AIMC Topic: Vitaceae

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Combining stable isotopes and multi-elements with machine learning chemometric models to identify the geographical origins of Tetrastigma hemsleyanum Diels et Gilg.

Food chemistry Dec 24, 2024
Tetrastigma hemsleyanum Diels et Gilg (T. hemsleyanum) is an edible plant with considerable medicinal properties, the quality of which varies depending on its origin. Therefore economically motivated adulteration has emerged. So there is an urgent ne...
Machine Learning Trace Elements Geography Carbon Isotopes Vitaceae Chemometrics
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Discrimination of Tetrastigma hemsleyanum according to geographical origin by near-infrared spectroscopy combined with a deep learning approach.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Apr 23, 2020
Recently, deep learning has presented as a powerful approach to overcome the deficiencies of the conventional biochemical approaches. In this study, a method for discriminating medicinal plant Tetrastigma hemsleyanum from different origins was propos...
Neural Networks, Computer Algorithms Spectroscopy, Near-Infrared Deep Learning Vitaceae
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Enhancing spatial resolution of (18)F positron imaging with the Timepix detector by classification of primary fired pixels using support vector machine.

Physics in medicine and biology Jun 18, 2015
Position-sensitive positron cameras using silicon pixel detectors have been applied for some preclinical and intraoperative clinical applications. However, the spatial resolution of a positron camera is limited by positron multiple scattering in the ...
Humans Algorithms Computer Simulation Support Vector Machine Plant Leaves Radiopharmaceuticals Electrons Positron-Emission Tomography Image Interpretation, Computer-Assisted Fluorodeoxyglucose F18 Monte Carlo Method Vitaceae
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