Development of predictive models evaluating the spoilage-delaying effect of a bioprotective culture on different yeast species in yogurt.

Yeast spoilage of fermented dairy products causes challenges for the dairy industry, including economic losses due to wasted product. Food cultures with bioprotective effects are becoming more widely used to help ensure product quality throughout product shelf life. To assist the dairy industry when evaluating product quality throughout shelf life and the effect of bioprotective cultures, we aimed to build stochastic models that provide reliable predictions of yeast spoilage in yogurt with and without bioprotective culture. Growth characterizations of Debaryomyces hansenii, Yarrowia lipolytica, Saccharomyces cerevisiae, and Kluyveromyces marxianus at storage temperatures of 7, 12, and 16°C during a 30-d storage period were conducted in yogurt with and without a bioprotective culture containing Lacticaseibacillus rhamnosus strains. The kinetic growth parameters were calculated using the Buchanan growth model, and these parameters were used as baseline values in Monte Carlo models to translate the yeast growth into spoilage levels. The models were developed using 100,000 simulations and they predicted yeast spoilage levels in yogurt by the 4 yeast types. Each modeled yogurt batch was set to be contaminated with yeast at a concentration drawn from a normal distribution with a mean of 1 log10 cfu/mL and standard deviation of 1 log10 cfu/mL and stored for 30 d at a temperature drawn from a normal distribution with a mean of 6.1°C and a standard deviation of 2.8°C. Considering a spoilage level of 5 log10 cfu/mL, the predicted number of spoiled samples was reduced 3-fold during the first 10 d and by 2-fold at the end of shelf life when a bioprotective culture was added to the yogurt. The models were evaluated by sensitivity analyses, where the main effect factors were maximum yeast population, storage temperature, and yeast strain. The models were validated by comparing the model output to actual observed spoilage data from a European dairy using the bioprotective culture. When the model prediction, based on a mixture of the 4 specific yeast strains, was compared with spoilage data from the European dairy, the observed effect of bioprotective cultures was considerably higher than predicted, potentially influenced by the presence of contaminating strains more sensitive to a bioprotective culture than those characterized here. The developed Monte Carlo models can predict yeast spoilage levels in yogurt at specific production settings and how this may be affected by various parameters and addition of bioprotective cultures.

Monitoring the Microevolution of Salmonella enterica in Healthy Dairy Cattle Populations at the Individual Farm Level Using Whole-Genome Sequencing.

Livestock represent a possible reservoir for facilitating the transmission of the zoonotic foodborne pathogen Salmonella enterica to humans; there is also concern that strains can acquire resistance to antimicrobials in the farm environment. Here, whole-genome sequencing (WGS) was used to characterize Salmonella strains (n = 128) isolated from healthy dairy cattle and their associated environments on 13 New York State farms to assess the diversity and microevolution of this important pathogen at the level of the individual herd. Additionally, the accuracy and concordance of multiple in silico tools are assessed, including: (i) two in silico serotyping tools, (ii) combinations of five antimicrobial resistance (AMR) determinant detection tools and one to five AMR determinant databases, and (iii) one antimicrobial minimum inhibitory concentration (MIC) prediction tool. For the isolates sequenced here, in silico serotyping methods outperformed traditional serotyping and resolved all un-typable and/or ambiguous serotype assignments. Serotypes assigned in silico showed greater congruency with the Salmonella whole-genome phylogeny than traditional serotype assignments, and in silico methods showed high concordance (99% agreement). In silico AMR determinant detection methods additionally showed a high degree of concordance, regardless of the pipeline or database used (≥98% agreement among susceptible/resistant assignments for all pipeline/database combinations). For AMR detection methods that relied exclusively on nucleotide BLAST, accuracy could be maximized by using a range of minimum nucleotide identity and coverage thresholds, with thresholds of 75% nucleotide identity and 50-60% coverage adequate for most pipeline/database combinations. In silico characterization of the microevolution and AMR dynamics of each of six serotype groups (S. Anatum, Cerro, Kentucky, Meleagridis, Newport, Typhimurium/Typhimurium variant Copenhagen) revealed that some lineages were strongly associated with individual farms, while others were distributed across multiple farms. Numerous AMR determinant acquisition and loss events were identified, including the recent acquisition of cephalosporin resistance-conferring bla CMY- and bla CTX-M-type beta-lactamases. The results presented here provide high-resolution insight into the temporal dynamics of AMR Salmonella at the scale of the individual farm and highlight both the strengths and limitations of WGS in tracking zoonotic pathogens and their associated AMR determinants at the livestock-human interface.

Validation Using Diverse, Difficult-to-Detect Salmonella Strains and a Dark Chocolate Matrix Highlights the Critical Role of Strain Selection for Evaluation of Simplified, Rapid PCR-Based Methods Offering Next-Day Time to Results.

ABSTRACT: Modifications to pathogen detection kits to accomplish simplified protocols with reduced time to results may impact method performance, particularly when combining shortened enrichment times and simplified enrichment procedures. We used Salmonella detection in dark chocolate as a model to test the impact of different enrichment times (minimum and maximum validated times) and procedures on detection of low levels of difficult-to-detect Salmonella strains, for three PCR kits that were AOAC International Performance Tested Method certified for detection of Salmonella spp. in dark chocolate. Initial inclusivity studies with pure cultures showed that all three kits detected 70 of 70 Salmonella spp. strains at 1 log above the theoretical limit of detection, with some strains yielding later cycle threshold values or having variable detection among technical replicates, indicating reduced assay performance for these strains. Based on these data, we selected a S. enterica subsp. enterica serovar Poona strain as well as three non-subsp. enterica strains to test the ability of the three kits to detect Salmonella in dark chocolate inoculated at low levels (0.06 to 1.18 most probable number per 25 g). With primary enrichment in skim milk at 35°C, detection frequency for all assays did not significantly differ from the reference method for both the minimum and maximum validated enrichment times. However, a pilot study that used primary enrichment in buffered peptone water at 42°C yielded significantly fewer positive samples (13 of 80) than were obtained with the U.S. Food and Drug Administration Bacteriological Analytical Manual method using enrichment in skim milk at 35°C (40 of 80 positive samples); strains representing subsp. houtenae and salamae were detected in significantly fewer chocolate samples than enrichment with skim milk. Our data indicate that continued efforts to simplify rapid pathogen detection kits may reduce kit performance in a way that can only be detected with stringent evaluation protocols that are designed to identify kit failure modes.

Evaluation of invA Diversity among Salmonella Species Suggests Why Some Commercially Available Rapid Detection Kits May Fail To Detect Multiple Salmonella Subspecies and Species.

HIGHLIGHTS: Salmonella exhibits tremendous diversity, with 2,659 documented serovars. invA is a common gene target for detecting Salmonella spp. Detection methods should be validated with a sufficiently diverse strain set.