Spectroscopy and Chemometrics News Weekly #30, 2020

NIR Calibration-Model Services

Development of quantitative Multivariate Prediction Models for Near Infrared Spectrometers | NIRS HSI LINK

Spectroscopy and Chemometrics News Weekly 29, 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 29, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 29, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

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Near-Infrared Spectroscopy (NIRS)

“Non-Destructive Determination of Quality Traits of Cashew Apples (Anacardium Occidentale, L.) Using a Portable near Infrared Spectrophotometer” LINK

“Non-destructive classification and prediction of aflatoxin-B1 concentration in maize kernels using Vis–NIR (400–1000 nm) hyperspectral imaging” LINK

“Determination of glucose content with a concentration within the physiological range by FT-NIR spectroscopy in a trans-reflectance mode” LINK

“Evaluating taste-related attributes of black tea by micro-NIRS” LINK

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

“Rapid authentication of Pseudostellaria heterophylla (Taizishen) from different regions by nearinfrared spectroscopy combined with chemometric methods” LINK

“Agronomy, Vol. 10, Pages 828: Estimating Sensory Properties with Near-Infrared Spectroscopy: A Tool for Quality Control and Breeding of Calçots (Allium cepa L.)” LINK

“Spectral observation of agarwood by infrared spectroscopy: The differences of infected and normal Aquilaria microcarpa” LINK

“Quantitative near infrared spectroscopic analysis of Tricholoma matsutake based on information extraction using the elastic net” LINK

“Visible-near infrared spectroscopy for detection of blood in sheep faeces” LINK

” … dans le proche infrarouge et techniques de chimiométrie Detection of addition of barley to coffee using near infrared spectroscopy and chemometric techniques” LINK

“Forests, Vol. 11, Pages 644: A Comparison of the Loading Direction for Bending Strength with Different Wood Measurement Surfaces Using Near-Infrared Spectroscopy” LINK

“Rapid assessment of soil condition in Kenya through development of near infrared spectral indicatators” LINK

Chemometrics and Machine Learning

“Determination of apple varieties by near infrared reflectance spectroscopy coupled with improved possibilistic Gath–Geva clustering algorithm” LINK

“Two-Dimensional Correlation Spectroscopy: The Power of Power Spectra” LINK

“Simple and fast spectrophotometric method based on chemometrics for the measurement of multicomponent adsorption kinetics” LINK

“Real time detection of amphetamine in oral fluids by MicroNIR/Chemometrics.” LINK

“In‐vitro digestion of the bioactives originating from the Lamiaceae family herbal teas: A kinetic and PLS modeling study” LINK

“Models for predicting the within-tree and regional variation of tracheid length and width for plantation loblolly pine” LINK

Research on Spectroscopy

“Study on rapid quality analysis method of Shengxuebao Mixture” LINK

“MD dating: molecular decay (MD) in pinewood as a dating method” LINK

Altersbestimmung von Holz mittels FTIR-Spektroskopie: Durch die Zusammenarbeit von Holz-, Materialwissenschaftler*innen und Statistikern konnte nach über 70 Jahren eine dritte Datierungsmethode neben der Jahrringanalyse und der Radiokarbonmethode im… LINK

Equipment for Spectroscopy

“Quality assessment of instant green tea using portable NIR spectrometer.” LINK

Process Control and NIR Sensors

“From powder to tablets: Investigation of residence time distributions in a continuous manufacturing process train as basis for continuous process verification” LINK

“Non-destructive, non-invasive, in-line real-time phase-based reflectance for quality monitoring of fruit” LINK

Agriculture NIR-Spectroscopy Usage

“Estimating soil organic carbon density in Northern China’s agro-pastoral ecotone using vis-NIR spectroscopy” LINK

“Retrieval of aboveground crop nitrogen content with a hybrid machine learning method” LINK

“Prediction of Soil Oxalate Phosphorus using Visible and Near-Infrared Spectroscopy in Natural and Cultivated System Soils of Madagascar” LINK

“Sensors, Vol. 20, Pages 3208: Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants” LINK

“The application of R language in the selection of characteristic bands for the prediction of protein content in milk powder by Near Infrared Spectroscopy” LINK

“Onsite nutritional diagnosis of tea plants using micro near-infrared spectrometer coupled with chemometrics” LINK

Horticulture NIR-Spectroscopy Applications

“Improving the accuracy of near-infrared (NIR) spectroscopy method to predict the oil content of oil palm fresh fruits” LINK

Laboratory and NIR-Spectroscopy

“Non-destructive determination of apple quality parameters of variety’red jonaprince’using near infrared spectroscopy.” LINK

“Laboratory Methods for Evaluating Forage Quality” LINK


“Automatic Walnut Sorting System Based on Adaptive Fuzzy Control” LINK

“Industrial gas chromatographs” LINK

Why use fully Automated Model Building for NIR-Spectroscopy?

  • lowers the barriers to using NIR-Spectroscopy based analysis technology
    by reducing the technical expertise required to build mathematical prediction models.

  • reduces human error, automates repetitive tasks,
    and enables the development of more effective models.

  • interpret-able trained models by access to the detailed calibration report
    that contains the intellectual property (IP) how the model is constructed and validated.

  • deploy models yourselves in a production-ready state
    (identifiable versioned models that are secured against changes and tampering)

  • free NIR-Predictor software with graphical user interface (GUI)
    and also optional with a software development kit (SDK)
    and source code samples using the application programming interface (API)
    to transform spectral raw data to prediction results.

  • allows to process anonymous data,
    we don’t need to know what type of sample you have measured or
    what name the ingredient has, you can just name it A or B.

read more : Main Advantages

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