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Published on: June 10, 2017
Structure analysis and non-invasive detection of cadmium-phytochelatin2 complexes in plant by deep learning Raman
Yinglei Zhao1, Jinnuo Zhang2, Mostafa Gouda3
1Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China; College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China.
Phytochelatins (PCs) chelate toxic heavy metals like cadmium (Cd) in plants. This study used Raman spectroscopy and deep learning to detect and visualize these Cd-PC complexes in plants for food safety and phytoremediation.
Area of Science:
- Plant biochemistry and environmental science
- Spectroscopy and computational chemistry
- Food safety and agricultural technology
Background:
- Plants produce phytochelatins (PCs) to detoxify heavy metals, forming complexes that aid stress tolerance.
- Detecting these heavy metal-PC complexes is crucial for identifying plants with high phytoremediation potential and ensuring food safety.
- Current methods for detecting these complexes can be complex and time-consuming.
Purpose of the Study:
- To characterize phytochelatin2 (PC2) and its cadmium (Cd) complexes using confocal Raman spectroscopy.
- To develop a non-invasive method for detecting and quantifying heavy metal-PC complexes in plants.
- To simplify the identification of high phytoremediation cultivars and assess food safety regarding heavy metal contamination.
Main Methods:
- Confocal Raman spectroscopy was employed to analyze phytochelatin2 (PC2) and its mixtures with Cd2+.
- Density functional theory (DFT) was used to characterize the structure of PC2 and Cd-PC2 complexes.
- Deep learning models were trained on Raman spectra for visualization, quantification, and classification of Cd-PC2 in plant tissues.
Main Results:
- PC2 chelates Cd2+ in a 2:1 ratio, forming Cd(PC2)2 complexes with characteristic Cd-S bond vibrations at 305 and 610 cm-1.
- PC2 acted as a natural probe, stabilizing Cd and enhancing its Raman signal for detection.
- Deep learning models successfully visualized, quantified, and classified Cd(PC2)2 within pak choi leaves using raw spectral data.
Conclusions:
- A general protocol using Raman spectroscopy for structural analysis and non-invasive detection of heavy metal-PC complexes in plants was established.
- This approach offers a novel method for simplifying the identification of high phytoremediation cultivars.
- The study provides a valuable tool for assessing heavy metal-related food safety and environmental monitoring.
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