Detecting and Combating Heavy Metal Lead Contamination in Wheat Seedlings Using LIBS-Raman


In a recent study, collaborative spectroscopic methods, such as LIBS-Raman, were used for detecting and combatting heavy metal lead contamination in wheat seedlings, revealing critical insights for food security and human health.

Over the past few years, the accumulation of heavy metal lead (Pb) in agricultural soil has emerged as a significant threat to human health by impacting crops. The presence of Pb in soil poses a substantial risk to wheat grain quality, for example, because this toxic metal can infiltrate the plant through its roots and stems. The consequential increase in Pb concentration not only jeopardizes the food security associated with wheat, but it also exposes consumers to potential health hazards.

Harvest concept, close up of male hand in the wheat field with copy space | Image Credit: © rangizzz -

Harvest concept, close up of male hand in the wheat field with copy space | Image Credit: © rangizzz -

A recent study published in the Journal of Analytical Atomic Spectroscopy presents a spectroscopic approach that is designed to address this critical issue (1). Researchers from Northwest A&F University, Ministry of Agriculture and Rural Affairs, and the Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service in China used spectroscopic techniques such as double-pulse laser-induced breakdown spectroscopy (DP-LIBS) and surface-enhanced Raman spectroscopy (SERS) to analyze the effects of Pb stress on wheat seedlings (1).

The study cultivated wheat seedlings under varying Pb stress levels, ranging from 0 mg/L (control) to 5000 mg/L (1). The research team closely examined the root, stem, and leaf of the seedlings using DP-LIBS and SERS spectrometers to obtain the enhanced spectral data. Through using principal component analysis (PCA), the analysis revealed distinct differences in the samples' spectral profiles, distinguishing the wheat seedling roots and leaves under different Pb concentration stresses (1).

The study also went further by employing advanced discriminant models, including least squares support vector machines (LS-SVM), multi-layer perceptron-artificial neural network (MLP-ANN), radial basis function-artificial neural network (RBF-ANN), and probabilistic neural networks (PNN) to enhance classification accuracy (1). The researchers found out that the model constructed using a fusion of DP-LIBS and SERS data, referred to as LIBS-Raman, significantly outperformed models built solely on SERS and DP-LIBS data (1).

This study also delved into the migration of Pb in the substrate-root-leaf system. Interestingly, at the control concentration (0 mg/L), Pb was undetectable in both the substrate and wheat seedling root and leaf. Moreover, at this concentration, the researchers observed that the root retained a higher level of Pb stress than the leaf. This observation suggests the existence of a self-protection mechanism in wheat seedlings, potentially limiting their ability to transport Pb ions (1).

By employing innovative spectral analysis techniques and utilizing collaborative spectroscopy, this study contributes to the development of more accurate methods for detecting heavy metal contamination in plants. It sheds light on the intricate interaction between heavy metals and wheat seedlings, ultimately providing vital support for ensuring the safety and quality of one of the world's most essential crops.

This article was written with the help of artificial intelligence and has been edited to ensure accuracy and clarity. You can read more about our policy for using AI here.


(1) Yang, Z.; Li, J.; Zuo, L.; Zhao, Y.; Yu, K. Collaborative Estimation of Heavy Metal Stress in Wheat Seedlings based on LIBS-Raman Spectroscopy Coupled with Machine Learning. J. Anal. At. Spectrom. 2023, ASAP. DOI: 10.1039/D3JA00243H

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