Classification of Chinese Vinegars Using Optimized Artificial Neural Networks by Genetic Algorithm and Other Discriminant Techniques

Biopolymer Food Packaging Films Incorporated with Essential Oils

Petroleum-based packaging materials are typically nonbiodegradable, which leads to significant adverse environmental and health issues. Therefore, developing novel efficient, biodegradable, and nontoxic food packaging film materials has attracted increasing attention from researchers. Due to significant research and advanced technology, synthetic additives in packaging materials are progressively replaced with natural substances such as essential oils (EOs). EOs demonstrate favorable antioxidant and antibacterial properties, which would be an economical and effective alternative to synthetic additives. This review summarized the possible antioxidant and antimicrobial mechanisms of various EOs. We analyzed the properties and performance of food packaging films based on various biopolymers incorporated with EOs. The progress in intelligent packaging materials has been discussed as a prospect of food packaging materials. Finally, the current challenges regarding the practical application of EOs-containing biopolymer films in food packaging and areas of future research have been summarized.

Characterization and identification of different Chinese fermented vinegars based on their volatile components

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.