Spectroscopy and Chemometrics News Weekly #35, 2019

Near Infrared (NIR)

“Evaluation of diesel exhaust fluid (DEF) using near-infrared spectroscopy and multivariate calibration” LINK

“A weighted ensemble method based on wavelength selection for near-infrared spectroscopic calibration” LINK

“110th Anniversary: Real-Time Endpoint Detection of Fluidized Bed Drying Process Based on a Switching Model of Near-Infrared Spectroscopy” LINK

“Supervised Dictionary Learning with Regularization for Near-infrared Spectroscopy Classification” tobacco NIRS LINK

“Evaluation of NIRS as non-destructive test to evaluate quality traits of purple passion fruit” LINK

“A Comparison of Sparse Partial Least Squares and Elastic Net in Wavelength Selection on NIR Spectroscopy Data” LINK

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

“Comparative study on the use of three different near infrared spectroscopy recording methodologies for varietal discrimination of walnuts” LINK

“Support vector machine regression on selected wavelength regions for quantitative analysis of caffeine in tea leaves by near infrared spectroscopy” LINK

“Use of near infrared spectroscopy and spectral database to assess the quality of pharmaceutical products and aid characterization of unknown components” LINK

“Determination of soil organic matter using visible-near infrared spectroscopy and machinelearning” LINK

“Evaluation of acetic acid and ethanol concentration in a rice vinegar internal venturi injector bioreactor using Fourier transform near infrared spectroscopy” LINK


“Determination of the superficial citral content on microparticles: An application of NIR spectroscopy coupled with chemometric tools” LINK

“Non-Invasive Tools to Detect Smoke Contamination in Grapevine Canopies, Berries and Wine: A Remote Sensing and Machine Learning Modeling Approach” LINK

“Optimization of soluble solids content prediction models in ‘Hami’melons by means of Vis-NIR spectroscopy and chemometric tools” LINK

“Fast quantitative detection of Black Pepper and Cumin adulterations by near-infrared spectroscopy and multivariate modeling” LINK

“Prediction of toughness and other beef quality characteristics of the m. longissimus thoracis using polarized near-infrared reflectance spectroscopy” LINK

“Application of near infrared for on-line monitoring of heavy fuel oil at thermoelectric power plants. Part I: Development of chemometric models” LINK

What’s a Near-Infrared-sensor for combines? Donau Soja collects data from soya fields for the soya yield- and protein prediction model within CYBELE_H2020. The NIR sensor measures & maps protein, oil and other quality parameters in real time during the upcoming harvest season! LINK

“NIRs calibration models for chemical composition and fatty acid families of raw and freeze-dried beef: a comparison” LINK

“Honey botanical origin classification using hyperspectral imaging and machine learning” LINK


Spectroscopy and Chemometrics News Weekly 34, 2019 | NIRS NIR Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Software Sensors QAQC Testing Quality Checking LabManagers laboratory digitalization LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 34, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse Qualitätslabor labdata LINK

Spettroscopia e Chemiometria Weekly News 34, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem prediction models LINK


Most people now know companies like Google & Facebook collect & sell your data. Yet some people still think, “So what? I have nothing to hide.” Here’s five compelling reasons to tell them why your privacy is worth more than you think! LINK


“Relevance of a near infrared spectral index for assessing tillage and fertilization effects on soil water retention” LINK


“Fast And Simultaneous Prediction Of Agricultural Soil Nutrients Content Using Infrared Spectroscopy” LINK

“Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed!” LINK

“Prediction of macronutrients in plant leaves using chemometric analysis and wavelength selection” LINK


“Spectroscopic data supporting investment decisions” LINK


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