Robust and Accurate Classification of Mutton Adulteration Under Food Additives Effect Based on Multi-Part Depth Fusion Features and Optimized Support Vector Machine-Reference-Cited by-同舟云学术

Robust and Accurate Classification of Mutton Adulteration Under Food Additives Effect Based on Multi-Part Depth Fusion Features and Optimized Support Vector Machine

Published:2023-03-18 Issue:5 Volume:16 Page:933-946
ISSN:1936-9751
Container-title:Food Analytical Methods
language:en
Short-container-title:Food Anal. Methods

Author:

Huang Zhongtao,Li Bin,Wang Shichang,Zhu Rongguang,Cui Xiaomin,Yao Xuedong

Funder

National Natural Science Foundation of China

Bingtuan Innovation Leadership Program in Sciences and Technologies for Young and Middle-aged Scientists

Publisher

Springer Science and Business Media LLC

Subject

Safety Research,Safety, Risk, Reliability and Quality,Applied Microbiology and Biotechnology,Food Science,Analytical Chemistry

Link

https://link.springer.com/content/pdf/10.1007/s12161-023-02459-8.pdf

Reference42 articles.

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3. Burns MJ et al (2021) Predicting moisture content during maize nixtamalization using machine learning with NIR spectroscopy. Theor Appl Genet 134:3743–3757. https://doi.org/10.1007/s00122-021-03926-8

4. Ciocca G, Napoletano P, Schettini R (2018) CNN-based features for retrieval and classification of food images. Comput vis Image Underst 176–177:70–77. https://doi.org/10.1016/j.cviu.2018.09.001

5. Fan B, Zhu R, He D, Wang S, Cui X, Yao X (2022) Evaluation of mutton adulteration under the effect of mutton flavour essence using hyperspectral imaging combined with machine learning and sparrow search algorithm. Foods 11. https://doi.org/10.3390/foods11152278

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