Machine learning-based identification of strain-specific aroma markers in fermented pumpkin juice: An integrated 1D-CNN and random forest approach
Year: 2026
Type: article
Abstract: This study investigated the impact of Levilactobacillus brevis , Lactiplantibacillus plantarum , and Leuconostoc citreum on the flavor and functional profiles of pumpkin juice fermented at 30 °C for 12 h. An integrated Random Forest (RF) and 1D-Convolutional Neural Network (1D-CNN) framework, interpreted via SHAP, identified strain-specific metabolic differences and volatile patterns. Under these laboratory-scale fermentation conditions, Leu. citreum PX3-5 produced a superior profile, significantly enhancing both total phenolic (54.61 mg GAE/kg) and flavonoid (79.64 mg RE/kg) contents, while enhancing creamy/buttery notes (acetoin OAV = 3.99, 2,3-butanedione OAV = 3.08). Conversely, L. plantarum HY1-3 was characterized by pronounced floral notes (β-ionone OAV = 31.07), while Lb. brevis PL6-1 imparted distinct mushroom/earthy notes (1-octen-3-ol OAV = 4.69). The integrated RF-CNN model robustly identified acetoin and 2-heptanone as differential markers. Notably, the CNN highlighted acetoin within a local associative pattern, suggesting its potential link to citrate metabolism. These chemical attributes aligned with the sensory profile of group PX3-5 as evaluated by a trained panel (n = 13), demonstrating consistency between the model-identified volatile markers and the observed sensory preference. This work provides a data-driven basis for developing value-added pumpkin products and proposes an analytical strategy for deciphering complex flavor systems in fermented plant-based foods. • Logistic models accurately described strain-specific LAB growth (R 2 =0.984-0.998). • RF-CNN identifies non-linear associative aroma patterns missed by PCA and PLS-DA. • Strain-specific metabolism dictates divergent flavor profiles (creamy vs. earthy). • Leu. citreum PX3-5 yields creamy flavor and enhances antioxidant capacity. • Acetoin and 2-heptanone differentiate fermentation profiles.
Source: LWT
Authors: Yun Cen, Xingye Sun, Qingyang Zhang, Weiye Cheng, Xiaoming Pan, Hansheng Gong, Chen Yu, Wenli Liu, Xiaoping Liu, Kanghee Ko, Huamin Li
Language: English
Subfield: Food Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $3,240
Awards: G183256472, G197662784, G7909662319
OpenAlex ID: W7143701641
