Predicting partition coefficients of migrants in food simulant/polymer systems using adaptive neuro-fuzzy inference system

Journal of the Brazilian Chemical Society

Parviz Shahbazikhah²  Mohammad Asadollahi-baboli²  Ramin Khaksar¹  Reza Fareghi Alamdari²  Vali Zare-shahabadi¹ 

[1] ,Islamic Azad University Deepartment of Chemistry and Young Researchers Club Mahshahr,Iran

关键词: quantitative structure property relationship (QSPR);    adaptive neuro-fuzzy inference system (ANFIS);    partition coefficients;    additive migration;    food safety;   

DOI  :  10.1590/S0103-50532011000800007

来源: SciELO

【 摘 要 】

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.

【 授权许可】

CC BY   
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