Drying plays a crucial role in preserving the quality of agricultural products. Nevertheless, suboptimal conditions in drying systems have an adverse effect on drying characteristics and energy efficiency. Machine learning approaches are innovative and reliable that have been successfully used to solve such challenges and achieve optimization in drying processes. In this study, five machine learning approaches (multilayer perceptron [MLP], gaussian processes [GP], support vector regression [SVR], k‐nearest neighbors [kN], and random forest [RF]) were used to estimate moisture content and moisture ratio of apricot in five various dryers (convective [CV], microwave [MW], infrared [IR], microwave‐convective [MW‐CV], and infrared‐convective [IR‐CV]). Also, the values of specific energy consumption (SEC) and effective moisture diffusivity (D_eff) were calculated in these dryers. Accordingly, the best result of the D_eff (3.14 × 10⁻¹⁰ m²/s) and the minimum value of the drying time (130 min) and SEC (18.67 MJ/kg) were obtained using MW‐CV hybrid dryer. While the lowest values of D_eff (2.09 × 10⁻¹¹ m²/s) and highest drying time (18.5 h) and SEC (209.34 MJ/kg) were detected in CV dryer at 50°C. The best correlation coefficients (R) for the estimation of moisture content were gained using RF technique for k‐fold cross validation and train‐test split with the values of 0.9908 and 0.9912, respectively. Moreover, moisture ratio results showed that the MLP achieved the highest R value over 0.9985 for both validation methodologies. In the discrimination of the drying methods, the MLP had the greatest accuracy as 82.00% and 86.00% for k‐fold cross validation and train‐test split, respectively. The results showed that the RF and ML models could potentially be used for estimation and discrimination for drying applications.Practical Applications: Recently, there has been an increased interest in healthy food choices such as foodstuffs, snacks, and dried products. This trend has captured the attention of both dietitians and conscious consumers. Apricots are a prime example of a valuable dried product that can be dry in various conditions. Machine learning techniques can be used for rapid and non‐destructive determination of drying characteristics and such techniques yield objective and accurate results. Present findings revealed that texture machine learning models could be used as an effective and reliable discrimination tool for dried products.

The present study was aimed to develop the powder from Moldavian balm extract using a spray dryer to preserve the valuable phytochemicals such as hydroxycinnamic acid and flavonoids. In order to produce optimum Moldavian balm spray‐dried powder, response surface methodology was applied. The inlet air temperature (120–180°C), compressed airflow rate (5–10 L/min), and carriers’ concentration (10%–30%) were kept as independent variables, while moisture content, drying performance, porosity, total phenol content, total flavonoid content, and antioxidant activity were selected as responses. The process was optimized with inlet air temperature of 140.36°C, compressed airflow rate of 9.13 L/min and carriers’ concentration of 18%, resulting in powder with moisture content of 7.68%, drying performance of 62.52%, porosity of 76.4%, total phenol content of 6.295 mg GAE/g, total flavonoid content of 0.378 mg QUE/g, and antioxidant activity of 51.78%. The optimized process led to attain the powder having significantly better phytochemical properties compared with others.

This study evaluates the impact of ultrasound (US), ethanol (ET), and combined ultrasound-ethanol (ET/US) pretreatments on the drying efficiency and quality of Rosa damascena petals during hot air drying. All pretreatments significantly reduced the drying time and specific energy consumption (p < 0.05). The highest reduction in drying time (52.38%) and specific energy consumption (64%) was observed for the ET/US pretreatment. Ultrasound alone (US30) and in combination with ethanol (ET/US) significantly improved rehydration ability and essential oil yield (up to 1.31%, p < 0.05). Furthermore, the ET/US treatment preserved more antioxidant activity, total phenolic content, and flavonoid content compared to other pretreatments and control (p < 0.05). The results of this study can show new perspectives for using the potential of ethanol- ultrasound as a cost-effective and energy-efficient method for industrial drying sensitive medicinal plants. Future studies could explore its scalability and applicability to other aromatic or perishable botanical species.