Principal component analysis of normalized SERS spectra for trace-level analyte quantification

doi:10.1016/j.jmst.2025.03.073

Abstract

Abstract: Development of approaches for reliable quantification of trace-level analytes by surface enhanced Raman scattering (SERS) is of great significance due to its potential applications in a diversity of fields. We developed an approach by principal component analysis (PCA) of the normalized SERS spectrum and the relative scattering ability (RSA) between molecules to quantify trace-level analytes. It was found that both the normalized SERS spectra and the RSA between molecules on a SERS substrate can be experimentally measured. Due to their material-determined nature, once measured they can serve as a database for quantification by SERS on substrates of the same material. PCA can greatly reduce the dimension of spectral data and list the few non-zero data in the spectra in sequence first. Thus, it makes SERS quantification simple and automated, with less error. This approach enables SERS to be an applicable technique in trace-level identifications in a diversity of fields.

Key words: Normalized SERS spectrum, Principal component analysis, Scattering ability, Quantitative analysis by SERS

Xin Xie, Yuanhao Zheng, Fengtong Zhao, Weipeng Wang, Wangyang Fu, Yunhan Ling, Zhengjun Zhang. Principal component analysis of normalized SERS spectra for trace-level analyte quantification[J]. J. Mater. Sci. Technol., 2026, 241: 107-113.

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