Raman Spectroscopic Diagnostics of Arsenic Bioaccumulation in Rice

Modern agriculture faces the interconnected issue of increasing productivity while managing threats from climate change, pests, and environmental contaminants. Sensing technologies are one of the leading solutions in research due to their rapid, non-invasive nature, enabling quick detection of crop stress and timely implementation of mitigation strategies by agriculturalists. Among these technologies, Raman spectroscopy (RS) stands out for its ability to provide biochemically specific data, and thereby more robust diagnostics. However, many studies implementing RS fall short by failing to link spectral changes to their underlying biochemical mechanisms. They also often overlook field complexities, such as the co-occurrence of multiple stressors and variations in stress severity. Therefore, this project aims to (i) identify the molecular species responsible for the underlying stress response detected by RS and (ii) evaluate the technique's specificity and sensitivity for detecting an agricultural stressor. Using rice as our model crop and arsenic as a representative environmental stressor, we specifically investigated the potential of RS to detect arsenic contamination, given rice's central role in global food security and its high propensity to accumulate heavy metals. First, we found that utilization of carotenoids and activation of phenylpropanoid pathways caused changes within the Raman spectra, serving as key biomarkers of arsenic stress in rice. We investigated these changes in relative biomolecular content using high-performance liquid chromatography to identify key molecular species and inductively coupled plasma mass spectrometry to quantify arsenic uptake. Then, we further demonstrated that RS had the specificity to distinguish arsenic-induced biochemical changes from nitrogen deficiency and other heavy metals across rice development, including during combined stressor scenarios. Each stressor utilized in these experiments elicited at least partially distinct responses within the crops' spectra, demonstrating RS's potential for a broad range of disease detection. Finally, we also demonstrated that RS had the sensitivity to diagnose the bioaccumulation of arsenic and other heavy metals at levels relevant to environmental contamination levels. In all experiments, Raman spectra were coupled with partial least squares discriminant analysis to develop diagnostic models that could accurately determine arsenic exposure under the variety of conditions tested. These findings overall demonstrate the capabilities of RS as a diagnostic tool for agricultural stressors, addressing critical gaps in current knowledge.

Name thesis:degree_name

Doctor of Philosophy

Discipline thesis:degree_discipline

Toxicology

Grantor

Texas A&M University

Year dc:date.issued

2025

Author dc:creator

• Juárez Hinojosa, Isaac Daniel 2001-

Advisor dc:contributor.advisor

• Kurouski, Dzmitry

Committee members dc:contributor.committeemember

• Sokolov, Alexei
• Septiningsih, Endang
• Rusyn, Ivan

• Environmental Sciences
• Agriculture, Plant Pathology
• Biophysics, General

Language dc:language.iso

English

Handle dc:identifier.uri

https://hdl.handle.net/1969.1/1599877

OAI record oai:identifier

https://oaktrust.library.tamu.edu/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Aoaktrust.library.tamu.edu%3A1969.1%2F1599877