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<p>Faba bean (<i>Vicia faba</i> L.) is an underutilized legume that accumulates the antiparkinson drug L-3,4-dihydroxyphenylalanine (L-DOPA) and co-occurring catecholamine (CA) intermediates, i.e., L-tyrosine and tyramine, in its leaf tissues. This study investigated the optimization of simultaneous extraction conditions for these bioactive compounds using a full factorial design (33 = 27 combinations, 81 observations with triplication) incorporating three factors: temperature (30&#xa0;°C, 45&#xa0;°C, 60&#xa0;°C), sample-to-liquid ratio (SL ratio: 1:10, 1:20, 1:30), and hydrochloric acid concentration (HCl: 0.01 N, 0.105 N, 0.20 N). Quantification was performed using Ultra-Performance Liquid Chromatography (UPLC) with validated recovery (87–96%) and intra-day precision (RSD <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\le \)</EquationSource> </InlineEquation> 3.5%). The yield datasets exhibited significant non-normality (Shapiro–Wilk test, <i>p</i> &lt; 0.001 for L-DOPA and tyramine), high skewness (tyramine: 1.27), and non-linear dependency structures (BDS test, <i>p</i> &lt; 0.01), characteristics that limit the reliability of conventional linear regression for optimisation. Accordingly, a Classification and Regression Trees (CART) model was developed, framing yield optimisation as a classification task by categorising responses into Low, Medium, and High-performance tiers based on interquartile thresholds. The CART model achieved classification accuracies of 84%, 84%, and 68% for L-DOPA, tyramine, and L-tyrosine, respectively, on a held-out test set. A naive ensemble of CART, Random Forest, and Gradient Boosting further improved accuracy for L-DOPA to 92%. Variable importance analysis identified HCl concentration as the dominant extraction driver for L-DOPA and tyramine, while SL ratio governed L-tyrosine yield. The identified optimal conditions were: L-DOPA at 30–45&#xa0;°C, SL 1:20, 0.105 N HCl; tyramine at 45–60&#xa0;°C, SL 1:10, 0.01 N HCl; L-tyrosine at 45&#xa0;°C, SL 1:30, 0.01 N HCl. These results support the potential utility of CART-based classification as a complementary, interpretable tool for multi-compound phytochemical extraction optimisation, with full assessment of reproducibility across genotypes and industrial-scale applicability remaining as important future work.</p> Graphical abstract <p></p>

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A classification-based machine learning strategy for nonlinear optimization of the antiparkinson drug L-DOPA and relevant catecholamine intermediates from Faba bean (Vicia faba L.) leaves

  • 期刊论文

  • 发表日期：

    2026年6月25日

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  • Mohammad Danish Jawed Ansari,
  • Sandip Garai,
  • K. K. Kanaka,
  • Chinta Sravani,
  • Tushar Kashyap,
  • Tanmaya Kumar Sahu,
  • Arnab Roy Choudhury,
  • Vijai Pal Bhadana,
  • Sujay Rakshit,
  • Rakesh Kumar Sinha,
  • Sujit Kumar Bishi

摘要

Faba bean (Vicia faba L.) is an underutilized legume that accumulates the antiparkinson drug L-3,4-dihydroxyphenylalanine (L-DOPA) and co-occurring catecholamine (CA) intermediates, i.e., L-tyrosine and tyramine, in its leaf tissues. This study investigated the optimization of simultaneous extraction conditions for these bioactive compounds using a full factorial design (33 = 27 combinations, 81 observations with triplication) incorporating three factors: temperature (30 °C, 45 °C, 60 °C), sample-to-liquid ratio (SL ratio: 1:10, 1:20, 1:30), and hydrochloric acid concentration (HCl: 0.01 N, 0.105 N, 0.20 N). Quantification was performed using Ultra-Performance Liquid Chromatography (UPLC) with validated recovery (87–96%) and intra-day precision (RSD \(\le \) 3.5%). The yield datasets exhibited significant non-normality (Shapiro–Wilk test, p < 0.001 for L-DOPA and tyramine), high skewness (tyramine: 1.27), and non-linear dependency structures (BDS test, p < 0.01), characteristics that limit the reliability of conventional linear regression for optimisation. Accordingly, a Classification and Regression Trees (CART) model was developed, framing yield optimisation as a classification task by categorising responses into Low, Medium, and High-performance tiers based on interquartile thresholds. The CART model achieved classification accuracies of 84%, 84%, and 68% for L-DOPA, tyramine, and L-tyrosine, respectively, on a held-out test set. A naive ensemble of CART, Random Forest, and Gradient Boosting further improved accuracy for L-DOPA to 92%. Variable importance analysis identified HCl concentration as the dominant extraction driver for L-DOPA and tyramine, while SL ratio governed L-tyrosine yield. The identified optimal conditions were: L-DOPA at 30–45 °C, SL 1:20, 0.105 N HCl; tyramine at 45–60 °C, SL 1:10, 0.01 N HCl; L-tyrosine at 45 °C, SL 1:30, 0.01 N HCl. These results support the potential utility of CART-based classification as a complementary, interpretable tool for multi-compound phytochemical extraction optimisation, with full assessment of reproducibility across genotypes and industrial-scale applicability remaining as important future work.

Graphical abstract