In this work, the results on the detection and identification of Bacillus thuringiensis (Bt) cells by using surface-enhanced Raman spectroscopy (SERS) are presented. Bt has been chosen as a harmless surrogate of the pathogen Bacillus anthracis (Ba) responsible for the deadly Anthrax disease, because of their genetic similarities. Drops of 200 µL of Bt suspensions, with concentrations 102 CFU/mL, 104 CFU/mL, 106 CFU/mL, were deposited on a SERS chip and sampled after water evaporation. To minimize the contribution to the SERS data given by naturally occurring interferents present in a real scenario, the SERS chip was functionalized with specific phage receptors BtCS33, that bind Bt (or Ba) cells to the SERS surface and allow to rinse the chip removing unwanted contaminants. Different chemometric approaches were applied to the SERS data to classify spectra from Bt-contaminated and uncontaminated areas of the chip: Principal Component Regression (PCR), Partial Least Squares Regression (PLSR) and Data Driven Soft Independent Modeling of Class Analogy (DD-SIMCA). The first two was tested and trained by using data from both contaminated and un-contaminated chips, the last was trained by using data from un-contaminated chips only and tested with all the available data. All of them were able to correctly classify the SERS spectra with great accuracy, the last being suitable for an automated recognition procedure.
Bacillus thuringiensis cells selectively captured by phages and identified by surface enhanced raman spectroscopy technique
Almaviva S.;Palucci A.;Aruffo E.;Rufoloni A.;Lai A.
2021-01-01
Abstract
In this work, the results on the detection and identification of Bacillus thuringiensis (Bt) cells by using surface-enhanced Raman spectroscopy (SERS) are presented. Bt has been chosen as a harmless surrogate of the pathogen Bacillus anthracis (Ba) responsible for the deadly Anthrax disease, because of their genetic similarities. Drops of 200 µL of Bt suspensions, with concentrations 102 CFU/mL, 104 CFU/mL, 106 CFU/mL, were deposited on a SERS chip and sampled after water evaporation. To minimize the contribution to the SERS data given by naturally occurring interferents present in a real scenario, the SERS chip was functionalized with specific phage receptors BtCS33, that bind Bt (or Ba) cells to the SERS surface and allow to rinse the chip removing unwanted contaminants. Different chemometric approaches were applied to the SERS data to classify spectra from Bt-contaminated and uncontaminated areas of the chip: Principal Component Regression (PCR), Partial Least Squares Regression (PLSR) and Data Driven Soft Independent Modeling of Class Analogy (DD-SIMCA). The first two was tested and trained by using data from both contaminated and un-contaminated chips, the last was trained by using data from un-contaminated chips only and tested with all the available data. All of them were able to correctly classify the SERS spectra with great accuracy, the last being suitable for an automated recognition procedure.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
