The detection of Clostridium in milk poses a significant challenge for the dairy industry since traditional methods are time-consuming and lack specificity towards these bacteria. Conversely, microbiological techniques are costly and demand skilled personnel. Clostridium in the form of spores can survive pasteurization and revert to their vegetative form during cheese aging. The gas-producing metabolism of Clostridium, characterized by the production of carbon dioxide and hydrogen, leads to the formation of cracks in the cheese and off-flavors. However, the analysis of gases produced in the headspace can be exploited to determine the presence of Clostridium in milk. This study aims present a Raman spectroscopy-based instrument that enables rapid and cost-effective identification of Clostridium in milk. The methodology aligns with the widely adopted most probable number (MPN) method, as established by Brändle et al. (2016), where vials are considered positive for growth after incubation. However, our innovation lies in the integration of an actual multigas sensing instrument to determine vial positivity, thereby enhancing accuracy. Notably, we emphasize the meticulous selection of vials and the optimization of headspace volume, crucial factors contributing to the heightened performance of the proposed instrument.
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