Swiss Cheese Model of food safety incidents: Preventing foodborne illness through multiple layers of defence

This study aims to discuss the use of multiple layers of defence to prevent foodborne illness in restaurants. A defence model was developed based on Reason’s Swiss Cheese Model. Reason’s model was extended by adding the concept of Hazard Analysis and Critical Control Points, as well as Five Keys to Safer Food. The defence system was divided into seven layers of defence: 1) adequate facilities and 2) training as administrative controls; 3) safe ingredients and water; 4) environmental hygiene; 5) personal and food hygiene and 6) safe food temperature as behavioural controls; and 7) control and systems. The hypothesis was that the layers would act as barriers to prevent hazards from causing losses. To test the model, a dataset (secondary data) of food safety assessments from 1,536 different restaurant establishments in Brazil was used. A checklist of 51 items was organised into seven layers of defence system. The model was tested with a Partial Least Square Structural Equation Model. Errors in administrative controls (facilities and training) led to errors in behavioural controls. A 'cascade effect' was observed where errors in distal behavioural controls (safe ingredients and water and environmental hygiene) impacted proximal controls (personal and food hygiene and; safe food temperature) and system controls. It was discussed how latent conditions and active failures can string together and cause a foodborne illness incident. The Swiss Cheese Model of food safety incidents is proposed as a new perspective for food safety. This model can be used for risk management and food safety education

What Motivates Consumer Food Safety Perceptions and Beliefs? A Scoping Review in BRICS Countries

This scoping review aimed to map the main evidence in the existing literature regarding consumer perceptions and beliefs regarding food safety in the context of BRICS (Brazil, Russia, India, China, and South Africa) countries. Articles were searched in the Web of Science, Scopus, and SciELO databases. The last search was performed on November 2021. Only the studies conducted within BRICS countries were included. The synthesis aimed to group similarities in consumer beliefs and perceptions of food safety. After screening, 74 eligible articles were included in the study. Of the 74 studies analyzed, 49 (66.2%) were carried out in China, 14 (18.9%) in Brazil, 5 (6.8%) in India,4 (5.4%) in South Africa, and 2 (2.7%) in Russia. Thirty-three motivators of perceptions and beliefs regarding food safety were identified. Food safety motivators were grouped into three categories: (1) sociodemographic characteristics, (2) cognitive aspects, and (3) other. In the “sociodemographic characteristics” category, the motivator with the highest number of corresponding results was education level (results = 22), followed by income (results = 22), both positive drivers for food safety perceptions. The “cognitive aspects” category comprised the majority of the identified motivators. Concern for food safety (results = 32) and risk perception (results = 30) were the motivators with the highest number of results among all categories and motivators. Finally, the main motivator in the “other” category was place of consumption/purchase (results = 8), focusing on consumers underestimating the risk of having a foodborne disease when eating away from home. China and Brazil are leading the way in studies on this topic. Consumers’ perceptions are influenced by socioeconomic characteristics (e.g., age, gender, education level, income), cognitive aspects (e.g., knowledge, risk perception, food concerns, previous experience with food safety incidents) and other situational factors (e.g., price, place of purchase, traceability)

Exploring dark kitchens in Brazilian urban centres: a study of delivery-only restaurants with food delivery apps

Dark kitchen is a delivery-only restaurant that operates without direct contact with the consumer, has no premises for local consumption and sells exclusively through online platforms. The main objective of this work is to identify and characterise dark kitchens in three urban centres featured in the most used food delivery app in Brazil. To this end, data collection was conducted in two phases. In the first phase, through data mining, we collected information from restaurants in three cities (Limeira, Campinas, and São Paulo - Brazil) that were provided in the food delivery app. A total of 22,520 establishments were searched from the central point of each of the cities. In the second phase, the first 1,000 restaurants in each city were classified as dark kitchens, standard, or undefined restaurants. A thematic content analysis was conducted to further distinguish the dark kitchen models. Of the restaurants evaluated, 1,749 (65.2%) were classified as standard restaurants, 727 (27.1%) as dark kitchens, and 206 (7.7%) as undefined. In terms of the characteristics of dark kitchens, they were more dispersed and located further away from the central points compared to standard restaurants. Meals in dark kitchens were cheaper than in standard restaurants, and had a lower number of user reviews. Most of the dark kitchens in São Paulo served Brazilian dishes, while in the smaller cities, Limeira and Campinas, it was mainly snacks and desserts. Six different models of dark kitchen were identified: Independent dark kitchen; shell-type (hub); franchise; virtual kitchen in a standard restaurant (different menu); virtual kitchen in a standard restaurant (similar menu but different name); and home-based dark kitchen. The modelling approach and methodology used to classify and identify dark kitchens is considered a contribution to science as it allows a better understanding of this fast growing sector of the food industry. This in turn can help to develop management strategies and policies for the sector. Our study is also of value to regulators to determine their proliferation through urban planning and to promote appropriate guidelines for dark kitchens as they differ from standard restaurants