Extraction and identification of mixed pesticides’ Raman signal and establishment of their prediction models

Authors: ZHAI, CHEN - China Agricultural University; PENG, YANKUN - China Agricultural University; LI, YONGYU - China Agricultural University; Chao, Kuanglin - Kevin Chao

Submitted to: Journal of Raman Spectroscopy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/26/2017
Publication Date: 6/20/2017
Citation: Chen, Z., Peng, Y., Li, Y., Chao, K. 2017. Extraction and identification of mixed pesticides’ Raman signal and establishment of their prediction models. Raman Spectroscopy. 48(3):494-500.

Interpretive Summary: The use of conventional chromatography and electrochemical methods to evaluate pesticide residues on foods are limited in practical use due to issues related to instrument portability, operational complexity, and sample preparation requirements. A sensitive and non-destructive method to detect and measure residues of three pesticides (acetamidprid, chlorpyrifos, and carbendazim) applied to intact apples was developed using silver nanoparticles to enhance the low-intensity signals from the pesticide residues, a technique called surface-enhanced Raman spectroscopy. The mixed spectral signals, which contained information from apple skin and flesh, three pesticides, and silver particles, were then analyzed to extract information for each of the three pesticides. The method was demonstrated capable of detection at 5.4, 64, and 14 parts per billion for acetamidprid, chlorpyrifos, and carbendazim, respectively. Non-destructive sample evaluation could be completed within one hour and with minimal sample preparation, compared to conventional analytical methods that take up to 24 hours. This method using surface-enhanced Raman spectroscopy shows great promise for use in pesticide residue monitoring. The results of this research will benefit producers interested in monitoring or minimizing their pesticide applications as well as food processors and regulatory agencies interested in identifying pesticide residues on fresh produce products or ensuring that any such residues present are within acceptable limits.

Technical Abstract: A nondestructive and sensitive method was developed to detect the presence of mixed pesticides of acetamiprid, chlorpyrifos and carbendazim on apples by surface-enhanced Raman spectroscopy (SERS). Self-modeling mixture analysis (SMA) was used to extract and identify the Raman spectra of individual pesticides from spectra acquired for mixed pesticides on apples. Experimental results indicate that the spectral peaks observed for each pesticide when extracted from the mixed-pesticide spectra show no obvious differences compared to spectral peaks measured for individual samples of the pure pesticides. The lowest detectable levels of acetamiprid, chlorpyrifos and carbendazim on apple were 0.0054 mg/kg, 0.064 mg/kg and 0.014 mg/kg, respectively, which are below typical pesticide residue limits of interest. Quantitative prediction models were developed to use the mixed-pesticide spectra to predict the concentrations of the individual pesticides, resulting in correlation coefficients of 0.893 for acetamiprid, 0.926 for chlorpyrifos and 0.938 for carbendazim. This study demonstrates the use of ultrasensitive SERS for quantification of residual pesticides on apples with minimal sample preparation, showing great potential to serve as a useful means for monitoring pesticide residues on intact fruits and vegetables.