Exploring Surface-Enhanced Raman Spectroscopy (SERS) Characteristic Peaks Screening Methods for the Rapid...

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Food Safety

Background:

• Pesticide residues in food pose significant health risks.
• Accurate and rapid detection methods for chlorpyrifos (CM) are crucial for food safety.
• Traditional detection methods can be time-consuming and complex.

Purpose of the Study:

• To develop a rapid and sensitive method for detecting chlorpyrifos (CM) pesticide residues in rice.
• To optimize characteristic peak selection for quantitative analysis using SERS.
• To validate the developed method for real-world sample analysis.

Main Methods:

• Surface-enhanced Raman spectroscopy (SERS) utilizing gold nanoparticles (AuNPs) for signal amplification.
• Sample preparation involving magnesium sulfate (MgSO4), primary secondary amine (PSA), and C18 extraction.
• Successive projections algorithm (SPA) for identifying optimal spectral features.
• Support vector machine (SVM) and partial least squares (PLS) for quantitative modeling.

Main Results:

• Six characteristic Raman peaks (671, 834, 1016, 1114, 1436, 1444 cm⁻¹) were identified by SPA and SVM.
• The optimized model using six peaks achieved high performance (R² = 0.97, RMSEP = 2.89, RPD = 4.26).
• Analysis time per sample was reduced to within 10 minutes with excellent recovery rates (97.45-103.96%).

Conclusions:

• SERS coupled with characteristic peak screening offers a rapid and reliable method for chlorpyrifos residue monitoring in rice.
• The developed technique provides high accuracy and efficiency for food safety applications.
• This approach facilitates quick screening of pesticide contamination in agricultural products.