研究目的
To develop a rapid and reliable method for bacterial discrimination using surface-enhanced Raman spectroscopy (SERS) and partial least squares discriminant analysis (PLS-DA), aiming to classify and identify bacteria at the genus and species levels with minimal sample preparation.
研究成果
The SERS/PLS-DA-based method was successfully applied to classify and identify bacteria at the genus and species levels with 100% efficiency, sensitivity, and specificity rates for all test samples. The method avoids the need for tedious nanoparticle surface modifications and its use may be implemented in the field of bacterial discovery.
研究不足
The study acknowledges the high similarity of bacterial cell walls, especially at the species level, as a challenge for discrimination. The stochastic nature of the resampling bootstrap technique used for uncertainty estimation may lead to different results even under the same conditions.
1：Experimental Design and Method Selection
The study employed a label-free method for bacterial discrimination using SERS and PLS-DA. Filter paper decorated with gold nanoparticles was fabricated by the dip-coating method and used as a flexible and highly efficient SERS substrate.
2：Sample Selection and Data Sources
Bacterial samples from three genera and six species were directly deposited on the filter paper–based SERS substrates before measurements. A potential new species of bacteria was also analyzed.
3：List of Experimental Equipment and Materials
Field emission scanning electron microscopy (FESEM, Quanta FEG 250), dispersive spectrometer Raman Station 400 F (PerkinElmer, MA, USA), tetrachloroauric acid solution (HAuCl4, 30% m/m), anhydrous sodium citrate, trypticase soy agar (TSA), sodium chloride, adenine, guanine, uric acid, inosine, guanosine, and adenosine, filter paper (Whatman No. 40).
4：Experimental Procedures and Operational Workflow
The gold nanoparticle–coated paper was fabricated by the dip-coating method. Bacterial suspensions were directly deposited on the fabricated SERS substrates before measurements. PLS-DA was employed as a multivariate supervised model to classify and identify bacteria.
5：Data Analysis Methods
PLS-DA was used for classification and identification of bacteria. Variable importance in projection (VIP) scores were analyzed for spectral interpretation. Confidence intervals for each sample in the PLS-DA model were calculated using a resampling bootstrap procedure.
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