化学计量学
天麻
气相色谱-质谱法
色谱法
可追溯性
钥匙(锁)
化学
气相色谱法
质谱法
数学
生物
医学
统计
病理
中医药
替代医学
生态学
作者
Yingfeng Zhong,Jieqing Li,Honggao Liu,Yuanzhong Wang
标识
DOI:10.1016/j.fochx.2025.102770
摘要
Gastrodia elata Blume (G. elata) is highly favored in the edible sector owing to its rich nutritional content and distinct flavor. Herein, headspace solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) and Fourier transform infrared spectroscopy (FTIR) technology were employed to classify the origin of G. elata and quantify volatile organic compounds (VOCs). GC-MS revealed that sweet, fruity, and nutty are the key flavor characteristics of G. elata, with samples from Zhaotong City, Yunnan Province, exhibiting superior flavor and richness Based on FTIR data, the gray wolf optimizer-support vector machine and residual convolutional neural network achieved 100 % accuracy in G. elata traceability, with an F1 of 1.000. Additionally, the partial least squares regression model successfully quantified the main components 2-Nonenal and 2(3H)-Furanone, dihydro-5-propyl- in G. elata, with prediction set residual deviations of 2.6003 and 2.3883, respectively. This approach offers a novel framework for monitoring VOCs quality control in other foods.
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