Food quality monitoring through bioinformatics and big data

Jeevanandam, Jaison; Agyei, Dominic; Danquah, Michael K.; Udenigwe, Chibuike C.

doi:10.1016/b978-0-323-91001-9.00036-0

Cited by 10 publications

(1 citation statement)

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“…Deep learning has demonstrated substantial benefits in automatically learning data representations, transfer learning, coping with the enormous amount of data, and achieving improved performance and higher precision (Ng et al, 2015;Kamilaris and Prenafeta-Boldu, 2018). Also, Jeevanandam et al (2022) reported that due to the ability of deep learning to feature learning based on multi-layer artificial neural networks, it has received significant attention. In recent years, automatic identification of fish, sizing as well as counting has been performed by applying deep learning (Ovalle et al, 2022).…”

Section: Deep Learningmentioning

confidence: 99%

Reduction of Losses and Wastage in Seafoods: The Role of Smart Tools and Biosensors Based on Artificial Intelligence

Ayısı Larbı,

Osei

2024

Journal of AI

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This paper reviews current knowledge on the role of smart tools and biosensors based on artificial intelligence in reducing seafood loss and wastage. This study shows that a variety of biosensors, categorised according to how they function, can be used to measure the quality of seafood. These include optical biosensors, enzyme-based biosensors, immunosensors, microbial biosensors, DNA-based biosensors, electrochemical biosensors, optical biosensors, tissue-based biosensors, and piezoelectric biosensors. Among these biosensors, optical biosensors, electrochemical biosensors, and mechanical biosensors are the most significant. Again, this study report that, for seafood traceability and management, a variety of smart solutions including blockchain technology, quick response (QR) codes, data analytics, digital twins, and radio frequency identification (RFID) tags can be utilised. Catch data, vessel tracking data, and data from the processing plant are some of the different data sources that can be utilised to trace seafood products. Artificial intelligence tools like neural networks, deep learning, machine learning, and others can be used to forecast and improve seafood quality. It is crucial to study the development of biosensors that can properly identify the earliest signs of seafood contamination or rotting.

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Section: Deep Learningmentioning

confidence: 99%