Rapid Identification Platform of Spores Based on Raman Spectroscopy

Abstract

The food processing stage facilitates the survival of microorganisms in the form of spores within food matrices, with common spore risk factors being Clostridium and Bacillus spores, posing significant food safety risks. However, current methods for detecting and identifying spore contamination in food do not allow for rapid comparison and classification. This method utilizes Raman spectroscopy, combined with a Python-based platform for comparison and analysis. Six common Clostridium and Bacillus spores were analyzed, with distinct Raman spectral peaks identified at 838 cm−1, 895 cm−1, 1052 cm−1, 1200 cm−1, 1400 cm−1, 1577 cm−1, 1666 cm−1, 1722 cm−1, 2970 cm−1, and 3000 cm−1. The platform was validated using bone gelatin products, successfully identifying Clostridium perfringens, Bacillus subtilis, Bacillus cereus, and Bacillus thuringiensis spores. This method enables faster and more efficient spore detection and classification, providing valuable technical support for improving food safety and contamination control strategies.

Keywords: Spore contamination, food safety, Raman spectroscopy, Spore identification, Python-based platform, Microbial contaminants