Characterization of Contaminants from a Sanitized Milk Processing Plant

Milk processing lines offer a wide variety of microenvironments where a diversity of microorganisms can proliferate. We sampled crevices and junctions where, due to deficient reach by typical sanitizing procedures, bacteria can survive and establish biofilms. The sampling sites were the holding cell, cold storage tank, pasteurizer and storage tank - transfer pump junction. The culturable bacteria that were isolated after the sanitation procedure were predominantly Pseudomonas spp., Serratia spp, Staphylococcus sciuri and Stenotrophomonas maltophilia. We assayed several phenotypic characteristics such as the ability to secrete enzymes and siderophores, as well as the capacity of the strains to form biofilms that might contribute to their survival in a mixed species environment. The Pseudomonas spp. isolates were found to either produce proteases or lecithinases at high levels. Interestingly, protease production showed an inverse correlation with siderophore production. Furthermore, all of the Serratia spp. isolates were strong biofilm formers and spoilage enzymes producers. The organisms identified were not mere contaminants, but also producers of proteins with the potential to lower the quality and shelf-life of milk. In addition, we found that a considerable number of the Serratia and Pseudomonas spp. isolated from the pasteurizer were capable of secreting compounds with antimicrobial properties

Effect of additional cycles and sonication on the amount of rain.

<p>Round dots represent the results for TC1507. Triangles represent the results for MON88017. <b>Panel A</b> shows the effects of running additional cycles: four conditions of increasing number of cycles were tested (45, 60, 75, and 90 cycles). <b>Panel B</b> shows the effects of sonication: six conditions of increasing sonication were tested (ranging from 0 seconds to 15 seconds in steps of 3 seconds).</p

Microbiota of Karaka\u10danski skakutanac, an artisanal fresh sheep cheese studied by culture-independent PCR-ARDRA and PCR-DGGE

Karaka\u10danski skakutanac is an artisanal fresh sheep cheese produced on a small scale in a limited area of eastern Croatia. It is manufactured from unrefrigerated raw sheep milk immediately after milking, without the addition of starter culture, and coagulated with industrial rennet. To date, no microbiological or molecular characterization of the biodiversity of the microbiota has been performed. The objective of this study was to obtain an initial insight into the biodiversity of the microbial community associated with this cheese during the production season and shelf life period. Eleven cheeses were obtained from a dairy farm at 14-day intervals during the lactation period of east Friesian sheep in 2007. Bacterial DNA was isolated directly from cheese on the first, second and third day of the cheese shelf life, resulting in a total of 33 DNA samples. Extracted DNA was used as a template for PCR-ARDRA and PCR-DGGE analysis. The use of dual culture-independent approaches revealed similar results and indicated predominance of Lactococcus lactis

Performance parameters for each target under optimised conditions.

<p>Performance parameters for each target under optimised conditions.</p

Measuring Digital PCR Quality: Performance Parameters and Their Optimization

<div><p>Digital PCR is rapidly being adopted in the field of DNA-based food analysis. The direct, absolute quantification it offers makes it an attractive technology for routine analysis of food and feed samples for their composition, possible GMO content, and compliance with labelling requirements. However, assessing the performance of dPCR assays is not yet well established. This article introduces three straightforward parameters based on statistical principles that allow users to evaluate if their assays are robust. In addition, we present post-run evaluation criteria to check if quantification was accurate. Finally, we evaluate the usefulness of Poisson confidence intervals and present an alternative strategy to better capture the variability in the analytical chain.</p></div

Digital PCR confidence limits.

<p><b>Panel A</b> shows the relative width of Poisson confidence intervals as a function of the number of target sequences per partition (<i>λ</i>). <b>Panel B</b> and <b>C</b> illustrate how many percent of the <i>λ</i> estimates of a 20 000 partition system contain 25% or more error as calculated via parametric bootstrap for different numbers of repeated analysis (single reaction, duplicate, triplicate, and four repeats). Replicates are averaged to obtain the final estimate. The black lines show the actual percentages obtained, the blue lines show a smoothed version of the curve. The dashed horizontal line represents five percent.</p

Effect of reaction conditions on digital PCR resolution.

<p><b>Panel A</b> shows the effect of primer concentration. For each of the twelve targets, four primer concentrations were tested (150, 300, 450, and 600 nM). Probe concentrations are such that the primer to probe ratio is the same as for the validated conditions (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153317#pone.0153317.s001" target="_blank">S1 Table</a>). <b>Panel B</b> shows the effect of annealing/elongation temperature. For each of the twelve targets, eight temperatures between 62 and 56°C were tested.</p

Poisson Confidence intervals for 48 independent dilutions of a rare target.

<p>The bottom axis shows the number of copies in the reaction, the top axis shows the number of droplets analysed (as indicated by the green bar-plots). The vertical line indicates the median estimate, confidence intervals that contain this value are coloured black, confidence intervals that do not contain this value are represented as empty boxes. <b>Panels A through D</b> show the decrease in confidence width as a consequence of merging the reactions, the respective CI coverage factors for the panels are: 93.75, 81.25, 75, and 70.83 percent.</p

Illustration of multiple fluorescence populations.

<p><b>Panel A</b> shows the results for the <i>acp1</i> target, an additional population of fluorescence measurements is visible with values closely situated to the negatives. <b>Panel B</b> shows the results for the <i>cruA</i> target, an additional population of fluorescence measurements is visible with values closely situated to the positives. Contrary to ‘rain’, the measurements with intermediate fluorescence are not uniformly spread.</p