Explainable machine learning for assessing the metabolomic and elemental profiles of tomatoes irrigated with treated wastewater.

While reusing treated wastewater (TWW) for irrigation provides a sustainable solution to water scarcity, it can potentially introduce contaminants that may threaten crop safety and quality. Consequently, further research is needed to understand the effects of TWW on the metabolomic and elemental profiles of irrigated crops. This study investigated how using TWW affect metabolism and element uptake in tomatoes grown in soil (lysimeters) and soilless (hydroponics) systems. Soil-grown tomatoes were irrigated with potable water, treated wastewater, and treated wastewater spiked with 14 CECs (0.1 mg/L), which included bisphenols, non-steroidal anti-inflammatory drugs, estrogens, and caffeine. Hydroponically grown tomatoes were grown in a nutrient solution, with or without CECs. Tomatoes were assessed by analysing sugars, organic acids, polyphenols, carotenoids, amino acids, fatty acids, and elements. Classification machine learning models were applied, and the best-performing model, a decision tree classifier, achieved 88% accuracy in distinguishing treatments under stratified five-fold cross-validation. The SHAP method identified key metabolites (ascorbic, palmitic, margaric, oleic, linoleic, behenic acids) and elements (Cd, Co, Cs, Cu, P, Na) that drive treatment differentiation. This study demonstrates how explainable machine learning can decode complex metabolic interactions, providing insights into the effects of using treated wastewater in agriculture.