Analytical and Bioanalytical Chemistry

, Volume 411, Issue 3, pp 705–713 | Cite as

Fast discrimination of bacteria using a filter paper–based SERS platform and PLS-DA with uncertainty estimation

  • Javier E. L. Villa
  • Nataly Ruiz Quiñones
  • Fabiana Fantinatti-Garboggini
  • Ronei J. PoppiEmail author
Research Paper


Rapid and reliable identification of bacteria is an important issue in food, medical, forensic, and environmental sciences; however, conventional procedures are time-consuming and often require extensive financial and human resources. Herein, we present a label-free method for bacterial discrimination using surface-enhanced Raman spectroscopy (SERS) and partial least squares discriminant analysis (PLS-DA). Filter paper decorated with gold nanoparticles was fabricated by the dip-coating method and it was utilized as a flexible and highly efficient SERS substrate. Suspensions of bacterial samples from three genera and six species were directly deposited on the filter paper–based SERS substrates before measurements. PLS-DA was successfully employed as a multivariate supervised model to classify and identify bacteria with efficiency, sensitivity, and specificity rates of 100% for all test samples. Variable importance in projection was associated with the presence/absence of some purine metabolites, whereas confidence intervals for each sample in the PLS-DA model were calculated using a resampling bootstrap procedure. Additionally, a potential new species of bacteria was analyzed by the proposed method and the result was in agreement with that obtained via 16S rRNA gene sequence analysis, thereby indicating that the SERS/PLS-DA approach has the potential to be a valuable tool for the discovery of novel bacteria.

Graphical abstract

This paper describes the discrimination of bacteria at the genus and species levels, after minimal sample preparation, using paper-based SERS substrates and PLS-DA with uncertainty estimation.


Surface-enhanced Raman spectroscopy Gold nanoparticles Chemometrics, partial least squares discriminant analysis Reliability estimation 16S rRNA gene sequence analysis 


Funding information

This study was financially supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (process 303994/2017-7 and 140377/2015-8) and by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 .

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

216_2018_1485_MOESM1_ESM.pdf (575 kb)
ESM 1 (PDF 539 kb)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Javier E. L. Villa
    • 1
  • Nataly Ruiz Quiñones
    • 2
    • 3
  • Fabiana Fantinatti-Garboggini
    • 2
  • Ronei J. Poppi
    • 1
    Email author
  1. 1.Institute of ChemistryUniversity of Campinas (UNICAMP)CampinasBrazil
  2. 2.Chemical, Biological and Agricultural Pluridisciplinary Research Center (CPQBA)University of Campinas (UNICAMP)PauliniaBrazil
  3. 3.Graduate Program in Genetics and Molecular Biology, Institute of BiologyUniversity of Campinas (UNICAMP)CampinasBrazil

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