysis of its components — like explanatory DataScience. Get your p… (2018.06.05) LINK

“Rice Classification Using Hyperspectral Imaging and Deep Convolutional Neural Network” DCNN (2018.05.31) LINK

“Development and comparison of regression models for determination of quality parameters in margarine spread samples using NIR spectroscopy” (2018.05.31) LINK

“Application of FTIR Spectroscopy and Chemometrics for Halal Authentication of Beef Meatball Adulterated with Dog Meat” (2018.05.31) LINK

Prediction of amino acids, caffeine, theaflavins and water extract in black tea by FT-NIR spectroscopy coupled chemometrics algorithms (2018.05.31) LINK

Chemometrics in Analytical Chemistry (CAC) Conference, Halifax, 17th CAC Meeting, June 25-29, 2018 (2018.05.31) LINK

Spatially Resolved Spectral Powder Analysis: Experiments and Modeling (2018.04.05) LINK

Calibration Transfer based on the Weight Matrix (CTWM) of PLS for near infrared (NIR) spectral analysis (2018.04.05) LINK

“A novel multivariate calibration method based on variable adaptive boosting partial least squares algorithm” (2018.03.28) LINK

“How Many ML Models You Have NOT Built?” (2018.03.28) LINK

“Calibration model maintenance in melamine resin production: Integrating drift detection, smart sample selection and model adaptation” (2018.03.27) LINK

“Application of NIR spectroscopy and chemometrics for revealing of the ‘high quality fakes’ among the medicines” (2018.03.27) LINK

“Dual-Domain Calibration Transfer Using Orthogonal Projection” (2018.03.14) LINK

“A Review of Calibration Transfer Practices and Instrument Differences in Spectroscopy” (2018.03.14) LINK



Near Infrared

“Near-infrared chemical imaging used for in-line analysis of functional finishes on textiles.” (2018.07.25) LINK

“A comparison between NIR and ATR-FTIR spectroscopy for varietal differentiation of Spanish intact almonds” (2018.07.25) LINK

Worked examples of MSC and SNV correction for NIR spectroscopy in Python. nirs (2018.07.25) LINK

“Evolution of Frying Oil Quality Using Fourier Transform Near-Infrared (FT-NIR) Spectroscopy” – FryingOil FTNIR (2018.07.25) LINK

“Evaluation of drying of edible coating on bread using NIR spectroscopy” (2018.07.25) LINK

very interesting article. NIRS is cheaper than molecular markers (2018.07.13) LINK

FlowChemMondays application of a portable near-infrared spectrophotometer (MicroNIR) for in-line monitoring of the synthesis of 5-hydroxymethylfurfural (5-HMF) from D-fructose is described in OPRD | flowchemistry (2018.07.03) LINK

“Accurate and rapid detection of soil and fertilizer properties based on visible/near-infrared spectroscopy” (2018.06.25) LINK

“Detection of Veterinary Antimicrobial Residues in Milk through Near-Infrared Absorption Spectroscopy” (2018.06.05) LINK

“Calibration is key – the calibration is the most important part of the NIRS method” near-infrared reflectance spectroscopy NIRS – from the lab to the field… forage quality agchat handheldNIRS Lab2Field (2018.06.05) LINK

“Assessment of tomato soluble solids content and pH by spatially-resolved and conventional Vis/NIR spectroscopy” (2018.05.31) LINK

“A novel method for geographical origin identification of Tetrastigma hemsleyanum (Sanyeqing) by near-infrared spectroscopy” (2018.05.31) LINK

“Effects of moisture on automatic textile fiber identification by NIR spectroscopy” (2018.05.31) LINK

“Rapid, noninvasive detection of Zika virus in Aedes aegypti mosquitoes by near-infrared spectroscopy” (2018.05.31) LINK

Method for Identifying Maize Haploid Seeds by Applying Diffuse Transmission Near-Infrared Spectroscopy (2018.04.05) LINK

This article is about NIR Spectroscopy. (2018.03.27) LINK

“Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS-NIR Spectroscopy” (2018.03.27) LINK

“Concentration monitoring with near infrared chemical imaging in a tableting press” (2018.03.14) LINK

“NIR Chemical Imaging Can Help Maintain the Safety of Pharmaceutical Tablets” | NIRCI (2018.03.14) LINK



Infrared

“High Throughput Screening of Elite Loblolly Pine Families for Chemical and Bioenergy Traits with Near Infrared Spectroscopy” (2018.07.25) LINK

“Non-Destructive Methodology to Determine Modulus of Elasticity (MOE) in Static Bending of Quercus mongolica Using Near-Infrared Spectroscopy” wood (2018.06.27) LINK

“Near infrared spectroscopy as an alternative method for rapid evaluation of toluene swell of natural rubber latex and its products” (2018.06.25) LINK

“The Application of Discrete Wavelet Transform with Improved Partial Least-Squares Method for the Estimation of Soil Properties with Visible and Near-Infrared Spectral Data” (2018.06.05) LINK

“Mutual factor analysis for quantitative analysis by temperature dependent near infrared spectra.” (2018.03.27) LINK

“Evaluating the Applications of the Near-Infrared Region in Mapping Foliar N in the Miombo Woodlands” (2018.03.27) LINK



Raman

“Differentiating Donor Age Groups Based on Raman Spectroscopy of Bloodstains for Forensic Purposes” (2018.06.27) LINK



Hyperspectral

“Evaluation of Near-Infrared Hyperspectral Imaging for Detection of Peanut and Walnut Powders in Whole Wheat Flour” (2018.07.03) LINK

“Potential of Visible and Near-Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves” (2018.07.03) LINK

“Quantification of leghaemoglobin content in pea nodules based on near infrared hyperspectral imaging spectroscopy and chemometrics” (2018.06.15) LINK

Optics

“Spectral Fiber Sensors for Cancer Diagnostics” by artphotonics | Optical Fibers (2018.07.03) LINK





Facts

“This is your brain detecting patterns. It is different from other kinds of learning, study shows” (2018.06.01) LINK



Research

Android Tricorder: Google übernimmt Startup, das Körperwerte mit dem Smartphone messen kann (2017.08.16) LINK





Equipment

“Comparing the qualitative performances of handheld NIR and Raman spectrometers for the analysis of falsified pharmaceutical products.” (2018.07.25) LINK

“Tiny $25 Spectrometer Aims to Identify Materials with Ease” (2018.05.31) LINK

“Comparison of Portable and Bench-Top Spectrometers for Mid-Infrared Diffuse Reflectance Measurements of Soils” (2018.03.28) LINK



Environment

Nonlinear Regression with High-Dimensional Space Mapping for Blood Component Spectral Quantitative Analysis (2018.02.02) LINK



Agriculture

“Bluret Protein Measurement Machine, a technological disrupter of its day” (2018.07.03) LINK

“Identification and quantification of microplastics in table sea salts using micro-NIR imaging methods” (2018.05.31) LINK



Food & Feed

“Non-Invasive Methodology to Estimate Polyphenol Content in Extra Virgin Olive Oil Based on Stepwise Multilinear Regression” (2018.03.27) LINK

Rapid Determination of Active Compounds and Antioxidant Activity of Okra Seeds Using Fourier Transform Near Infrared (FT-NIR) Spectroscopy FTNIR Polyphenols Flavonoids AntioxidantActivity | (2018.03.03) LINK



Pharma

“Quantification of pharmaceuticals contaminants in wastewaters by NIR spectroscopy” (2018.07.25) LINK



Laboratory

Spectroscopy used to be confined to the laboratory. Today, portable NeoSpectra SpectralSensors bring the power of NIR out of the lab and into the field. (2018.06.20) LINK



Other

“Non-Destructive Methodology to Determine Modulus of Elasticity (MOE) in Static Bending of Quercus mongolica Usin… (2018.06.27) LINK

“The Application of Discrete Wavelet Transform with Improved Partial Least-Squares Method for the Estimation of… (2018.06.05) LINK

: The emission spectrum of each element is a unique identifier — like the DNA of the element — and the spectral analysis of a light source is essentially a Principal Component Analysis of its components — like explanatory DataScience. Get your p… (2018.06.05) LINK

An Innovative Approach to Exploit the Reflection Spectroscopy of Liquid Characteristics (2018.04.05) LINK

“Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS-NIR Spectroscopy” (2018.03.27) LINK



Spectroscopy and Chemometrics News Weekly #51+52, 2016

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Chemometrics

Quantification of quality parameters in castanhola fruits by NIRS for the development of prediction models using… LINK


Infrared

USB3.0 SWIR OEM Linescan Camera for Machine Vision & Spectroscopy — Princeton Infrared Technologies, Inc. LINK


Near Infrared

An easy and effective way to screen chemicals in cosmetics? Introducing, Near Infrared Analyser (NIR)! LINK


Raman

Identification and Evaluation of Composition in Food Powder Using Point-Scan Raman Spectral Imaging LINK


Spectral Imaging

Low-Cost Spectral Imaging on the Horizon | SpectralImaging LINK


Food & Feed

The plastic rice scam reaches Africa (Nigeria to be accurate). Food fraudsters compared to drug dealers LINK!


Equipment

Handheld Raman Spectrometer for On-Site Verification of Materials in Seconds – Mira M-3 LINK

“Bruker Announces Acquisition of Active Spectrum Micro-ESR Business” (Electron Spin Resonance Spectroscopy) LINK


Future

Disruptive Technology in 201X and Beyond IoT MobileFirst TechnologyTrends LINK


CalibrationModel.com

Efficient Spectroscopic Analysis Method Development and Data Analysis for Robust and Exact NIRS Measurement Results LINK

Increase Your Profit with optimized NIRS Accuracy FoodSafety Qualitätssicherung LINK

Services for Optimization of Chemometric Application Methods of Near-Infrared Spectroscopy | NIRS Quality Control LINK