SERS characterization and concentration prediction of Salmonella in pork

Abstract

The SERS technique has great potential for the rapid detection of foodborne microorganisms. However, the rapid detection of Salmonella in pork is difficult because of the complex chemical composition contained in the food matrix.In order to resolve more effective data information from the Raman spectra of complex samples, the spectral data were firstly subjected to data preprocessing such as smoothing, de-baselining, and feature extraction. And the optimal number of windows for SG smoothing was explored. Then, the study performed PLSR analysis on three groups of extracted Raman feature: peak intensities, widths, and area of regions. Besides, the three groups of features were screened using cars. The modeling results showed that compared to the single group of features, the prediction of the model built by the Raman intensity at the 670 cm -1 , 737 cm -1 , 804 cm -1 , 896 cm -1 , 1096 cm -1 , 1231 cm -1 , 1377 cm -1 , and 1603 cm -1 Raman shifts, the widths of the characteristic peaks at the 1096 cm -1 and 1341 cm -1 shifts, and the areas of peak regions at the 737 cm -1 , 804 cm -1 , 1096 cm -1 , 1341 cm -1 , and 1377 cm -1 was improved to a large extent. A predictive correlation of 0.9058 and a predictive root mean square error of 0.9706 were achieved. The method mined more data related to the parameters to be measured from the Raman mapping features and provided a methodological reference for the SERS mapping resolution of complex samples.