Thin layer chromatography coupled with surface enhanced Raman scattering for rapid separation and on-site detection of multi-components

Han, Caiqin; Wang, Qin; Yao, Yue; Zhang, Qian; Huang, Jiawei; Zhang, Hengchang; Qu, Lulu

doi:10.1016/j.chroma.2023.464217

Cited by 10 publications

(1 citation statement)

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“…The sample can then be simply transported for additional analysis for the qualitative measurement of pollutants. By concentrating the sample for easy measurement and enabling clear separation between analyte types based on mass, this technique combines chromatography and spectroscopy, opening the door to in situ real-time food quality monitoring. , Researchers have reported using TLC-SERS to detect analytes at incredibly low concentrations (even at the ppb level). − Since then, it has been shown that the technique may be applied to SERS-based environmental analysis including pesticide analysis.…”

Section: Comparative Analysis Of Pesticide Detection Technologies In ...mentioning

confidence: 99%

Raman Spectroscopy-Based Chemometrics for Pesticide Residue Detection: Current Approaches and Future Challenges

Sharma,

Kolašinac,

Jiang

et al. 2024

ACS Agric. Sci. Technol.

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Inappropriate pesticide usage leads to unsustainable agricultural practices and deteriorates the quality of fruits and vegetables by introducing potentially hazardous substances. Raman spectroscopy, specifically surface-enhanced Raman spectroscopy (SERS), offers high-sensitivity in situ monitoring of pesticide residues. This review emphasizes the importance of advanced databases and algorithms in interpreting Raman signals. Various statistical models are introduced for spectral analysis, including self-modeling curve resolution, multivariate curve resolution, and self-modeling mixture analysis. Additionally, this study provides comprehensive information on different SERS substrates and shows great potential in the determination of food pesticide residues. However, a multicomponent analysis is needed for pesticide mixtures. The overlapping of the bands needs to be considered due to the complex matrices of biological samples. Artificial neural networks (ANNs) are applied as nonlinear models when the analytes are in a multicomponent mixture. Further research is needed to establish standardized protocols for SERS-based pesticide quantitative detection, including sample preparation and data analysis.

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