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Article Abstract
The food processing stage facilitates the survival of spores from microorganisms such as and species, thereby posing food safety risks. However, current methods for detecting and classifying spore contamination in food are slow, inefficient, and unsuitable for rapid comparison. This method utilizes Raman spectroscopy, combined with a Python-based platform for comparison and analysis. Six common and spores were analyzed, with distinct Raman spectral peaks identified at 838 cm, 895 cm, 1052 cm, 1200 cm, 1400 cm, 1577 cm, 1666 cm, 1722 cm, 2970 cm, and 3000 cm. The platform was validated using bone gelatin products, successfully identifying , , , and spores. This method enables faster and more efficient spore detection and classification, providing valuable technical support for improving food safety and contamination control strategies.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12362135 | PMC |
| http://dx.doi.org/10.1016/j.fochx.2025.102844 | DOI Listing |
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