食物基质对姜黄素体外口服生物可及性影响的概率模型分析

Foods Pub Date : 2024-07-16 DOI:10.3390/foods13142234
Kevin de Castro Cogle, M. Kubo, F. Merlier, Alexandra Josse, M. Anastasiadi, F. Mohareb, Claire Rossi
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引用次数: 0

摘要

生物活性化合物的生物可及性对食物的营养价值起着重要作用,但目前还缺乏系统研究来评估食物基质对生物可及性的影响。姜黄素是从姜黄中提取的植物化学物质,因其宣称的健康益处而备受公众关注。本研究的目的是根据不同的纤维类型配方,建立一个数学模型来预测姜黄素在饼干和奶油冻中的生物可及性。通过体外消化获得了富含姜黄素的吉士和饼干的生物可及性,并对食品的理化特性进行了表征。在贝叶斯分层线性回归模型中,观察到常量营养素浓度与生物可及性之间存在很强的相关性(p = 0.89),并将其选为主解释变量。此外,食物基质对生物可及性的影响模式在蛋黄酱和饼干中并不相同;例如,半纤维素含量与饼干中的生物可及性呈中等强度的正相关(p = 0.66),而在蛋黄酱中则不显著(p = 0.12)。使用贝叶斯分层方法对这些相互作用进行建模,优化结果为 r2 = 0.97,留空交叉验证得分 (LOOCV) 为 r2 = 0.93。该决策支持系统可帮助食品行业优化新型食品的配方,使消费者能够做出更明智的选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Probabilistic Modelling of the Food Matrix Effects on Curcuminoid’s In Vitro Oral Bioaccessibility
The bioaccessibility of bioactive compounds plays a major role in the nutritional value of foods, but there is a lack of systematic studies assessing the effect of the food matrix on bioaccessibility. Curcuminoids are phytochemicals extracted from Curcuma longa that have captured public attention due to claimed health benefits. The aim of this study is to develop a mathematical model to predict curcuminoid’s bioaccessibility in biscuits and custard based on different fibre type formulations. Bioaccessibilities for curcumin-enriched custards and biscuits were obtained through in vitro digestion, and physicochemical food properties were characterised. A strong correlation between macronutrient concentration and bioaccessibility was observed (p = 0.89) and chosen as a main explanatory variable in a Bayesian hierarchical linear regression model. Additionally, the patterns of food matrix effects on bioaccessibility were not the same in custards as in biscuits; for example, the hemicellulose content had a moderately strong positive correlation to bioaccessibility in biscuits (p = 0.66) which was non-significant in custards (p = 0.12). Using a Bayesian hierarchical approach to model these interactions resulted in an optimisation performance of r2 = 0.97 and a leave-one-out cross-validation score (LOOCV) of r2 = 0.93. This decision-support system could assist the food industry in optimising the formulation of novel food products and enable consumers to make more informed choices.
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