淀粉的体外消化率是人类营养研究中的常见分析,通常包括在特定条件下通过α-淀粉酶进行淀粉的水解。类似的体外测定也用于其他研究领域,可以使用不同的方法。总的来说,天然淀粉的体外水解是所有这些方法之间的桥梁。在这篇文献综述中,我们在最近的出版物中研究了淀粉分解测定法的使用,这些出版物研究了复杂的淀粉结构-淀粉分解关系。本综述分为两部分:(1)对影响淀粉水解的因素的简要回顾;(2)对2016-2020年期间出版物中使用的实验设计和方法的系统回顾。后者报道了淀粉材料,调查的因素,淀粉水解动力学和数据分析技术的表征。这篇综述表明,主要的研究策略有利于通过结晶度最常描述的一些淀粉样品之间的比较,颗粒类型,直链淀粉和链长分布具有显著特征。该策略旨在通过关注特定特征来规避淀粉消化机制的多因素方面。一种替代策略依赖于计算方法,如多变量统计分析和机器学习技术,以破译每个因素对淀粉分解的作用。虽然承诺解决复杂性,有限的计算方法的使用可以解释为小尺寸的实验数据集在大多数出版物。这篇综述表明,已经有了生产更大数据集的关键步骤,特别是快速水解测定的推广和大多数分析结果的定量方法的发展。
In vitro digestibility of starch is a common analysis in human nutrition research, and generally consists of performing the hydrolysis of starch by α-amylase in specific conditions. Similar in vitro assays are also used in other research fields, where different methods can be used. Overall, the in vitro hydrolysis of native starch is a bridge between all of these methods. In this literature
review, we examine the use of amylolysis assays in recent publications investigating the complex starch structure-amylolysis relation. This
review is divided in two parts: (1) a brief
review of the factors influencing the hydrolysis of starch and (2) a systematic
review of the experimental designs and methods used in publications for the period 2016-2020. The latter reports on starch materials, factors investigated, characterization of the starch hydrolysis kinetics and data analysis techniques. This
review shows that the dominant research strategy favors the comparison between a few starch samples most frequently described through crystallinity, granule type, amylose and chain length distribution with marked characteristics. This strategy aims at circumventing the multifactorial aspect of the starch digestion mechanism by focusing on specific features. An alternative strategy relies on computational approaches such as multivariate statistical analysis and machine learning techniques to decipher the role of each factor on amylolysis. While promising to address complexity, the limited use of a computational approach can be explained by the small size of the experimental datasets in most publications. This
review shows that key steps towards the production of larger datasets are already available, in particular the generalization of rapid hydrolysis assays and the development of quantification approaches for most analytical results.