Simulation Evaluation of Power of Sampling Plans to Detect Cronobacter in Powdered Infant Formula Production

Highlights

  • Sampling by Codex guidelines detects Cronobacter in a recalled PIF batch profile.
  • Sampling would not reliably detect Cronobacter in a non-recalled batch profile.
  • Sampling PIF with stratification is potentially more powerful than random sampling.
  • Taking more samples, even if smaller, increases the power to detect contamination.

Abstract

Cronobacter is a hazard in Powdered Infant Formula (PIF) products that is hard to detect due to localized and low-level contamination. We adapted a previously published sampling simulation to PIF sampling and benchmarked industry-relevant sampling plans across different numbers of grabs, total sample mass, and sampling patterns. We evaluated performance to detect published Cronobacter contamination profiles for a recalled PIF batch [42% prevalence, −1.8 ± 0.7 log(CFU/g)] and a reference, nonrecalled, PIF batch [1% prevalence, −2.4 ± 0.8 log(CFU/g)]. Simulating a range of numbers of grabs [n = 1–22,000 (representing testing every finished package)] with 300 g total composite mass showed that taking 30 or more grabs detected contamination reliably (<1% median probability to accept the recalled batch). Benchmarking representative sampling plans ([n = 30, mass grab = 10g], [n = 30, m = 25g], [n = 60, m = 25g], [n = 180, m = 25g]) showed that all plans would reject the recalled batch (<1% median probability to accept) but would rarely reject the reference batch (>50% median probability of acceptance, all plans). Overall, (i) systematic or stratified random sampling patterns are equal to or more powerful than random sampling of the same sample size and total sampled mass, and, (ii) taking more samples, even if smaller, can increase the power to detect contamination.

DOI

https://doi.org/10.1016/j.jfp.2023.100115

Evaluation of the Impact of Skewness, Clustering, and Probe Sampling Plan on Aflatoxin Detection in Corn

The schematic diagram of the simulation process: (1) simulating contaminated corn kernels in a container; (2) taking probe samples by a specific sampling strategy; (3) assaying the samples; and (4) accepting or rejecting the container. The inner iteration layer produces an acceptance probability for one grain container and the outer iteration layer produces multiple acceptance probabilities, each corresponding to a different container.

Abstract

Probe sampling plans for aflatoxin in corn attempt to reliably estimate concentrations in bulk corn given complications like skewed contamination distribution and hotspots. To evaluate and improve sampling plans, three sampling strategies (simple random sampling, stratified random sampling, systematic sampling with U.S. GIPSA sampling schemes), three numbers of probes (5, 10, 100, the last a proxy for autosampling), four clustering levels (1, 10, 100, 1,000 kernels/cluster source), and six aflatoxin concentrations (5, 10, 20, 40, 80, 100 ppb) were assessed by Monte-Carlo simulation. Aflatoxin distribution was approximated by PERT and Gamma distributions of experimental aflatoxin data for uncontaminated and naturally contaminated single kernels. The model was validated against published data repeatedly sampling 18 grain lots contaminated with 5.8–680 ppb aflatoxin. All empirical acceptance probabilities fell within the range of simulated acceptance probabilities. Sensitivity analysis with partial rank correlation coefficients found acceptance probability more sensitive to aflatoxin concentration (−0.87) and clustering level (0.28) than number of probes (−0.09) and sampling strategy (0.04). Comparison of operating characteristic curves indicate all sampling strategies have similar average performance at the 20 ppb threshold (0.8–3.5% absolute marginal change), but systematic sampling has larger variability at clustering levels above 100. Taking extra probes improves detection (1.8% increase in absolute marginal change) when aflatoxin is spatially clustered at 1,000 kernels/cluster, but not when contaminated grains are homogenously distributed. Therefore, taking many small samples, for example, autosampling, may increase sampling plan reliability. The simulation is provided as an R Shiny web app for stakeholder use evaluating grain sampling plans.

DOI

https://doi.org/10.1111/risa.13721

Stasiewicz Food Safety Laboratory
Email: mstasie@illinois.edu
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