With a high moisture content, fresh peppers are perishable and rot easily. Drying is essential for shelf-life extension. The natural thin wax layer on the pepper surface hinders moisture transfer. Traditionally, chemical dipping or mechanical pricking is used to remove this wax layer. However, in chemical dipping, chemical residues can trigger food-safety issues, while the low efficiency of mechanical pricking hinders its industrial application. Feasible pretreatment methods are advantageous for industrial use. Here, an emerging pretreatment technique (high-humidity hot-air impingement blanching, HHAIB) was used for peppers before drying and its effects on drying characteristics, microstructure, and polyphenol oxidase (PPO) activity were explored. The impact of drying temperature on color parameters and red pigment content of pulsed-vacuum-dried peppers was also evaluated. PPO activity was reduced to less than 20% after blanching at 110 °C for 60 s. HHAIB reduced drying time and PPO activity and promoted chemical-substance release. Effective water diffusivity was highest (5.01 × 10⁻¹⁰ m²/s) after blanching at 110 °C for 90 s, and the brightness value and red pigment content were highest (9.94 g/kg) at 70 °C. HHAIB and pulsed vacuum drying are promising pretreatment and drying methods for enhancing the drying rate and quality of red peppers.

The effects of temperature, air velocity, and infrared radiation distances on the drying characteristics and quality of apple slices were investigated using infrared-assisted-hot air drying (IRAHAD). Drying temperature and air velocity had remarkable effects on the drying kinetics, color, total phenol content, total flavonoid content, and vitamin C content (VCC) of apple slices. Infrared radiation distance demonstrated similar results, other than for VCC and color. The shortest drying time was obtained at 70 °C, air velocity of 3 m/s and infrared radiation distance of 10 cm. A deep neural network (DNN) was developed, based on 4526 groups of apple slice drying data, and was applied to predict changes in moisture ratio (MR) and dry basis moisture content (DBMC) of apple slices during drying. DNN predicted that the coefficient of determination (R²) was 0.9975 and 1.0000, and the mean absolute error (MAE) was 0.001100 and 0.000127, for MR and DBMC, respectively. Furthermore, DNN obtained the highest R² and lowest MAE values when compared with multilayer perceptron (MLP) and support vector regression (SVR). Therefore, DNN can provide new ideas for the rapid detection of apple moisture and guide apple processing in order to improve quality and intelligent control in the drying process.