Welcome

This is an interactive web app designed to run sampling simulation in a food safety context. The simulation model and the web app were built by Xianbin (Eric) Cheng, a graduated PhD student, and are maintained by researchers of the Stasiewicz Food Safety Lab under the department of Food Science and Human Nutrition at the University of Illinois at Urbana-Champaign . For more information about the Stasiewicz Food Safety Lab, please click this link .

Who We Are

We are a group of researchers in the department of Food Science and Human Nutrition at the University of Illinois at Urbana-Champaign, dedicated to using risk analysis, at the interface between microbiology and engineering, to analyze and develop solutions to applied problems in food safety and security. We endeavor to create a world where everyone and everywhere is food secure.

Goal

We aim to provide a tool to simulate bulk sampling in 1D (e.g. Dairy Powders), 2D (e.g. a produce field) or 3D (e.g. a grain container) scenarios and evaluate the performance of any specific sampling plan.

Funding Sources

This web app is the product of three research projects funded by International Life Sciences Institute (ILSI), The Institute for the Advancement of Food and Nutrition Sciences (IAFNS), and Center for Produce Safety (CPS).

Major Outputs

This simulation model and web app have been used extensively in the following research articles or posters.

Cheng, X., Stasiewicz, M. J. (2021). Evaluation of the impact of skewness, clustering, and probe sampling plan on aflatoxin detection in corn. Risk Analysis. Link
Stasiewicz, M. J., Wiedmann, M. (2019). Simulation analysis of in-field produce sampling for risk-based sampling plan development. CPS. Link
Quintanilla Portillo J., Cheng X., Belias A.M., Weller D.L., Wiedmann M., Stasiewicz, M. J. (2023). A Validated Preharvest Sampling Simulation Shows that Sampling Plans with a Larger Number of Randomly Located Samples Perform Better than Typical Sampling Plans in Detecting Representative Point-Source and Widespread Hazards in Leafy Green Fields Link
Kim M.,Reyes G.A., Cheng X., Stasiewicz, M. J. (2023). Simulation Evaluation of Power of Sampling Plans to Detect Cronobacter in Powdered Infant Formula Production Link

Version

The current version is 4.0.2 and was updated on: 06/16/2023. Previous versions and source code can be found on GitHub ( link ).

Contacts

Please feel free to contact us if you have any questions.

Matthew Stasiewicz : Principal Investigator, Assistant Professor, PhD. Email: mstasie@illinois.edu

Xianbin (Eric) Cheng Corn Sampling Lead, Email: ericxbcheng@gmail.com LinkedIn

Jorge Quintanilla : Produce Sampling Lead. Email: jfq@illinois.edu

Minho Kim : Powder Sampling Lead. Email: minho3@illinois.edu

Questionnaire

Q1: Which type of product do you want to simulate?

Produce in a field Grains in a bin

Visualization for one iteration

Powder Sampling Input Parameters

Production Time (h)

Production Rate (tonne/h)

Contamination proportion, prevalence (%)

Mean contamination level (log CFU/g)

Standard deviation of contamination level (log CFU/g)

Sampling strategy

Number of sample points

Number of strata

Number of sample points per strata

Individual grab mass (g)

Iteration section

Number of contamination patterns

Number of sampling patterns per contamination pattern

Visualization for one iteration

2D Input Parameters

Length (m)

Width (m)

Geometry

Number of contamination points

Radius of contamination area (m)

Mean contamination level (log CFU/g)

Standard deviation of contamination level (log CFU/g)

Background level (CFU/g)

Decay function

Limit of contamination contribution (0 - 1)

Number of sample points

Sampling strategy

Stratify by

Number of strata

Number of row strata (2D only)

Number of column strata (2D only)

Case

Individual sample mass (g)

Detection method

Iteration section

Number of contamination patterns

Number of sampling patterns per contamination pattern

Number of tuning parameters

Visualization for one iteration

3D Input Parameters

Length (m)

Width (m)

Height (m)

Mycotoxin

Overall mycotoxin level (ppb)

Mycotoxin distribution in contaminated grains

Mycotoxin level in contaminated grains(ppb)

Mode (Most frequent level)(ppb)

Lower bound (ppb)

Number of grains in a cluster

Cluster covariance matrix

Length

Width

Height

Sampling strategy

Number of probes

Stratify by

Number of strata

Number of row strata (2D only)

Number of column strata (2D only)

Grain container (SS Only)

Number of compartments

Hopper car type

Probe diameter (m)

Single kernel mass (g)

Density (g/cm^3)

How homogeneous should the samples be after grinding?

Detection method

Mc (ppb)

Iteration section

Number of contamination patterns

Number of sampling patterns per contamination pattern

Number of tuning parameters

Visualization for one iteration

Selected parameters

Visualization for multiple iterations

Download the simulation data

Click the following button to download the csv file that contains the simulation data.

Download

Variable interpretation

The csv file contains a header with multiple variables. The interpretation is as follows.

seed : The random seed that determines the locations of contaminated kernels.

P_rej : Probability of rejection

Paccept : Probability of acceptance = 1 - P_rej

param : The primary tuning parameter

param2 : The secondary tuning parameter

c_true : The true mycotoxin concentration in the container (ppb).