Specificity and Selectivity of Raman Spectroscopy for the Detection of Dose-Dependent Heavy Metal Toxicities

Contamination of farmland with heavy metals (HMs), particularly arsenic, cadmium, and lead, poses significant risks to human health and food security, especially through HM bioaccumulation in rice (Oryza Sativa). Current methods of detection for HMs, such as ICP-MS, provide accurate measurements but are destructive and labor-intensive, limiting their feasibility for widespread agricultural use. In this study, we investigated the potential of Raman spectroscopy (RS) as a nondestructive, cost-effective alternative for the detection of HM stress and thereby uptake in rice. Using a dose-response experimental design, we examined the sensitivity of RS for detecting varying levels of arsenic, cadmium, and lead-induced stress. Our analyses revealed several dose-dependent changes in Raman peaks associated with carotenoid and phenylpropanoid abundance. We found these changes were specific to each HM, reflecting the activation of distinct stress-response mechanisms. We also performed ICP-MS of harvested rice tissue, allowing us to build Raman-based calibration curves for predicting the HM concentration within rice. Lastly, we built a machine-learning algorithm that could interpret the Raman spectra to diagnose the specific type of HM toxicity with an average of 84.5% accuracy after only 1 week of HM stress. These findings highlight the promise of RS as a valuable tool for real-time, nondestructive monitoring of HM contamination in rice crops. Notably, the dose-response experimental design demonstrated RS's ability to detect HM stress levels that aligned with typical environmental contamination.