# Evaluation of Different Drying Methods on the Quality Parameters of _Acanthopanax semicosus_ Fruits
## 4 Discussion
Specifically, DPPH and ABTS assays primarily evaluate radical scavenging, inherently linked to polyphenic content. Consequently, drying techniques affecting polyphenol stability naturally impact antioxidant capacities. Future studies should employ complementary analytical techniques, such as HPLC or UPLC, enabling precise identification and quantification of individual bioactive phenolic and flavonoid compounds, thus minimizing assay-related interference. The characterization of volatile profiles in this study was primarily conducted using PCA, which accounted for 64.2% of the total variance (PC1: 35.9%, PC2: 28.3%). Although PCA effectively captured major compositional trends, it did not reach the typically recommended threshold (\(\geq\)70%) for robust dimensionality reduction, likely due to the inherent complexity and diverse responses of volatile compounds to drying treatments. Moreover, variance distribution and interpretability may be affected by data normalization and scaling methods. Alternative statistical techniques such as OPLS-DA or t-distributed Stochastic Neighbor Embedding (t-SNE) could provide superior class separation and deeper insightsinto the volatile changes induced by drying. A previous study found that PCA explained 66.47% variance in strawberry tree fruit volatile analysis, emphasizing the importance of complementary statistical approaches for interpreting complex metabolomic data [79]. Future studies should incorporate these methods to enhance the robustness and accuracy of volatile profiling. Constraints of fixed drying parameters. This study evaluated five drying methods under fixed experimental conditions; however, bioactive compound retention and drying efficiency depend on multiple parameters, including temperature gradients, drying duration, airflow, and humidity. The standardized conditions applied here might not fully capture these dynamic interactions, potentially limiting insights into the retention mechanisms of phenolics, flavonoids, and volatile compounds. Recent studies emphasize the importance of optimizing drying parameters using advanced statistical and computational methods. A recent study optimized the hybrid microwave-hot-air drying of sweet potatoes using response surface methodology (RSM), demonstrating that optimal conditions can significantly improve drying time and rehydration ability and minimize bioactive compound loss [80]. Furthermore, machine learning approaches have been increasingly employed to enhance drying efficiency. Artificial neural networks (ANNs) combined with genetic algorithms (GAs) were applied to optimize the spray drying of probiotic beverages, demonstrating their effectiveness in modeling and optimizing drying parameters to enhance bioactive compound retention [81]. Future studies should, thus, explore broader drying parameter ranges using optimization techniques such as RSM or machine learning to determine conditions that best preserve bioactive compounds in AS fruits. Integrating these advanced approaches will facilitate the development of more efficient drying processes tailored for nutraceutical applications. Lack of long-term stability assessments. This study primarily addressed the immediate impact of drying methods on AS fruit quality, neglecting the critical factor of post-processing stability. Bioactive compounds can degrade significantly over time due to oxidative, enzymatic, and structural changes, potentially affecting long-term efficacy and bioavailability. Storage conditions were found to have a significant impact on the stability of anthocyanin and ascorbic acid in dried raspberry products [82]. Future studies should, therefore, include accelerated aging tests and long-term storage trials to better understand phenolic, flavonoid, antioxidant, and volatile compound stability, ensuring improved shelf-life predictions and functional quality of dried AS fruits. Generalizability and sample representativeness.