Integrating Machine Learning and SHAP Analysis to Boost Callus Growth and Rutin Biosynthesis in Capparis spinosa L.

Capparis spinosa L. is a Mediterranean medicinal species of high economic value, yet its large-scale propagation and metabolite production remain constrained by conventional approaches. A full factorial design was used to evaluate the effects of four plant growth regulators, 6-benzylaminopurine, kinetin, 2,4-dichlorophenoxyacetic acid, and 1-naphthaleneacetic acid, on fresh weight gain from leaf explants. Data from twenty hormonal treatments were modeled using four machine learning algorithms: Random Forest, Gradient Boosting, Extreme Gradient Boosting, and second-degree polynomial regression. Random Forest provided the highest predictive accuracy. SHapley Additive exPlanations analysis identified 2,4-dichlorophenoxyacetic acid as the dominant factor driving callogenesis, with 6-benzylaminopurine exerting a secondary synergistic effect, whereas kinetin and 1-naphthaleneacetic acid showed minimal or inhibitory influence. Experimental validation confirmed the five best Random Forest–predicted hormonal combinations, including the optimal mixture of 6-benzylaminopurine and 2,4-dichlorophenoxyacetic acid, which produced the highest increase in callus fresh weight gain. Rutin, the major bioactive flavonoid of C. spinosa , was identified by LC-QTOF-MS/MS tandem mass spectrometry and semi-quantified by LC-TQ-MS/MS under 6-benzylaminopurine and 2,4-dichlorophenoxyacetic acid combinations. A stacked Random Forest model integrating fresh weight gain predictions successfully estimated rutin accumulation, with maximal production at moderate hormone levels. This integrative machine learning and SHapley Additive exPlanations framework offers an interpretable and scalable strategy for optimizing callus culture and enhancing high-value metabolite production in C. spinosa . Moreover, callus culture represents a promising and sustainable alternative for large-scale production of valuable metabolites, reducing reliance on wild plant resources.