## 4 Conclusions  
The utilization of a microwave vacuum dryer combined with PEF treatment exerted a substantial influence on the moisture ratio of the sample throughout the drying process. With a rise in the quantity of microwave energy, the drying rate increased significantly. However, elevating the specific energy of PEF did not impact the drying kinetics. The employed mathematical models for predicting the MR yielded satisfactory results, with the Weibull model offering the most accurate prediction (R\({}^{2}\) = 0.970, RMSE = 0.099 and MAE = 0.064). Furthermore, in pursuit of enhanced accuracy and the incorporation of more parameters for MR prediction, the optimized computational intelligence methods were employed. The outcomes from the developed ANN and SVR highlight that the machine learning approach can be contemplated for online control of the drying process due to its outstanding performance. Finally, an exploration of various qualitative indices of dried apples was conducted, revealing that the escalation of microwave energy exerted an influence on the color parameters, mechanical properties and sensory evaluation of the dried apple slices. Also, the PEF treatment influenced the characterization of the dried samples, even though not in a statistically significant manner. In conclusion, drying apple slices under 100 W of microwave energy with the aid of PEF treatment not only shortens the drying time but also preserves the product's quality.  
The results obtained indicated that employing PEF as a pretreatment prior to drying holds promise for fostering more sustainable processes, ensuring the nutritional attributes and maintaining the flavor of agricultural products. In addition, by leveraging ML algorithms, drying parameters such as temperature and humidity can be optimized for improved efficiency and product quality. ML models can analyze real-time data from sensors to predict drying times, detect anomalies or irregularities in the process and suggest adjustments to enhance energy usage and reduce waste. Additionally, ML techniques enable the development of predictive maintenance systems, allowing for proactive identification of equipment failures or performance degradation, thus minimizing downtime and ensuring continuous operation of drying systems. However, some challenges such as achieving more accuracy and versatility of developed MV dryers and applied ML models were found in this study. To overcome these kinds of challenges, it is suggested that the dyer be integrated with some novel technologies such as machine vision systems. This involves aligning the imaging setup to monitor the product consistently and possibly synchronizing it with the drying parameters. In addition, implementing the trained models within the drying system's control software can be a helpful action to achieve more accuracy. The models should process image processing data in real time to predict moisture content and other relevant parameters. Moreover, closing a feedback loop in which the predictions of the model can enable a dynamic adjustment of the drying conditions.  
M.R.: Conceptualization, Data curation, Methodology, Software, Writing--original draft. M.N.: Visualization, Writing--review and editing. J.P.: Visualization, Writing--review and editing. H.Z.: Visualization, Writing--review and editing. A.H.F.: Conceptualization, Project administration, Supervision, Visualization, Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
This project received support from the National Food Institute of the Technical University of Denmark. Additionally, the authors wish to express their gratitude to [PERSON] from the Food Production Engineering Research Group at DTU for his assistance in developing the laboratory equipment utilized in this project.  
The authors declare no conflicts of interest.