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Poor storage capacity of soybean makes it prone to fungal spoilage and heating during storage, resulting in lower quality. Early prediction of the fungal spoilage in stored soybeans is very difficult because fungi are often too small to be seen with the naked eye. Here a new method for fungus to early detection is adopted: it is called counting fungal spores. Soybeans with moisture contents of 11.4, 12.1, 13.0, 13.9, 14.3 and 14.7%, were held at 6 temperatures 10, 15, 20, 25, 30 and 35? for180d. Samples were taken at regular intervals and the fungal spores counted. The safe storage conditions (temperature, moisture content, duration) were estimated by means of a curve fitted using the power function fitting. It can predict of soybean spoilage by fungus before there is visible damage.

Grain mildew is a significant hazard that causes food loss and poses a serious threat to human health when severe. Therefore, effective prediction and determination of mildew grade is essential for the prevention and control of mildew and global food security. In the present study, a model for predicting and determining the mildew grade of rice was constructed using logistic regression, back propagation neural network and GS-SVM (a grid search-based support vector machine algorithm) based on laboratory culture data and actual data from a granary, respectively. The results show that the GS-SVM model has a better prediction effect, but the model cannot automatically adjust the parameters and is more subjective, and the accuracy may decrease when the data set changes. Therefore, this paper establishes a new model for a support vector machine based on a fruit fly optimization algorithm (FOA-SVM), which can achieve automatic parameter search and automatically adjust its parameters to find the best result when the data set changes, with a strong ability of self-adjustment of parameters. In addition, the FOA-SVM converges quickly and the model is stable. The results of this study provide a technical method for early identification of mildew grade during grain storage, which is beneficial for the prevention and control of rice mildew during grain storage. [ABSTRACT FROM AUTHOR]

Research interest in VO2, one of the well‐known transition metal oxides featured by the metal–insulator transition, has resulted in a half‐century's accumulation of fundamentals toward a crystal clear description of the transition mechanism. To understand the interplay between extra electrons and lattice across the transition in VO2‐based integrated electronic devices, a heterostructure of VO2/CH3NH3PbI3 is constructed to investigate the structural evolution when hot electrons are injected from CH3NH3PbI3 to VO2 upon light irradiation. The decrease in the resistance of VO2 confirms the presence of extra electrons injected from CH3NH3PbI3 upon light irradiation. Then, Raman spectra are used to study the structural evolution of VO2 with temperature. The damping in the Raman intensity and the shift in Raman peak of VO2 near transition temperature implicate that the interplay between extra electron injection and lattice weakens the V–V vibration in VO2. This study provides a fundamental framework for understanding the interplay between extra charge and lattice in VO2. [ABSTRACT FROM AUTHOR]

This study processed Jingxi rice 7233 into reasonably well milled (RWM, bran degree 2.8%, 4.9%) and well milled (WM, bran degree 1.4%) grades, and compared the processing characteristics, physicochemical properties, fatty acid composition, volatile composition and taste quality between the two grades. The results showed that the average rice yield and head rice yield of RWM were 1.3% and 1.8% higher than those of WM, respectively. The contents of total phenolic acids, water-soluble protein, unsaturated fatty acids, especially polyunsaturated fatty acids, in RWM rice were more than 1.5 times as high as those in RWM rice. The color difference between RWM and WM rice was greater than 2.0, which could be perceived with the naked eye. Totally 62 characteristic peaks were marked by gas chromatography-ion mobility spectrometry (GC-IMS), and 38 compounds were accurately identified. The contents of 58% of volatile components were significantly higher in RWM rice than in WM rice. The cooking time of RWM rice was significantly longer than that of WM rice, and the breakdown viscosity of starch and stickiness were significantly lower than those of WM rice. RWM rice has great advantages in paddy utilization rate, nutrition and health, but further research and optimization are needed in terms of appearance, smell and taste quality.