Application of Artificial Neural Network in the Baking Process of Salmon

Abstract

The global production of farmed Atlantic salmon amounts to over 2 million tons per year. Consumed all over the world, salmon is not only delicious but also nutritious. This paper deals with the relationship between moisture content, low-field nuclear magnetic resonance (LF-NMR), scanning electron microscope (SEM), and sensory evaluation in the baking process of salmon. An artificial neural network (ANN) model has been established to simulate the change of moisture content and energy consumed in the baking process. Through the study of LF-NMR, SEM, and sensory evaluation, it was found that the change of sensory indexes was consistent with the results observed by LF-NMR and SEM. With the increase of temperature, muscle fibers contracted, the interstices increased, the rate of water loss increased, and the sensory score decreased. Initial moisture content, baking time, baking temperature, baking humidity, and baking air velocity were employed as the baking control parameters for the ANN. ANN can be used to determine the moisture content and energy consumed of baking salmon. The best network topology occurred with 5 input layer neurons, 17 hidden layer neurons, and 2 output layer neurons, and the MSE was 0.00153, and Rall was 0.99661. According to the experiment, it was demonstrated that the ANN is a reliable software-based method.

Application of an artificial neural network model to predict the change of moisture during drying of sturgeon bone marrow

“…ANNS have been successfully employed in a variety of food processing systems, including drying, baking, and visual inspection, as well as in the prediction of food properties and quality indicators (Turkay et al, 2017;Poonpat et al, 2014). Furthermore, ANNs have been successfully used to describe the drying behavior of other natural products such as codfish (Camila et al, 2011), salmon (Jiang et al, 2022), and shrimp (Imran et al, 2014). The most important factors of effective modeling of drying systems are simulation, prediction, optimization, control, mode detection, and fault diagnosis (Mortaza et al, 2015).…”

Current Trends in Intelligent Control Neural Networks for Thermal Processing (Foods): Systematic Literature Review

“…Therefore, an Artificial Neural Network (ANN), which is a part of soft computing methods, can be developed and applied in many areas of food processing [28], [29], [30], [31], [32]. ANN has been used in food industries for many process modeling, such as estimation model of food antioxidant property, modeling food drying process, prediction model of products indicators (water content, crumb temperature, food color, relative volume) using several inputs (drying parameter, temperature of jet, speed of jet, and grilling time) [33].…”