A longitudinal study on the bacterial quality of baby spinach cultivated in Arizona and California.

In the U.S., baby spinach is mostly produced in Arizona (AZ) and California (CA). Characterizing the impact of growing region on the bacterial quality of baby spinach can inform quality management practices in industry. Between December 2021 and December 2022, baby spinach was sampled after harvest and packaging for microbiological testing, including shelf-life testing of packaged samples that were stored at 4°C. Samples were tested to (i) determine bacterial concentration, and (ii) obtain and identify bacterial isolates. Packaged samples from the Salinas, CA, area (n = 13), compared to those from the Yuma, AZ, area (n = 9), had a significantly higher bacterial concentration, on average, by 0.78 log10 CFU/g (P < 0.01, based on aerobic, mesophilic plate count data) or 0.67 log10 CFU/g (P < 0.01, based on psychrotolerant plate count data); the bacterial concentrations of harvest samples from the Yuma and Salinas areas were not significantly different. Our data also support that an increase in preharvest temperature is significantly associated with an increase in the bacterial concentration on harvested and packaged spinach. A Fisher's exact test and linear discriminant analysis (effect size), respectively, demonstrated that (i) the genera of 2,186 bacterial isolates were associated (P < 0.01) with growing region and (ii) Pseudomonas spp. and Exiguobacterium spp. were enriched in spinach from the Yuma and Salinas areas, respectively. Our findings provide preliminary evidence that growing region and preharvest temperature may impact the bacterial quality of spinach and thus could inform more targeted strategies to manage produce quality. IMPORTANCE: In the U.S., most spinach is produced in Arizona (AZ) and California (CA) seasonally; typically, spinach is cultivated in the Yuma, AZ, area during the winter and in the Salinas, CA, area during the summer. As the bacterial quality of baby spinach can influence consumer acceptance of the product, it is important to assess whether the bacterial quality of baby spinach can vary between spinach-growing regions. The findings of this study provide insights that could be used to support region-specific quality management strategies for baby spinach. Our results also highlight the value of further evaluating the impact of growing region and preharvest temperature on the bacterial quality of different produce commodities.

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.