Due to the nonlinearity, strong coupling and hysteresis of the parameters used in measuring the grain drying process, it is always a challenge to perform accurate control of the drying system. The purpose of this paper is to describe the design of a suitable neural network model to control the grain dryer more effectively. First, the operating mechanism of a continuous grain dryer and the principle of the heat and mass transfer that can be used in the process of grain drying was analyzed before an intelligent control system was designed accordingly. Second, an intelligent control system based on the Back Propagation Neural Network (BPNN) was developed. The BPNN was the optimal model selected based on a series of comparative test results. According to the BPNN prediction of the moisture content of dried rice, the system could adjust the rate of grain discharge of the dryer, and then control the drying process accurately. Finally, the neural network control model was simulated using computer simulation technology, and was optimized by comparing analysis results with experimental results. The results showed that the optimized intelligent control system using BPNN has the advantage of strong stability and good noise handling, and could have great potential for future implementation studies.

Non-aromatic rice is often sold at the price of aromatic rice to increase profits, seriously impairing consumer experience and brand credibility. The assessment of rice varieties origins in terms of their aroma traits is of great interest to protect consumers from fraud. To address this issue, the study identified differentially abundant metabolites between non-aromatic rice varieties and each of the three most popular aromatic rice varieties in the market using an untargeted metabolomics approach. The 656 metabolites of five rice grain varieties were determined by headspace solid-phase extraction gas chromatography-mass spectrometry, and the multivariate analyses were used to identify differences in metabolites among rice varieties. The metabolites most differentially abundant between Daohuaxiang 2 and non-aromatic rice included 2-acetyl-1-pyrroline and acetoin; the metabolites most differentially abundant between Meixiangzhan 2 and non-aromatic rice included acetoin and 2-methyloctylbenzene,; and the metabolites most differentially abundant between Yexiangyoulisi and non-aromatic rice included bicyclo[4.4.0]dec,1-ene-2-isopropyl-5-methyl-9-methylene and 2-methylfuran. Overall, acetoin was the metabolite that was most differentially abundant between the aromatic and non-aromatic rice. This study provides direct evidence of the outstanding advantages of aromatic rice and acts a reference for future rice authentication processes in the marketplace.

Rice drying is a complex and nonlinear process, with product quality being easily influenced by numerous factors. This study aimed to investigate the characteristics causing variation in rice quality and provide novel insights for regulating and controlling rice drying operations. To this end, response surface methodology was employed to examine the hot air drying of rice. The study was centered on investigating the impact of different levels of drying temperature (35–55 °C), relative humidity (30–50%), initial moisture content (20–28%), air velocity (0.36–0.84 m/s), and tempering ratio (1–4) on the process. The measured parameters included the net drying time, total drying time, additional crack percentage, and head rice yield. The experimental data were analyzed using Design–Expert, and the results indicated that all the response quadratic polynomial models were statistically significant. All the linear terms had a significant impact on the response variables except for the impact of air velocity on head rice yield. Finally, process reference charts of actual drying operating conditions were established based on the regression models to provide a scientific reference for guiding the control of rice drying quality and integrating into intelligent grain drying control systems in the future.

In this study, cold plasma treatments are employed to modify peanuts. This study systematically investigates the effects of various plasma treatment conditions, including power, duration, and gas type, on the microstructure of peanut seed coats and embryos. Observations under a scanning electron microscope (SEM) reveal that as plasma treatment power increases from 100 W to 500 W, the etching level of peanut seed coats significantly intensifies, surface roughness deepens, and concavities become more pronounced. Additionally, micro-pores on the seed coat gradually enlarge and form cracks. Specifically, when the plasma treatment is set at 200 W for 60 s, the oxygen (O₂) treatment group shows interconnected cracks on the peanut seed coat surface, with lipid particles exuding and protein particles and polymers decomposing. In contrast, the helium (He) treatment group displays clear cell structures and deep grooves, with no noticeable lipid particles exuding around surface cracks. The argon (Ar) treatment group exhibits a distinct rectangular cell structure with clear boundaries, and although surface cracks form, only a few protein particles escape from the cracks. The embryo surface structure becomes looser after plasma treatment, leading to the disintegration of lipid particles, protein particles, and polymers, affecting the fusion and migration of large and small lipid bodies within the peanut’s internal structure. Increasing treatment duration intensifies the etching phenomenon, resulting in more lipid particles exuding, which indicates a positive correlation between lipid particles exuding and treatment duration. This study sheds light on the mechanisms underlying changes in peanut microstructure due to cold plasma treatment, providing scientific evidence for improving peanut quality, enhancing oil extraction efficiency, and optimizing food processing techniques.

The use of microorganisms to manage aflatoxin contamination is a gentle and effective approach. The aim of this study was to test the removal of AFB₁ from AFB₁-contaminated peanut meal by a strain of Meyerozyma guilliermondii AF01 screened by the authors and to optimize the conditions of the biocontrol. A regression model with the removal ratio of AFB₁ as the response value was established by means of single-factor and response surface experiments. It was determined that the optimal conditions for the removal of AFB₁ from peanut meal by AF01 were 75 h at 29 °C under the natural pH, with an inoculum of 5.5%; the removal ratio of AFB₁ reached 69.31%. The results of simulating solid-state fermentation in production using shallow pans and fermentation bags showed that the removal ratio of AFB₁ was 68.85% and 70.31% in the scaled-up experiments, respectively. This indicated that AF01 had strong adaptability to the environment with facultative anaerobic fermentation detoxification ability. The removal ratio of AFB₁ showed a positive correlation with the growth of AF01, and there were no significant changes in the appearance and quality of the peanut meal after fermentation. This indicated that AF01 had the potential to be used in practical production.

Previous research has shown that the accumulated temperature can describe drying processes as well as crop growth. To describe the mass and heat transfer processes in the rice drying process more accurately, a mathematical model of rice drying was proposed based on the drying accumulated temperature, and the optimal tempering ratio for conventional hot air drying was obtained through data comparison and analysis. First, it was proven that there was an exponential relationship between the moisture ratio and the drying accumulated temperature of rice. Second, by comparing and analyzing the fitting results of seven different drying mathematical models, the model with the highest fitting degree was selected and reconstructed to obtain the drying accumulated temperature–moisture ratio model. Finally, the new model was used to fit the results of two drying experiments without and with tempering, and the tempering characteristics of rice drying were proved by comparing and analyzing the coefficient difference between the two models. The results showed that the optimal tempering ratio was 3. This study thus provides a reference for rice drying process parameters.