Crowdsourcing: A new conceptual view for food safety and quality

Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools

To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify emerging food safety risks and to provide early warning signals in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things as part of early warning and emerging risk identification tools and methods in the food safety domain. There is an ongoing rapid development of systems fed by numerous, real-time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real-time food safety risk early warning systems. Although these developments increase the feasibility and effectiveness of prospective early warning and emerging risk identification tools, their implementation may prove challenging, particularly for low- and middle-income countries due to low connectivity and data availability. It is advocated to overcome these challenges by improving the capability and capacity of national authorities, as well as by enhancing their collaboration with the private sector and international organizations.

Big Data Applications in Food Supply Chain Management: A Conceptual Framework

The paper provides a systematic review and analysis of the current literature on big data (BD) applications in the context of food supply chain management (FSCM) in order to categorize the state-of-the-art research trends exploring the adoption and implementation of big data analytics (BDA) across different segments of food supply chain (FSC). The use of BDA brings the digital transformation of FSCs closer providing sustainable implications and added value to their operation. Harnessing BD’s potential is becoming more and more relevant in addressing the constantly evolving complexities in food systems. However, the field of BD applications in the FSCM domain is severely fragmented and relatively “primitive”. The present research is one of the earliest attempts to recognize and present a comprehensive analysis for the BD applications across different segments of FSC proposing a conceptual framework that illustrates the role of BD in a data-driven FSCM environment. For the purposes of our research, we adopted the systematic literature review (SLR) method aiming at the identification of the dominant categories and themes within the research area. Based on the SLR findings, we propose a conceptual framework that captures the interconnection between FSC performance and BD applications by using the input-process-output (IPO) model within a data-driven FSCM context. The main research contribution lies on the thematic classification of relevant research, the conceptualization of this fragmented field, the development of a conceptual framework, and the presentation of a future research agenda pertaining to BD applications in a data-driven FSCM context.

Branded Foods Databases as a Tool to Support Nutrition Research and Monitoring of the Food Supply: Insights From the Slovenian Composition and Labeling Information System

Branded foods databases are becoming very valuable not only in nutrition research but also for clinical practice, policymakers, businesses, and general population. In contrast to generic foods, branded foods are marked by rapid changes in the food supply because of reformulations, the introduction of new foods, and the removal of existing ones from the market. Also, different branded foods are available in different countries. This not only complicates the compilation of branded foods datasets but also causes such datasets to become out of date quickly. In this review, we present different approaches to the compilation of branded foods datasets, describe the history and progress of building and updating such datasets in Slovenia, and present data to support nutrition research and monitoring of the food supply. Manufacturers are key sources of information for the compilation of branded foods databases, most commonly through food labels. In Slovenia, the branded food dataset is compiled using standard food monitoring studies conducted at all major retailers. Cross-sectional studies are conducted every few years, in which the food labels of all available branded foods are photographed. Studies are conducted using the Composition and Labeling Information System (CLAS) infrastructure, composed of a smartphone application for data collection and online data extraction and management tool. We reviewed various uses of branded foods datasets. Datasets can be used to assess the nutritional composition of food in the food supply (i.e., salt, sugar content), the use of specific ingredients, for example, food additives, for nutrient profiling, and assessment of marketing techniques on food labels. Such datasets are also valuable for other studies, for example, assessing nutrient intakes in dietary surveys. Additional approaches are also being tested to keep datasets updated between food monitoring studies. A promising approach is the exploitation of crowdsourcing through the mobile application VešKajJeš, which was launched in Slovenia to support consumers in making healthier dietary choices.

Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media

Foodborne outbreaks are a serious but preventable threat to public health that often lead to illness, loss of life, significant economic loss, and the erosion of consumer confidence. Understanding how consumers respond when interacting with foods, as well as extracting information from posts on social media may provide new means of reducing the risks and curtailing the outbreaks. In recent years, Twitter has been employed as a new tool for identifying unreported foodborne illnesses. However, there is a huge gap between the identification of sporadic illnesses and the early detection of a potential outbreak. In this work, the dual-task BERTweet model was developed to identify unreported foodborne illnesses and extract foodborne-illness-related entities from Twitter. Unlike previous methods, our model leveraged the mutually beneficial relationships between the two tasks. The results showed that the F1-score of relevance prediction was 0.87, and the F1-score of entity extraction was 0.61. Key elements such as time, location, and food detected from sentences indicating foodborne illnesses were used to analyze potential foodborne outbreaks in massive historical tweets. A case study on tweets indicating foodborne illnesses showed that the discovered trend is consistent with the true outbreaks that occurred during the same period.

Application Research: Big Data in Food Industry

A huge amount of data is being produced in the food industry, but the application of big data—regulatory, food enterprise, and food-related media data—is still in its infancy. Each data source has the potential to develop the food industry, and big data has broad application prospects in areas like social co-governance, exploit of consumption markets, quantitative production, new dishes, take-out services, precise nutrition and health management. However, there are urgent problems in technology, health and sustainable development that need to be solved to enable the application of big data to the food industry.

Crowdsourced Mapping for Healthy Food Accessibility in Dallas, Texas: A Feasibility Study

Since its first use for describing a neighborhood lacking access to food in the 1990's, “food deserts” has been widely addressed by researchers and adopted as an indicator of neighborhood-level food insecurity by governmental agencies, such as USDA. However, mostly due to cost and difficulty in collecting georeferenced data and characteristics of grocery stores, the USDA Food Access Research Atlas is infrequently released, and considers only income, vehicle ownership, and distance to the nearest grocery store. In this paper, we explored the feasibility of a crowdsourced geospatial data source, coupled with additional measures, in supplementing the USDA's current designation of food deserts. We used Yelp data to map food deserts in the city of Dallas and compared them with those based on the 2015 USDA food retailer database. Although direct comparison was not possible due to time mismatch between the two data sources, the discrepancies highlighted the need of a more frequent identification of food deserts for timely policy intervention. Furthermore, we extended mapping to reveal other potential areas of concerns, by adding the Transit Score metric and Yelp's price descriptor of businesses. The resulting maps illustrated the areas with grocery stores nearby but with limited accessibility due to lack of public transit or potential financial barriers in purchasing foods due to high prices. Our findings demonstrate the current status and future potential of up-to-date crowdsourced, georeferenced data as a complement of official government data, which could serve to extend food access research and guide health policies.

Consumers' Awareness and Trust Toward Food Safety News on Social Media in Malaysia

ABSTRACT

Social media offers numerous advantages for personal users and organizations to communicate, socialize, and market their products. When used correctly, social media is an effective tool to communicate and to share food safety news and good practices. However, there have been reports of fake food safety news shared via social media, fueling panic and resulting in a loss of revenue. Thus, this study aimed to investigate the consumers' awareness, trust, and usage of social media in communicating food safety news in Malaysia. A questionnaire divided into five sections-(i) demographics, (ii) reaction to food safety news, (iii) consumers' awareness, (iv) social media truth and level of trust, and (v) social media uses and content creation-was created and shared online. A total of 341 questionnaires were returned of which 339 surveys were valid. This study revealed that less than one-third of the study group (27.1%) knew which of the food safety news were fake. Most respondents (67.8%) were less likely to purchase the affected foods if the foods were featured in social media as problematic, although no differences were made between true and fake news and how that would influence respondents' willingness to purchase affected foods. Overall, 62% of the respondents agreed or strongly agreed about the usage of social media and its ability to prevent food poisoning cases, while more than 50% of the respondents were in total agreement that social media allow consumers to act more responsibly by sharing food safety news. Respondents tended to trust information shared by scientists (67.5%) and family members and friends (33%). Respondents would most often share the news after verifying its authenticity (46%). If respondents experienced a personal food safety issue (e.g., discovered a fly in their meal), they seldom or never took photos to post online (56.1%). It is possible that the respondents preferred to inform the food handlers and/or shop owners about the affected products rather than post the photos online. It is suggested that targeted food safety information and media literacy be provided to improve consumers' awareness and to positively influence self-verification of the food safety information before sharing. This study provides crucial insights for a range of stakeholders, particularly public authorities, food bloggers, and the public, in using social media effectively to build consumers' awareness and trust in food safety information.

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