Ag NP-filter paper based SERS sensor coupled with multivariate analysis for rapid identification of bacteria.Rapid and accurate identification of bacteria is essential to ensure food safety and prevent pathogenic bacterial infection.  In this study, a highly efficient method was established for accurately identifying bacterial species by applying Ag NP-filter paper based Surface enhanced Raman spectroscopy (SERS) analysis and Partial Least Squares-Discriminant Analysis (PLS-DA) statistical methods.  This exploratory study demonstrates that a Ag NP-filter paper based SERS sensor coupled with PLS-DA has great potential for rapid and effective detection and identification of bacteria.
Ag NP-filter paper
In this study, a highly efficient method was established for accurately identifying bacterial species by applying Ag NP-filter paper based Surface enhanced Raman spectroscopy (SERS) analysis and Partial Least Squares-Discriminant Analysis (PLS-DA) statistical methods.  Furthermore, PLS-DA was successfully employed to distinguish SERS spectra from S. aureus CMCC 26003, E. faecalis ATCC29212 and L. monocytogenes ATCC 19115 with a sensitivity of 93.3-100%, specificity of 96.7-97%, and overall predicting accuracy of 95.8%.  This exploratory study demonstrates that a Ag NP-filter paper based SERS sensor coupled with PLS-DA has great potential for rapid and effective detection and identification of bacteria.
PLS-DA
In this study, a highly efficient method was established for accurately identifying bacterial species by applying Ag NP-filter paper based Surface enhanced Raman spectroscopy (SERS) analysis and Partial Least Squares-Discriminant Analysis (PLS-DA) statistical methods.  Furthermore, PLS-DA was successfully employed to distinguish SERS spectra from S. aureus CMCC 26003, E. faecalis ATCC29212 and L. monocytogenes ATCC 19115 with a sensitivity of 93.3-100%, specificity of 96.7-97%, and overall predicting accuracy of 95.8%.  This exploratory study demonstrates that a Ag NP-filter paper based SERS sensor coupled with PLS-DA has great potential for rapid and effective detection and identification of bacteria.
PLS-DA
In this study, a highly efficient method was established for accurately identifying bacterial species by applying Ag NP-filter paper based Surface enhanced Raman spectroscopy (SERS) analysis and Partial Least Squares-Discriminant Analysis (PLS-DA) statistical methods.  Furthermore, PLS-DA was successfully employed to distinguish SERS spectra from S. aureus CMCC 26003, E. faecalis ATCC29212 and L. monocytogenes ATCC 19115 with a sensitivity of 93.3-100%, specificity of 96.7-97%, and overall predicting accuracy of 95.8%.  This exploratory study demonstrates that a Ag NP-filter paper based SERS sensor coupled with PLS-DA has great potential for rapid and effective detection and identification of bacteria.
PLS-DA
In this study, a highly efficient method was established for accurately identifying bacterial species by applying Ag NP-filter paper based Surface enhanced Raman spectroscopy (SERS) analysis and Partial Least Squares-Discriminant Analysis (PLS-DA) statistical methods.  Furthermore, PLS-DA was successfully employed to distinguish SERS spectra from S. aureus CMCC 26003, E. faecalis ATCC29212 and L. monocytogenes ATCC 19115 with a sensitivity of 93.3-100%, specificity of 96.7-97%, and overall predicting accuracy of 95.8%.  This exploratory study demonstrates that a Ag NP-filter paper based SERS sensor coupled with PLS-DA has great potential for rapid and effective detection and identification of bacteria.
PLS-DA