In the last decade, the use of multivariate statistical techniques developed for analytical chemistry has been adopted widely in food science and technology. Usually, chemometrics is applied when there is a large and complex dataset, in terms of sample numbers, types, and responses. The results are used for authentication of geographical origin, farming systems, or even to trace adulteration of high value‐added commodities. In this article, we provide an extensive practical and pragmatic overview on the use of the main chemometrics tools in food science studies, focusing on the effects of process variables on chemical composition and on the authentication of foods based on chemical markers. Pattern recognition methods, such as principal component analysis and cluster analysis, have been used to associate the level of bioactive components with in vitro functional properties, although supervised multivariate statistical methods have been used for authentication purposes. Overall, chemometrics is a useful aid when extensive, multiple, and complex real‐life problems need to be addressed in a multifactorial and holistic context. Undoubtedly, chemometrics should be used by governmental bodies and industries that need to monitor the quality of foods, raw materials, and processes when high‐dimensional data are available. We have focused on practical examples and listed the pros and cons of the most used chemometric tools to help the user choose the most appropriate statistical approach for analysis of complex and multivariate data.

中文翻译：

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化学计量学的趋势：食品认证，微生物学和加工效果

在过去的十年中，为分析化学开发的多元统计技术的使用已在食品科学和技术中被广泛采用。通常，当存在庞大而复杂的数据集时，就样品数量，类型和响应而言，应采用化学计量学。结果用于验证地理来源，耕作系统，甚至用于追踪高附加值商品的掺假。在本文中，我们对食品科学研究中主要化学计量学工具的使用提供了广泛的实践和实用概述，重点是过程变量对化学成分的影响以及基于化学标记的食品认证。模式识别方法（例如主成分分析和聚类分析）已用于将生物活性成分的水平与尽管有监督的多元统计方法已用于身份验证目的，但仍具有体外功能特性。总体而言，当需要在多因素和整体环境下解决广泛，多重和复杂的现实生活问题时，化学计量学将是有用的帮助。毫无疑问，需要高维数据的政府机构和行业应该使用化学计量学来监控食品，原材料和过程的质量。我们重点研究了实际示例，并列出了最常用的化学计量工具的利弊，以帮助用户选择最合适的统计方法来分析复杂和多变量数据。