A Novel Method Based on Multi-Molecular Infrared (MM-IR) AlexNet for Rapid Detection of Trace Harmful Substances in Flour-Reference-Cited by-同舟云学术

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A Novel Method Based on Multi-Molecular Infrared (MM-IR) AlexNet for Rapid Detection of Trace Harmful Substances in Flour

Published:2022-12-13 Issue:3 Volume:16 Page:667-676
ISSN:1935-5130
Container-title:Food and Bioprocess Technology
language:en
Short-container-title:Food Bioprocess Technol

Author:

Lin Xiao-Wen,Li Fei-Li,Wang Song,Xie Jun,Pan Qian-Nan,Wang Ping,Xu Chang-Hua

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Process Chemistry and Technology,Safety, Risk, Reliability and Quality,Food Science

Link

https://link.springer.com/content/pdf/10.1007/s11947-022-02964-4.pdf

Reference35 articles.

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2. Altomare, C., Logrieco, A. F., & Gallo, A. (2021) Mycotoxins and mycotoxigenic fungi: Risk and management. A challenge for future global food safety and security. In: Zaragoza, Ó., & Casadevall, A. (eds) Encyclopedia of Mycology. pp 64–93. Elsevier, Oxford. https://doi.org/10.1016/B978-0-12-819990-9.00032-9

3. Benmouna, B., Garcia-Mateos, G., Sabzi, S., Fernandez-Beltran, R., Parras-Burgos, D., & Molina-Martinez, J. M. (2022). Convolutional neural networks for estimating the ripening state of Fuji apples using visible and near-infrared spectroscopy. Food and Bioprocess Technolgy, 15(10), 2226–2236. https://doi.org/10.1007/s11947-022-02880-7

4. Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines. Acm Transactions on Intelligent Systems and Technology, 2(3), 27. https://doi.org/10.1145/1961189.1961199

5. Ghazi, M. M., Yanikoglu, B., & Aptoula, E. (2017). Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing, 235, 228–235. https://doi.org/10.1016/j.neucom.2017.01.018

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