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United States Department of Agriculture

Agricultural Research Service

Research Project: AERIAL APPLICATION RESEARCH FOR EFFICIENT CROP PRODUCTION Title: Rapid quantitative analysis of Dimethoate pesticide using surface enhanced raman spectroscopy

Authors
item Liu, Yande -
item Wan, Changlan -
item Hao, Yong -
item Lan, Yubin

Submitted to: Food and Bioprocess Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: April 2, 2013
Publication Date: July 10, 2013
Citation: Liu, Y., Wan, C., Hao, Y., Lan, Y. 2013. Rapid quantitative analysis of Dimethoate pesticide using surface enhanced raman spectroscopy. Food and Bioprocess Technology. 56:1043-1049.

Interpretive Summary: Pesticides are used to prevent, destroy, repel or mitigate any agricultural pest, including insects, rodents, and weed. Because of their acute toxicity and wide use, they have attracted public concern worldwide about trace amounts of the residues in agricultural products that might cause long-term non-fatal health effects. Although chemical analysis methods ca be used to detect tract amount of pesticide residues, they require complicated and time-consuming sample pretreatments such as solvent evaporation, boiling, and dilutions. We applied surface enhanced raman scattering (SERS) to make quantitative detection of dimethoate pesticide with klarite SERS substrates. The application of the SERS technique coupled with klarite substrates in trace analysis of dimethoate at part-per-million levels were demonstrated. The new method establishes a practical application for pesticide residue analysis of food.

Technical Abstract: A method for rapid quantitative detection of dimethoate pesticide by using surface-enhanced Raman spectroscopy (SERS) has been described. Significantly enhanced Raman signals of pesticide in low concentrations of 0.5 ~ 10 ug/mL were acquired by confocal raman micro-spectrometry with renishaw diagnostics’ klarite SERS substrates. Multivariate statistical method-partial least squares (PLS) combined with different preprocessing methods was applied to develop quantitative models. The best model with the highest correlation coefficient (R P, R P = 0.969) and the lowest root mean square error of predictions (RMSEP, RMSEP=0.626) was achieved when the spectra were preprocessed by first derivative combined with SNV in the regions of 1845.5-1186.9 and 1023-199.5, which demonstrated that SERS coupled with klarite SERS substrates was an excellent tool for rapid determination of pesticides residue at ppm level.

Last Modified: 4/23/2014