An effective deep learning fusion method for predicting the TVB-N and TVC contents of chicken breasts using dual hyperspectral imaging systems
文献类型： 外文期刊
作者： Cai, Mingrui 1 ; Li, Xiaoxin 2 ; Liang, Juntao 2 ; Liao, Ming 3 ; Han, Yuxing 1 ;
作者机构： 1.Tsinghua Univ, Shenzhen Int Grad Sch, Shenzhen 518055, Peoples R China
2.South China Agr Univ, Coll Elect Engn, Coll Artificial Intelligence, 483 Wushan Rd, Guangzhou 510642, Peoples R China
3.State Key Lab Swine & Poultry Breeding Ind, Guangzhou 510640, Peoples R China
4.South China Agr Univ, Natl Ctr Int Collaborat Res Precis Agr Aviat Pesti, Guangzhou 510642, Peoples R China
5.Minist Agr & Rural Affairs, Key Lab Prevent & Control Avian Influenza & Other, Guangzhou 510640, Peoples R China
6.Key Lab Livestock Dis Prevent Guangdong Prov, Guangzhou 510640, Peoples R China
关键词： Chicken breast; Hyperspectral imaging techniques; Deep learning; Data fusion; Attention mechanism; Pyramid structure
期刊名称：FOOD CHEMISTRY （ 影响因子：8.5； 五年影响因子：8.2 ）
ISSN： 0308-8146
年卷期： 2024 年 456 卷
页码：
收录情况： SCI
摘要： Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are important freshness indicators of meat. Hyperspectral imaging combined with chemometrics has been proven to be effective in meat detection. However, a challenge with chemometrics is the lack of a universally applicable processing combination, requiring trial-anderror experiments with different datasets. This study proposes an end-to-end deep learning model, pyramid attention features fusion model (PAFFM), integrating CNN, attention mechanism and pyramid structure. PAFFM fuses the raw visible and near-infrared range (VNIR) and shortwave near-infrared range (SWIR) spectral data for predicting TVB-N and TVC in chicken breasts. Compared with the CNN and chemometric models, PAFFM obtains excellent results without a complicated processing combinatorial optimization process. Important wavelengths that contributed significantly to PAFFM performance are visualized and interpreted. This study offers valuable references and technical support for the market application of spectral detection, benefiting related research and practical fields.
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