To explore the effect of micronization on the structural, functional, and antioxidant properties of wheat bran, wheat bran with mean particle size (D₅₀) of 46.08, 34.29, 26.51, 26.35, and 26.05 μm were prepared by using an ultrafine pulverizer under different rolling frequencies (0, 6, 9, 12, and 15 times). The main chemical components and structural, functional, and antioxidant properties of the wheat bran were compared and a correlation analysis was conducted. As the D₅₀ of the wheat bran decreased from 46.08 μm to 26.05 μm, the micromorphology exhibited the destruction of the bundle structure, which is formed by starch and fiber, during which the starch particles peeled off, the fiber fragments destructed, and some of the slim fiber fragments attached to the surfaces of the starch granules. According to the X-ray diffraction pattern, part of the crystalline structure was transformed into an amorphous structure and the crystallization index decreased from 13.08% to 3.95%. According to the near-infrared spectrum, more active groups, such as the hydroxyl group, were exposed; however, no new groups were generated. These structural changes accordingly caused changes in the chemical components, functional properties, and antioxidant properties of the wheat bran. Specifically, the protein, total phenols, total flavonoids, and fatty acid content increased by 6.72%, 23.47%, 19.07%, and 172.88%, respectively. The lipase activity, antioxidant activity in vitro (DPPH• scavenging activity, ABTS⁺• scavenging activity, and ferric reducing antioxidant power), and the water-holding, cholesterol-adsorption, sodium nitrite-adsorption, and cation-exchange capacities, were enhanced to some extent. The oil-holding capacity decreased from 3.01 g/g to 1.32 g/g. The swelling capacity decreased first and then increased and the swelling capacity of the wheat bran with a D₅₀ of 34.29 μm was the lowest (3.62 mL/g). Therefore, the micronization could be used as a pretreatment method to improve the functional and antioxidant properties of wheat bran; however, the optimal particle size of wheat bran is based on the function of the product.

Sweet cherry has many cultivars with different storability and nutritional properties. To reveal the reasons for the differences in storability among cultivars and improve the quality of sweet cherries, the surface microstructure of four representative sweet cherry cultivars (Red Light, Ranier, Red Agate, Friendship) epidermis and peduncle at harvest were examined and the effects of carboxymethyl chitosan-gelatin (CMCS-GL) based edible coating incorporating CaCl₂ and ascorbic acid (AA) (AA-CaCl₂-CMCS-GL) on the quality and nutritional characteristics of sweet cherry were evaluated. Results showed there were significant differences in the wax distribution of the epidermis and the number of stomata on the peduncle surface between four cultivars of sweet cherries at harvest, which was closely related to fruit decay ratio during storage. AA-CaCl₂-CMCS-GL coating delayed the onset of decay and the fruit decay ratio in coated groups (3.0%–15.3%) was significantly lower than in control groups (17.7%–63.0%) after 33 d storage. The coating also helped to maintain the quality and nutritional characteristics of four sweet cherry cultivars, including reducing weight loss, maintaining better skin color, peduncle freshness, higher fruit firmness, titratable acidity, AA, total phenolics content, total anthocyanins concentration, and antioxidant capacity. These results suggested that AA-CaCl₂-CMCS-GL coating could be considered as a new preservation method for improving postharvest quality and nutritional properties of different sweet cherry cultivars.

The particle size of wheat bran plays an important role in the quality of reconstituted whole-wheat flour and its products. The effects of wheat bran particle size on the quality of reconstituted whole-wheat flour and its cooked noodles were analyzed; the mean particle size (D₅₀) of wheat bran ranged from 26.05 to 46.08 μm. Results show that the decreases in D₅₀ of wheat bran induced the changes in the quality of whole-wheat flour and its noodles. Specifically, the damaged starch content, water absorption, and the solvent retention capacity of sodium carbonate and sucrose of whole-wheat flour increased at various degrees, while pasting viscosity decreased, and the gluten index and SDS-sedimentation volume increased first and then decreased. The cooking yield, cooking loss, and break rate of fresh noodles decreased first and reached a trough at D₅₀ of 26.05 μm, and then increased. The adhesiveness of cooked noodles increased, the score of smoothness, taste, appearance, and color increased to a stable value, but the hardness, springiness, cohesiveness, resilience, firmness score, and elasticity score increased first and then decreased. These turning points of changing trends of indexes mostly occurred when the D₅₀ of wheat bran was 26.51 μm. In conclusion, whole-wheat noodles with wheat bran of D₅₀ of 26.51 μm addition exhibit better cooking, textural, and sensory properties than those with smaller or larger wheat bran. Excessive crushing of wheat bran not only costs highly in terms of energy, but also has a negative impact on the quality of the noodles.

To explore the effects of particle size distribution on its physicochemical, functional, and structural properties, alfalfa leaf powders with mean particle sizes (D₅₀) of 506.1, 246.3, 209.8, 92.01, and 20.68 μm were prepared by sieving. The physicochemical, functional, and structural properties of alfalfa were compared, and correlation and principal component analyses were conducted. As the D₅₀ of alfalfa leaf decreased, the bulk density, tap density, and the swelling capacity increased first and then decreased, but the compressibility, transition temperature, and melting temperature exhibited an opposite trend. The solubility, lightness, and inhibition of angiotensin-converting enzymes and tyrosinase were enhanced. Specifically, the alfalfa leaf with a D₅₀ of 209.8 μm exhibited a higher bulk density and swelling capacity and a lower compressibility, transition temperature, and melting temperature. The alfalfa leaf with a D₅₀ of 20.68 μm presented better solubility, lightness, and inhibition of angiotensin-converting enzymes and tyrosinase. Additionally, the surface roughness and the number of surface hydroxyls improved and the crystallinity index decreased, but the type of surface functional groups was unchanged. These changes in microstructure can provide an explanation for the trend of the physicochemical and functional properties. Moreover, based on the results of the correlation analysis and principal component analysis, it can be concluded that there are strong correlations among the particle size, physicochemical properties, and functional properties of alfalfa leaf. Overall, this conclusion can help determine the appropriate grinding particle size range for alfalfa leaf in different functional food products.

The chlorophyll content is an important index reflecting the growth status and nutritional level of plants. The rapid, accurate and nondestructive monitoring of the SPAD content of apple trees can provide a basis for large-scale monitoring and scientific management of the growth status of apple trees. In this study, the canopy leaves of apple trees at different growth stages in the same year were taken as the research object, and remote sensing images of fruit trees in different growth stages (flower-falling stage, fruit-setting stage, fruit expansion stage, fruit-coloring stage and fruit-maturing stage) were acquired via a DJI MAVIC 3 multispectral unmanned aerial vehicle (UAV). Then, the spectral reflectance was extracted to calculate 15 common vegetation indexes as eigenvalues, the 5 vegetation indexes with the highest correlation were screened out through Pearson correlation analysis as the feature combination, and the measured SPAD values in the leaves of the fruit trees were gained using a handheld chlorophyll meter in the same stages. The estimation models for the SPAD values in different growth stages were, respectively, established through five machine learning algorithms: multiple linear regression (MLR), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost). Additionally, the model performance was assessed by selecting the coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE). The results show that the SPAD estimation results vary from stage to stage, where the best estimation model for the flower-falling stage, fruit-setting stage and fruit-maturing stage is RF and those for the fruit expansion stage and fruit-coloring stage are PLSR and MLR, respectively. Among the estimation models in the different growth stages, the model accuracy for the fruit expansion stage is the highest, with R² = 0.787, RMSE = 0.87 and MAE = 0.644. The RF model, which outperforms the other models in terms of the prediction effect in multiple growth stages, can effectively predict the SPAD value in the leaves of apple trees and provide a reference for the growth status monitoring and precise management of orchards.