dioxins: dioxins Bayesian Network

In bnRep: A Repository of Bayesian Networks from the Academic Literature

dioxins Bayesian Network

Description

Designing optimal food safety monitoring schemes using Bayesian network and integer programming: The case of monitoring dioxins and DL‐PCBs.

Format

A discrete Bayesian network to optimize the use of resources for food safety monitoring. The Bayesian network is learned as in the referenced paper. The vertices are:

screeningResults

The results from the screening DR CALUX method (negative, suspect);

year

The monitoring year (2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017);

trimester

The quarter of the year (1, 2, 3, 4);

animalSpecies

The animal species monitored (bovine animal, bovine animal for fattening, broiler, calf for fattening, cow, deer, duck, eel, fishm goat, goose, hen, horse, pig, poultry, rabbit, sheep, trout);

product

The food product type (egg, liver, meat, milk);

sampling place

The control points (aquaculture, farm, slaughterhouse);

euMonitoring

The number of samples analyzed for EU monitoring to estimate background contamination in different products (0, 1, ..., 31);

gcResults

The results from the GC/MS method (0, n, p);

sampleSize

The number of samples collected during the monitoring period (196, 226, 254, 340, 352, 358, 365, 366, 379, 425).

Value

An object of class bn.fit. Refer to the documentation of bnlearn for details.

References

Wang, Z., van der Fels-Klerx, H. J., & Oude Lansink, A. G. J. M. (2023). Designing optimal food safety monitoring schemes using Bayesian network and integer programming: The case of monitoring dioxins and DL-PCBs. Risk Analysis, 43(7), 1400-1413.

bnRep documentation built on April 12, 2025, 1:13 a.m.