基于 SERS 技术的基底增强效应对比及食源性芽孢快速检测.

The spores of foodborne pathogens are hardly eradicated in the food industry, due to the extreme resistance. The potential risks to food safety have been from the serious foodborne diseases upon germinating and infecting. Taking three common pathogenic C.perfringens, C.difficile, and C. sporogenes spores as the research objects, rapid identification was proposed using Surface-enhanced Raman spectroscopy (SERS). A comparison was made on the enhancing ability of three noble-metal colloids substrate of AuNPs, AgNPs, and Au@AgNPs. An optimal SERS substrate material was then achieved to rapidly identify the fingerprints of foodborne spores. Three noble metal nanoparticle colloids SERS substrates were fabricated via hydrothermal reduction. The irregular spherical structure was obtained with an average size of 82.26 nm AuNPs, 49.83 nm AgNPs, and 57.85 nm Au@AgNPs. Notably, the structure of Au core and Ag shell indicated the successful preparation of Au@AgNPs. The uniform AgNPs and Au@AgNPs with the nano-scale distribution were superior to the AuNPs. Particularly, the homogeneous nanostructure provided a solid foundation for excellent SERS performance and signal reproducibility. The nanoparticle morphologies were characterized by a field-emission scanning electron microscope. The prominent characteristic peaks were selected in the spectra of spores for further analysis of enhancement performance. Among them, the Ca2+-DPA was known as the biomarker for the inner core of spores. The results showed that the optimal SERS performance of Au@AgNPs was achieved to detect the foodborne pathogenic spores with an analytical enhancement factor (AEF) of 3.49×104, indicating excellent spectroscopic specificity and signal reproducibility. According to the Raman shift and characteristic peaks, the characteristic peaks of Ca2+-DPA around 1 017, 1 440, and 1 570 cm-1 were the common specific peaks but with different intensities. Specifically, the C. perfringens spores presented the specific characteristic peaks at around 740, 787, 821, 1 203, 1 308, and 1 627 cm-1, whereas, the specific characteristic peaks at 852 cm-1 were found in the C. sporogenes spores. The intensities of characteristic peaks at 1 017 cm-1(Ca2+-DPA), and 1 081 cm-1 (DNA, O-P-O) were ranked in the order of the C. perfringens>C. sporogenes>C. difficile spores. However, the intensity at around 1332 cm-1 (adenine nucleic acid in nucleic acid) and 1 440 cm-1 (Ca2+-DPA) of C. difficile spores was stronger than that of C. sporogenes spores, but lower than that of C. perfringens spores, indicating the remarkable differences of Raman shifts and intensity in SERS spectra of three spores. A qualitative identification and discrimination model was established to combine the SERS spectra of three spores with the multivariate statistical analysis (PCA and LDA). The PCA demonstrated that the accumulated variance contribution rate for the top three principal components was up to 92.90%, and the recognition rate of LDA analysis reached 100%, fully meeting the specificity and sensitivity. The SERS performances of three noble-metal nanoparticles substrates were utilized to analyze the fingerprints spectra of three spores using SERS and multivariate statistical analysis. A rapid and accurate performance was achieved in the classification and identification of the different strains, indicating the favorable purpose of detection. An effective and reliable risk detection can be expected to serve as the food safety control and rapid identification of microorganisms as a bio-sensor platform. The finding can provide a strong reference for safety control in the subsequent food processing. [ABSTRACT FROM AUTHOR]