# Surface-Enhanced Raman Spectroscopy (SERS) Method for Rapid Detection of Neomycin and Chloramphenicol Residues in Chicken Meat

## TL;DR

This paper presents a rapid method using SERS and PCA-LDA to detect neomycin and chloramphenicol residues in chicken meat, improving food safety.

## Contribution

A novel SERS-based classification method using PCA-LDA with optimized preprocessing for detecting antibiotic residues in chicken meat.

## Key findings

- The optimal SERS conditions included a 4-minute adsorption time and a 14th-generation gold colloid.
- PCA-LDA with second derivative and baseline correction achieved 100% classification accuracy for antibiotic residues.
- Characteristic peaks at 546 and 666 cm−1 effectively distinguished neomycin and chloramphenicol residues.

## Abstract

In the process of chicken breeding, there has been a great deal of abuse of antibiotics. Antibiotics can enter the human body along with the chicken meat, comprising a possible risk to human health. In this paper, principal component analysis (PCA)–linear discriminant analysis (LDA) was chosen to classify neomycin (NEO) and chloramphenicol (CAP) residues in chicken meat. A total of 400 chicken meat samples were used for the classification, of which 268 samples and 132 samples were used as the training sets and the test sets, respectively. The experimental condition of SERS spectrum collection was optimized, including the use of a gold colloid and active agent, and an improvement in the adsorption time. The optimal measurement conditions for the SERS spectra were an adsorption time of 4 min and the use of a 14th-generation gold colloid as the enhanced substrate without a surfactant. For three groups of different spectral preprocessing methods, the classification accuracies of PCA-LDA models for test sets were 78.79% for baseline correction, 84.85% for the second derivative and 100% for the second derivative combined with baseline correction. LDA was used to establish a classification model to realize the quick determination of NEO and CAP residues in chicken meat by SERS. The results showed that the characteristic peaks at 546 and 666 cm−1 could be used to distinguish NEO and CAP residues in chicken meat. The classification model based on PCA-LDA had higher classification accuracy, sensitivity and specificity using a second derivative combined with baseline correction as the spectral preprocessing method, which shows that the SERS method based on PCA-LDA could be used to perform the classification of NEO and CAP residues in chicken meat quickly and effectively. It also verified the feasibility of PCA-LDA to effectively classify chicken meat samples into four types. This research method could provide a reference for the measurement of such antibiotic residues in chicken meat in the future.