Machine learning (ML) has proven to be a useful technology for data analysis and modeling in a wide variety of domains, including food science and engineering. The use of ML models for the monitoring and prediction of food safety is growing in recent years. Currently, several studies have reviewed ML applications on foodborne disease and deep learning applications on food. This article presents a literature review on ML applications for monitoring and predicting food safety. The paper summarizes and categorizes ML applications in this domain, categorizes and discusses data types used for ML modeling, and provides suggestions for data sources and input variables for future ML applications. The review is based on three scientific literature databases: Scopus, CAB Abstracts, and IEEE. It includes studies that were published in English in the period from January 1, 2011 to April 1, 2021. Results show that most studies applied Bayesian networks, Neural networks, or Support vector machines. Of the various ML models reviewed, all relevant studies showed high prediction accuracy by the validation process. Based on the ML applications, this article identifies several avenues for future studies applying ML models for the monitoring and prediction of food safety, in addition to providing suggestions for data sources and input variables.

In response to the publics concerns about animal welfare in swine husbandry, the pig (Sus scrofa domesticus) sector introduced improved measures to focus on single rather than multiple dimensions of animal welfare concerns without accounting for their impact on public attitudes. These measures failed to improve attitudes to pig husbandry. The present study uses a more comprehensive approach by evaluating animal welfare measures in terms of their effect on animal welfare, farm income and public attitudes. Four measures were defined for each of the following societal aspects of sow husbandry: piglet mortality; tail biting and the indoor housing of gestating sows. A simulation model was developed to estimate the effects of the measures and Data Envelopment Analysis used to compare measures in terms of their effects on animal welfare, farm income and public attitudes. Only piglet mortality measures were found to have a positive effect on farm income but they showed a relatively low effect on animal welfare and public attitudes. The most efficient measure was that which included straw provision, daylight and increased group sizes for gestating sows. The level of improvement of a measure on animal welfare did not necessarily equate to the same level of improvement in public attitudes or decrease in farm income.