Machine learning-driven multimodal optimization of selenium biotransformation and flavor profiling in fermented apple–Yacon functional beverages

Guan T., Gong J., Lin J., Palanisamy C. P., Pei J., Hassibelnaby A. M. A.

Innovative Food Science and Emerging Technologies, cilt.105, 2025 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 105
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.ifset.2025.104198
  • Dergi Adı: Innovative Food Science and Emerging Technologies
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Biotechnology Research Abstracts, CAB Abstracts, Compendex, Food Science & Technology Abstracts, Veterinary Science Database
  • Anahtar Kelimeler: Aroma compound evolution, Berenriched fermentation, Multivariate data integration, Predictive modeling algorithms, Response surface methodology
  • Atatürk Üniversitesi Adresli: Evet

Özet

This study developed an artificial intelligence (AI)-driven framework to optimize selenium-enriched Yacon-apple juice fermentation. By integrating response surface methodology (RSM) and extreme gradient boosting (XGBoost) modeling, key parameters (34.8 °C, 1:2.2 apple:yacon ratio, 0.65 g/L enzyme) were identified, resulting in 89.78 % selenium conversion and high bioactive yields (149.42 mg/100 mL polysaccharides; 1.250 mg/mL flavonoids). XGBoost demonstrated superior predictive accuracy (R2 = 0.953) over traditional RSM, revealing temperature thresholds (34–35 °C) critical for Lactiplantibacillus plantarum YKX (L. plantarum YKX) activity. Headspace-gas chromatography–ion mobility spectrometry (HS–GC–IMS) analysis revealed fermentation-driven flavor evolution: 442 % ester accumulation (ethyl acetate) at 4 days correlated with sensory improvement (r = 0.91), whereas the content of aldehydes decreased by 23 %. Multimodal machine learning linked polysaccharide metabolism to flavor enhancement (R2 = 0.927) and identified a critical 12–24 h selenium conversion window (1.52 %/h rate). This work bridges empirical optimization with explainable AI, providing actionable guidelines (temperature control ±0.5 °C, enzyme synergy) for scaling functional foods. Limitations in dataset size highlight the need for sensor-augmented monitoring. This approach advances precision fermentation technologies to balance nutrient bioavailability, flavor complexity, and bioactive retention in selenium-enriched beverages.