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A Sensitive SERS Sensor Combined with Intelligent Variable Selection Models for Detecting Chlorpyrifos Residue in Tea
Hanhua Yang1, Hao Qian1, Yi Xu2
1School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, China.
Foods (Basel, Switzerland)
|August 10, 2024
Summary
A new method uses surface-enhanced Raman spectroscopy (SERS) with advanced models to detect chlorpyrifos insecticide residue in tea. This rapid and reliable technique offers accurate results comparable to traditional methods.
Area of Science:
- Analytical Chemistry
- Spectroscopy
- Agricultural Science
Background:
- Chlorpyrifos is a widely used agricultural insecticide with potential toxicity and presence in food products like tea.
- Accurate and rapid detection methods for chlorpyrifos residue are essential for food safety and regulatory compliance.
Purpose of the Study:
- To develop a novel, rapid, and reliable strategy for detecting chlorpyrifos residue in tea samples.
- To compare the performance of surface-enhanced Raman spectroscopy (SERS) combined with various intelligent variable selection models against traditional methods.
Main Methods:
- Fabrication of gold nanostars as SERS sensors for spectral measurement.
- Preprocessing of raw SERS spectra for quantitative analysis.
- Development and comparison of partial least squares and four intelligent variable selection models (MC-UVE, CAVE, ITRIV, VISSA) for chlorpyrifos detection.
Main Results:
- The SERS strategy demonstrated excellent stability, repeatability, and reproducibility.
- Sensitivity was assessed through limit of detection values for each model.
- The proposed method's accuracy was statistically equivalent to gas chromatography-mass spectrometry (GC-MS).
Conclusions:
- The combined SERS and intelligent variable selection approach provides a promising, accurate, and stable method for chlorpyrifos residue determination in tea.
- This strategy offers a viable alternative to conventional analytical techniques for rapid food safety monitoring.
