a Food Quality & Design Group , Wageningen University & Research , Wageningen , The Netherlands.
Crit Rev Food Sci Nutr. 2018;58(16):2814-2828. doi: 10.1080/10408398.2017.1342595. Epub 2017 Aug 28.
The same chemical reaction may be different in terms of its position of the equilibrium (i.e., thermodynamics) and its kinetics when studied in different foods. The diversity in the chemical composition of food and in its structural organization at macro-, meso-, and microscopic levels, that is, the food matrix, is responsible for this difference. In this viewpoint paper, the multiple, and interconnected ways the food matrix can affect chemical reactivity are summarized. Moreover, mechanistic and empirical approaches to explain and predict the effect of food matrix on chemical reactivity are described. Mechanistic models aim to quantify the effect of food matrix based on a detailed understanding of the chemical and physical phenomena occurring in food. Their applicability is limited at the moment to very simple food systems. Empirical modeling based on machine learning combined with data-mining techniques may represent an alternative, useful option to predict the effect of the food matrix on chemical reactivity and to identify chemical and physical properties to be further tested. In such a way the mechanistic understanding of the effect of the food matrix on chemical reactions can be improved.
同一化学反应在不同食物中的平衡位置（即热力学）和动力学方面可能有所不同。食物中化学成分的多样性及其在宏观、介观和微观水平上的结构组织，即食物基质，是造成这种差异的原因。在本观点文章中，总结了食物基质影响化学反应性的多种相互关联的方式。此外，还描述了用于解释和预测食物基质对化学反应性影响的机理和经验方法。基于对食品中发生的化学和物理现象的详细了解，机理模型旨在量化食品基质的影响。目前，它们的适用性仅限于非常简单的食品体系。基于机器学习与数据挖掘技术相结合的经验模型可能是预测食物基质对化学反应性影响并识别有待进一步测试的化学和物理性质的一种替代、有用的选择。通过这种方式，可以提高对食物基质对化学反应影响的机理理解。
