Bioactivity of soy-based fermented foods: A review.

Background Food sensory science and flavour analysis are key processes in new product development, and is essential in understanding consumers by bridging the gap between product characteristics and consumer perception and acceptance. Scope and approach This article provides a critical review of computer-based approaches to flavour and sensory analysis, including optimal design approaches to sensory experimental designs, and incorporation of nonlinear modelling methods such as artificial neural network into the analysis of results. The advantages and disadvantages of these methods, as well as their statistical background will be discussed. The incorporation of these statistical and mathematical methods into existing analytical processes is briefly covered, along with an overview of available computer software packages. Key findings and conclusions Food flavour and sensory analysis is an information gathering process, and can be divided into two main stages: (1) the design of the experiment; (2) analyses and interpretation of results. The choice of an analytical procedure in sensory and flavour science is crucial in obtaining information correlating food products and consumers. Traditionally, sensory analysis is based on classical experimental designs and linear multivariate analysis techniques. Computer algorithm-based methods such as optimal designs in the design of experiments, and artificial neural network as a non-linear regression method may be used in conjunction with current methods, or adopted to overcome potential shortfalls of existing methods.

Design of experiments and regression modelling in food flavour and sensory analysis: A review

Traditional fermented soybean food has emerged as an important part of people's dietary structure because of the unique flavors and improved health benefit. During fermentation, the nutrients in soybean undergo a series of biochemical reactions catalyzed naturally by microorganism secreted enzymes. Thereafter, many functional and bioactive substances such as bioactive peptides, unsaturated fatty acids, free soy isoflavones, vitamins and minerals are produced, making fermented soy products more advantageous in nutrition and health. This review comprehensively discusses the historical evolution, distribution, traditional fermentation processing, main sources and characteristics of fermented strains, flavor components, nutritional properties, and biological activities of four traditional fermented soybean foods including douchi, sufu, dajiang, and soy sauce. In the end, we introduce four major challenges encountered by traditional fermented soybean foods including high salt content, formation of biogenic amine, the presence of pathogenic microorganisms and mycotoxins, and quality inconsistency. We conclude that the establishment of scientific quality standard and innovated fermentation processing is the potential solutions to combat the issues and improve the safety of traditional fermented soybean products.

Traditional fermented soybean products: processing, flavor formation, nutritional and biological activities.

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

Dynamics of microbial communities and metabolites in ganjang, a traditional Korean fermented soy sauce, during fermentation.

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.

Soy sauce classification by geographic region and fermentation based on artificial neural network and genetic algorithm.

Chili oil, which contains large amounts of capsaicin and capsorubin, is one of the most consumed seasonings in China. These compounds significantly affect the quality, antioxidant activity, pungency, and color of chili oil. This study aimed to investigate the effect of stewing temperature, stewing time, and amount of oil on the capsaicin and capsorubin contents of Chinese chili oil. The partial least squares (PLS) regression and genetic algorithm–artificial neural network models were established and used to predict capsaicin and capsorubin contents. The genetic algorithm was applied to optimize the parameters of the network. The developed genetic algorithm–artificial neural network, which included ten hidden neurons, predicted capsaicin and capsorubin contents with correlation coefficients of 0.995 and 0.986, respectively. The neural network exhibited more accurate prediction and practicability compared with the PLS regression model.

Genetic Algorithm–Artificial Neural Network Modeling of Capsaicin and Capsorubin Content of Chinese Chili Oil

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 (R2 > 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 Applications

The 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.

Analysis of the Hydrolytic Capacities of Aspergillus oryzae Proteases on Soybean Protein Using Artificial Neural Networks

Abstract 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.

Libin Xu

Papers

Soy sauce classification by geographic region and fermentation based on artificial neural network and genetic algorithm.

Genetic Algorithm–Artificial Neural Network Modeling of Capsaicin and Capsorubin Content of Chinese Chili Oil

Analysis of the Hydrolytic Capacities of Aspergillus oryzae Proteases on Soybean Protein Using Artificial Neural Networks

Modeling of Furfural and 5-Hydroxymethylfurfural Content of Fermented Lotus Root: Artificial Neural Networks and a Genetic Algorithm Approach