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.

Culture-independent bacterial cell extraction from fluid milk and oat-based beverage for basic qualitative microscopy

Butterfat and protein complicate attempts to extract bacterial cells from milk by centrifugation for use in basic microscopy. Some types of bacteria preferentially separate into the butterfat layer upon centrifugation and are lost when this layer is discarded, and the action of bacterial protease enzymes can cause milk proteins to precipitate and partition into the centrifugal pellet. Butterfat and precipitated protein remaining in the centrifugal pellet along with the desired bacterial cells can confound the results of differential staining and microscopy. Oat- and other plant-based beverages, which are often manufactured by dairy processors on shared equipment, present similar hurdles to bacterial extraction and microscopic visualization because of the presence of oils, starch granules, and dietary fiber particles in these products. Herein we describe methods for centrifugal separation of bacterial cells for microscopy from unflavored milk, chocolate milk, and oat-based beverage. Cell suspensions prepared through these methods were used for phase-contrast microscopy, Gram staining, and viability staining. These techniques can be used to provide rapid, culture-independent diagnostic information when bacterial cells are expected to be present in high concentrations, as in the event of sporadic product spoilage or mass product spoilage incidents.

Microbial Burden Estimation of Food Items, Built Environments, and the International Space Station Using Film Media

The use of film media involves considerably less preparation, waste, and incubator space than conventional agar-media-based assays and has proven in past studies to provide counts of cultivable microbes similar to those of traditional agar media. Film media also have the advantage of allowing sample volumes similar to those used in pour plates and, therefore, are well-suited for cultivable microbial counts in extremely low-biomass environments such as clean rooms or space habitats, particularly where the subsequent isolation of colonies is necessary. As the preparation of film media plates relies on water cohesion/adhesion rather than manual spreading, they may have future applications in low- or microgravity settings. In this study, cultivable microbial count performance was compared between agar media and film media in three kinds of samples: food items, surfaces in built environments on Earth (homes), and on the environmental surfaces of the International Space Station (ISS). Easy Plates (Kikkoman Corporation) and Petrifilm (3M) were compared with traditional agar plating for food and home surfaces, while only Easy Plates were compared with agar for ISS samples. For both food items and built environments on Earth, both types of film media performed comparably to agar media for bacterial counts, with R² values of 0.94–0.96. Fungal counts for built-environment samples had a lower correlation between film and agar counts, with R² values of 0.72–0.73. Samples from the ISS, which ranged from below detection to 10³ CFU per 100 cm², had R² values of 0.80 for bacterial counts and 0.73 for fungal counts, partially due to multiple samples recording below the detection limit for agar or too numerous to count, and the growth of fungal species on R2A medium. The species compositions of isolates picked from agar vs. film media plates were similar; however, further phylogenetic analysis is needed to confirm the differential microbial diversity composition. Overall, film media such as Easy Plates and Petrifilm are viable alternatives to agar plates for low-biomass built environments as well as for food samples, and the two brands tested in this study performed equally well.

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.

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