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标题：Sensopeptidomic Kinetic Approach Combined with Decision Trees and Random Forests to Study the Bitterness during Enzymatic Hydrolysis Kinetics of Micellar Caseins
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作者：Dahlia Daher ; Barbara Deracinois ; Philippe Courcoux 等
期刊名称：Foods
电子版ISSN：2304-8158
出版年度：2021
卷号：10
期号：6
页码：1312
DOI：10.3390/foods10061312
出版社：MDPI Publishing
摘要：Protein hydrolysates are, in general, mixtures of amino acids and small peptides able to supply the body with the constituent elements of proteins in a directly assimilable form. They are therefore characterised as products with high nutritional value. However, hydrolysed proteins display an unpleasant bitter taste and possible off-flavours which limit the field of their nutrition applications. The successful identification and characterisation of bitter protein hydrolysates and, more precisely, the peptides responsible for this unpleasant taste are essential for nutritional research. Due to the large number of peptides generated during hydrolysis, there is an urgent need to develop methods in order to rapidly characterise the bitterness of protein hydrolysates. In this article, two enzymatic hydrolysis kinetics of micellar milk caseins were performed for 9 h. For both kinetics, the optimal time to obtain a hydrolysate with appreciable organoleptic qualities is 5 h. Then, the influence of the presence or absence of peptides and their intensity over time compared to the different sensory characteristics of hydrolysates was studied using heat maps, random forests and regression trees. A total of 22 peptides formed during the enzymatic proteolysis of micellar caseins and influencing the bitterness the most were identified. These methods represent simple and efficient tools to identify the peptides susceptibly responsible for bitterness intensity and predict the main sensory feature of micellar casein enzymatic hydrolysates.
关键词：bitterness; enzymatic hydrolysis; micellar caseins; off-flavours; peptidomics; random forests; regression trees; sensory analysis bitterness ; enzymatic hydrolysis ; micellar caseins ; off-flavours ; peptidomics ; random forests ; regression trees ; sensory analysis