Endophytes from blueberry (Vaccinium sp.) fruit: Characterization of yeast and bacteria via label-free surface-enhanced Raman spectroscopy (SERS)

Advances in surface‐enhanced Raman spectroscopy technology for detection of foodborne pathogens

“…The label‐free SERS method, also known as direct detection, has the advantages of simplicity of operation, rapidity, and low cost by incubating the SERS substrate directly with the suspension of pathogenic bacteria to bring the bacteria close to the surface of the substrate, enhancing the Raman signal of the pathogens and obtaining their intrinsic fingerprint information (Liu et al., 2021; Zhang, Wu, et al., 2022). Due to the complex biological composition and rich spectral information of microorganism (Vaitiekunaite et al., 2022), relatively small Raman scattering cross section of most biomolecules (Chuesiang et al., 2021), presence of impurities in the actual samples, and other objective factors (Qu et al., 2021), research on label‐free detection is primarily devoted to three aspects: (i) isolation of pathogenic bacteria from complex food matrices and reduction of the interference of other components with the SERS spectra of target bacteria, (ii) preparation of SERS substrates with excellent sensitivity and repeatability to improve label‐free detection performance, and (iii) analysis of SERS spectra of bacteria and combination of multivariate statistical analysis techniques and machine learning algorithms to extract effective information in the spectra for identification and classification purposes.…”

Recent advances of Raman spectroscopy for the analysis of bacteria

“…Traditional linear classification models are challenging when the Raman spectra of bacteria are highly similar. Different multivariate data analysis methods have been developed for the identification and discrimination of bacteria with Raman spectroscopy, such as principal component analysis (PCA) in Figure 3A , 61 , 62 , 63 , 64 partial least square discriminant analysis (PLS‐DA), 63 , 65 , 66 and discriminant function analysis (DFA). 64 These methods have been applied to discriminate waterborne pathogen species in drinking water, 62 to discriminate bacterial strains with different biofilm forming abilities, 63 to identify bacteria and yeast species in blueberries (Figure 3B ), 64 and to differentiate common microbes in urine.…”

“…Different multivariate data analysis methods have been developed for the identification and discrimination of bacteria with Raman spectroscopy, such as principal component analysis (PCA) in Figure 3A , 61 , 62 , 63 , 64 partial least square discriminant analysis (PLS‐DA), 63 , 65 , 66 and discriminant function analysis (DFA). 64 These methods have been applied to discriminate waterborne pathogen species in drinking water, 62 to discriminate bacterial strains with different biofilm forming abilities, 63 to identify bacteria and yeast species in blueberries (Figure 3B ), 64 and to differentiate common microbes in urine. 67 PCA is a multivariate analysis and the most commonly used method to analyze Raman spectra.…”