Artificial intelligence predictability of moisture, fats and fatty acids composition of fish using low frequency Nuclear Magnetic Resonance (LF-NMR) relaxation-Reference-Cited by-同舟云学术

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Artificial intelligence predictability of moisture, fats and fatty acids composition of fish using low frequency Nuclear Magnetic Resonance (LF-NMR) relaxation

Published:2024-04-14 Issue: Volume: Page:
ISSN:0022-1155
Container-title:Journal of Food Science and Technology
language:en
Short-container-title:J Food Sci Technol

Author:

Al-Habsi Nasser^ORCID,Al-Julandani Ruqaya,Al-Hadhrami Afrah,Al-Ruqaishi Houda,Al-Sabahi Jamal,Al-Attabi Zaher,Rahman Mohammad Shafiur

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s13197-024-05977-3.pdf

Reference42 articles.

1. Al-Habsi NA, Al-Hadhrami S, Al-Kasbi H, Rahman MS (2017) Molecular mobility of fish flesh measured by low-field nuclear magnetic resonance (LF-NMR) relaxation: effects of freeze–thaw cycles. Fish Sci 83(5):845–851

2. AOAC (2000) Official methods of analysis of the association of official analytical chemists, 17th edn. Association of Official Analytical Chemists Inc., Virginia

3. Bennett-Lenane H, Griffin BT, O’Shea JP (2022) Machine learning methods for prediction of food effects on bioavailability: a comparison of support vector machines and artificial neural networks. Eur J Pharmac Sci 168:1–13

4. Bourquin J, Schmidli H, van Hoogevest P, Leuenberger H (1997) Basic concepts of artificial neural networks (ANN) modeling in the application to pharmaceutical development. Pharmac Dev Technol 2(2):95–109

5. Brar D S, Aggarwal A K, Nanda V, Saxena S, Gautam S (2024) AI and CV based 2D-CNN algorithm: botanical authentication of Indian honey. Sustainable Food Technology (in press)

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