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

第一作者： Cai, Mingrui

作者： Cai, Mingrui;Han, Yuxing;Li, Xiaoxin;Liang, Juntao;Liao, Ming;Li, Xiaoxin;Liang, Juntao;Liao, Ming;Liao, Ming

作者机构：

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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