Application of Artificial Neural Networks for Process Simulation and Quality Control of Dry-sausage
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Abstract:
The purpose of this study was to achieve rapid detection and evaluation of the quality of air-dried sausage under natural conditions. In this paper, different processing conditions (such as wind speed, temperature, humidity) were used through simulating the natural conditions, and the resulting physicochemical indices, lipid oxidation and protein oxidation of dried sausage were compared. Then an artificial neural network modelling method was used to construct a neural network model for predicting and simulating the processing conditions for dry sausage production. The obtained results showed that sausages with low fat and high protein (protein content 38.65% and fat content 16.06%) were obtained under the following processing conditions: wind speed 0.80 m/s, daytime temperature 23.60 ℃, daytime humidity 40.42%, night-time temperature 16.89 ℃, and night-time humidity 54.21%. The established artificial model r values above 0.99 and rmse values below 0.4, indicating that the model can make highly accurate predictions and can predict the optimal sausage processing conditions. Therefore, this method can describe the effect of processing conditions on the quality of air-dried sausage and provide a theoretical basis for the establishment of an artificial climate and quality and safety control for large-scale processing of air-dried sausage.
