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A case of 'blown pack' spoilage of vacuum-packaged pork likely associated with Clostridium estertheticum in Canada.

P ZhangP WardLynn M McMullenXianqin Yang
Published in: Letters in applied microbiology (2019)
This study investigated the potential causative agents for vacuum-packaged pork that had shown gross package extension during a routine storage life study in a Canadian pork plant using both conventional and culture-independent methods. The spoilage-associated bacteria in purge samples from two packages were enumerated using selective media and profiled using 16S rDNA amplicon analysis. The presence of Clostridium estertheticum was detected using species-specific real-time PCR. An enrichment procedure was used to isolate C. estertheticum from one of the purge samples. The average population density in the two purge samples of total aerobes, lactic acid bacteria (LAB), coliforms and Brochothrix thermosphacta was 9·4, 9·1, 6·0 and 4·6 log CFU per ml respectively, as determined by plating. The estimated numbers of C. estertheticum were >7 log cells per ml. Clostridium estertheticum was recovered although the enrichment condition used for isolation favoured the growth of LAB more than that of Clostridium spp. Based on 16S rDNA amplicon analysis, the microbiota in the two purge samples had 64·7 and 20·7% of Clostridium spp., and 32·5 and 70·1% of LAB respectively. SIGNIFICANCE AND IMPACT OF THE STUDY: Blown pack spoilage of vacuum-packaged meat may lead to severe economic losses and is often associated with beef, venison and lamb. This study is the first to report vacuum-packaged chilled pork can also be subject to blown pack spoilage, and data support the conclusion that the causative agent is likely Clostridium estertheticum. The lysozyme-digestion step greatly improved the isolation efficiency for C. estertheticum, a spore-forming anaerobic organism that has been proven to be difficult to recover. This method can be used for isolating spore-forming organisms from food samples.
Keyphrases
  • microbial community
  • machine learning
  • induced apoptosis
  • early onset
  • endoplasmic reticulum stress
  • risk assessment
  • cell death
  • mass spectrometry
  • multidrug resistant
  • big data