Application of Artificial Neural Network in the Baking Process of Salmon

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.

Parameters of the fermentation of soybean flour by Monascus purpureus or Aspergillus oryzae on the production of bioactive compounds and antioxidant activity

The objective of this work was to evaluate the effects the solid-state fermentation parameters of defatted soybean flour (DSF) by Monascus purpureus or Aspergillus oryzae on the bioactive compounds. Central composite rotatable design, multi-response optimization, and Pearson's correlation were used. The fermentation parameters as initial pH (X1), DSF-to-water ratio (X2), and incubation temperature (X3) were taken as independent variables. The function responses were isoflavone content, total phenolic content (TPC), and antioxidant activity. All fermentation parameters affected the isoflavone content when fermented by Monascus purpureus, whereas the TPC or antioxidant activities remained almost unchanged. For the fermentation by Aspergillus oryzae, all the function responses were influenced by X2 and X3 and were independent of the X1. Estimated optimum conditions were found as x1 = 6.0, x2 = 1:1, and x3 = 30 °C for both fungi. Achieving suitable fermentation parameters is essential to increase bioactive compounds in the DSF that makes it promising for food industrial applications.

Artificial neuronal networks (ANN) to model the hydrolysis of goat milk protein by subtilisin and trypsin

The enzymatic hydrolysis of milk proteins yield final products with improved properties and reduced allergenicity. The degree of hydrolysis (DH) influences both technological (e.g., solubility, water binding capacity) and biological (e.g., angiotensin-converting enzyme (ACE) inhibition, antioxidation) properties of the resulting hydrolysate. Phenomenological models are unable to reproduce the complexity of enzymatic reactions in dairy systems. However, empirical approaches offer high predictability and can be easily transposed to different substrates and enzymes. In this work, the DH of goat milk protein by subtilisin and trypsin was modelled by feedforward artificial neural networks (ANN). To this end, we produced a set of protein hydrolysates, employing various reaction temperatures and enzyme/substrate ratios, based on an experimental design. The time evolution of the DH was monitored and processed to generate the ANN models. Extensive hydrolysis is desirable because a high DH enhances some bioactivities in the final hydrolysate, such as antioxidant or antihypertensive. The optimization of both ANN models led to a maximal DH of 23·47% at 56·4 °C and enzyme-substrate ratio of 5% for subtilisin, while hydrolysis with trypsin reached a maximum of 21·3% at 35 °C and an enzyme-substrate ratio of 4%.