P1-79 Factors Affecting Dry Cross-contamination of Salmonella during Almond Processing

Sunday, July 26, 2015
Exhibit Hall (Oregon Convention Center)
Joanna Carroll , Michigan State University , East Lansing , MI
Sanghyup Jeong , Michigan State University , East Lansing , MI
Quincy Suehr , Michigan State University , East Lansing , MI
Bradley Marks , Michigan State University , East Lansing , MI
Introduction: Outbreaks of Salmonella associated with low-moisture foods, such as almonds, are an important concern in food processing, which necessitates an improved understanding of the mechanism of bacterial transfer and physical/environmental factors affecting the process.  

Purpose: The goal of this study was to quantify the effect of physical and environmental factors on the transfer of Salmonella within bulk almonds.

Methods: Un-inoculated almond kernels (200 g) and inoculated almonds (5 g, 7.85 log CFU/g) were conditioned at 0.2 and 0.4 water activity (aw), placed in a stainless-steel drum (140 mm diameter and 64 mm depth), and rotated for four durations (60 - 600 s) with three rotational speeds (8, 16, and 24 rpm) in an environment chamber (in triplicate). At each condition, a four almond sample (~4 g) was retrieved from the drum, plated on modified tryptic soy agar, incubated, and enumerated.

Results: Bacterial transfer rate was compared based on physical and environmental factors. Bacterial transfer rates were higher (P < 0.05) at the higher aw (0.0009 ± 0.0006% and 0.006 2 ± 0.0050% at 0.2 and 0.4 aw , respectively).  As the total number of revolution increased, the total bacterial transfer reached statistically different (95% CI) asymptotic values, 3.02 ± 0.13 and 3.80 ± 0.20 log CFU/g.  However, the rotational speed did not affect (P > 0.05) transfer rates.

Significance: Environmental factors, such as aw, appear to be critical factors affecting bacterial transfer in low-moisture products. A subsequent secondary model as a function of aw will significantly increase the accuracy of an existing first-principle-based discrete element model, which will ultimately contribute to more accurate risk modeling and assessment for low-moisture food safety.