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

IF 2.3 Q1 AGRICULTURE, MULTIDISCIPLINARY ACS agricultural science & technology Pub Date : 2024-03-22 DOI:10.1021/acsagscitech.4c00005
Shailja Sharma, Stefan Kolašinac, Xingyi Jiang, Juan Gao, Deeksha Kumari, Shiva Biswas, Ujjal Kumar Sur, Zora Dajić-Stevanović, Qinchun Rao*, Priyankar Raha and Santanu Mukherjee*, 
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Abstract

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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基于拉曼光谱的农药残留检测化学计量学:当前方法与未来挑战
杀虫剂的不当使用会导致不可持续的农业生产方式,并通过引入潜在的有害物质而降低水果和蔬菜的质量。拉曼光谱,特别是表面增强拉曼光谱(SERS),可对农药残留进行高灵敏度的原位监测。本综述强调了先进数据库和算法在解读拉曼信号方面的重要性。介绍了用于光谱分析的各种统计模型,包括自建模曲线分辨率、多元曲线分辨率和自建模混合物分析。此外,这项研究还提供了有关不同 SERS 底物的全面信息,并显示出在食品农药残留测定方面的巨大潜力。然而,农药混合物需要进行多组分分析。由于生物样本的基质复杂,需要考虑条带重叠的问题。当被分析物处于多组分混合物中时,可将人工神经网络(ANN)用作非线性模型。需要进一步开展研究,以建立基于 SERS 的农药定量检测的标准化方案,包括样品制备和数据分析。
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