Liquid chromatography-tandem mass spectrometry method for the analysis of N-(3-aminopropyl)-N-dodecylpropane-1,3-diamine, a biocidal disinfectant, in dairy products

Genomic elements located in the accessory repertoire drive the adaptation to biocides in Listeria monocytogenes strains from different ecological niches

In response to the massive use of biocides for controlling Listeria monocytogenes (hereafter Lm) contaminations along the food chain, strains showing biocide tolerance emerged. Here, accessory genomic elements were associated with biocide tolerance through pangenome-wide associations performed on 197 Lm strains from different lineages, ecological, geographical and temporal origins. Mobile elements, including prophage-related loci, the Tn6188_qacH transposon and pLMST6_emrC plasmid, were widespread across lineage I and II food strains and associated with tolerance to benzalkonium-chloride (BC), a quaternary ammonium compound (QAC) widely used in food processing. The pLMST6_emrC was also associated with tolerance to another QAC, the didecyldimethylammonium-chloride, displaying a pleiotropic effect. While no associations were detected for chemically reactive biocides (alcohols and chlorines), genes encoding for cell-surface proteins were associated with BC or polymeric biguanide tolerance. The latter was restricted to lineage I strains from animal and the environment. In conclusion, different genetic markers, with polygenic nature or not, appear to have driven the Lm adaptation to biocide, especially in food strains but also from animal and the environment. These markers could aid to monitor and predict the spread of biocide tolerant Lm genotypes across different ecological niches, finally reducing the risk of such strains in food industrial settings.

Detection of Potential Microbial Contaminants and Their Toxins in Fermented Dairy Products: a Comprehensive Review

Fermented dairy products are dominant constituents of daily diets around the world due to their desired organoleptic properties, long shelf life, and high nutritional value. Probiotics are often incorporated into these products for their health and technological benefits. However, the safety and possible contamination of fermented dairy products during the manufacturing process could have significant deleterious health and economic impacts. Pathogenic microorganisms and toxins from different sources in fermented dairy products contribute to outbreaks and toxicity cases. Although the health and nutritional benefits of fermented dairy products have been extensively investigated, safety hazards due to contamination are relatively less explored. As a preventive measure, it is crucial to accurately identify and determine the associated microbiota or their toxins. It is noteworthy to highlight the importance of detecting not only the pathogenic microbiota but also their toxic metabolites so that putative outbreaks can thereby be prevented or detected even before they cause harmful effects to human health. In this context, this review focuses on describing techniques designed to detect potential contaminants; also, the advantages and disadvantages of these techniques were summarized. Moreover, this review compiles the most recent and efficient analytical methods for detecting microbial hazards and toxins in different fermented dairy products of different origins. Causative agents behind contamination incidences are also discussed briefly to aid in future prevention measures, as well as detection approaches and technologies employed. Such approach enables the elucidation of the best strategies to control contamination in fermented dairy product manufacturing processes.

The statistical fusion identification of dairy products based on extracted Raman spectroscopy

At present, practical and rapid identification techniques for dairy products are still scarce. Taking different brands of pasteurized milk as an example, they are all milky white in appearance, and their Raman spectra are very similar, so it is not feasible to identify them directly using the naked eye. In the current work, a clear feature extraction and fusion strategy based on a combination of Raman spectroscopy and a support vector machine (SVM) algorithm was demonstrated. The results showed a 58% average recognition accuracy rate for dairy products as based on the original Raman full spectral data and up to nearly 70% based on a single spectral interval. Data normalization processing effectively improved the recognition accuracy rate. The average recognition accuracy rate of dairy products reached 91% based on the normalized Raman full spectral data or nearly 85% based on a normalized single spectral interval. The fusion of multispectral feature regions yielded high accuracy and operation efficiency. After screening and optimizing based on SVM algorithm, the best spectral feature intervals were determined to be 335–354 cm⁻¹, 435–454 cm⁻¹, 485–540 cm⁻¹, 820–915 cm⁻¹, 1155–1185 cm⁻¹, 1300–1414 cm⁻¹, and 1415–1520 cm⁻¹ under the experimental conditions, and the average identification accuracy rate here reached 93%. The developed scheme has the advantages of clear feature extraction and fusion, and short identification time, and it provides a technical reference for food quality control.

At present, practical and rapid identification techniques for dairy products are still scarce.