A Scalable Aggregated and Nondestructive Sampling Approach for Food Safety Monitoring of Fresh-Cut Produce During...

Representative sampling is critical for detecting low-prevalence microbial contamination in fresh produce, particularly fresh-cut produce. Conventional grab sampling may miss localized contamination due to heterogeneous microbial distribution. This study evaluated a cloth-based aggregated, nondestructive sampling approach against traditional grab sampling across five fresh-cut produce matrices (onions, lettuce, carrots, tomatoes, and peppers) under commercial processing conditions. Paired samples were collected concurrently, with aggregated cloth and grab sampling conducted side-by-side to reflect current industry practices. Cloth samples were obtained by fitting the cloth onto the distribution cone of a multipocket scale for 2 h, whereas grab samples (150 g) were collected using a gloved hand at approximately 1-min intervals from the product stream during the same 2-h period. Samples were analyzed using a panel of index targets representing indicator microorganisms and microbial groups, and detection frequencies were compared using the exact McNemar test. Cloth sampling yielded significantly higher detection rates for tetA (37.5%) and tetB (98.4%) in diced carrots (p < 0.05), indicating enhanced recovery of low-prevalence targets. For all other evaluated targets and matrices, aggregated cloth sampling demonstrated detection performance equivalent to grab sampling (p > 0.05). PCR targeting Enterobacteriaceae (EB-set 1) showed near-universal detection across both methods, whereas low-prevalence targets such as phoE and fliC in certain matrices provided limited discriminatory power, highlighting the importance of target selection. By increasing sampled surface area, aggregated cloth sampling generated representative samples while reducing labor and standardizing sampling procedures. Overall, aggregated cloth sampling was shown to be a scalable alternative to conventional grab sampling under commercial processing conditions.