Journal
EUROPEAN FOOD RESEARCH AND TECHNOLOGY
Volume 245, Issue 11, Pages 2539-2547Publisher
SPRINGER
DOI: 10.1007/s00217-019-03369-y
Keywords
Processed cheese; Shelf-life modelling; Artificial neural network; Quality deterioration
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The aim of the study was to compare the ability of multiple linear regression (MLR) and Artificial Neural Network (ANN) to predict the overall quality of spreadable Gouda cheese during storage at 8 degrees C, 20 degrees C and 30 degrees C. The ANN used five factors selected by Principal Component Analysis, which was used as input data for the ANN calculation. The datasets were divided into three subsets: a training set, a validation set, and a test set. The multiple regression models were highly significant with high determination coefficients: R-2 = 0.99, 0.87 and 0.87 for 8, 20 and 30 degrees C, respectively, which made them a useful tool to predict quality deterioration. Simultaneously, the artificial neural networks models with determination coefficient of R-2 = 0.99, 0.96 and 0.96 for 8, 20 and 30 degrees C, respectively were built. The models based on ANNs with higher values of determination coefficients and lower RMSE values proved to be more accurate. The best fit of the model to the experimental data was found for processed cheese stored at 8 degrees C.
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