首页|Yunnan Agricultural University Reports Findings in Machine Learning (Rapid and a ccurate identification of Gastrodia elata Blume species based on FTIR and NIR sp ectroscopy combined with chemometric methods)
Yunnan Agricultural University Reports Findings in Machine Learning (Rapid and a ccurate identification of Gastrodia elata Blume species based on FTIR and NIR sp ectroscopy combined with chemometric methods)
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New research on Machine Learning is th e subject of a report. According to news reporting originating in Kunming, Peopl e's Republic of China, by NewsRx journalists, research stated, "Different variet ies of Gastrodia elata Blume (G. elata Bl.) have different qualities and differe nt contents of active ingredients, such as polysaccharide and gastrodin, and it is generally believed that the higher the active ingredients, the better the qua lity of G. elata Bl. and the stronger the medicinal effects. Therefore, effectiv e identification of G. elata Bl. species is crucial and has important theoretica l and practical significance." The news reporters obtained a quote from the research from Yunnan Agricultural U niversity, "In this study, first unsupervised PCA and t-SNE are established for data visualisation, follow by traditional machine learning (PLS-DA, OPLS-DA and SVM) models and deep learning (ResNet) models were established based on the four ier transform infrared (FTIR) and near infrared (NIR) spectra data of three G. e lata Bl. species. The results show that PLS-DA, OPLS-DA and SVM models require c omplex preprocessing of spectral data to build stable and reliable models."
KunmingPeople's Republic of ChinaAsi aChemometricCyborgsEmerging TechnologiesMachine Learning
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