Optimization of medium composition for two-step fermentation of vitamin C based on artificial neural network–genetic algorithm techniques

Comparison of an Artificial Neural Network and a Response Surface Model during the Extraction of Selenium-Containing Protein from Selenium-Enriched Brassica napus L

In this study, the extraction conditions for selenium-enriched rape protein (SEP) were optimized by applying a response surface methodology (RSM) and artificial neural network (ANN) model, and then, the optimal conditions were obtained using a genetic algorithm (GA). Then, the antioxidant power of the SEP was examined by using the DPPH, ABTS, and CCK-8 (Cell Counting Kit-8), and its anticancer activities were explored by conducting a cell migration test. The results showed that compared with the RSM model, the ANN model was more accurate with a higher determination coefficient and fewer errors when it was applied to optimize the extraction method. The data obtained for SEP using a GA were as follows: the extraction temperature was 59.4 °C, the extraction time was 3.0 h, the alkaline concentration was 0.24 mol/L, the liquid-to-material ratio was 65.2 mL/g, and the predicted content of protein was 58.04 mg/g. The protein was extracted under the conditions obtained by the GA; the real content of protein was 57.69 mg/g, and the protein yield was 61.71%. Finally, as the concentration of the selenium-containing protein increased, it showed increased ability in scavenging free radicals and was influential in inhibiting the proliferation and migration of HepG2 cells.

Strain Development, Substrate Utilization, and Downstream Purification of Vitamin C

Vitamin C, C6H8O6, is a water-soluble vitamin that is widespread in nature. It is an essential nutrient involved in many biological processes in the living organisms: it enhances collagen biosynthesis, ensures the optimal functioning of enzymes and the immune system, has a major role in lipid and iron metabolism, and it enhances the biosynthesis of l-carnitine. Due to its antioxidant activity, vitamin C can neutralize the tissue-damaging effects of free radicals. Vitamin C is being related to the prevention of cancer and cardiovascular diseases. This review includes current information on the biosynthesis of ascorbic acid, as new methods are now challenging the traditional Reichstein process for vitamin C’s industrial-scale production. Different strains were analyzed in correlation with their ability to synthesize ascorbic acid, and several separation techniques were investigated for a more effective production of vitamin C.

High Throughput Screening Platform for a FAD-Dependent L-Sorbose Dehydrogenase

2-Keto-L-gulonic acid (2-KLG) is the direct precursor for the production of L-ascorbic acid (L-Asc) on industrial scale. Currently, the production of L-Asc in the industry is a two-step fermentation process. Owing to many unstable factors in the fermentation process, the conversion rate of L-sorbose to 2-KLG has remained at about 90% for many years. In order to further improve the production efficiency of 2-KLG, a FAD-dependent sorbose dehydrogenase (SDH) has been obtained in our previous research. The SDH can directly convert L-sorbose to 2-KLG at a very high efficiency. However, the enzyme activity of the SDH is relatively low. In order to further improve the enzyme activity of the SDH, a high throughput screening platform the dehydrogenase is essential. By optimizing the promoter, host and sorbosone dehydrogenase (SNDH), knockout of the aldosterone reductases and PTS related genes, a reliable platform for high-throughput screening of more efficient FAD-dependent SDH has been established. By using the high-throughput screening platform, the titer of the 2-KLG has been improved by 14.1%. The method established here could be useful for further enhancing the FAD-dependent SDH, which is important to achieve the efficient one-strain-single-step fermentation production of 2-KLG.

Optimization of microfluidic gallotannic acid extraction using artificial neural network and genetic algorithm

The current study presents the outcomes of modeling and optimizing extraction of gallotannic acid from Quercus leaves using a microfluidic system. In this study, the effects of various experimental parameters were investigated using the method of design expert. Number of experiments suggested is 31 by central composite design of Design Expert. The experimental results of design expert were analyzed by artificial neural network (ANN). Based on the results of ANN, independent variables experiment: temperature (T), flow rate ratio (FR) and pH have shown a negative effect on extraction yield (dependent variable), while the residence time (RT) has shown a positive effect. In trained network, ${R^2} = 0.9805$ and RMSE = 0.0166 shows good agreement between the predicted values of ANN and experimental results. Optimum extraction conditions, to reach maximum yield by genetic algorithms (GA), were FR = 0.53, RT = 26.4, pH = 2.06 and T = 21.44 ${R^2} = 0.9805$ . The extraction yield under the optimum predicated conditions was 96.4 %, which was well matched with the experimental value 95.01 % $\pm 0.63$ . Based on the obtained results, it was found that the ANN model could be employed successfully in estimating the gallotannic acid extraction efficiency using microfluidic extraction method.