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中国精品科技期刊2020
王佳俊,肖乾煌,阮松林,等. 基于广泛靶向代谢组学解析衢陈皮陈化机制J. 食品工业科技,2026,47(5):1−9. doi: 10.13386/j.issn1002-0306.2025010271.
引用本文: 王佳俊,肖乾煌,阮松林,等. 基于广泛靶向代谢组学解析衢陈皮陈化机制J. 食品工业科技,2026,47(5):1−9. doi: 10.13386/j.issn1002-0306.2025010271.
WANG Jiajun, XIAO Qianhuang, RUAN Songlin, et al. Analysis the Aging Mechanism of Qu Chenpi Based on Widely-Targeted MetabolomicsJ. Science and Technology of Food Industry, 2026, 47(5): 1−9. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2025010271.
Citation: WANG Jiajun, XIAO Qianhuang, RUAN Songlin, et al. Analysis the Aging Mechanism of Qu Chenpi Based on Widely-Targeted MetabolomicsJ. Science and Technology of Food Industry, 2026, 47(5): 1−9. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2025010271.

基于广泛靶向代谢组学解析衢陈皮陈化机制

Analysis the Aging Mechanism of Qu Chenpi Based on Widely-Targeted Metabolomics

  • 摘要: 目的:探究不同陈化年限衢陈皮的差异代谢物,为衢陈皮的陈化机制和开发利用提供基础和科学依据。方法:采用超高液相色谱-串联质谱(Ultra Performance Liquid Chromatography Tandem Mass Spectrometry,UPLC-MS/MS)广泛靶向代谢组学技术对4种陈化年限(Y1、Y3、Y5、Y7)衢陈皮的代谢物进行检测和分析;使用主成分分析(Principal Component Analysis,PCA)和正交偏最小二乘法-判别分析(Orthogonal Projections to Latent Structures-Discriminant Analysis,OPLS-DA)统计分析方法鉴定差异代谢物。结果:共鉴定出2019个代谢物,主要为酚酸类、黄酮类、萜类和生物碱类化合物。根据变量投影重要度(Variable Importance Projection,VIP)值大于1且P-value小于0.05,筛选出显著差异代谢物280个,利用KEGG数据库对显著差异代谢物进行KEGG通路富集分析,发现显著差异代谢物主要涉及单萜类生物合成、色氨酸代谢、黄酮和黄酮醇生物合成、花青素的生物合成及托烷、哌啶和吡啶生物碱的生物合成等23条代谢途径。进一步对显著差异代谢物的嗅、味特性进行分析,发现苯乙醇、5-羟甲基糠醛、间甲苯酚、柚皮素、3-甲基苯甲醛、二氢猕猴桃内酯、香兰素等13种呈味代谢物质含量的显著变化可能是衢陈皮陈化过程中颜色、气味、滋味改变的主要物质基础。结论:广泛靶向代谢组学技术可以高效地对衢陈皮化学成分进行组分鉴别和分析,为衢陈皮陈化机制研究和开发利用奠定了重要的技术基础。

     

    Abstract: Objective: To analyze the differential metabolites of Qu Chenpi, and to provide the scientific basis for the aging mechanism, development, and utilization of Qu Chenpi. Methods: UPLC-MS/MS widely-targeted metabolomics technology was used to detect and analyze the chemical constituents of four kinds of Qu Chenpi aged one year, three years, five years and seven years. PCA and OPLS-DA statistical analysis methods were used to identify differential metabolites. Results: A total of 2019 compounds were identified. Main components were phenolic acids, flavonoids, terpenoids, and alkaloids. According to the VIP value greater than 1 and the P-value less than 0.05, 280 differential metabolites were screened. KEGG enrichment analysis revealed a total of 23 metabolic pathways associated with these significant differential metabolites. Notably, the significant differential metabolites were predominantly enriched in pathways related to Monoterpenoid biosynthesis, Tryptophan metabolism, Flavone and flavonol biosynthesis, Anthocyanin biosynthesis, and Tropane piperidine and pyridine alkaloid among others. The results of the analysis on the physiological effects of HMIDB showed the 13 flavor substances of phenethyl alcohol, 5-hydroxymethylfurfural, M-cresol, naringenin, 3-methylbenzaldehyde, dihydroactinidiolide, vanillin were the main material basis of Qu Chenpi change in the odor and taste. Conclusion: Widely-targeted metabolomics technology can be used to identify and analyze the chemical components of Qu Chenpi with high efficiency. It has established important technical foundation for researching the aging mechanism, development and utilization of Qu Chenpi.

     

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