Diagnosing arsenic-mediated biochemical responses in rice cultivars using Raman spectroscopy

• Published in: Frontiers in plant science Vol. 15; p. 1371748
• Main Authors: Juárez, Isaac D, Dou, Tianyi, Biswas, Sudip, Septiningsih, Endang M, Kurouski, Dmitry
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 25-03-2024
• Subjects: analytical techniques; carotenoids; non-invasive analysis; Oryza sativa; phenylpropanoids; Plant Science; stress pathways; Oryza sativa; carotenoids; non-invasive analysis; analytical techniques; bioaccumulation; phenylpropanoids; stress pathways
• ISSN: 1664-462X; 1664-462X
• DOI: 10.3389/fpls.2024.1371748

Rice (
) is the primary crop for nearly half of the world's population. Groundwater in many rice-growing parts of the world often has elevated levels of arsenite and arsenate. At the same time, rice can accumulate up to 20 times more arsenic compared to other staple crops. This places an enormous amount of people at risk of chronic arsenic poisoning. In this study, we investigated whether Raman spectroscopy (RS) could be used to diagnose arsenic toxicity in rice based on biochemical changes that were induced by arsenic accumulation. We modeled arsenite and arsenate stresses in four different rice cultivars grown in hydroponics over a nine-day window. Our results demonstrate that Raman spectra acquired from rice leaves, coupled with partial least squares-discriminant analysis, enabled accurate detection and identification of arsenic stress with approximately 89% accuracy. We also performed high-performance liquid chromatography (HPLC)-analysis of rice leaves to identify the key molecular analytes sensed by RS in confirming arsenic poisoning. We found that RS primarily detected a decrease in the concentration of lutein and an increase in the concentration of vanillic and ferulic acids due to the accumulation of arsenite and arsenate in rice. This showed that these molecules are detectable indicators of biochemical response to arsenic accumulation. Finally, a cross-correlation of RS with HPLC and ICP-MS demonstrated RS's potential for a label-free, non-invasive, and non-destructive quantification of arsenic accumulation in rice.