Lemon juice vesicles have abundant flavor components that can undergo complex changes during drying. Three drying methods, including integrated freeze-drying (IFD), conventional freeze-drying (CFD), and hot-air drying (AD), were studied to determine their effects on the dynamic changes in the flavor compounds in lemon juice vesicles. Compared with the fresh samples, the final dried samples that underwent IFD, CFD, and AD lost seven, seven, and six volatile flavor compounds and three, four, and five amino acids, respectively; the order of the loss ratios with respect to the volatile compound content was: 82.73% in CFD > 71.22% in IFD > 28.78% in AD. AD resulted in the highest total amino acid content (10.83 ± 0.20 mg/g), which was 1.39 and 5.54 mg/g higher than that of IFD and CFD, respectively; CFD resulted in the highest total organic acid content (45.94 ± 0.34 mg/g), which was 8.01 and 7.87 mg/g higher than that of IFD and AD, respectively; and AD contributed to the highest total soluble sugars (17.12 ± 0.20 mg/g), which was 1.24 and 1.49 mg/g higher than that of IFD and CFD, respectively. A correlation analysis demonstrated that most of the amino acids and the soluble sugars were closely related to the profiles of the volatile compounds in the lemon juice vesicles during drying.

At present, ginkgo nut shellers have many problems, such as low shelling rate and high damage rate. To address these problems, a multifunctional ginkgo nut sheller was designed. The equipment had functions for shell breaking, shelling, and separation of shell from the kernel. The influencing factors of the shelling process were analyzed. A three-factor two-level response surface test was conducted, and mathematical models for the response surface were established. The influence of each factor on the operation quality was analyzed, and a combination of parameters was optimized. The experimental results showed that the factors in the order of importance of their influence on shelling rate was as follows: feeding rate, chainplate speed, and shelling gap. The factors in the order of importance of their influence on damage rate was as follows: shelling gap, chainplate speed, and feeding rate. The results of the interaction analysis conducted showed that the interaction between the chainplate speed and shelling gap had the highest significant effect on the shelling rate, followed by the interaction between the feeding rate and chainplate speed. The interaction between the chainplate speed and shelling gap had a significant effect on damage rate. The interaction between the other factors had no significant effect on shelling and damage rates. The optimal combination of parameters was as follows: the feeding rate was 30.2 g/s, the chainplate speed was 0.48 m/s, and the shelling gap was 12.3 mm. The optimal combination was employed, and the validation test resulted in a shelling rate of 98.02% and damage rate of 4.45%. The relative error between the measured and theoretical values was less than 5%, indicating that the models were reliable. This study can provide a reference for subsequent research on shelling equipment of ginkgo nut.

In order to ensure the reliability of the simulation test on the shelling process of ginkgo nuts, the physical parameters (characteristic dimensions, thousand grain mass, moisture content, density, Poisson’s ratio, elastic modulus, and shear modulus), contact parameters (restitution coefficient, static friction coefficient, and dynamic friction coefficient) and actual stacking angle of the ginkgo nuts were measured by physical test methods. A stacking angle test model was established, and simulation tests were performed. Parameter intervals for the simulation were selected according to the measured parameters. Three factors affecting the stacking angle significantly were determined using the Plackett–Burman test: static and dynamic friction coefficients of ginkgo nut–ginkgo nut and the restitution coefficient of the ginkgo nut–steel plate. Each factor’s range was selected using the steepest climbing test. A quadratic polynomial regression model of the stacking angle considering these factors was obtained using the Box–Behnken test. Taking the actual stacking angle of 39.59° as the optimization objective, the optimal combination was formed: the static and dynamic friction coefficients were 0.5 and 0.36, respectively, and the restitution coefficient was 0.42. This combination was used for the verification test. The stacking angle was found to be 41.12°. The relative error was 3.86%, indicating that the model is reliable and can provide a useful reference for relevant research.

A cylindrical grader is an important piece of equipment used to grade maize seeds. However, the motion and distribution patterns of seeds within the cylindrical grading process remain poorly understood, leading to a heavy reliance on empirical adjustments of operational parameters during grading. This results in issues such as low grading efficiency, unstable operational performance, and failure to meet practical production requirements. To investigate the motion and distribution patterns of maize seeds during cylindrical grading, key simulation parameters characterizing the maize seeds and grading cylinders were experimentally determined. Discrete element models of maize seeds and the grading cylinder were subsequently developed using EDEM 2018 software. Variations in seed motion velocity and the coefficient of variation (CV) along the circumferential and axial directions were analyzed under different operational parameters, including cylinder rotational speed, inclination angle, and feeding rate. Discrete element simulations combined with orthogonal experiments revealed that the order of influence of these factors on the grading qualification rate was as follows: inclination angle > rotational speed > feeding rate. The results of the interaction analysis showed that the interaction between the inclination angle and rotational speed significantly affected the grading qualification rate, while the interactions among the other factors had no significant effect. The optimized parameter combination was identified as a rotational speed of 47.08 r/min, an inclination angle of 0.52°‌, and a feeding rate of ‌303.07 g/s‌, achieving a theoretical grading qualification rate of 97.24%. Validation experiments conducted with this optimal combination yielded a practical grading qualification rate of ‌93.83%‌, with the relative error between the experimental and predicted values below 4%‌. These results confirm the validity of discrete element simulations for analyzing maize seed motion dynamics and provide a valuable reference for further research in this field.

This study conducted a simulation analysis of the seed grading process to address the challenges of excessive dependence on empirical parameter adjustments, low grading efficiency, and operational instability in maize cylinder grading systems. After determining the relevant characteristic parameters of maize seeds and the grading cylinder, discrete element models were established using EDEM software. The simulation investigated axial mean velocity, intra-cylinder quantity distribution, and post-sieving quantity distribution of seeds under varying cylinder speeds (14–22 r/min), inclination angles (0°–4°), and feeding rates (1,150–1,350 g/s). Orthogonal experiments and range analysis were conducted to determine the significance hierarchy of factors: for qualification rate, cylinder speed > inclination angle > feeding rate. For productivity, inclination angle > feeding rate > cylinder speed. The optimal parameter combination was cylinder speed 16 r/min, inclination angle 0°, and feeding rate 1,300 g/s. At this point, the qualification rate was 93.21%, and the productivity was 1,033 g/s. Using the optimal combination for validation experiments, the obtained qualification rate was 90.15%, and the productivity was 985 g/s. The relative errors between the experimental values and the predicted values were less than 5%, validating the simulation’s reliability and providing critical engineering implications for industrial seed grading optimization.