This study combined an artificial neural network (ANN) with a genetic algorithm (GA) to obtain the model and optimal process parameters of drying-assisted walnut breaking. Walnuts were dried at different IR temperatures (40 °C, 45 °C, 50 °C, and 55 °C) and air velocities (1, 2, 3, and 4 m/s) to different moisture contents (10%, 15%, 20%, and 25%) by using air-impingement technology. Subsequently, the dried walnuts were broken in different loading directions (sutural, longitudinal, and vertical). The drying time (DT), specific energy consumption (SEC), high kernel rate (HR), whole kernel rate (WR), and shell-breaking rate (SR) were determined as response variables. An ANN optimized by a GA was applied to simulate the influence of IR temperature, air velocity, moisture content, and loading direction on the five response variables, from which the objective functions of DT, SEC, HR, WR, and SR were developed. A GA was applied for the simultaneous maximization of HR, WR, and SR and minimization of DT and SEC to determine the optimized process parameters. The ANN model had a satisfactory prediction ability, with the coefficients of determination of 0.996, 0.998, 0.990, 0.991, and 0.993 for DT, SEC, HR, WR, and SR, respectively. The optimized process parameters were found to be 54.9 °C of IR temperature, 3.66 m/s of air velocity, 10.9% of moisture content, and vertical loading direction. The model combining an ANN and a GA was proven to be an effective method for predicting and optimizing the process parameters of walnut breaking. The predicted values under optimized process parameters fitted the experimental data well, with a low relative error value of 2.51–3.96%. This study can help improve the quality of walnut breaking, processing efficiency, and energy conservation. The ANN modeling and GA multiobjective optimization method developed in this study provide references for the process optimization of walnut and other similar commodities.

The main objective of the present work was to study the drying kinetics and obtain the optimum process parameters of cantaloupe slices using short-and medium-wave infrared radiation (SMIR) drying technology. The effect of three independent variables of infrared radiation temperature (55–65 $°\mathrm{C}$), slice thickness (5–9 mm) and radiation distance (80–160 mm) on the L value, color difference (∆E), hardness and vitamin C content were investigated by using the Response Surface Methodology (RSM). The results showed that the Page model can adequately predict the moisture content between 55 and 65 $°\mathrm{C}$ ($R^2$ > 0.99). The effective moisture diffusivity ($D_{eff}$) varied from 5.26 × 10⁻¹⁰ to 2.09 × 10⁻⁹ m²/s and the activation energy ($Ea$) of the SMIR drying was 31.84 kJ/mol. Infrared radiation temperature and slice thickness exerted extremely significant effects on L value and color difference (ΔE) (p < 0.01), with higher infrared radiation temperature and thin slice thickness leading to a decrease in the L value and an increase in ΔE. Hardness and vitamin C content were significantly affected by infrared radiation temperature, slice thickness and radiation distance, of which the slice thickness was the most distinct factor affecting the hardness value. Higher infrared radiation temperature and larger slice thickness and radiation distance resulted in higher vitamin C degradation. For the given constraints (maximized vitamin C content and L value, minimized ΔE and hardness value), the optimum drying parameters were infrared radiation temperature 58.2 $°\mathrm{C}$, slice thickness 6 mm and radiation distance 90 mm. Under the optimum drying combination conditions, the experimental values were 65.58 (L value), 8.57 (∆E), 10.49 N (hardness) and 106.58 mg/100 g (vitamin C content), respectively. This study is beneficial to the development of the cantaloupe food processing industry and provides more insights for the application of SMIR drying technology to improve the drying rate and product quality of cantaloupe.

The evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared hyperspectral imaging (NIR-HSI, 1000–2500 nm) combined with machine learning (ML) and sparrow search algorithm (SSA), were proposed in this study. After spectral preprocessing via first derivative combined with multiple scattering correction (1D + MSC), classification and quantification models were established using back propagation neural network (BP), extreme learning machine (ELM) and support vector machine/regression (SVM/SVR). SSA was further used to explore the global optimal parameters of these models. Results showed that the performance of models improves after optimisation via the SSA. SSA-SVM achieved the optimal discrimination result, with an accuracy of 99.79% in the prediction set; SSA-SVR achieved the optimal prediction result, with an R_P² of 0.9304 and an RMSEP of 0.0458 g·g⁻¹. Hence, NIR-HSI combined with ML and SSA is feasible for classification and quantification of mutton adulteration under the effect of mutton flavour essence. This study can provide a theoretical and practical reference for the evaluation and supervision of food quality under complex conditions.

A drying temperature precision control system was studied to provide technical support for developing and further proving the superiority of the variable-temperature drying process. In this study, an improved neural network (INN) proportional–integral–derivative (PID) controller (INN-PID) was designed. The dynamic performance of the PID, neural network PID (NN-PID) and INN-PID controllers was simulated with unit step signals as an input in MATLAB software. A drying temperature precision control system was set up in an air impingement dryer, and the drying temperature control experiment was carried out to verify the performance of the three controllers. Linear variable-temperature (LVT) and constant-temperature drying experiments of cantaloupe slices were carried out based on the system. Moreover, the experimental results were evaluated comprehensively with the brightness (L value), colour difference (ΔE), vitamin C content, chewiness, drying time and energy consumption (EC) as evaluation indexes. The simulation results show that the INN-PID controller outperforms the other two controllers in terms of control accuracy and regulation time. In the drying temperature control experiment at 50 °C–55 °C, the peak time of the INN-PID controller is 237.37 s, the regulation time is 134.91 s and the maximum overshoot is 4.74%. The INN-PID controller can quickly and effectively regulate the temperature of the inner chamber of the air impingement dryer. Compared with constant-temperature drying, LVT is a more effective drying mode as it ensures the quality of the material and reduces the drying time and EC. The drying temperature precision control system based on the INN-PID controller meets the temperature control requirements of the variable-temperature drying process. This system provides practical and effective technical support for the variable-temperature drying process and lays the foundation for further research. The LVT drying experiments of cantaloupe slices also show that variable-temperature drying is a better process than constant-temperature drying and is worthy of further study to be applied in production.

Jujubes have been favored by consumers because of their rich nutrition and wide use. Hot air drying has been commercially and typically used to prolong shelf life and acquire the dried produce. Jujube slices were dried with hot air combined with radio frequency (RF) at different drying stages, namely, early (0–2 h, E-HA + RF), middle (2–4 h, M-HA + RF), later (4–6 h, L-HA + RF), and whole (0–6 h, W-HA + RF) stages. This study aimed to investigate the effects of different RF application stages on the microstructure, moisture absorption rate, color, and ascorbic acid of jujube slices. Compared with the hot air drying (HA) group, the E-HA + RF group obtained the best results among the experimental groups because it reduced the cells with a roundness of less than 0.4 by 5%. Moreover, the M-HA + RF group showed better results than those of other groups, with an 18.6% and 48.8% reduction in cells for a cross-sectional area less than 200 µm² and a perimeter less than 25 µm, respectively. The minimum total color difference (ΔE = 9.21 ± 0.31) and maximum retention of ascorbic acid (285.06 mg/100 g) were also observed in this group. Therefore, the method of hot air drying assisted by phased RF is viable in the drying industry to improve the quality of dried agricultural products and reduce energy consumption.