Artificial neural network modelling of the electrical conductivity property of recombined milk

Nantawan Therdthai, Weibiao Zhou

    Research output: Contribution to journalArticle

    33 Citations (Scopus)

    Abstract

    This paper focuses on modelling the electrical conductivity of recombined milk by artificial neural network (ANN). It aims to establish a non-linear relationship that accounts for the effect of milk constituents (protein, lactose, and fat) and temperature on the electrical conductivity of recombined milk. Various ANNs of 3-layer and 4-layer were investigated. Compared with 3-layer ANN models, 4-layer ANN models provide better model performance. In addition, log-sigmoid transfer function is proved to perform more practically than tan-sigmoid transfer function. The best ANN model has a 4-4-4-1 structure with log-sigmoid transfer function. After being trained for 4.4×105 epochs by back-propagation, the model produced a correlation coefficient of 0.9937 between the actual electrical conductivity (actual EC) and the modelled electrical conductivity (modelled EC) and a SSE of 0.4864.
    Original languageEnglish
    JournalJournal of Food Engineering
    Publication statusPublished - 2001

    Keywords

    • artificial neural network
    • electrical conductivity
    • reconstituted milk

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