Structural characterization and catalytic sterilization performance of a TiO2 nano-photocatalyst.

In view of the food safety and hygiene issues caused by pathogenic microorganisms, tetrabutyl titanate was used as a precursor for the preparation of a TiO2 nano-semiconductor photocatalyst via the sol-gel process. The plate count method was then adopted to investigate the photocatalytic sterilization performance of the synthesized TiO2 nanoparticles toward Escherichia coli, Staphylococcus aureus, and Candida albicans. Subsequently, a backpropagation (BP) neural network model was developed to predict the photocatalytic sterilization performance. The photocatalyst was structurally characterized by the Brunauer-Emmett-Teller method for specific surface area determination, transmission electron microscopy, X-ray diffraction, and X-ray photoelectron spectroscopy. The results indicated that the prepared TiO2 nano-photocatalyst was of high purity with a specific surface area of 76.5 m2/g and the particle size range 15-18 nm. The nanoparticles exhibited characteristic peaks corresponding to the oxide component Ti-O, hydroxyl group ˙OH and oxygen chemisorbed and presented an anatase-dominated multiphase structure that enhanced the photocatalytic performance. UV irradiation at 254 nm produced better sterilization effects on E. coli, S. aureus, and C. albicans, with elimination rates after 30 min of reaction of 97.8%, 99.4%, and 93.6%, respectively. These results indicated that the TiO2 nano-photocatalyst is a promising environmentally friendly catalyst with good sterilization performance. The constructed BP neural network also exhibited high training accuracy and good generalization ability, with correlation coefficients between the network-predicted and experimental target values of 0.9789. These results support research on the intelligent processing of photocatalytic sterilization with TiO2 nanoparticles.

Research of extraction behavior of heavy metal Cd in tea based on backpropagation neural network.

In order to meet the increasing demand for food and beverage safety and quality, this study focused on the application of a back propagation (BP) neural network to determine the leaching rate of heavy metal in tea to improve the scientific health of tea drinking. The evaluation index and target expectations have been determined based on the extraction experiment of heavy metal Cd in tea soaking, with 3 evaluation index values taken as input layer parameters and the heavy metal extraction rate taken as output layer parameter. Then, employ the sample data standardized by min-max linearization method to train and test the network model and get the satisfactory results, which showed that the constructed BP neural network expressed a fast convergence speed and the systematic error was as low as 0.0003509. Additionally, there was no significance between Cd leaching rate of experimental results and neural network model results by reliability testing with a correlation coefficient was .9895. These results revealed that the network model established possessed an outstanding training accuracy and generalization performance, which effectively reflected the extraction rate of heavy metal in tea soaking and improved the safety of tea drinking.