Characteristic substance analysis and rapid detection of bacteria spores in cooked meat products by surface enhanced Raman scattering based on Ag@AuNP array substrate

• Published in: Analytica chimica acta 2024-06, Vol.1308, p.342616-342616, Article 342616
• Main Authors: Liu, Shijie, Zhu, Yaodi, Zhao, Lijun, Li, Miaoyun, Liang, Dong, Li, Mengya, Zhao, Gaiming, Ma, Yangyang, Tu, Qiancheng
• Format: Article
• Language: English
• Subjects: Ag@AuNP array substrate; Characteristic substance; Cooking; Food Contamination - analysis; Food Microbiology - methods; Meat Products - analysis; Meat Products - microbiology; Metal Nanoparticles - chemistry; Rapid detection; SERS; Silver - chemistry; Spectrum Analysis, Raman - methods; Spores; Spores, Bacterial - chemistry; Spores, Bacterial - isolation & purification; Surface Properties
• ISSN: 0003-2670; 1873-4324
• DOI: 10.1016/j.aca.2024.342616

Bacterial spores are the main potential hazard in medium- and high-temperature sterilized meat products, and their germination and subsequent reproduction and metabolism can lead to food spoilage. Moreover, the spores of some species pose a health and safety threat to consumers. The rapid detection, prevention, and control of bacterial spores has always been a scientific problem and a major challenge for the medium and high-temperature meat industry. Early and sensitive identification of spores in meat products is a decisive factor in contributing to consumer health and safety. In this study, we developed a novel and stable Ag@AuNP array substrate by using a two-step synthesis approach and a liquid-interface self-assembly method that can directly detect bacterial spores in actual meat product samples without the need for additional in vitro bacterial culture. The results indicate that the Ag@AuNP array substrate exhibits high reproducibility and Raman enhancement effects (1.35 × 105). The differentiation in the Surface enhanced Raman scattering (SERS) spectra of five bacterial spores primarily arises from proteins in the spore coat and inner membrane, peptidoglycan of cortex, and Ca2⁺-DPA within the spore core. The correct recognition rate of linear discriminant analysis for spores in the meat product matrix can reach 100 %. The average recovery accuracy of the SERS quantitative model was at around 101.77 %, and the limit of detection can reach below 10 CFU/mL. It provides a promising technological strategy for the characteristic substance analysis and timely monitoring of spores in meat products. This method can quickly identify and quantitatively detect five common bacterial spores: C. perfringens spores, C. sporogenes spores, C. difficile spores, B. subtilis spores, and B. cereus spores. As such, it offers effective technical support to ensure meat product safety. The study design was as follows: (1) preparation of compact and highly ordered Ag@AuNP array SERS substrate by interfacial self-assembly method, (2) resolving the Raman spectral feature structures of five bacterial spores using SERS detection, (3) synthesizing chemometric methods (PCA, HCA, PLS-DA, and LDA) to achieve multi-dimensional identification and quantitative detection of spores, and (4) practical application and verification of spore detection for meat products. [Display omitted] •We have developed a novel SERS substrate that exhibits good stability over 45 days.•The method directly d