Comparison of optimization approaches (response surface methodology and artificial neural network‐genetic algorithm) for a novel mixed culture approach in soybean meal fermentation

Statistical versus neural network-embedded swarm intelligence optimization of a metallo-neutral-protease production: activity kinetics and food industry applications

An integrated approach involving response surface methodology (RSM) and artificial neural network-ant-colony hybrid optimization (ANN-ACO) was adopted to develop a bioprocess medium to increase the yield of Bacillus cereus neutral protease under submerged fermentation conditions. The ANN-ACO model was comparatively superior (predicted r2 = 98.5%, mean squared error [MSE] = 0.0353) to RSM model (predicted r2 = 86.4%, MSE = 23.85) in predictive capability arising from its low performance error. The hybrid model recommended a medium containing (gL-1) molasses 45.00, urea 9.81, casein 25.45, Ca2+ 1.23, Zn2+ 0.021, Mn2+ 0.020, and 4.45% (vv-1) inoculum, for a 6.75-fold increase in protease activity from a baseline of 76.63 UmL-1. Yield was further increased in a 5-L bioreactor to a final volumetric productivity of 3.472 mg(Lh)-1. The 10.0-fold purified 46.6-kDa-enzyme had maximum activity at pH 6.5, 45-55 °C, with Km of 6.92 mM, Vmax of 769.23 µmolmL-1 min-1, kcat of 28.49 s-1, and kcat/Km of 4.117 × 103 M-1 s-1, at 45 °C, pH 6.5. The enzyme was stabilized by Ca2+, activated by Zn2+ but inhibited by EDTA suggesting that it was a metallo-protease. The biomolecule significantly clarified orange and pineapple juices indicating its food industry application.

Optimization of adsorption performance of cerium-loaded intercalated bentonite by CCD-RSM and GA-BPNN and its application in simultaneous removal of phosphorus and ammonia nitrogen

Excessive phosphorus (P) and ammonia nitrogen (NH₃-N) in water bodies can lead to eutrophication of the aquatic environment. Therefore, it is important to develop a technology that can efficiently remove P and NH₃-N from water. Here, the adsorption performance of cerium-loaded intercalated bentonite (Ce-bentonite) was optimized based on single-factor experiments using central composite design-response surface methodology (CCD-RSM) and genetic algorithm-back propagation neural network (GA-BPNN) models. Based on the determination coefficient (R²), mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE), the GA-BPNN model was found to be more accurate in predicting adsorption conditions than the CCD-RSM model. The validation results showed that the removal efficiency of P and NH₃-N by Ce-bentonite under optimal adsorption conditions (adsorbent dosage = 1.0 g, adsorption time = 60 min, pH = 8, initial concentration = 30 mg/L) reached 95.70% and 65.93%. Furthermore, based on the application of these optimal conditions in simultaneous removal of P and NH₃-N by Ce-bentonite, pseudo-second order and Freundlich models were able to better analyze adsorption kinetics and isotherms. It is concluded that the optimization of experimental conditions by GA-BPNN has some guidance and provides a new approach to explore adsorption performance after optimizing the conditions.

EFFECTS OF DIFFERENT FORMS OF PRACTICING A SPORT ON ATHLETES’ NERVE FUNCTION

ABSTRACT Introduction: Poor reaction time can cause irritability, easier distractions, and interfere with normal activities that require concentration. Objective: To study the impact of simulated hypoxic training on the immune system of female middle- and long-distance runners. Methods: Observation of 50 female middle- and long-distance runners living high and training low (HiHiLo) in regard to changes in CD3+, CD4+, CD8+, NK cells and NKT cells during the process of living low and training high (LoHi). Research indicates: HiHiLo training does have a significant effect on these indicators to enhance the antioxidant capacity of the brain and nervous system. Results: Living high and training low (HiHiLo) can improve the excitability of the nervous system of long-distance endurance athletes and has a very significant impact on the stability of the nervous system. This improves cardiopulmonary function under hypoxic conditions and has a role in promoting the ability to mobilize the skeletal muscles. Conclusions: It has been proved that different forms of exercise can effectively improve the athlete’s nervous response. Level of evidence II; Therapeutic studies - investigation of treatment results.

L-asparaginase production in solid-state fermentation using Aspergillus niger: process modeling by artificial neural network approach

L-asparaginase has proven itself as a potential anti-cancer drug and in the mitigation of acrylamide formation in the food industry. In the present investigation, a novel utilization of niger (Guizotia abyssinica) de-oiled cake as the sole source for the cost-effective production of L-asparaginase was evaluated and compared with different agro-substrates in solid-state fermentation. The substrate provided a favorable C/N content for the L-asparaginase production as evident from the chemical composition (CHNS analysis) of the substrate. The influential process parameters viz; autoclaving time, moisture content, temperature and pH were optimized and modeled using machine-learning based artificial neural network (ANN) and statistical-based response surface methodology (RSM). The maximum enzyme activity of 34.65 ± 2.18 IU/gds was observed at 30.3 min of autoclaving time, 62% moisture content, 30 °C temperature and 6.2 pH in 96 h. A 1.36 fold improvement in enzyme activity was observed on utilizing optimized parameters. In comparison with RSM, the ANN model showed superior prediction with a low mean squared error of 0.072, low root mean squared error of 0.268 and 0.99 value of regression coefficient. The present study demonstrates the novel utilization of inexpensive and readily available agro-industrial waste for the development of cost-effective L-asparaginase production process.

Valorization of Vegetable Food Waste and By-Products Through Fermentation Processes

There is a general interest in finding new ways of valorizing fruit and vegetable processing by-products. With this aim, applications of industrial fermentation to improve nutritional value, or to produce biologically active compounds, have been developed. In this sense, the fermentation of a wide variety of by-products including rice, barley, soya, citrus, and milling by-products has been reported. This minireview gives an overview of recent fermentation-based valorization strategies developed in the last 2 years. To aid the designing of new bioprocesses of industrial interest, this minireview also provides a detailed comparison of the fermentation conditions needed to produce specific bioactive compounds through a simple artificial neural network model. Different applications reported have been focused on increasing the nutritional value of vegetable by-products, while several lactic acid bacteria and Penicillium species have been used to produce high purity lactic acid. Bacteria and fungi like Bacillus subtilis, Rhizopus oligosporus, or Fusarium flocciferum may be used to efficiently produce protein extracts with high biological value and a wide variety of functional carbohydrates and glycosidases have been produced employing Aspergillus, Yarrowia, and Trichoderma species. Fermentative patterns summarized may guide the production of functional ingredients for novel food formulation and the development of low-cost bioprocesses leading to a transition toward a bioeconomy model.