首页|Researchers from Nanjing University of Chinese Medicine Describe Findings in Machine Learning (Using Hs-gc-ms and Flash Gc E-nose In Combination With Chemometric Analysis and Machine Learning Algorithms To Identify the Varieties, Geographical ...)
Researchers from Nanjing University of Chinese Medicine Describe Findings in Machine Learning (Using Hs-gc-ms and Flash Gc E-nose In Combination With Chemometric Analysis and Machine Learning Algorithms To Identify the Varieties, Geographical ...)
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Elsevier
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Fresh data on Machine Learning are presented in a new report. According to news reporting from Nanjing, People’s Republic of China, by NewsRx journalists, research stated, “Atractylodes lancea (AL) is argued to be the best botanical source of the atractylodes rhizome (AR), which is used within traditional Chinese medicine. However, in recent years there have been a number of issues around the production and use of AR, including authenticity, confusion, and mislabeling between AL and Atractylodes chinensis (AC) isolates, geographical origins, and production modes.” Funders for this research include Key project at central government level: The ability establishment of sustainable use for valuable Chinese medicine resources, Research of assurance-ability improvement of Chinese medicinal resources. The news correspondents obtained a quote from the research from the Nanjing University of Chinese Medicine, “These discrepancies can impact both the quality and commercial value of the crop. In this study, volatile organic compounds from 173 batches of AR isolated from both AL and AC plants were compared using a flash gas chromatography electronic nose (flash GC e-nose) and headspace gas chromatography- mass spectrometry (HS-GC-MS). The flash GC e-nose revealed that the main aromas of AR were spicy, sweety, and fruity, and the flavor differences of Atractylodes lancea from different geographical origins are mainly reflected in sweetness and spicy taste. Furthermore, HS-GC-MS showed that terpenoids are key indicators for determining the quality and further clarifying the origin of AL. Eight terpenoids including 2-pinen-10-ol and beta-elemene were higher in abundance in AL than AC; seven terpenoids including alpha-curcumene and alpha-pinene were higher in abundance in wild AL than cultivated AL; and there were significantly different quantities of ten terpenoids including agarospirol and beta-bisabolene present in samples of AL taken from Jiangsu, Henan and Hubei provinces. Finally, the performance of eight machine- learning algorithms to distinguish between AL and AC, and recognize different regions and production patterns of AL, were compared. Among them, XGBoost had the highest differentiation accuracy of 86.17 +/- 7.48%.”
NanjingPeople’s Republic of ChinaAsiaAlgorithmsChemo- metricCyborgsEmerging TechnologiesMachine LearningNanjing University of Chinese Medicine
NETL
NSTL
Elsevier
