Structure analysis and non-invasive detection of cadmium-phytochelatin2 complexes in plant by deep learning Raman spectrum

Plants synthesize phytochelatins to chelate in vivo toxic heavy metal ions and produce nontoxic complexes for tolerating the stress. Detection of the complexes would simplify the identification of high phytoremediation cultivars, as well as assessment of plant food for safe consumption. Thus, a confocal Raman spectroscopy combined with density functional theory and deep learning was used for characterizing phytochelatin2 (PC2), and Cd-PC2 mixtures. Results showed the PC2 chelate Cd2+ in a 2:1 ratio to produce Cd(PC2)(2) ; Cd-S bonds of the Cd(PC2)(2) have signature Raman vibrations at 305 and 610 cm(-1) which are the most distinctive spectral signatures for varieties of Cd-PCs complexes. The PC2 was used as a natural probe to stabilize the chemical status of Cd, and to enrich and magnify Raman signature of the trace Cd for deep learning models which enabled condition of the Cd(PC2)(2) in pak choi leaf to be visualized, quantified, and classified by directly using raw spectra of the leaf. This study provides a general protocol by using Raman information for structure analysis and noninvasive detection of heavy metal-PCs complexes in plants and provides a novel idea for simplifying identification of high phytoremediation cultivars, as well as assessment of heavy metal related food safeties.

Automated summary

This study used confocal Raman spectroscopy combined with density functional theory and deep learning to characterize phytochelatin2 (PC2) and heavy metal-PC2 mixtures. The results provide a general protocol for structure analysis and noninvasive detection of heavy metal-PCs complexes in plants, and offer a novel idea for simplifying identification of high phytoremediation cultivars and assessment of heavy metal related food safeties.