Enhanced Food Safety Through Deep Learning for Food Recalls Prediction

2020 ◽  
pp. 566-580
Author(s):  
Georgios Makridis ◽  
Philip Mavrepis ◽  
Dimosthenis Kyriazis ◽  
Ioanna Polychronou ◽  
Stathis Kaloudis
Keyword(s):  
Deep Learning ◽  
Food Safety ◽  
Food Recalls

scholarly journals Perceived Knowledge, Coping Efficacy and Consumer Consumption Changes in Response to Food Recall

2020 ◽  
Vol 12 (7) ◽  
pp. 2696
Author(s):  
Chuanhui Liao ◽  
Huang Yu ◽  
Weiwei Zhu
Keyword(s):  
Food Safety ◽  
Structural Equation ◽  
Safety Management ◽  
Explanatory Power ◽  
Future Research ◽  
Protection Motivation ◽  
Perceived Knowledge ◽  
Major Food ◽  
Food Recall ◽  
Food Recalls

With a serious food safety situation in China, lots of major food recalls have been initiated. This study examined the key determinants underlying consumers’ protection and behavioral intention in response to major food recalls. An augmented protection motivation theory model (PMT) was developed by incorporating trust in food safety management and food recall concern into the original PMT. Structural equation model analysis was conducted using survey data in China (N = 631). The results showed that perceived knowledge significantly and positively influence protection motivation via its positive influence on the threat appraisal and coping appraisal. Moreover, protection motivation, trust in food safety management (TFSM), and food recall concern (FRC) significantly affect protection behavior intention. It was indicated that the inclusion of TFSM and FRC into the PMT significantly increase the explanatory power of the PMT model. Further analysis of quadratic regression demonstrated that the relationship between perceived knowledge and protection motivation presented an inverted U shape, which indicates the importance of continuous education in developing consumers’ food safety knowledge. Implications for future research are discussed.

Analysis and Assessment of Food Traceability Status in China

2011 ◽  
Vol 396-398 ◽  
pp. 1353-1357 ◽  
Author(s):  
Li Mei Liu ◽  
Heng Qian ◽  
Yong Chao Gao ◽  
Ding Wang
Keyword(s):  
Food Safety ◽  
Food Chains ◽  
Support Tool ◽  
Food Traceability ◽  
The Status ◽  
Food Recalls ◽  
Further Development

Food traceability is a support tool for preventing and improving food safety problems. The purpose of food traceability is to collect the flow and transformation information of food-related materials in the food chains. When faced with a food safety crisis, we can find the source of the problem and track the flow of products from the information, and effectively carry out food recalls. In this paper, the status of food traceability in China is deeply analyzed from the laws, regulations, standards, traceability techniques and systems construction, and validity of internal and external traceability in food chains is assessed retrospectively. Then some recommendations for the further development of China's food traceability are proposed.

scholarly journals MajraDoc an Image based Disease Detection App for Agricultural Plants using Deep Learning Techniques

2021 ◽  
Author(s):  
Sara Saleh Alfozan ◽  
Mohamad Mahdi Hassan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Food Safety ◽  
Early Detection ◽  
Deep Neural Network ◽  
High Accuracy ◽  
Vital Role ◽  
Mobile App ◽  
Learning Techniques ◽  
Agricultural Plants

Infection of agricultural plants is a serious threat to food safety. It can severely damage plants' yielding capacity. Farmers are the primary victims of this threat. Due to the advancement of AI, image-based intelligent apps can play a vital role in mitigating this threat by quick and early detection of plants infections. In this paper, we present a mobile app in this regard. We have developed MajraDoc to detect some common diseases in local agricultural plants. We have created a dataset of 10886 images for ten classes of plants diseases to train the deep neural network. The VGG-19 network model was modified and trained using transfer learning techniques. The model achieved high accuracy, and the application performed well in predicting all ten classes of infections.

scholarly journals Research on Intelligent Analysis of Illegal Food Safety Behavior Based on Deep Learning Algorithm

2021 ◽  
Vol 292 ◽  
pp. 03012
Author(s):  
Bing Yang ◽  
Kai Chen ◽  
Yajie Wang ◽  
Hong Tan ◽  
Fugui Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Food Safety ◽  
Learning Algorithm ◽  
Consumer Trust ◽  
Consumer Health ◽  
Safety Behavior ◽  
Deep Learning Algorithm ◽  
Intelligent Analysis ◽  
Food Quality And Safety ◽  
Made In

Food safety has been a major concern in recent years as a result of numerous food safety events in many nations. This could increase the health risks associated with eating low-quality food, lowering customer confidence in food safety. It is critical to overcome this challenge and gain consumer trust in order to improve food quality and safety. To address this issue, we suggested an intelligent deep learning method for identifying which foods are potentially harmful to human health based on chemical and additive qualities, which could have a significant impact on consumer health. The findings of our survey show that deep learning surpasses other methods such as manual feature extractors, as well as the promising findings of categorization of hazardous food, further research efforts to apply deep learning to the field of food will be made in the future.

Make It Safe

2010 ◽  
Author(s):  
Keyword(s):  
Food Safety ◽  
Small Businesses ◽  
Manufacturing Industry ◽  
Small Scale ◽  
Safety Hazards ◽  
Safety Risks ◽  
Food And Beverage ◽  
Control Food ◽  
Food Recalls ◽  
Safety Systems

All people involved with preparation of food for the commercial or retail market need a sound understanding of the food safety risks associated with their specific products and, importantly, how to control these risks. Failure to control food safety hazards can have devastating consequences for not only the consumer, but also the food manufacturer. Make It Safe provides practical guidance on how to control food safety hazards, with a specific focus on controls suitable for small-scale businesses to implement. Small businesses make up around two-thirds of businesses in Australia’s food and beverage manufacturing industry. This book is aimed at those small-scale businesses already in or considering entering food manufacture. Those already operating a small business will develop a better understanding of key food safety systems, while those who are in the ‘start-up’ phase will gain knowledge essential to provide their business with a solid food safety foundation while also learning about Australian food regulations relevant to food safety. The content will also be useful for students studying food technology or hospitality who wish to seek employment in the manufacturing industry or are planning on establishing their own manufacturing operation. Illustrated in full colour throughout, Make It Safe outlines the major food safety hazards – microbial, chemical and physical – which must be controlled when manufacturing all types of food products. The control of microbial hazards is given special emphasis as this is the greatest challenge to food manufacturers. Topics covered include: premises, equipment, staff, product recipes, raw ingredients, preparation, processing, packaging, shelf-life, labelling and food recalls. Key messages are highlighted at the end of each chapter.

scholarly journals Detecting reports of unsafe foods in consumer product reviews

JAMIA Open ◽  
2019 ◽  
Vol 2 (3) ◽  
pp. 330-338 ◽  
Author(s):  
Adyasha Maharana ◽  
Kunlin Cai ◽  
Joseph Hellerstein ◽  
Yulin Hswen ◽  
Michael Munsell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Food Safety ◽  
Food Product ◽  
Food Products ◽  
Good Health ◽  
Consumer Product ◽  
Product Reviews ◽  
Applied Machine Learning ◽  
Under Sampling ◽  
Food Recalls

Abstract Objectives Access to safe and nutritious food is essential for good health. However, food can become unsafe due to contamination with pathogens, chemicals or toxins, or mislabeling of allergens. Illness resulting from the consumption of unsafe foods is a global health problem. Here, we develop a machine learning approach for detecting reports of unsafe food products in consumer product reviews from Amazon.com. Materials and Methods We linked Amazon.com food product reviews to Food and Drug Administration (FDA) food recalls from 2012 to 2014 using text matching approaches in a PostGres relational database. We applied machine learning methods and over- and under-sampling methods to the linked data to automate the detection of reports of unsafe food products. Results Our data consisted of 1 297 156 product reviews from Amazon.com. Only 5149 (0.4%) were linked to recalled food products. Bidirectional Encoder Representation from Transformations performed best in identifying unsafe food reviews, achieving an F1 score, precision and recall of 0.74, 0.78, and 0.71, respectively. We also identified synonyms for terms associated with FDA recalls in more than 20 000 reviews, most of which were associated with nonrecalled products. This might suggest that many more products should have been recalled or investigated. Discussion and Conclusion Challenges to improving food safety include, urbanization which has led to a longer food chain, underreporting of illness and difficulty in linking contaminated food to illness. Our approach can improve food safety by enabling early identification of unsafe foods which can lead to timely recall thereby limiting the health and economic impact on the public.

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