Nowadays the Clostridium detection in milk for the dairy industry still is a challenging problem since traditional methods are time-consuming and lack specificity towards these bacteria. The use of microbiological techniques is possible but is expensive in terms of response time and requires qualified personnel. Pasteurization is ineffective against Clostridium spores which can survive the process and later revert to their vegetative form during cheese aging. The Clostridium metabolism is characterized by the production of carbon dioxide and hydrogen, which can lead to the formation of cracks and slits in the cheese altering its taste and structure. The analysis of gas production is indicative of the presence of Clostridia; therefore, it can be exploited to detect their presence. This study presents a Raman spectroscopy-based instrument for a rapid and cost-effective identification of Clostridium in milk. The methodology relies on the widely adopted Most Probable Number (MPN) method, as established by Brändle et al. (2016). However, our innovation lies in adoption of a Raman-based instrument to speed up the vial positivity detection. The instrument also enables the discrimination Clostridia infection from non-hydrogen-producing bacteria.
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.
Despite developments in the dairy industry, blowing defects in cheese due to Clostridium contamination continues to be a problem. Traditional microbiological detection methods used in dairy industries to detect spores are time consuming and limited in efficiency and sensitivity. Many alternative approaches of detecting butyric acid clostridia in milk still pose a major challenge for microbiologists. According to the fermentative butyric acid production, carbon dioxide and hydrogen are also produced as byproducts during fermentation. As a consequence, during the incubation of contaminated milk in a sealed container (such as a cuvette), the measurement of CO2 and H2 in the headspace can be a promising possibility for early-stage detection of the spores. A multi-gas sensor based on Raman spectroscopy was used for the measurement of CO2 and H2 concentration. The aim of this paper is to demonstrate the applicability of Raman gas analysis to the detection of multiple gas species in sealed, transparent samples for early detection of spore contamination in milk. Two different contaminant spores were used: Clostridium tyrobutyricum and Bacillus. The first one showed an increase of CO2 and H2 content within 16h of incubation. The second one showed an increase of CO2 content within 48 h of incubation. For the first time, Raman gas spectroscopy has been applied for contactless measurement of the time resolved evolution of multiple gaseous species in milk samples for spore analysis.
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