Migration modelling of phthalate from non-alcoholic beer bottles by adaptive neuro-fuzzy inference system

There are limitations to the basic knowledge regarding various ways by which packaging components migrate into food as well as ways by which various conditions, elements and molecules related to this phenomenon are analysed. This research aimed to model phthalate migration from polyethylene terephthalate bottles containing non-alcoholic beer by performing adaptive neuro-fuzzy inference system (ANFIS) analysis.

The data showed that storage temperature, contact surface and storage period correlates with the rate of migration. Migration of phthalate increases with storage duration gradually and reduces under different temperatures and contact surface. Moreover, increased temperature and storage duration resulted in an increase in migration level ranging from 0.6 μg L^-1 to 2.9 μg L^-1 . In summary, the present study used an ANFIS architecture which consists of three inputs (temperature, surface and storage period), Gauss-bell membership functions for each input variable and one output layer, which represent the migration level. The validation and training models showed an excellent match between the experimental and predicted values of ANFIS.

Analysis of the model showed that ANFIS is a powerful tool for predicting phthalate migration from bottles containing non-alcoholic beer. © 2017 Society of Chemical Industry.