Modeling the Formation Mechanism of Food Safety Risk in Catering O2O Distribution Link

This paper aims to solve the problem of food safety in catering O2O distribution link. We applied the system dynamics method to model the formation mechanism of food safety risk in the distribution link. The results of our experiment include identifying the risk factors that may be faced by food safety in the distribution link from five perspectives: O2O catering enterprise’s own risk, logistics distribution team’s distribution risk, O2O catering platform supervision risk, user-supervision risk, and government department supervision risk, and establishing a risk index evaluation system based on the Analytic Hierarchy Process. With the help of the system dynamics model, the corresponding risk formation mechanism system model flow diagram is established, and the model simulation analysis is carried out. Through this research, we concluded that we can use the risk model to understand the risks faced by different subjects so as to make targeted countermeasures.

Ensemble Learning Models for Food Safety Risk Prediction

Ensemble learning was adopted to design risk prediction models with the aim of improving border inspection methods for food imported into Taiwan. Specifically, we constructed a set of prediction models to enhance the hit rate of non-conforming products, thus strengthening the border control of food products to safeguard public health. Using five algorithms, we developed models to provide recommendations for the risk assessment of each imported food batch. The models were evaluated by constructing a confusion matrix to calculate predictive performance indicators, including the positive prediction value (PPV), recall, harmonic mean of PPV and recall (F1 score), and area under the curve. Our results showed that ensemble learning achieved better and more stable prediction results than any single algorithm. When the results of comparable data periods were examined, the non-conformity hit rate was found to increase significantly after online implementation of the ensemble learning models, indicating that ensemble learning was effective at risk prediction. In addition to enhancing the inspection hit rate of non-conforming food, the results of this study can serve as a reference for the improvement of existing random inspection methods, thus strengthening capabilities in food risk management.

Principle-based recommendations for big data and machine learning in food safety: the P-SAFETY model

Big data and Machine learning Techniques are reshaping the way in which food safety risk assessment is conducted. The ongoing ‘datafication’ of food safety risk assessment activities and the progressive deployment of probabilistic models in their practices requires a discussion on the advantages and disadvantages of these advances. In particular, the low level of trust in EU food safety risk assessment framework highlighted in 2019 by an EU-funded survey could be exacerbated by novel methods of analysis. The variety of processed data raises unique questions regarding the interplay of multiple regulatory systems alongside food safety legislation. Provisions aiming to preserve the confidentiality of data and protect personal information are juxtaposed to norms prescribing the public disclosure of scientific information. This research is intended to provide guidance for data governance and data ownership issues that unfold from the ongoing transformation of the technical and legal domains of food safety risk assessment. Following the reconstruction of technological advances in data collection and analysis and the description of recent amendments to food safety legislation, emerging concerns are discussed in light of the individual, collective and social implications of the deployment of cutting-edge Big Data collection and analysis techniques. Then, a set of principle-based recommendations is proposed by adapting high-level principles enshrined in institutional documents about Artificial Intelligence to the realm of food safety risk assessment. The proposed set of recommendations adopts Safety, Accountability, Fairness, Explainability, Transparency as core principles (SAFETY), whereas Privacy and data protection are used as a meta-principle.

Microbial Spoilage of Plant-Based Meat Analogues

Plant-based meat analogues (i.e., plant-based meat alternatives or substitutes, or vegan meats) are becoming more and more popular. The quality of the available products is constantly increasing therefore their consumption is also increasing. The primary role of meat analogues is to replace the meat component in meals while appropriate nutrient content and hedonic value will be provided as well. The food safety aspects of these newly emerging food products are less investigated. The aim of this study is to compare the microbial spoilage of identical meals prepared with meat and meat analogues to evaluate the food safety risk of meat analogues. In this work, raw protein materials were tested. Moreover, three pairs of meals prepared with or without meat were microbiologically examined during a storage experiment. Microbial contaminants were low in raw protein sources. In the case of hot meals, the microbial proliferation was faster in samples containing meat analogue, especially if the meals were not cooled. The food safety risk of meals prepared with meat analogues is slightly higher than their meat-containing counterparts, therefore more attention needs to be paid to the preparation, processing, and storage of these foods.

Predicting Food-Safety Risk and Determining Cost-Effective Risk-Reduction Strategies

Food safety is a major risk for agribusiness firms. According to the Centers for Disease Control and Prevention (CDC), approximately 5000 people die annually, and 36,000 people are hospitalized as a result of foodborne outbreaks in the United States. Globally, the death estimate is about 42,000 people per year. A single outbreak could cost a particular segment of the food industry hundreds of millions of dollars due to recalls and liability; these instances might amount to billions of dollars annually. Despite U.S. advancements and regulations, such as pathogen reduction/hazard analysis critical control points (PR/HACCP) in 1996 and the Food Modernization Act in 2010, to reduce food-safety risk, retail meat facilities continue to experience recalls and major outbreaks. We developed a stochastic-optimization framework and used stochastic-dominance methods to evaluate the effectiveness for three strategies that are used by retail meat facilities. Copula value-at-risk (CVaR) was utilized to predict the magnitude of the risk exposure associated with alternative, cost-effective risk-reduction strategies. The results showed that optimal retail-intervention strategies vary by meat and pathogen types, and that having a single Salmonella performance standard for PR/HACCP could be inefficient for reducing other pathogens and food-safety risks.