Cellulose paper-based SERS sensor for sensitive detection of 2,4-D residue levels in tea coupled uninformative variable elimination-partial least squares

Food safety is a growing concern in recent years. This work presents the design of a simple and sensitive method for predicting 2,4-D (2,4-dichlorophenoxyacetic acid) residue levels in green tea extract employing surface-enhanced Raman spectroscopy (SERS) coupled uninformative variable elimination-partial least squares (UVE-PLS). Herein, SERS active citrate functionalized silver nanoparticles (AgNPs) with enhancement factor 1.51 x 10(8) was used to prepare cellulose paper (common office) templated SERS sensor for acquiring SERS spectra of 2,4-D. The principle of the work was based on the interaction between 2,4-D and citrate group of AgNPs via chlorine atoms in the concentration range 1.0 x 10(-4) to 1.0 x 10(3) mu g/g. Three different wavenumber selection chemometric algorithms were studied comparatively to build an optimum calibration model, among them UVE-PLS showed enhanced performance as evident from the RPD value of 6.01 and Rp = 0.9864. Under optimized experimental condition proposed paper-based SERS sensor exhibited detection limit and RSD of 1.0 x 10(-4) mu g/g and <5%, respectively. In addition, the validation results by HPLC method were satisfactory (p > 0.05). (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This study presents a simple and sensitive method for predicting 2,4-D residue levels in green tea extract using surface-enhanced Raman spectroscopy (SERS) coupled with uninformative variable elimination-partial least squares (UVE-PLS). The paper-based SERS sensor showed low detection limit and good reproducibility under optimized conditions, with satisfactory validation results when compared to the HPLC method.