Using HS-GC-MS and flash GC e-nose in combination with chemometric analysis and machine learning algorithms to identify the varieties, geographical origins and production modes of Atractylodes lancea

算法 气相色谱-质谱法 电子鼻 闪光灯(摄影) 生产(经济) 人工智能 计算机科学 模式识别(心理学) 机器学习 质谱法 化学 色谱法 物理 光学 经济 宏观经济学
作者
Yifu Gan,Tao Yang,Wei Gu,Lanping Guo,Rongli Qiu,Sheng Wang,Yan Zhang,Min Tang,Zengcai Yang
出处
期刊:Industrial Crops and Products [Elsevier]
卷期号:209: 117955-117955 被引量:19
标识
DOI:10.1016/j.indcrop.2023.117955
摘要

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. 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 β-elemene were higher in abundance in AL than AC; seven terpenoids including α-curcumene and α-pinene were higher in abundance in wild AL than cultivated AL; and there were significantly different quantities of ten terpenoids including agarospirol and β-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%. This study provides a rapid and accurate strategy for addressing quality control and market regulation issues for AL and other industrial crops.
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