Predicting partition coefficients of migrants in food simulant/polymer systems using adaptive neuro-fuzzy inference system
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.
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Sociedade Brasileira de Química
2011
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oai:scielo:S0103-505320110008000072011-08-04Predicting partition coefficients of migrants in food simulant/polymer systems using adaptive neuro-fuzzy inference systemShahbazikhah,ParvizAsadollahi-Baboli,MohammadKhaksar,RaminAlamdari,Reza FareghiZare-Shahabadi,Vali quantitative structure property relationship (QSPR) adaptive neuro-fuzzy inference system (ANFIS) partition coefficients additive migration food safety 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.info:eu-repo/semantics/openAccessSociedade Brasileira de QuímicaJournal of the Brazilian Chemical Society v.22 n.8 20112011-08-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532011000800007en10.1590/S0103-50532011000800007 |
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Shahbazikhah,Parviz Asadollahi-Baboli,Mohammad Khaksar,Ramin Alamdari,Reza Fareghi Zare-Shahabadi,Vali |
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Shahbazikhah,Parviz Asadollahi-Baboli,Mohammad Khaksar,Ramin Alamdari,Reza Fareghi Zare-Shahabadi,Vali Predicting partition coefficients of migrants in food simulant/polymer systems using adaptive neuro-fuzzy inference system |
author_facet |
Shahbazikhah,Parviz Asadollahi-Baboli,Mohammad Khaksar,Ramin Alamdari,Reza Fareghi Zare-Shahabadi,Vali |
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Shahbazikhah,Parviz |
title |
Predicting partition coefficients of migrants in food simulant/polymer systems using adaptive neuro-fuzzy inference system |
title_short |
Predicting partition coefficients of migrants in food simulant/polymer systems using adaptive neuro-fuzzy inference system |
title_full |
Predicting partition coefficients of migrants in food simulant/polymer systems using adaptive neuro-fuzzy inference system |
title_fullStr |
Predicting partition coefficients of migrants in food simulant/polymer systems using adaptive neuro-fuzzy inference system |
title_full_unstemmed |
Predicting partition coefficients of migrants in food simulant/polymer systems using adaptive neuro-fuzzy inference system |
title_sort |
predicting partition coefficients of migrants in food simulant/polymer systems using adaptive neuro-fuzzy inference system |
description |
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. |
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Sociedade Brasileira de Química |
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2011 |
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http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532011000800007 |
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1756403444563836928 |