Spectroscopy and Chemometrics News Weekly #34, 2019

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Spectroscopy and Chemometrics News Weekly 33, 2019 | NIRS NIR Spectrometer Analytical Chemistry Chemical Analysis Lab Labs Laboratories QAQC Testing Quality LabManager LabManagers laboratory digitalization labdata laboratorydata LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 33, 2019 | NIRS NIR FTNIR Spektroskopie Chemometrie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysemethode Laborleiter Laboranalyse Qualitätskontrolle LINK

Spettroscopia e Chemiometria Weekly News 33, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità prediction controllo qualità LINK




Chemometrics

“A Spectral Fitting Algorithm to Retrieve the Fluorescence Spectrum from Canopy Radiance” Remote Sensing RemoteSensing LINK

“A hyperspectral GA-PLSR model for prediction of pine wilt disease” LINK

“Hyperspectral Anomaly Detection via Convolutional Neural Network and Low Rank With Density-Based Clustering” LINK

“Use of near-infrared hyperspectral (NIR-HS) imaging to visualize and model the maturity of long-ripening hard cheeses” LINK

“Identification of lactic acid bacteria Enterococcus and Lactococcus by near-infrared spectroscopy and multivariate classification.” LINK

“A practical convolutional neural network model for discriminating Raman spectra of human and animal blood” LINK

“Non-destructive prediction of texture of frozen/thaw raw beef by Raman spectroscopy” LINK

“Incorporating brand variability into classification of edible oils by Raman spectroscopy” LINK

“Three-way data splits (training, test and validation) for model selection and performance estimation” LINK

“Importance of spatial predictor variable selection in machine learning applications — Moving from data reproduction to spatial prediction.” LINK

“Tracing the dune activation of Badain Jaran Desert and Tengger Desert by using near infrared spectroscopy and chemometrics” LINK




Near Infrared

“On-The-Go VIS + SW – NIR Spectroscopy as a Reliable Monitoring Tool for Grape Composition within the Vineyard.” LINK

“Improved Functional Near Infrared Spectroscopy Enables Enhanced Brain Imaging” fNIR FDNIR LINK

“Estabilishing A Calibration For Neutral Detergent Fiber (NDF) Value by Using Near Infrared Spectroscopy (NIR) in Corn Grain” LINK

“Using Near Infrared Spectroscopy and Machine Learning to diagnose Systemic Sclerosis.” LINK

“Strategies for the efficient estimation of soil organic carbon at the field scale with vis-NIR spectroscopy: Spectral libraries and spiking vs. local calibrations” LINK

“Sensomics-from conventional to functional NIR spectroscopy-shining light over the aroma and taste of foods” LINK




Infrared

“Assessment of Spinal Cord Ischemia With Near-Infrared Spectroscopy: Myth or Reality?” LINK

“Identification of antibiotic mycelia residues in cottonseed meal using Fourier transform near-infrared microspectroscopic imaging.” LINK

“Application of near-infrared spectroscopy for frozen-thawed characterization of cuttlefish (Sepia officinalis)” Aquaphotomics LINK

” Identification of Tilletia foetida, Ustilago tritici, and Urocystis tritici Based on Near-Infrared Spectroscopy” LINK

“Assessment of meat freshness and spoilage detection utilizing visible to near-infrared spectroscopy” LINK




Hyperspectral

“Estimating the severity of apple mosaic disease with hyperspectral images” LINK

“Spectral filter design based on in-field hyperspectral imaging and machine learning for mango ripeness estimation” LINK




Equipment

“Feasibility Study of the Use of Handheld NIR Spectrometer for Simultaneous Authentication and Quantification of Quality Parameters in Intact Pineapple Fruits” LINK




Agriculture

“Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy” FTNIR FTMIR LINK




Forestry

“Near-infrared spectroscopy analysis-a useful tool to detect apple proliferation diseased trees?”LINK

“Evaluation of near infrared spectroscopy to non-destructively measure growth strain in trees” LINK




Other

“Spectral Screening Based on Comprehensive Similarity and Support Vector Machine” LINK

“Aquaphotomics-From Innovative Knowledge to Integrative Platform in Science and Technology.” LINK





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NIR Spectroscopy and Chemometric surveys, inquiry, polls and assessments (Part 3)


10. NIR in Supply Chain
Where in the supply chain are your NIR instruments located?

11. NIR Usage
How long has your company used NIR spectroscopy?

12. NIR instruments
How many NIR instrument units are in use in your company?

13. NIR Mobile
How much is the mobile hand-held percentage of total NIR devices in your company?

14. Calibration Source
How do you get the NIR Calibrations developed?

15. Calibration Training
How often do the operators get training about NIR Spectroscopy and Chemometrics?

16. NIR PreCalibrations
How many NIR Pre-Calibration, NIR factory calibrations or NIR starter calibrations have you in use?

17. Calibration Spectra
How many Spectra does your quantitative Calibration have in average?

Please vote and see the assessments below.

Part 1, Part 2
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Part 1, Part 2

Extend NIR calibrations (re-calibration)

How to extend a NIR calibration with new measured sample spectra and optimize the calibration again? The important thing is not stubbornly cling to the existing chemometric model settings, but to evaluate the whole data set and newly re-modeling, so as to enable better performance. In practice, it is so that new NIR data fill the gaps in the base model, bring in concentration range extensions with them, the raw materials or the process has changed somewhat, and thus a new calibration optimization is certainly useful. Because the added new variations in the data pool generates a totally new picture, so that a different pre-processing and a modified wavelength selection can provide a much better and robust compensation for unwanted effects and increase the overall calibration performance and improve the models accuracy.