Application of Ultrasound and Deep Networks in Recognizing the Presence of Heavy Metals Contaminated in Sweet Potatoes

Received: 23-03-2020

Accepted: 02-11-2020

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Issue: Vol. 19 No. 4 (2021)

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KỸ THUẬT VÀ CÔNG NGHỆ

How to Cite:

Hien, N., Dung, L., & Kuong, N. (2024). Application of Ultrasound and Deep Networks in Recognizing the Presence of Heavy Metals Contaminated in Sweet Potatoes. Vietnam Journal of Agricultural Sciences, 19(4), 427–434. http://testtapchi.vnua.edu.vn/index.php/vjasvn/article/view/808

Application of Ultrasound and Deep Networks in Recognizing the Presence of Heavy Metals Contaminated in Sweet Potatoes

Nguyen Tien Hien (*) 1 , Le Van Dung 1 , Nguyen Trong Kuong 1

  • 1 Khoa Công nghệ thông tin, Học viện Nông nghiệp Việt Nam

    • Keywords

    Food safety, Artificial intelligence, machine learning, neural network, Boltzmann machine, deep learning

    Abstract


    Food contaminated with heavy metals causes serious consequences for human health, it is always the prime concern of any food safety control systems, even required through costly processes from sample collecting and evaluating contaminated components in the food samples. This study aimed to use ultrasound coupling with deep networks to assess the presence of heavy metals in sweet potatoes, while ultrasound is safe. To classify the acquired ultrasound data sets, we used deep networks that presently become a powerful tool and attract many researchers in order to recognize the data associating with the presence of lead sulfate in samples of sweet potatoes. For the 31 ultrasonic data sets of sweet potato samples acquired, the application of Neuron Network (NN) and Deep Boltzmann machine (DBM) as our target deep networks yielded the results showing that the accuracies of the NN was 62% for training set and 55% for testing set, and of DBM was 68% for training set and 65% for testing set, respectively.

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