The aim of this study was to investigate the effect of different pretreatment and reducing sugar content on furfural (F) and 5-hydroxymethylfurfural (HMF) contents of fermented lotus root by vinegar. The lotus root samples were fermented using vinegar for 15 days, at different solution concentrations and temperatures. The processing conditions were considered as inputs of neural network to predict the F and HMF contents of lotus root. Genetic algorithm was applied to optimize the structure and learning parameters of ANN. The developed genetic algorithm-artificial neural network (GA-ANN) which included 23 and 17 neurons in the first and second hidden layers, respectively, gives the lowest mean squared error (MSE). The correlation coefficient of ANN was compared with multiple linear regression-based models. The GA-ANN model was found to be a more accurate prediction method for the F and HMF contents of fermented lotus root than linear regression-based models. In addition, sensitivity analysis and Pearson’s correlation coefficient were also analyzed to find out the relation between input and output variables.

This work demonstrated the possibility of using artificial neural networks to classify soy sauce from China. The aroma profiles of different soy sauce samples were differentiated using headspace solid-phase microextraction. The soy sauce samples were analyzed by gas chromatography-mass spectrometry, and 22 and 15 volatile aroma compounds were selected for sensitivity analysis to classify the samples by fermentation and geographic region, respectively. The 15 selected samples can be classified by fermentation and geographic region with a prediction success rate of 100%. Furans and phenols represented the variables with the greatest contribution in classifying soy sauce samples by fermentation and geographic region, respectively.

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An artificial neural network (ANN) model was established to predict the hydrolytic capacities of Aspergillus oryzae proteases on soybean protein. The available training data were split in two subsets: training and testing data, which comprised 25 and six groups of proteases, respectively. These data served as the inputs of ANN to predict small peptide content, degree of hydrolysis and free amino nitrogen content. This network included three neurons in the single hidden layer with a low mean squared error. The predicted results were similar to the actual values (R 2 > 0.92) and were superior to those of multiple linear regression. Sensitivity analysis revealed that there is a correlation between protease and soy protein hydrolysates. It was also verified that protease and soy protein hydrolysates could serve as inputs and outputs in the ANN. Among the tested proteases, aminopeptidase showed the highest hydrolytic capacity for soybean protein with sensitivity analysis. PRACTICAL APPLICATIONSThe artificial neural network model is a powerful technique to predict the hydrolytic capacities of Aspergillus oryzae proteases for soybean protein. The results of this study could be used to test the amount of yielded hydrolysates of soybean protein under one combination protease, and also explained the mechanism underlying the protease-catalyzed hydrolysis of soybean.

The discriminative correlation filter (DCF)-based tracking methods have achieved remarkable performance in visual tracking. However, the existing DCF paradigm still suffers from dilemmas such as boundary effect, filter degradation, and aberrance. To address these problems, we propose a spatio-temporal joint aberrance suppressed regularization (STAR) correlation filter tracker under a unified framework of response map. Specifically, a dynamic spatio-temporal regularizer is introduced into the DCF to alleviate the boundary effect and filter degradation, simultaneously. Meanwhile, an aberrance suppressed regularizer is exploited to reduce the interference of background clutter. The proposed STAR model is effectively optimized using the alternating direction method of multipliers (ADMM). Finally, comprehensive experiments on TC128, OTB2013, OTB2015 and UAV123 benchmarks demonstrate that the STAR tracker achieves compelling performance compared with the state-of-the-art (SOTA) trackers.