Characterization of volatile profiles and markers prediction of eleven popular edible boletes using SDE-GC-MS and FT-NIR combined with chemometric analysis
文献类型： 外文期刊
作者： Yuan, Tianjun 1 ; Zhao, Yanli 2 ; Zhang, Ji 2 ; Li, Shuhong 3 ; Hou, Ying 4 ; Yang, Yan 5 ; Wang, Yuanzhong 2 ;
作者机构： 1.Mae Fah Luang Univ, Sch Sci, Chiang Rai 57100, Thailand
2.Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Peoples R China
3.Yunnan Acad Agr Sci, Biotechnol & Germplasm Resources Inst, Kunming 650205, Peoples R China
4.Southwest Forestry Univ, Mat & Chem Engn Coll, Kunming 650224, Peoples R China
5.Yunnan Forestry Technol Coll, Kunming 650224, Peoples R China
关键词： Aroma characterization; Chemometrics; FT-NIR; Relative odor activity value; Wild bolete mushrooms
期刊名称：FOOD RESEARCH INTERNATIONAL （ 影响因子：8.0； 五年影响因子：8.5 ）
ISSN： 0963-9969
年卷期： 2024 年 196 卷
页码：
收录情况： SCI
摘要： Wild edible boletes mushrooms are regarded as a delicacy in many countries and regions due to their rich nutritional contents and strong aromatic compounds. This study aimed to identify 445 samples of 11 boletes species collected from Yunnan and Sichuan provinces through molecular analysis. Using simultaneous distillation-extraction (SDE) combined with gas chromatography-mass spectrometry (GC-MS), 97 volatile compounds were identified. Chemometric methods were then applied to analyze the heterogeneity of these volatile compounds among the different species. The results showed that, 22 and 21 volatile compounds were selected using variable importance in projection (VIP > 1) and relative odor activity values (ROAV > 0.1), respectively. Partial least squares discrimnatint analysis (PLS-DA) was then employed to develop pattern recognition models for 11 species, which demonstrated strong identification performance. Furthermore, correlation heat maps, volcano plots, and Fisher linear discriminant analysis identified five volatile organic compounds, including methyl (9E)-9-octadecenoate, 2, 6-dimethylpyrazine, 1-decen-3-one, furfural, and methional as markers for distinguishing 11 boletes species. Ultimately, the rapid content prediction models of partial least squares regression (PLSR) were established by combining Fourier Transform Near-Infrared Spectroscopy (FT-NIR) with the concentrations of these five marker compounds. These findings provide a methodological strategy for the effective species identification of wild edible mushrooms and the rapid prediction of their characteristic aroma compounds.
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