利用气相色谱-质谱法和化学计量法分析不同葡萄籽油基因型的挥发性有机化合物

IF 5.6 1区 农林科学 Q1 AGRICULTURAL ENGINEERING
Niloofar Rahmani, Ahmad Mani-Varnosfaderani
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引用次数: 0

摘要

本研究利用气相色谱-质谱法(GC-MS)对取自伊朗五种主要葡萄基因型的葡萄籽油(GSO)进行了非目标代谢组学分析。在 GSO 中总共鉴定出 175 种挥发性有机化合物 (VOC),其中 20 种被鉴定为核心分子,存在于所有基因型和样品中,155 种被鉴定为辅助和稀有分子,存在于≥10% 但为 100% 的样品中。我们假设,GSO 基因型中的特定挥发性有机化合物可以作为可靠的指标来区分基因型和评估其质量。核心分子主要包括碳氢化合物(35%)、脂肪酸(30%)、醛类(15%)和酯类(5%),其中 7 种化合物有推测名称,10 种化合物有推测配方。在 155 种附属分子和稀有分子中,有 12 种挥发性化合物在不同的 GSO 基因型中得到了唯一鉴定,表明了与不同 GSO 基因型相关的特定表型特征。在 20 个核心分子中,有 10 个分子的重要性在 Boruta 特征选择算法的 70 次迭代中一直排名较高。脂肪酸(包括亚油酸和油酸)成为评估 GSO 样品质量的关键化合物。使用 10 种核心分子作为预测因子,随机森林、支持向量机、偏最小二乘判别分析和 k 近邻等监督学习方法在对训练集和测试集的不同 GSO 基因型进行分类时,准确率、灵敏度、特异性和精确度均达到了 100%。鉴定出的代谢物可作为预测质量和区分基因型的潜在标记物,凸显了代谢组学分析在分析 GSO 变异和深入了解 GSO 质量方面的高效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Profiling volatile organic compounds of different grape seed oil genotypes using gas chromatography-mass spectrometry and chemometric methods
This study utilized gas chromatography-mass spectrometry (GC-MS) for untargeted metabolomic profiling of grape seed oils (GSO) taken from five major grape genotypes in Iran. A total of 175 volatile organic compounds (VOCs) were identified in the GSO, with 20 identified as core molecules being present in all genotypes and samples, and 155 identified as accessory and rare molecules, found in ≥10 % but <100 % of the samples. We hypothesized that specific VOCs in GSO genotypes could be used as reliable indicators to differentiate genotypes and assess their quality. The core molecules mainly consisted of hydrocarbons (35 %), fatty acids (30 %), aldehydes (15 %), and esters (5 %), with putative names assigned to 7 compounds and putative formulas to 10. Of the 155 accessory and rare molecules, 12 volatile compounds were uniquely identified in distinct GSO genotypes, indicating specific phenotypic characteristics associated with different GSO genotypes. Among 20 core molecules, ten were consistently ranked higher in importance through 70 iterations of the Boruta feature selection algorithm. Fatty acids, including Linoleic and Oleic acid, emerged as key compounds for assessing the quality of the GSO samples. Using 10 core molecules as predictors, supervised learning methods such as random forest, support vector machine, partial least squares discriminant analysis, and k-nearest neighbor achieved 100 % accuracy, sensitivity, specificity, and precision in classifying different GSO genotypes for both training and test sets. The identified metabolites served as potential markers for predicting quality and distinguishing genotypes, highlighting the efficiency of metabolomic profiling in analyzing GSO variations and providing insights into GSO quality.
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来源期刊
Industrial Crops and Products
Industrial Crops and Products 农林科学-农业工程
CiteScore
9.50
自引率
8.50%
发文量
1518
审稿时长
43 days
期刊介绍: Industrial Crops and Products is an International Journal publishing academic and industrial research on industrial (defined as non-food/non-feed) crops and products. Papers concern both crop-oriented and bio-based materials from crops-oriented research, and should be of interest to an international audience, hypothesis driven, and where comparisons are made statistics performed.
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