A Comparative Evaluation of Greek Fig Cultivars Utilizing Instrumental Analytical Methodologies, In Silico Studies and Machine Learning Prediction

The present study provides a comprehensive comparative evaluation of three Greek fig cultivars through integrated instrumental, computational, and chemometric approaches. Fresh fig peel and flesh samples were analyzed to determine total soluble solids, total phenolic and flavonoid content, as well as antioxidant and antiradical activities, complemented by attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy for structural profiling. Significant varietal and tissue-dependent differences were observed, with fig peel exhibiting higher levels of phenolic compounds and antioxidant activity compared to flesh. ATR-FTIR spectral patterns revealed the presence of characteristic functional groups associated with carbohydrates, phenolic compounds, carboxylic acids, and volatile compounds, reflecting the influence of variety, pollination requirements, and geographical origin. In parallel, to explore potential neuroprotective relevance, 30 phytochemicals reported in figs were subjected to molecular docking against human β-secretase 1 (hBACE1), a key enzyme in Alzheimer’s disease (AD) pathogenesis. Phenolic acids and flavonoids displayed favorable binding affinities and interaction profiles with the catalytic Asp32–Asp228 dyad and with the flap domain. A machine learning model (XGBoost) trained on known BACE1 inhibitors further classified all examined fig metabolites as active candidates. Collectively, these findings highlight Greek figs as chemically rich fruits with potential biological properties, supporting future targeted studies on their bioactive potential.