Rice is a staple food for over 60% of the world's population, and its production faces challenges such as water scarcity, land urbanization, climate change, and disruptions from the COVID-19 pandemic. Pesticide use is crucial for increasing rice yields, but excessive application leads to environmental pollution and contamination of the food chain, posing risks to human health. The Rapid Alert System for Food and Feed (RASFF) reports frequent exceedances of Maximum Residue Limits (MRLs) for acephate, carbendazim, thiamethoxam, and tricyclazole in Basmati rice. Conventional methods for pesticide analysis (LC-MS, GC-MS) are accurate but require complex extractions, lengthy analysis times, and expensive equipment, making on-site analysis difficult. Surface-Enhanced Raman Spectroscopy (SERS) offers a potential solution for rapid, cost-effective, and portable on-site analysis. While lab-based SERS techniques have been developed for pesticide detection in various food matrices, on-site analysis currently relies on less sensitive methods. This study aims to develop a handheld-SERS-based platform combined with QuEChERS extraction to detect the four pesticides mentioned above in Basmati rice, addressing the need for rapid and sensitive on-site analysis.

Previous research has demonstrated the effectiveness of SERS for detecting various food and environmental contaminants, including mycotoxins, antibiotic residues, mercury, and tropane alkaloids. Several studies have explored SERS-based methods for detecting pesticide residues in different food matrices, utilizing various substrates like gold nanoparticles (AuNPs), silver-coated AuNPs, and gold nanorod arrays. However, these methods primarily focus on laboratory-based techniques, lacking the portability required for on-site analysis. Various extraction procedures have been developed for pesticide residue analysis, including swab techniques, original QuEChERS, and buffered QuEChERS. QuEChERS, designed for high moisture and low-fat content matrices, has been adapted for grains and rice, but primarily with LC-MS and GC-MS analysis. This study builds upon existing research by integrating handheld-SERS with QuEChERS extraction, aiming to improve sensitivity and enable on-site analysis.

The study involved several key steps: 1. **Raman Spectral Characterization:** Raman spectral data was acquired for solid pesticide powders using both a benchtop microscope and a handheld instrument to establish the handheld device's performance and identify characteristic peaks. 2. **Synthesis and Characterization of AuNP Substrates:** AuNPs were synthesized using the Frens method, varying sodium citrate concentration to optimize particle size and SERS enhancement. UV-Vis spectroscopy and Dynamic Light Scattering (DLS) were used to characterize the AuNPs. The SERS enhancement was assessed using Rhodamine 6G (R6G) as a probe molecule. 3. **Optimization of Pesticide Detection:** Experimental parameters, including Au substrate concentration, pesticide-to-Au ratio, HCl concentration and incubation time were optimized for maximum SERS enhancement. UV-Vis analysis was used to assess the stability of AuNPs in the presence and absence of pesticides and HCl. 4. **Analysis of Pesticide Standards:** Pesticide standards at varying concentrations were analyzed using handheld-SERS to determine the working range and detection limits. The results were validated against a benchtop Raman microscope. 5. **Extraction of Pesticide Residues from Rice:** Four extraction methods were evaluated for recovering TRI from spiked Basmati rice: swab extraction, solvent extraction, QuEChERS acetate, and original QuEChERS. The optimal extraction method was determined based on recovery efficiency. The impact of sample grinding and hydration time on extraction efficiency was also investigated. 6. **Analysis of Pesticide Residues in Rice:** Basmati rice spiked with different concentrations of the four pesticides was extracted using the chosen method and analyzed with handheld-SERS. Results were validated using a benchtop Raman microscope. The release factor was calculated to determine the extraction efficiency. 7. **Multiplex Analysis:** A mixture of the four pesticides at different concentrations was analyzed in solvent and after extraction from spiked rice to evaluate the multiplexing capabilities of the method.

The handheld SERS device successfully produced Raman fingerprint spectra comparable to those obtained with a benchtop Raman microscope. AuNPs synthesized with a λmax of 528 nm were found to be the most suitable SERS substrate for pesticide analysis. The addition of HCl (2 M) facilitated the aggregation of AuNPs, enhancing SERS signals. Detection limits for pesticide standards in solvent were 62 ppb (acephate), 47 ppb (carbendazim), 75 ppb (thiamethoxam), and 5 ppb (tricyclazole). QuEChERs acetate extraction proved most effective for extracting the pesticides from Basmati rice, significantly improving detection limits in matrix conditions. Three out of four pesticides (carbendazim, thiamethoxam, and tricyclazole) were detected below the EU MRL of 10 ppb in rice. The improved sensitivity in matrix conditions was attributed to the high ionic strength and low pH of the extraction medium, promoting AuNP aggregation. Multiplex analysis showed that while the four pesticides could be detected simultaneously, spectral overlapping, particularly with tricyclazole, presented a challenge to accurate quantification of the other pesticides.

This study successfully demonstrates a rapid and sensitive handheld SERS-based method for detecting multiple pesticide residues in Basmati rice. The combination of optimized AuNP substrates, QuEChERS acetate extraction, and handheld SERS significantly enhanced the detection limits compared to analysis of standards in solvent. The improved sensitivity in the rice matrix is attributed to the favorable conditions of the QuEChERs acetate extraction, which promotes AuNP aggregation and enhances SERS signal. While the method successfully detected three of the four pesticides below the EU MRLs, further improvements are needed to enhance sensitivity for acephate and address spectral overlapping in multiplexed analysis. The development of more specific AuNP substrates and application of machine learning algorithms could potentially mitigate spectral interference and improve multiplexing capabilities.

This research presents a novel handheld SERS platform coupled with QuEChERS acetate extraction for the rapid and sensitive detection of pesticide residues in Basmati rice. The method successfully detected three out of four target pesticides below the EU MRLs, highlighting its potential as a valuable on-site screening tool. Future research should focus on improving the selectivity and sensitivity of the method, particularly for acephate, and exploring the application of machine learning techniques to enhance multiplexing capabilities. This technology holds promise for improving food safety and environmental monitoring.

The main limitation of the current method is the spectral overlapping observed during multiplex analysis, especially with tricyclazole. This overlapping hinders the accurate quantification of other pesticides in the mixture. The sensitivity for acephate remains relatively low, even with the optimized extraction method. The study focused on Basmati rice, and further investigation is needed to determine the method's generalizability to other food matrices and different types of pesticides. The recovery rates for some pesticides were not 100%, suggesting some residues may not have been completely released during extraction.