Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

中文翻译：

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使用非线性方法比较食品分析中的化学计量问题

食品分析是一个具有挑战性的分析问题，通常使用可产生大量数据集的复杂实验室方法来解决。线性和非线性多元方法可用于处理这些类型的数据集并回答诸如产品来源是否准确标记或产品是否可以安全食用等问题。在这篇综述中，我们介绍了非线性方法，如人工神经网络、支持向量机、自组织图和多层人工神经网络在与食品分析相关的化学计量学领域的应用。我们讨论了确定何时更适合使用非线性方法而不是传统方法的标准。描述了算法的原理，并给出了解决探索性分析、分类和预测问题的示例。