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首页> 外文期刊>Journal of the Brazilian Chemical Society >Predicting partition coefficients of migrants in food simulant/polymer systems using adaptive neuro-fuzzy inference system
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Predicting partition coefficients of migrants in food simulant/polymer systems using adaptive neuro-fuzzy inference system

机译：使用自适应神经模糊推理系统预测食品模拟物/聚合物系统中移民的分配系数

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Food contaminations by migration of low molecular weight additives into foodstuffs can result from direct contact between packaging materials and food. The amount of migration is related to the structural properties of the additive as well as to the nature of packaging material. The goal of this study is to develop a quantitative structure-property relationship (QSPR) model by the adaptive neuro-fuzzy inference system (ANFIS) for prediction of the partition coefficient K in food/packaging system. The partition coefficients of a set of 44 systems consisted of 4 food simulants, 6 migrants and 2 packaging materials were investigated. A set of 6 molecular descriptors representing various structural characteristics of food simulants (2 descriptors), migrants (3 descriptors) and polymers (1 descriptor) was used as data set. This data set was divided into three subsets: training, test and prediction. ANFIS as a new modeling technique was applied for the first time in this field. The final model has a root mean square error (RMSE) of 0.0006 and correlation coefficient (R2) for the prediction set of 0.9920.
机译：低分子量添加剂迁移到食品中会导致食品污染，这是由于包装材料与食品之间的直接接触所致。迁移量与添加剂的结构特性以及包装材料的性质有关。这项研究的目标是通过自适应神经模糊推理系统（ANFIS）开发定量结构-属性关系（QSPR）模型，以预测食品/包装系统中的分配系数K。研究了由4种食品模拟物，6种移民和2种包装材料组成的44个系统的分配系数。代表食物模拟物（2个描述符），移民（3个描述符）和聚合物（1个描述符）的各种结构特征的6个分子描述符被用作数据集。该数据集分为三个子集：训练，测试和预测。 ANFIS作为一种新的建模技术首次在该领域中应用。最终模型的均方根误差（RMSE）为0.0006，预测集的相关系数（R2）为0.9920。

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