Strain-specific Growth Parameters are Important to Accurately Model Bacterial Growth on Baby Spinach in Simulation Models.

Digital tools to predict produce shelf life have the potential to reduce food waste and improve consumer satisfaction. To address this need, we (i) performed an observational study on the microbial quality of baby spinach, (ii) completed growth experiments of bacteria that are representative of the baby spinach microbiota, and (iii) developed an initial simulation model of bacterial growth on baby spinach. Our observational data showed that the predominant genera found on baby spinach were Pseudomonas, Pantoea and Exiguobacterium. Rifampicin-resistant mutants (rifR mutants) of representative bacterial subtypes were subsequently generated to obtain strain-specific growth parameters on baby spinach. These experiments showed that: (i) it is difficult to select rifR mutants that do not have fitness costs affecting growth (9 of 15 rifR mutants showed substantial differences in growth, compared to their corresponding wild-type strain) and (ii) based on estimates from primary growth models, the mean (geometric) maximum population of rifR mutants on baby spinach (7.6 log10 CFU/g, at 6°C) appears lower than that of the spinach microbiota (9.6 log10 CFU/g, at 6°C), even if rifR mutants did not have substantial growth-related fitness costs. Thus, a simulation model, parameterized with the data obtained here as well as literature data on home refrigeration temperatures, underestimated bacterial growth on baby spinach. The root mean square error of the simulation's output, compared against data from the observational study, was 1.11 log10 CFU/g. Sensitivity analysis was used to identify key parameters (e.g., strain maximum population) that impact the simulation model's output, allowing for prioritization of future data collection to improve the simulation model. Overall, this study provides a roadmap for the development of models to predict bacterial growth on leafy vegetables with strain-specific parameters and suggests that additional data are required to improve these models.

Low-cost, on-farm intervention to reduce spores in bulk tank raw milk benefits producers, processors, and consumers.

Bacterial spores, which are found in raw milk, can survive harsh processing conditions encountered in dairy manufacturing, including pasteurization and drying. Low-spore raw milk is desirable for dairy industry stakeholders, especially those who want to extend the shelf life of their product, expand their distribution channels, or reduce product spoilage. A recent previous study showed that an on-farm intervention that included washing towels with chlorine bleach and drying them completely, as well as training milking parlor employees to focus on teat end cleaning, significantly reduced spore levels in bulk tank raw milk. As a follow up to that previous study, here we calculate the costs associated with that previously described intervention as ranging from $9.49 to $13.35 per cow per year, depending on farm size. A Monte Carlo model was used to predict the shelf life of high temperature, short time fluid milk processed from raw milk before and after this low-cost intervention was applied, based on experimental data collected in a previous study. The model predicted that 18.24% of half-gallon containers of fluid milk processed from raw milk receiving no spore intervention would exceed the pasteurized milk ordinance limit of 20,000 cfu/mL by 17 d after pasteurization, while only 16.99% of containers processed from raw milk receiving the spore intervention would reach this level 17 d after pasteurization (a reduction of 1.25 percentage points and a 6.85% reduction). Finally, a survey of consumer milk use was conducted to determine how many consumers regularly consume fluid milk near or past the date printed on the package (i.e., code date), which revealed that over 50% of fluid milk consumers surveyed continue to consume fluid milk after this date, indicating that a considerable proportion of consumers are exposed to fluid milk that is likely to have high levels spore-forming bacterial growth and possibly associated quality defects (e.g., flavor or odor defects). This further highlights the importance of reducing spore levels in raw milk to extend pasteurized fluid milk shelf life and thereby reducing the risk of adverse consumer experiences. Processors who are interested in extending fluid milk shelf life by controlling the levels of spores in the raw milk supply should consider incentivizing low-spore raw milk through premium payments to producers.

Growth and survival of aerobic and Gram-negative bacteria on fresh spinach in a Chinese supply chain from harvest through distribution and refrigerated storage.

Spinach is a highly perishable product that degrades over time, including due to bacteria contaminating the product prior to packaging, yet the dynamics of bacterial spoilage and factors that affect it are not well understood. Notably, while China is the top producer of spinach globally, there is limited available microbiological data in the literature for spinach supply chains in China. The overall goal of this foundational study was to establish a baseline understanding of bacterial population dynamics on spinach from harvest to 10 days postprocessing for a Chinese supply chain that includes distribution via traditional grocery (a local physical store) and eCommerce (an online store). To this end, organic spinach samples were collected at different stages in a Chinese supply chain by following the same 3 lots, starting at point-of-harvest through processing and distribution via a local grocery store and eCommerce. After distribution, the same 3 lots were stored at 4 °C with microbiological testing performed on multiple days up to day 10 postprocessing, simulating storage at the point-of-consumer. Results showed aerobic plate counts and total Gram-negative counts did not significantly differ across stages in the supply chain from harvest through processing. However, packaged spinach from the same processing facility and lots, exhibited different patterns in bacterial levels across 0 to 10 days postprocessing, depending on whether it was distributed via the local grocery store or eCommerce. Evaluation of bacterial populations performed on a subset of the packaged spinach samples indicated Gram-negative bacteria, in particular Pseudomonas, were predominant across all days of testing (days 0, 3, and 10 postprocessing), with populations differing at the genus level by day. Overall, this study improves our understanding of the dynamics of bacterial populations on spinach and provides baseline data needed for future studies.

Characterization of the roles of activated charcoal and Chelex in the induction of PrfA regulon expression in complex medium.

The foodborne pathogen Listeria monocytogenes is able to survive across a wide range of intra- and extra-host environments by appropriately modulating gene expression patterns in response to different stimuli. Positive Regulatory Factor A (PrfA) is the major transcriptional regulator of virulence gene expression in L. monocytogenes. It has long been known that activated charcoal is required to induce the expression of PrfA-regulated genes in complex media, such as Brain Heart Infusion (BHI), but not in chemically defined media. In this study, we show that the expression of the PrfA-regulated hly, which encodes listeriolysin O, is induced 5- and 8-fold in L. monocytogenes cells grown in Chelex-treated BHI (Ch-BHI) and in the presence of activated charcoal (AC-BHI), respectively, relative to cells grown in BHI medium. Specifically, we show that metal ions present in BHI broth plays a role in the reduced expression of the PrfA regulon. In addition, we show that expression of hly is induced when the levels of bioavailable extra- or intercellular iron are reduced. L. monocytogenes cells grown Ch-BHI and AC-BHI media showed similar levels of resistance to the iron-activated antibiotic, streptonigrin, indicating that activated charcoal reduces the intracellular labile iron pool. Metal depletion and exogenously added glutathione contributed synergistically to PrfA-regulated gene expression since glutathione further increased hly expression in metal-depleted BHI but not in BHI medium. Analyses of transcriptional reporter fusion expression patterns revealed that genes in the PrfA regulon are differentially expressed in response to metal depletion, metal excess and exogenous glutathione. Our results suggest that metal ion abundance plays a role in modulating expression of PrfA-regulated virulence genes in L. monocytogenes.

Machine Learning and Advanced Statistical Modeling Can Identify Key Quality Management Practices That Affect Postpasteurization Contamination of Fluid Milk.

ABSTRACT: Spoilage of high-temperature, short-time (HTST)- and vat-pasteurized fluid milk due to the introduction of gram-negative bacteria postpasteurization remains a challenge for the dairy industry. Although processing facility-level practices (e.g., sanitation practices) are known to impact the frequency of postpasteurization contamination (PPC), the relative importance of different practices is not well defined, thereby affecting the ability of facilities to select intervention targets that reduce PPC and provide the greatest return on investment. Thus, the goal of this study was to use an existing longitudinal data set of bacterial spoilage indicators obtained for pasteurized fluid milk samples collected from 23 processing facilities between July 2015 and November 2017 (with three to five samplings per facility) and data from a survey on fluid milk quality management practices, to identify factors associated with PPC and rank their relative importance. This ranking was accomplished using two separate approaches: multimodel inference and conditional random forest. Data preprocessing for multimodel inference analysis showed (i) nearly all factors were significantly associated with PPC when assessed individually using univariable logistic regression and (ii) numerous pairs of factors were strongly associated with each other (Cramer's V ≥ 0.80). Multimodel inference and conditional random forest analyses identified similar drivers associated with PPC; factors identified as most important based on these analyses included cleaning and sanitation practices, activities related to good manufacturing practices, container type (a proxy for different filling equipment), in-house finished product testing, and designation of a quality department, indicating potential targets for reducing PPC. In addition, this study illustrates how machine learning approaches can be used with highly correlated and unbalanced data, as typical for food safety and quality, to facilitate improved data analyses and decision making.