Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese

• Published in: European food research & technology Vol. 245; no. 11; pp. 2539 - 2547
• Main Authors: Stangierski, J., Weiss, D., Kaczmarek, A.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2019; Springer Nature B.V
• Subjects: Agriculture; Analytical Chemistry; Artificial neural networks; Biotechnology; Cheese; Chemistry; Chemistry and Materials Science; Coefficients; Dairy products; Food Science; Forestry; Multiple regression models; Neural networks; Original Paper; Principal components analysis; Processed foods; Regression analysis; Artificial neural network; Shelf-life modelling; Processed cheese; Quality deterioration
• ISSN: 1438-2377; 1438-2385
• DOI: 10.1007/s00217-019-03369-y

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 °C, 20 °C and 30 °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 °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 °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 °C.