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Among these, electronic sensing for rapid diagnosis of food quality and a multiple linear regression model was reported to predict the shelf-life of roasted coffee, sterilized milk drinks or yogurt.
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Some predictive models developed for shelf-life evaluation are often expensive and laborious. The shelf-life stated on the product largely relies on commercial experience and conventional methods which are not consistent, whereas the use of predictive models to establish the shelf-life of spreadable processed cheese might not be adequate. īearing in mind all the reasons mentioned above, it is important to establish a shelf-life prediction model for accurate identification of shelf-life. During storage of processed cheese, we can observe major changes in its colour, aroma and flavour, and consistency. The main ones are the type and state of raw materials, the technological process, the microbiological state of the ready product, and the type of packaging. However, the stability of the sensory quality and physicochemical characteristics of a product depends on various factors. Processed cheese has several advantages over raw and ripened cheese, such as a uniquely pleasing taste and longer shelf-life. The manufacturing technique for this includes adding butter, water, salt, emulsifier, vegetables or meat products and optional spices. Processed cheese is generally manufactured from ripened Gouda or Cheddar cheese, but often a smaller quantity of fresh and less ripened cheese is also added.
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An important dairy product is spreadable cheese, which enjoys great popularity among consumers in Europe and elsewhere. Milk and dairy products are important nutrient sources and are considered primary sources of biological calcium. The best fit of the model to the experimental data was found for processed cheese stored at 8 ☌. The models based on ANNs with higher values of determination coefficients and lower RMSE values proved to be more accurate. Simultaneously, the artificial neural networks models with determination coefficient of R 2 = 0.99, 0.96 and 0.96 for 8, 20 and 30 ☌, respectively were built. 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 ☌, respectively, which made them a useful tool to predict quality deterioration. The datasets were divided into three subsets: a training set, a validation set, and a test set. The ANN used five factors selected by Principal Component Analysis, which was used as input data for the ANN calculation. 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 ☌, 20 ☌ and 30 ☌.