Highly sensitive and precise SERS quantification of P. gingivalis in complex saliva matrices via AgNPs@4-MPBA substrates coupled with the NiPLS-AMLP algorithm.

Authors

Lina Bai, Kai Hu, Jie Li, Guoqiang Fang, Wuliji Hasi, Siqingaowa Han

Published in

Analytica chimica acta. Volume 1420. Pages 345963. Oct 22, 2026. Epub Jul 10, 2026.

Abstract

Precise quantitative detection of Porphyromonas gingivalis (P. gingivalis, Pg) is important for developing a potential non-invasive screening platform for key periodontal pathogens. To address the challenge of specific P. gingivalis detection in complex saliva matrices by surface-enhanced Raman scattering (SERS), this study developed an intelligent quantitative sensing platform combining a SERS substrate based on 4-mercaptophenylboronic acid-functionalized silver nanoparticles (AgNPs@4-MPBA) with a nested interval partial least squares-attention mechanism multilayer perceptron (NiPLS-AMLP) framework. Based on the enhancement of the C-S-related characteristic peak at 674 cm^-1 and the bacterial control experiments, 4-MPBA may interact with the O-glycosylated gingipain glycans on the P. gingivalis outer membrane through its terminal boronic acid group, thereby promoting bacterial capture. Meanwhile, interactions between 4-MPBA and gingipain-related cysteine residues may contribute to the enhancement of the C-S-related Raman signal. For data analysis, the NiPLS-competitive adaptive reweighted sampling (CARS) strategy identified the 650-699 cm^-1 core spectral region, providing biochemical interpretability, whereas the AMLP model improved the quantitative accuracy for low-concentration P. gingivalis signals. To mitigate matrix effects caused by artificial and real human saliva matrices, semi-supervised transfer learning was further introduced to reduce cross-matrix domain shift. The platform achieved accurate P. gingivalis quantification in complex saliva matrices over the range of 2 x 10² to 2 x 10⁹ CFU/mL, with an R² of 0.98 and a limit of detection of 200 CFU/mL. These results support the use of this platform as a proof-of-concept approach for potential non-invasive P. gingivalis screening in complex saliva matrices and provide a strategy for intelligent SERS-based quantification of pathogens in complex biological samples.

PMID:
42648839
Bibliographic data and abstract were imported from PubMed on 27 Aug 2026.

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