Akademik Veri Yönetim Sistemi
ACS OMEGA, cilt.10, ss.59300-59313, 2025 (SCI-Expanded, Scopus)
This study aimed to improve the functional and nutritional
properties of garden cress (Lepidium sativum) juice using ultrasound and
optimize process parameters by modeling them with advanced machine learning
algorithms. Using a Box−Behnken experimental design, the effects of sonication
time (8−16 min) and amplitude (60−100%) on total chlorophyll, total phenolic
content (TPC), and ferric reducing antioxidant power (FRAP) were
investigated. Nonparametric, high-accuracy estimations were made using the
XGBoost algorithm. Optimum conditions were determined to be 12 min and
80% amplitude. Under these conditions, TPC (78.44 mg GAE/mL), FRAP
(59.80 mg TE/mL), and chlorophyll (7.15 g/100 mL) values were significantly
higher than those in control and pasteurized samples (p < 0.05). HPLC-DAD
analysis showed that ultrasound treatment positively impacted the phenolic
profile by increasing the release of quercetin, quercetin derivatives, caffeic acid,
and chrysin. GC-MS data revealed that volatile aroma compounds (especially 1-hexanol, benzaldehyde, and cinnamaldehyde) were
preserved mainly by ultrasound. In vitro digestion simulation showed that total postdigestion recovery rates in ultrasound-treated
samples were 34.96% for TPC, 32.50% for chlorophyll, and 28.81% for FRAP, demonstrating a significant increase in bioaccessibility.
PCA and hierarchical clustering analyses confirmed a significant biochemical separation of ultrasound-treated samples. The findings
indicate that ultrasound technology is a superior method for preserving bioactive compounds, maintaining the aroma profile, and
enhancing bioaccessibility compared to heat treatment. This enables data-driven process design. The developed model showed a
strong predictive performance under optimal conditions. However, the study is limited by the relatively small data set used for model
training.