PDF(Pubmed)
Abstract：
During the last few years, the increasing evidence of dietary antioxidant compounds and reducing chronic diseases and the relationship between diet and health has promoted an important innovation within the baked product sector, aiming at healthier formulations. This study aims to develop a tool based on mathematical models to predict baked goods\' total antioxidant capacity (TAC). The high variability of antioxidant properties of flours based on the aspects related to the type of grain, varieties, proximal composition, and processing, among others, makes it very difficult to innovate on food product development without specific analysis. Total phenol content (TP), oxygen radical absorbance capacity (ORAC), and ferric-reducing antioxidant power assay (FRAP) were used as markers to determine antioxidant capacity. Three Bayesian-type models are proposed based on a double exponential parameterized curve that reflects the initial decrease and subsequent increase as a consequence of the observed processes of degradation and generation, respectively, of the antioxidant compounds. Once the values of the main parameters of each curve were determined, support vector machines (SVM) with an exponential kernel allowed us to predict the values of TAC, based on baking conditions (temperature and time), proteins, and fibers of each native grain.
摘要：
在过去的几年里，越来越多的证据表明，膳食抗氧化剂化合物和减少慢性疾病以及饮食与健康之间的关系促进了烘焙产品部门的一项重要创新，旨在更健康的配方。本研究旨在开发一种基于数学模型的工具来预测烘焙食品的总抗氧化能力（TAC）。基于与谷物类型有关的方面，面粉的抗氧化性能具有很高的变异性，品种,近端成分，和加工，其中,如果没有具体分析，很难在食品产品开发方面进行创新。总酚含量(TP),氧自由基吸收能力（ORAC），以三价铁还原抗氧化能力测定(FRAP)为指标测定抗氧化能力。基于双指数参数化曲线提出了三种贝叶斯模型，该曲线反映了由于观察到的退化和生成过程而导致的初始减少和随后的增加，分别,抗氧化剂化合物。一旦确定了每条曲线的主要参数的值，具有指数核的支持向量机(SVM)允许我们预测TAC的值，根据烘烤条件（温度和时间），蛋白质，和每个天然谷物的纤维。
