Volume 18, Issue 115 (2021)                   FSCT 2021, 18(115): 247-257 | Back to browse issues page

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Bagheri Z, Motamedzadegan A, Khanbabaie R, Farhadi A. Foam Mat Drying of Ricotta Cheese and Predicting its Characteristics with Artificial Neural Network Model. FSCT 2021; 18 (115) :247-257
URL: http://fsct.modares.ac.ir/article-7-48219-en.html
1- Department of Food Science and Technology, Sari Agricultural Sciences and Natural Resources University
2- Department of Food Science and Technology, Sari Agricultural Sciences and Natural Resources University , amotgan@yahoo.com
3- Department of Physics, Babol Noushirovani University of Technology
4- Department of Animal Sciences, Sari Agricultural Sciences and Natural Resources University
Abstract:   (1536 Views)
Artificial neural networks are a set of nonlinear equations that have the ability to adapt to establish complex nonlinear relationships between input and output variables. Artificial neural network modeling was used to predict the production of Ricotta cheese powder with the desired quality. In this study, a 4-class artificial neural network with a multilayer perceptron model was used to predict foam and Ricotta cheese powder data prepared by foam mat drying. This modeling was performed by pattern recognition method and using machine learning algorithm. Pattern recognition is the ability to recognize the order of properties or data that gives information about a system or data set. The model used for this study had 10 neurons in the hidden layer. 4 different ratios of milk and whey (treatments) were considered as input and foam density, powder density, hygroscopy, water activity, water absorption and oil absorption as model outputs. In this model, 70% of the data were used for training, 15% for testing and 15% of the data for validation. The best validation performance occurred in the 20th period. The final results showed that the model used was able to accurately predict the data related to each class with 94.8% accuracy.
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Article Type: Original Research | Subject: Statistics, modeling and response levels in the food industry
Received: 2020/12/8 | Accepted: 2021/02/20 | Published: 2021/09/6

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