Emerging Applications of Machine Learning in Food Safety

Deng, Xiangyu; Cao, Shuhao; Horn, Abigail L.

doi:10.1146/annurev-food-071720-024112

Cited by 80 publications

(33 citation statements)

References 121 publications

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“…For example, in preharvest, field, and weather forecasts, toxin contaminations on farmlands were predicted, and in the retail environment, contactless audits and record-keeping were performed for 1.4 million months, and observations of roast chickens' actual annealing temperature were carried out for food safety. e consumer interactions with foods, including transactions, ingestion, comments, and share experience, also produce a huge amount of data at the end of the food supply chain [4].…”

Section: Introductionmentioning

confidence: 99%

“…rough digital platforms including social networking sites, search stories, fundraising sites, testimonials and remarks, and also Sales Revenue and Consumer Transactions, these unique data streams are becoming available. Extraction of this information is on the horizon in order to inform food safety and public health [4]. In tracking instances and agents of foodborne diseases, blockchain solutions have a vital role to play.…”

Section: Introductionmentioning

confidence: 99%

“…Image recognition is a term that describes a set of algorithms and technologies that attempt to evaluate image data and fully comprehend the disguised interpretations of characteristics beside them and implement these learned representations for specific activities like categorizing pixels' classifications instantaneously, understanding which items are available and where in an image, etc. ese technologies leverage numerous classic computer vision approaches as well as machine learning and deep learning algorithms to get essential results for solving such situations [4,14]. Image recognition is a wide and thorough work related to the more general difficulty of the design categorization.…”

Section: Brief Introduction Of Image Recognitionmentioning

confidence: 99%

See 2 more Smart Citations

The Use of Different Image Recognition Techniques in Food Safety: A Study

Khan¹,

Kumar

Dhingra³

et al. 2021

Journal of Food Quality

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Food safety refers to preparing, transporting, and storing food to avoid foodborne sickness and harm. From farm to factory and factory to fork, food items may meet various health dangers. Therefore, food safety is crucial both monetarily and morally. The implications of failing to comply with food safety requirements are varied. The requirement for accurate, quick, and nonpartisan quality assessments of these features in food products continues to rise with increased demands for dietary materials and high-quality requirements. Computer vision provides an automatic, nondestructive, and economic approach to achieving these aims. A substantial research has demonstrated its effectiveness for fruit and vegetable assessment and classification. It stresses the critical components of image processing technology and a survey of the most current advances across the food sector. This article outlines the essential parts of a computer vision system. In order to avoid foodborne disease and ensure food security, fast and effective detection of pathogenic microorganisms is crucial for public safety biomonitoring. Over the years, microorganism detection techniques have evolved.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Brief Introduction Of Image Recognitionmentioning

confidence: 99%

See 1 more Smart Citation

The Use of Different Image Recognition Techniques in Food Safety: A Study

Khan¹,

Kumar

Dhingra³

et al. 2021

Journal of Food Quality

View full text Add to dashboard Cite

show abstract

“…In recent years, the readily available and rapidly disseminated digital data (e.g., social media) have been utilized for detecting foodborne illnesses [12 -17] . Crowdsourcing, a method that leverages massive online data from user responses, coupled with machine learning approaches provide a new means for conducting food safety risk analysis and risk communications [18,19] . For example, Ordun et al (2013) used feeds from the open-source media outlets Twitter and Rich Site Summary to characterize the 2012 Salmonella event related to cantaloupes and estimate the numbers of sick, dead, and hospitalized [12] .…”

Section: Introductionmentioning

confidence: 99%

Crowdsourcing and Machine Learning Approaches for Extracting Entities Indicating Potential Foodborne Outbreaks From Social Media

Tao

Zhang

et al. 2021

Preprint

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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.

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“…Increasingly, machine learning algorithms are being applied to food and pharmaceutical research, such as fruit extracts, natural sweeteners, sustained‐release matrix tablets, and oral fast disintegrating tablets (Abhimanyu et al., 2020; Astray et al., 2020; Bhagya Raj & Dash, 2020; Deng et al., 2021; Goel et al., 2021; Han et al., 2019; Yang et al., 2019; Ye et al., 2019; Zhao et al., 2019). The use of state‐of‐the‐art machine learning algorithms in pharmaceutics and food research has improved the efficiency of drug and food development.…”

Section: Introductionmentioning

confidence: 99%

Interpretable machine learning methods for in vitro pharmaceutical formulation development

Ye¹,

Yang

et al. 2021

Food Frontiers

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Background Machine learning has become an alternative approach for pharmaceutical formulation development. However, many machine learning applications in pharmaceutics only focus on model performance rather than model interpretability. Aim This study aims to propose an attention‐based deep neural network (DNN) for pharmaceutical formulation development. Methods An attention‐based DNN, AttPharm, was proposed. AttPharm separately handled feature values and feature physical meaning by representation learning to successfully apply the attention mechanism to the pharmaceutical tabular data. Furthermore, the distributions of the attention weights were computed using AttPharm. Two post hoc methods, local interpretable model‐agnostic explanation (LIME) and TreeSHAP, were utilized to obtain the post hoc model interpretability for lightGBM. Results The results demonstrated that AttPharm significantly improved the model performance of plain neural networks on a pharmaceutical cyclodextrin dataset because the attention mechanism could extract related features and find minute variation. Notably, the attention weights were analyzed, which illustrated global and local feature‐level and sample‐level model interpretability, thus providing insights for formulation design. Comparing with post hoc methods, AttPharm can be used without the concern of the faithfulness of interpretability. Conclusion This is the first step in applying the attention‐based DNN to pharmaceutical formulation development. Considering the importance of model interpretability, the proposed approach may have a wide range of applications in pharmaceutics.

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Emerging Applications of Machine Learning in Food Safety

Cited by 80 publications

References 121 publications

The Use of Different Image Recognition Techniques in Food Safety: A Study

The Use of Different Image Recognition Techniques in Food Safety: A Study

Crowdsourcing and Machine Learning Approaches for Extracting Entities Indicating Potential Foodborne Outbreaks From Social Media

Interpretable machine learning methods for in vitro pharmaceutical formulation development

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