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

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dc.contributor.authorSharma S.
dc.contributor.authorKola�inac S.
dc.contributor.authorJiang X.
dc.contributor.authorGao J.
dc.contributor.authorKumari D.
dc.contributor.authorBiswas S.
dc.contributor.authorSur U.K.
dc.contributor.authorDaji?-Stevanovi? Z.
dc.contributor.authorRao Q.
dc.contributor.authorRaha P.
dc.contributor.authorMukherjee S.
dc.date.accessioned2025-01-13T07:05:46Z
dc.date.available2025-01-13T07:05:46Z
dc.date.issued2024
dc.description.abstractInappropriate 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. � 2024 American Chemical Society
dc.identifier.doi10.1021/acsagscitech.4c00005
dc.identifier.issn26921952
dc.identifier.urihttps://dl.bhu.ac.in/ir/handle/123456789/2051
dc.language.isoen
dc.publisherAmerican Chemical Society
dc.subjectalgorithms
dc.subjectANN
dc.subjectpesticide residues
dc.subjectself-modeling mixture
dc.subjectSERS
dc.titleRaman Spectroscopy-Based Chemometrics for Pesticide Residue Detection: Current Approaches and Future Challenges
dc.typeReview
journal.titleACS Agricultural Science and Technology
journalvolume.identifier.volume4

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