T5-10 Risk Factors for Prevalence and Concentration of Indicator Microorganisms on Fresh Tomatoes in the Postharvest Supply Chain

Monday, August 1, 2016: 4:15 PM
241 (America's Center - St. Louis)
Claire Zoellner, Cornell University, Ithaca, NY
Yrjo Grohn, Cornell University, Ithaca, NY
Randy W. Worobo, Cornell University, Ithaca, NY
Introduction: In risk assessment models, “prevalence” and “concentration” describe microbial populations in product units, capturing rare but significant occurrence of contamination.  While estimates for human pathogens are difficult to obtain experimentally or by regular surveillance, indicator microorganisms can demonstrate similar potential behavior if pathogens are introduced postharvest. 

Purpose: Here, the objective was to use microbial indicators of quality and safety on fresh tomatoes to determine and estimate the significant factors of the postharvest supply chain that influence these characteristics, prevalence and concentration. 

Methods: The factors included were location in the supply chain, harvest date, and days in transit.  Microbial count data (log CFU/tomato) for aerobic mesophiles (APC), total coliforms (TC) and yeasts/molds (YM) on the surface of Roma tomatoes (n=475) sampled within lots moving through a supply chain were used in mixed linear models (PROC MIXED, SAS 9.3) to determine significant factors for concentration.  Based on a detection limit of 0.3 log CFU/tomato, count data were converted into a binomial variable and modeled using logistic regression (PROC GENMOD, SAS 9.3) to estimate the prevalence of tomatoes with detectable indicators. 

Results: Location explained prevalence changes in TC (p=.0009) and YM (p<.0001), while days-in-transit best explained concentration dynamics in all populations (p<0.001), with each additional day contributing 0.5 log on average.  Used together, these models quantified the dynamics observed (% prevalence, LS mean±s.e.).  For example, at harvest TC had low prevalence in sampled tomatoes (13%), but high concentrations (2.7±0.5 log).  After packing, TC prevalence (53%) and concentration (3.1±0.4 log) increased, while at distribution both decreased (30%, 0.6±0.2 log).  At supermarkets, prevalence increased (55%) while concentration was variable (0.3-4.2 log/tomato).   

Significance: Locations with increased prevalence and variability were packinghouse and retail and the difference in concentration between a six- and ten-day supply chain was 2 log CFU/tomato.  These results can be used in future risk assessment models.