Food Analytical Methods

, Volume 11, Issue 9, pp 2518–2527 | Cite as

Detection of Omethoate Residues in Peach with Surface-Enhanced Raman Spectroscopy

  • Tehseen Yaseen
  • Da-Wen Sun
  • Hongbin Pu
  • Ting-Tiao Pan


In this work, surface-enhanced Raman spectroscopy (SERS) was used with silver colloid substrate for rapid detection of omethoate (an organophosphate pesticide) in standard solution and peach extract. The findings demonstrated that the characteristic wavenumber of the pesticide could be precisely identified using the SERS method. The calibration curve was plotted between concentrations and Raman intensities of the target peak at 1649 cm−1 for the peach extract and at 1647 cm−1 for the standard solution. The coefficients of determination (R2) of 0.9829 and 0.98 were obtained for standard solution and for peach extract, respectively. The calculated limits of detection for omethoate in standard solution and in peach extracts were 0.001 mg L−1 and 0.01 mg kg−1, respectively. This study revealed that the proposed method could be used for the analysis of trace contaminants like omethoate in multifaceted food matrices.


SERS Residue detection Omethoate Substrate Fruit 


Funding Information

The authors are grateful to the National Key R&D Program of China (2017YFD0400404) for its support. This research was also supported by the Collaborative Innovation Major Special Projects of Guangzhou City (201604020007), the Guangdong Provincial Science and Technology Plan Projects (2015A020209016, 2016A040403040), the Fundamental Research Funds for the Central Universities (2017MS067, 2017MS075), the Agricultural Development and Rural Work of Guangdong Province (2017LM4173), the S&T Project of Guangdong Province (2017B020207002), the Pearl River S&T Nova Program of Guangzhou (201610010104), the International and Hong Kong–Macau–Taiwan Collaborative Innovation Platform of Guangdong Province on Intelligent Food Quality Control and Process Technology and Equipment (2015KGJHZ001), the Guangdong Provincial R&D Centre for the Modern Agricultural Industry on Non-destructive Detection and Intensive Processing of Agricultural Products, the Common Technical Innovation Team of Guangdong Province on Preservation and Logistics of Agricultural Products (2016LM2154), and the Innovation Centre of Guangdong Province for Modern Agricultural Science and Technology on Intelligent Sensing and Precision Control of Agricultural Product Qualities. In addition, Tehseen Yaseen is a recipient of a PhD scholarship from the China Scholarship Council (CSC).

Compliance with Ethical Standards

Conflict of Interest

Tehseen Yaseen declares that she has no conflict of interest. Da-Wen Sun declares that he has no conflict of interest. Hongbin Pu declares that he has no conflict of interest. Ting-Tiao Pan declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.

Informed Consent

Not applicable.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Food Science and EngineeringSouth China University of TechnologyGuangzhouPeople’s Republic of China
  2. 2.Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega CenterSouth China University of TechnologyGuangzhouPeople’s Republic of China
  3. 3.Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain FoodsGuangzhou Higher Education Mega CentreGuangzhouChina
  4. 4.Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College DublinNational University of IrelandDublin 4Ireland

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