State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang, China.
J Food Biochem. 2021 Mar;45(3):e13670. doi: 10.1111/jfbc.13670. Epub 2021 Feb 22.
In this study, volatile components of 40 Chinese fermented vinegar samples, made from different raw materials, starters, and processing technologies, were collected from different geographic origins in China (Shanxi, Jiangsu, Sichuan, and Fujian Province) and their volatile components were analyzed by headspace-solid-phase microextraction-gas chromatography-mass spectrometry. Sixty-two aroma compounds have been identified by NIST library combined with retention index, mainly including esters, heterocyclics, acids, aldehydes, and ketones. In addition, multivariate analysis including principal component analysis and partial least squares-discriminant analysis (PLS-DA) were carried out to discriminate vinegars based on their composition of volatile components. For PLS-DA models, analysis of variance (ANOVA) or variable importance in the projection (VIP) value were used to select variables with the highest discriminatory power, and the Kennard-Stone algorithm was used to select the training and testing samples. The PLS-DA models (ANOVA or VIP) all provided a classification accuracy of 100% for the training set, and subsequent application of these models allowed the grouping of unknown samples (testing set) according to their characteristics (raw materials and processing technology). PRACTICAL APPLICATIONS: Traditional Chinese vinegars have a long history but nowadays adulterations of them are becoming a problem in the market. In this study, Chinese fermented vinegars from different varieties were identified based on volatile composition. We found that starter cultures and fermentation process have the greatest influence on the volatile components of vinegars, while the influence of raw material and steaming of raw material are weaker volatile components. Then, partial least squares-discriminant analysis models, we carried out could successfully be applied to predict unknown vinegar samples based on a database of volatile components. This study provided a strategy to detect the identity of different vinegars, which can also be used to monitor the quality and safety of traditional Chinese vinegars.
在这项研究中，收集了来自中国不同地理起源（山西、江苏、四川和福建省）的 40 种不同原料、发酵剂和加工技术的中国发酵醋样品的挥发性成分，并用顶空固相微萃取-气相色谱-质谱法对其挥发性成分进行了分析。通过 NIST 库结合保留指数，共鉴定出 62 种香气化合物，主要包括酯类、杂环类、酸类、醛类和酮类。此外，还进行了包括主成分分析和偏最小二乘判别分析（PLS-DA）在内的多元分析，以根据挥发性成分的组成来区分醋。对于 PLS-DA 模型，使用方差分析（ANOVA）或投影变量重要性（VIP）值来选择具有最高区分能力的变量，并使用 Kennard-Stone 算法选择训练和测试样本。PLS-DA 模型（ANOVA 或 VIP）对训练集的分类准确率均为 100%，随后应用这些模型可以根据原料和加工技术对未知样品（测试集）进行分组。实际应用：传统的中国醋历史悠久，但现在市场上出现了掺假的情况。在这项研究中，基于挥发性成分对不同品种的中国发酵醋进行了鉴定。我们发现，发酵剂和发酵过程对醋的挥发性成分影响最大，而原料和原料蒸制的影响较弱。然后，我们进行的偏最小二乘判别分析模型可以成功地应用于根据挥发性成分数据库预测未知醋样品。本研究提供了一种检测不同醋身份的策略，也可用于监测传统中国醋的质量和安全。
