4.7 Article

Modeling phenolic content during storage of cut fruit and vegetables: A consecutive reaction mechanism

期刊

JOURNAL OF FOOD ENGINEERING
卷 140, 期 -, 页码 1-8

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jfoodeng.2014.04.006

关键词

Phenolic compounds; Kinetics; Fresh-cut produce; Modeling; Phenylalanine ammonia-lyase

向作者/读者索取更多资源

The changes in the phenolic content of fresh-cut produce during storage are often characterized by an initial growth caused by several abiotic stresses that promote the increase of phenylalanine ammonia-lyase (PAL) activity, which is the first step in phenylpropanoid metabolism. A kinetic model based on a mechanism involving two consecutive reactions was developed to describe the changes in the phenolic content of fresh-cut produce during storage. Experimental data for purslane stored at 0 and 5 degrees C, apples and broccoli stored at 5 degrees C, as well as literature data for 'Lisbon' lemon and 'Palazzelli' mandarin samples were used to validate the model, which is consistent with the phenol changes under all of the studied conditions. By estimating model parameters, individually or as group, the storage temperature did not affect the de novo synthesis of phenols but did affect the oxidative degradation for purslane samples. For apples and broccoli samples, biological variability was very important and affected the initial phenolic content and synthesis. Moreover the model also explained the phenolic variation on mandarin segments and on cut lemons. The type of cut for lemon samples had a significant effect on the rate of synthesis of phenols, with an increase of 1.8-fold being observed for the 1/2 slice compared to the slices. The model may be a useful tool for obtaining a better understanding of the effects of processing and storage conditions on the changes in the phenolic content and improving the shelf life prediction of fresh-cut produce. (C) 2014 Elsevier Ltd. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据