Zinc oxide nanoparticles (ZnO-NPs) have gained significant interest in the agricultural and food industry as a means of killing or reducing the activity of microorganisms. The antibacterial properties of ZnO-NPs may improve food quality, which has a direct impact on human health. ZnO-NPs are one of the most investigated inorganic nanoparticles and have been used in various related sectors, with the potential to rapidly gain attention and increase interest in the agriculture and food industries. In this review, we describe various methods for preparing ZnO-NPs, their characterizations, modifications, applications, antimicrobial activity, testing procedures, and effects, including bactericidal and bacteriostatic mechanisms. It is hoped that this review could provide a better understanding of the preparation and application of ZnO nanoparticles in the field of food and agriculture, and promote their development to advance the field of food and agriculture.

(1) Background: Soybean storage quality is crucial for subsequent processing and consumption, making it essential to explore an objective, rapid, and non-destructive technology for assessing its quality. (2) Methods: crude fatty acid value is an important indicator for evaluating the storage quality of soybeans. In this study, three types of soybeans were subjected to accelerated aging to analyze trends in crude fatty acid values. The study focused on acquiring raw spectral information using hyperspectral imaging technology, preprocessing by the derivative method (1ST, 2ND), multiplicative scatter correction (MSC), and standard normal variate (SNV). The feature variables were extracted by a variable iterative space shrinkage approach (VISSA), competitive adaptive reweighted sampling (CARS), and a successive projections algorithm (SPA). Partial least squares regression (PLSR), support vector machine (SVM), and extreme learning machine (ELM) models were developed to predict crude fatty acid values of soybeans. The optimal model was used to visualize the dynamic distribution of these values. (3) Results: the crude fatty acid values exhibited a positive correlation with storage time, functioning as a direct indicator of soybean quality. The 1ST-VISSA-SVM model was the optimal predictive model for crude fatty acid values, achieving a coefficient of determination (R²) of 0.9888 and a root mean square error (RMSE) of 0.1857 and enabling the visualization of related chemical information. (4) Conclusions: it has been confirmed that hyperspectral imaging technology possesses the capability for the non-destructive and rapid detection of soybean storage quality.

(1) In order to accurately judge the new maturity of wheat and better serve the collection, storage, processing and utilization of wheat, it is urgent to explore a fast, convenient and non-destructively technology. (2) Methods: Catalase activity (CAT) is an important index to evaluate the ageing of wheat. In this study, hyperspectral imaging technology (850–1700 nm) combined with a BP neural network (BPNN) and a support vector machine (SVM) were used to establish a quantitative prediction model for the CAT of wheat with the classification of the ageing of wheat based on different storage durations. (3) Results: The results showed that the model of 1ST-SVM based on the full-band spectral data had the best prediction performance (R² = 0.9689). The SPA extracted eleven characteristic bands as the optimal wavelengths, and the established model of MSC-SPA-SVM showed the best prediction result with R² = 0.9664. (4) Conclusions: The model of MSC-SPA-SVM was used to visualize the CAT distribution of wheat ageing. In conclusion, hyperspectral imaging technology can be used to determine the CAT content and evaluate wheat ageing, rapidly and non-destructively.

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 research was conducted to explore the affiliation between the physicochemical properties of rice noodles and rice starch of early indica rice samples of varying amylose content. 3 various types of rice samples were analyzed to uncover how amylose content influences rice noodles' quality. Findings revealed that primarily sensory scores differ in palatability, while textural disparity lies in hardness and chewiness. The higher hardness and chewiness values were correlated with higher sensory scores. Rice with lower amylose content demonstrates elevated solubility, swelling power, and crystallinity along with poor retrogradation resulting in inferior-quality noodles. Sensory scores and textural properties were proved to be associated with the distribution of branched starch molecule chain lengths. Noodles of higher sensory scores had more stable gel structure and improved elasticity. These findings underscore the critical role of amylose content and starch molecular structure in determining the suitability of early indica rice for noodle preparation.

(1) Background: To achieve the rapid, non-destructive detection of corn freshness and staleness for better use in the storage, processing and utilization of corn. (2) Methods: In this study, three varieties of corn were subjected to accelerated aging treatment to study the trend in fatty acid values of corn. The study focused on the use of hyperspectral imaging technology to collect information from corn samples with different aging levels. Spectral data were preprocessed by a convolutional smoothing derivative method (SG, SG1, SG2), derivative method (D1, D2), multiple scattering correction (MSC), and standard normal transform (SNV); the characteristic wavelengths were extracted by the competitive adaptive reweighting method (CARS) and successive projection algorithm (SPA); a neural network (BP) and random forest (RF) were utilized to establish a prediction model for the quantification of fatty acid values of corn. And, the distribution of fatty acid values was visualized based on fatty acid values under the corresponding optimal prediction model. (3) Results: With the prolongation of the aging time, all three varieties of corn showed an overall increasing trend. The fatty acid value of corn can be used as the most important index for characterizing the degree of aging of corn. SG2-SPA-RF was the quantitative prediction model for optimal fatty acid values of corn. The model extracted 31 wavelengths, only 12.11% of the total number of wavelengths, where the coefficient of determination R_P² of the test set was 0.9655 and the root mean square error (RMSE) was 3.6255. (4) Conclusions: This study can provide a reliable and effective new method for the rapid non-destructive testing of corn freshness.