Ag@CDS SERS substrate coupled with lineshape correction algorithm and BP neural network to detect thiram in beverages

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Materials Science

Background:

• Surface-enhanced Raman scattering (SERS) offers high sensitivity for chemical analysis but faces challenges in extracting meaningful data from complex spectra.
• Developing robust methods for spectral preprocessing is crucial for accurate quantification using SERS.

Purpose of the Study:

• To develop a novel spectral preprocessing algorithm, the lineshape correction algorithm (LCA), for SERS analysis.
• To create a composite SERS substrate (Ag@CDS) with enhanced "hot spots" for improved analyte detection.
• To quantitatively detect thiram residues in various beverages using the developed SERS substrate and LCA.

Main Methods:

• Fabrication of a composite SERS substrate by encapsulating silver nanoparticles within dialdehyde starch (Ag@CDS).
• Development and application of the lineshape correction algorithm (LCA) for SERS spectral preprocessing.
• Quantitative analysis of thiram residues using a back propagation (BP) neural network regression model.

Main Results:

• The Ag@CDS substrate provided dense "hot spots" for SERS detection.
• LCA significantly improved the predictive performance of the BP model for thiram detection in apple juice, grape juice, and milk (R_p² increased from ~0.24-0.56 to ~0.91-0.93).
• The optimized model achieved a low limit of detection (1.0 × 10^-7 M) for thiram, well below regulatory limits.

Conclusions:

• The developed LCA is an effective and user-friendly method for preprocessing SERS spectra, enhancing quantitative analysis.
• The Ag@CDS substrate combined with LCA offers a sensitive and reliable platform for detecting thiram residues in food and beverages.
• This approach demonstrates the potential of integrated SERS substrate design and spectral processing algorithms for trace contaminant analysis.