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*,
{"title":"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","authors":"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*, ","doi":"10.1021/acsagscitech.4c00005","DOIUrl":null,"url":null,"abstract":"<p >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.</p>","PeriodicalId":93846,"journal":{"name":"ACS agricultural science & technology","volume":"4 4","pages":"389–404"},"PeriodicalIF":2.3000,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Raman Spectroscopy-Based Chemometrics for Pesticide Residue Detection: Current Approaches and Future Challenges\",\"authors\":\"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*, \",\"doi\":\"10.1021/acsagscitech.4c00005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >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.</p>\",\"PeriodicalId\":93846,\"journal\":{\"name\":\"ACS agricultural science & technology\",\"volume\":\"4 4\",\"pages\":\"389–404\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-03-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS agricultural science & technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://pubs.acs.org/doi/10.1021/acsagscitech.4c00005\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS agricultural science & technology","FirstCategoryId":"1085","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsagscitech.4c00005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
Raman Spectroscopy-Based Chemometrics for Pesticide Residue Detection: Current Approaches and Future Challenges
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.