Artificial neural network and genetic algorithm coupled fermentation kinetics to regulate L-lysine fermentation

Fermentation plays a pivotal role in the industrialization of bioproducts, yet there is a substantial lag in the fermentation process regulation. Here, an artificial neural network (ANN) and genetic algorithm (GA) coupled with fermentation kinetics were employed to establish an innovative lysine fermentation control. Firstly, the strategy of coupling GA with ANN was established. Secondly, specific lysine formation rate (qp), specific substrate consumption rate (qs), and specific cell growth rate (μ) were predicted and optimized by ANN-GA. The optimal ANN model adopts a three-layer feed-forward back-propagation structure (4:10:1). The optimal fermentation control parameters are obtained through GA. Finally, when the carbon to nitrogen ratio, residual sugar concentration, ammonia nitrogen concentration, and dissolved oxygen were [2.5, 4.5], [6.5, 9.5] g·L-1, [1.0, 2.0] g·L-1 and [20, 30] %, respectively, the lysine concentration reaches its peak at 213.0 ± 5.10 g·L-1. The novel control strategy holds significant potential for optimizing the fermentation of other bioproducts.

Wheat Germ Fermentation with Saccharomyces cerevisiae and Lactobacillus plantarum: Process Optimization for Enhanced Composition and Antioxidant Properties In Vitro

Wheat germ, a by-product of the flour milling industry, is currently commercialized mainly for animal feed applications. This study aims to explore and optimize the process of wheat germ fermentation to achieve products with enhanced nutritional composition and biological properties and further characterize the fermented products generated using these optimum conditions. The type of microorganism (Saccharomyces cerevisiae 5022 (yeast) and Lactobacillus plantarum strain 299v (bacteria)), pH (4.5, 6, and 7.5) and fermentation time (24, 48, and 72 h) were optimized using response surface methodology (RSM) aiming to achieve fermented products with high total phenol content (TPC), dimethoxy benzoquinone (DMBQ) and antioxidant activities. Optimum fermentation conditions were achieved using L. plantarum, pH 6, 48 h, generating extracts containing TPC (3.33 mg gallic acid equivalents/g), DMBQ (0.56 mg DMBQ/g), and DPPH radical scavenging (86.49%). These optimally fermented products had higher peptide concentrations (607 μg/mL), gamma-aminobutyric acid (GABA) (19,983.88 mg/kg) contents compared to non-fermented or yeast-fermented products. These findings highlight the influence of fermentation conditions of wheat germ and the promising industrial application of wheat germ fermentation for developing food products with enhanced biological properties promising for their commercialization as functional foods.

A cumulative-risk assessment method based on an artificial neural network model for the water environment

To analyze the cumulative risks to the water environment, the backpropagation artificial neural network (BP-ANN), a self-adapting algorithm, was proposed in this study. A new comprehensive indicator of cumulative risks was formed by combining the water risk assessment tool proposed by the World Wide Fund for Nature or World Wildlife Fund (WWF), Deutsche Investitions und Entwicklungsgesellschaft mbH (DEG), and the cumulative environmental risk assessment system proposed by the US Environmental Protection Agency (USEPA). Eleven training algorithms were selected and optimized based on the mean square error (MSE) of prediction results. Data concerning evaluating indicators and cumulative risk indexes of the Liao River collected from 2005 to 2017 in the cities of Tieling, Shenyang, and Panjin, China, were used as input and output data to train, validate, and test the BP-ANN. Levenberg Marquardt backpropagation was the most accurate algorithm, with an MSE of 3.33 × 10^-6. After optimization, there were six hidden layers in the model. The correlation coefficient of the BP-ANN with LM exceeded 80%. These findings suggest that the BP-ANN model is applicable to prediction of cumulative risks to the water environment. The model was sensitive to the number of wastewater treatment facilities and the wastewater treatment rate along the river. Based on the sensitivity analysis, the contributing factors can be controlled to reduce the cumulative risk.

Comprehensive study on applications of artificial neural network in food process modeling

Artificial neural network (ANN) is a simplified model of the biological nervous system consisting of nerve cells or neurons. The application of ANN to food process engineering is relatively novel. ANN had been employed in diverse applications like food safety and quality analyses, food image analysis, and modeling of various thermal and non-thermal food-processing operations. ANN has the ability to map nonlinear relationships without any prior knowledge and predicts responses even with incomplete information. Every neural network possesses data in the form of connection weights interconnecting lines between the input to hidden layer neurons and weights of hidden to output layer neurons, which has a significant role in predicting the output data. The applications of ANN in different unit operations in food processing were described that includes theoretical developments using intelligent characteristics for adaptability, automatic learning, classification, and prediction. The parallel architecture of ANN resulted in a fast response and low computational time making it suitable for application in real-time systems of different food process operations. The predicted responses obtained by the ANN model exhibited high accuracy due to lower relative deviation and root mean squared error and higher correlation coefficient. This paper presented the various applications of ANN for modeling nonlinear food engineering problems. The application of ANN in the modeling of the processes such as extraction, extrusion, drying, filtration, canning, fermentation, baking, dairy processing, and quality evaluation was reviewed.HIGHLIGHTS1. This paper discusses application of ANN in different emerging trends in food process.2. Application of ANN to develop non-linear multivariate modeling is illustrated.3. ANNs have been shown to be useful tool for prediction of outcomes with high accuracy.4. ANN resulted in fast response making it suitable for application in real time systems.

Effects of Agitation Speed and Kinetic Studies on Probiotication of Pomegranate Juice with Lactobacillus casei

The issues of lactose intolerance and vegetarianism have encouraged the introduction of non-dairy fermented food into the market. Therefore, this study aims to evaluate the effect of agitation speed on the bioactive compounds and functional characteristics of probioticated pomegranate juice. Pomegranate juice was fermented with Lactobacillus casei at different agitation speeds ranging from 0 (microaerophilic) to 150 rpm at 37 °C. The functional properties of probioticated pomegranate juice were evaluated in terms of growth (biomass), lactic acid production, antioxidant activity, total phenolic content, and key metabolites using LC-MS/MS. The growth kinetics of fermentation was monitored at the optimal condition using one factor at a time method. High cell growth (3.58 × 1010 cfu/mL or 7.9 gL-1) was observed for L. casei probioticated pomegranate juice agitated at 0 rpm. The findings of this study reveal the potential of pomegranate juice as a medium for L. casei cultivation without nutrient supplementation. The improvement of antioxidant activity in the probioticated juice could be due to the increment of quercetin-3-glucoside. Therefore, L. casei grew well in pomegranate juice with a high cell viability and antioxidant activity at a non-agitated condition. Probioticated pomegranate juice is a potentially functional drink.

Anti-Inflammatory Activity of Citric Acid-Treated Wheat Germ Extract in Lipopolysaccharide-Stimulated Macrophages

Until recently, fermentation was the only processing used to improve the functionality of wheat germ. The release of 2,6-dimethoxy-1,4-benzoquinone (DMBQ) from hydroquinone glycosides during the fermentation process is considered a marker of quality control. Here, we treated wheat germ extract with citric acid (CWG) to release DMBQ and examined the anti-inflammatory activity of this extract using a lipopolysaccharide-activated macrophage model. Treatment of wheat germ with citric acid resulted in detectable release of DMBQ but reduced total phenolic and total flavonoid contents compared with untreated wheat germ extract (UWG). CWG inhibited secretion of the pro-inflammatory cytokines tumor necrosis factor-α, interleukin (IL)-6, and IL-12 and the synthesis of cyclooxygenase-2, while UWG only decreased IL-12 production. CWG and UWG induced high levels of anti-inflammatory IL-10 and heme oxygenase-1. CWG specifically inhibited phosphorylation of NF-κB p65 and p38 kinase at 15 min after LPS stimulation. Our study showed that citric acid treatment enhanced the anti-inflammatory activity of wheat germ extract.

Artificial neural network - Genetic algorithm to optimize wheat germ fermentation condition: Application to the production of two anti-tumor benzoquinones

Methoxy-ρ-benzoquinone (MBQ) and 2, 6-dimethoxy-ρ-benzoquinone (DMBQ) are two potential anticancer compounds in fermented wheat germ. In present study, modeling and optimization of added macronutrients, microelements, vitamins for producing MBQ and DMBQ was investigated using artificial neural network (ANN) combined with genetic algorithm (GA). A configuration of 16-11-1 ANN model with Levenberg-Marquardt training algorithm was applied for modeling the complicated nonlinear interactions among 16 nutrients in fermentation process. Under the guidance of optimized scheme, the total contents of MBQ and DMBQ was improved by 117% compared with that in the control group. Further, by evaluating the relative importance of each nutrient in terms of the two benzoquinones' yield, macronutrients and microelements were found to have a greater influence than most of vitamins. It was also observed that a number of interactions between nutrients affected the yield of MBQ and DMBQ remarkably.