Surface‐enhanced Raman spectroscopy coupled with advanced chemometric models for quantification of sulfide anion in environmental water samples

• Published in: Journal of Raman spectroscopy 2022-02, Vol.53 (2), p.202-210
• Main Authors: Shi, Cai‐Xia, Li, Sha‐Sha, Tian, Yong, Xu, Jia‐Jia, Wang, Yan‐Qian, Wang, Yuan‐Yang, Xue, Yong‐Bing, Li, Peng, Wang, Yan
• Format: Article
• Language: English
• Subjects: Anions; Calibration; environmental water samples; multiplicative effects model; Nanoparticles; Physical properties; Quantitative analysis; Raman spectroscopy; Silver; Spectroscopy; Spectrum analysis; Substrates; sulfide anion; Sulfides; surface‐enhanced Raman spectroscopy; Water analysis; Water sampling
• ISSN: 0377-0486; 1097-4555
• DOI: 10.1002/jrs.6274

In this work, surface‐enhanced Raman spectroscopy (SERS) was employed for the quantification of sulfide anion (S2−) in aqueous samples. Silver nanoparticles modified with trithiocyanuric acid were used as the SERS enhancing substrate. To address the problem of poor reproducibility of SERS signal caused by the uncontrollable variations in physical properties of enhancing substrate, the performance of several calibration models for quantitative analysis of the SERS measurements of S2− samples were evaluated and compared. The calibration models studied were univariate ratiometric model based on the intensity ratio between the characteristic SERS peaks of trithiocyanuric acid, PLS models built on the raw and preprocessed SERS measurements, and the generalized ratiometric indicator based multiplicative effects model (MEMGRI). Experimental results showed that among the calibration models considered in this contribution, only MEMGRI model achieved accurate and precise concentration predictions for S2− in the spiked environmental water samples with recovery rates varying within the range of 94% to 106%. Therefore, it is reasonable to expect that the combination of SERS technique with MEMGRI model may be a competitive alternative for quantitative analysis of environmental samples. In this work, a general ratiometric multiplicative effects model has been established and applied successfully for the quantitative detection of sulfide anion based on SERS analysis.