Ensemble Learning Models for Food Safety Risk Prediction

Wu, Lei; Weng, Sung-Shun

doi:10.3390/su132112291

Cited by 21 publications

(4 citation statements)

References 21 publications

(26 reference statements)

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“…If 70% of data were used for training and 30% for testing, the combined model outperformed all single ones, reaching 95% accuracy, thereby helping to avoid perishing of foods before they reach the market (Liu & Hu, 2017). Wu and Weng (2021) explored the use of "ensemble learning," that is, using multiple machine-learning methods in parallel. They tested these on historic data for three types of problematic foodstuffs whilst considering 125 factors.…”

Section: Data Processing: Text Mining and Artificial Intelligencementioning

confidence: 99%

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

Mu,

Kleter,

Bouzembrak

et al. 2024

Comp Rev Food Sci Food Safe

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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.

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Section: Data Processing: Text Mining and Artificial Intelligencementioning

confidence: 99%

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

Mu,

Kleter,

Bouzembrak

et al. 2024

Comp Rev Food Sci Food Safe

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show abstract

“…The combination of the intricate characteristics of reinforced concrete buildings and the significant nonlinearity of embedded metal rusting, it is challenging to forecast corrosion parameters in particular processes [18]. To calibrate new experimental data when empirical coefficients are necessary but challenging to attain, most prediction models use empirical formulas.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Corrosion Prediction Initiation Time for Embedded Steel in Concrete with Shell Powders Using Deep Learning Techniques

et al. 2024

ijisae

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The embedded steel is integrated with concrete material, primarily used in buildings and infrastructure projects. "Embedded steel" refers to steel reinforcement bars or mesh embedded in concrete structures. Steel is added to the concrete to strengthen and support the structure. One of the primary challenges associated with embedded-based steel is anticipating its corrosion once it has been incorporated into building structures. It is necessary to monitor the initiation time of corrosion on the steel in the concrete, which is considered crucial to the environment. Early corrosion detection is challenging, and its accuracy helps design durable concrete. This process reduces the time and cost of embedded steel manufacturing. This research focuses on applying embedded deep-learning models to test the accuracy of the algorithms suggested for embedded steel. A-state of art technique reveals that convolutional neural network (CNN), Long short-term memory (LSTM), and Deep neural network (DNN) models can perform accurate predictions. In this study, the above deep learning models are embedded to validate the accuracy of the different algorithms. The study aimed to determine the corrosion initiation time on steel, which is Incorporated within concrete via corrosion potential measurement. To achieve this, concrete samples were arranged with conch shell powder as a partial replacement to Portland cement and exposed in 5% sodium chloride with following the requirements of ASTM C876 -15. During the exposure time, the steel embedded's corrosion potential was measured, and the resulting dataset was utilized for training three deep-learning models. These models were developed using input variables such as cement, conch shell powder, fine aggregate, coarse aggregate, exposure period, and water to estimate the corrosion initiation time on the embedded-steel based on the potential corrosion measurements.

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“…This information is used to build interventions that allocate resources more effectively while helping food safety specialists make well-informed decisions to guarantee a more secure food supply chain [50,72]. In the study by Wu and Weng [73], numerous ensemble learning models were utilized to forecast food safety hazards, with a specific emphasis on enhancing border inspection techniques for imported food in Taiwan. Online implementation of their approach models resulted in a notable improvement in the nonconformity hit rate, demonstrating the efficacy of ensemble learning in predicting food safety risks.…”

Section: Ai and ML In Predictive Analytics For Risk Assessmentmentioning

confidence: 99%

Enhancing Food Integrity through Artificial Intelligence and Machine Learning: A Comprehensive Review

Gbashi,

Njobeh

2024

Applied Sciences

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Herein, we examined the transformative potential of artificial intelligence (AI) and machine learning (ML) as new fronts in addressing some of the pertinent challenges posed by food integrity to human and animal health. In recent times, AI and ML, along with other Industry 4.0 technologies such as big data, blockchain, virtual reality, and the internet of things (IoT), have found profound applications within nearly all dimensions of the food industry with a key focus on enhancing food safety and quality and improving the resilience of the food supply chain. This paper provides an accessible scrutiny of these technologies (in particular, AI and ML) in relation to food integrity and gives a summary of their current advancements and applications within the field. Key areas of emphasis include the application of AI and ML in quality control and inspection, food fraud detection, process control, risk assessments, prediction, and management, and supply chain traceability, amongst other critical issues addressed. Based on the literature reviewed herein, the utilization of AI and ML in the food industry has unequivocally led to improved standards of food integrity and consequently enhanced public health and consumer trust, as well as boosting the resilience of the food supply chain. While these applications demonstrate significant promise, the paper also acknowledges some of the challenges associated with the domain-specific implementation of AI in the field of food integrity. The paper further examines the prospects and orientations, underscoring the significance of overcoming the obstacles in order to fully harness the capabilities of AI and ML in safeguarding the integrity of the food system.

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Ensemble Learning Models for Food Safety Risk Prediction

Cited by 21 publications

References 21 publications

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

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

Optimizing Corrosion Prediction Initiation Time for Embedded Steel in Concrete with Shell Powders Using Deep Learning Techniques

Enhancing Food Integrity through Artificial Intelligence and Machine Learning: A Comprehensive Review

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