Abstract：
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-and-error 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.
摘要：
总挥发性碱性氮（TVB-N）和总活菌数（TVC）是肉类重要的新鲜度指标。高光谱成像与化学计量学相结合已被证明在肉类检测中是有效的。然而,化学计量学的一个挑战是缺乏普遍适用的处理组合，需要使用不同的数据集进行试错实验。本研究提出了一种端到端的深度学习模型,金字塔注意力特征融合模型(PAFFM)，整合CNN,注意机制和金字塔结构。PAFFM融合原始可见光和近红外范围（VNIR）和短波近红外范围（SWIR）光谱数据，以预测鸡胸肉中的TVB-N和TVC。与CNN和化学计量学模型相比，PAFFM获得了优异的结果，而无需复杂的处理组合优化过程。可视化并解释了对PAFFM性能做出重大贡献的重要波长。本研究为光谱检测的市场应用提供了有价值的参考和技术支持,有利于相关研究和实践领域。
