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Development of a novel wavelength selection method for the trace determination of chlorpyrifos on Au@Ag NPs substrate coupled surface-enhanced Raman spectroscopy

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Abstract

A novel wavelength selection method, namely interval combination population analysis-minimal redundancy maximal relevance (ICPA-mRMR), was employed for the trace level detection of chlorpyrifos (CPS) coupled surface-enhanced Raman spectroscopy (SERS). Herein, a highly sensitive SERS enhancement substrate, Au@Ag nanoparticles (NPs), was synthesized possessing strong enhancement of Raman signals for CPS quantification (enhancement factor: 2.5 × 106). Compared with other established methods such as partial least squares (PLS), synergy interval partial least squares-genetic algorithm (siPLS-GA) and competitive adaptive reweighted sampling-partial least squares (CARS-PLS), ICPA-mRMR yielded the best results with higher correlation coefficients (Rc = 0.9917, RP = 0.9895), ratios of performance to deviation (RPD = 6.8797), and lower root mean square errors (RMSEC = 0.1998, RMSEP = 0.2271). The proposed method was employed for the determination of trace level CPS in tea samples, and the recovery percentages were in the range 90%–108%. Meanwhile, this method was validated using a standard GC-MS method indicating no significant difference (P > 0.05). The proposed methodology offers a rapid, sensitive and powerful analytical platform for the detection of pesticide residues in food.

Graphical abstract: Development of a novel wavelength selection method for the trace determination of chlorpyrifos on Au@Ag NPs substrate coupled surface-enhanced Raman spectroscopy

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Publication details

The article was received on 30 Oct 2018, accepted on 04 Dec 2018 and first published on 07 Dec 2018


Article type: Paper
DOI: 10.1039/C8AN02086H
Citation: Analyst, 2019, Advance Article
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    Development of a novel wavelength selection method for the trace determination of chlorpyrifos on Au@Ag NPs substrate coupled surface-enhanced Raman spectroscopy

    J. Zhu, W. Ahmad, Y. Xu, S. Liu, Q. Chen, Md. M. Hassan and Q. Ouyang, Analyst, 2019, Advance Article , DOI: 10.1039/C8AN02086H

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