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基于气相色谱-离子迁移谱技术结合多元统计建模分析不同水稻品种中挥发性有机化合物的差异

Analysis of the Differences in Volatile Organic Compounds in Different Rice Varieties Based on GC-IMS Technology Combined with Multivariate Statistical Modelling.

作者信息

Chen Jin, Liu Ying, Yang Mi, Shi Xinmin, Mei Yuqin, Li Juan, Yang Chunqi, Pu Shihuang, Wen Jiancheng

机构信息

Rice Research Institute, Yunnan Agricultural University, Kunming 650201, China.

Lincang Seed Management Station, Lincang 677000, China.

出版信息

Molecules. 2023 Nov 13;28(22):7566. doi: 10.3390/molecules28227566.

Abstract

In order to investigate the flavour characteristics of aromatic, glutinous, and nonaromatic rice, gas chromatography-ion mobility spectrometry (GC-IMS) was used to analyse the differences in volatile organic compounds (VOCs) amongst different rice varieties. The results showed that 103 signal peaks were detected in these rice varieties, and 91 volatile flavour substances were identified. Amongst them, 28 aldehydes (28.8931.17%), 24 alcohols (34.8540.52%), 14 ketones (12.2614.74%), 12 esters (2.304.15%), 5 acids (7.8010.85%), 3 furans (0.300.68%), 3 terpenes (0.340.64%), and 2 species of ethers (0.801.78%) were detected. SIMCA14.1 was used to perform principal component analysis (PCA) and orthogonal partial least squares discriminant analysis, and some potential character markers (VIP > 1) were further screened out of the 91 flavour substances identified based on the variable important projections, including ethanol, 1-hexanol, hexanal, heptanal, nonanal, (E)-2-heptenal, octanal, trans-2-octenal, pentanal, acetone, 6-methyl-5-hepten-2-one, ethyl acetate, propyl acetate, acetic acid, and dimethyl sulphide. Based on the established fingerprint information, combined with principal component analysis and orthogonal partial least squares discriminant analysis, different rice varieties were also effectively classified, and the results of this study provide data references for the improvement in aromatic rice varieties.

摘要

为了研究香稻、糯稻和非香稻的风味特征,采用气相色谱-离子迁移谱(GC-IMS)分析不同水稻品种挥发性有机化合物(VOCs)的差异。结果表明,在这些水稻品种中检测到103个信号峰,鉴定出91种挥发性风味物质。其中,检测到28种醛类(28.89%31.17%)、24种醇类(34.85%40.52%)、14种酮类(12.26%14.74%)、12种酯类(2.30%4.15%)、5种酸类(7.80%10.85%)、3种呋喃类(0.30%0.68%)、3种萜类(0.34%0.64%)和2种醚类(0.80%1.78%)。利用SIMCA14.1进行主成分分析(PCA)和正交偏最小二乘判别分析,并基于变量重要性投影从鉴定出的91种风味物质中进一步筛选出一些潜在的特征标记(VIP>1),包括乙醇、1-己醇、己醛、庚醛、壬醛、(E)-2-庚烯醛、辛醛、反式-2-辛烯醛、戊醛、丙酮、6-甲基-5-庚烯-2-酮、乙酸乙酯、乙酸丙酯、乙酸和二甲基硫醚。基于所建立的指纹信息,结合主成分分析和正交偏最小二乘判别分析,对不同水稻品种也进行了有效分类,本研究结果为香稻品种改良提供了数据参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6570/10673298/4c5dfb2ac02b/molecules-28-07566-g001.jpg

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