Detection of umami intensity in grass carp based on hyper-spectrum and multi-attention mechanisms

WAN Shiwen; FENG Yaoze; SHU Guoqiang; ZHAO Mingquan; WANG Yijian; KONG Liqin; ZHU Ming

doi:10.13300/j.cnki.hnlkxb.2025.05.028

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Detection of umami intensity in grass carp based on hyper-spectrum and multi-attention mechanisms

Food science and agricultural engineering | 更新时间：2026-11-05

- Detection of umami intensity in grass carp based on hyper-spectrum and multi-attention mechanisms
- Journal of Huazhong Agricultural University Vol. 44, Issue 5, Pages: 280-287(2025)
- 作者机构：
  
  1.华中农业大学信息学院，武汉 430070
  2.华中农业大学工学院，武汉 430070
  3.华中农业大学深圳营养与健康研究院，深圳 518000
  4.中国农业科学院深圳农业基因组研究所/ 岭南现代农业科学与技术广东省实验室深圳分中心，深圳 518120
  5.农业农村部长江中下游农业装备实验室，武汉 430070
  6.农业农村部水产养殖重点实验室，武汉 430070
- 作者简介：
- 基金信息：
- DOI：10.13300/j.cnki.hnlkxb.2025.05.028
  CLC： TP391.4
- Received：24 December 2024，
  
  Published：15 September 2025
- 稿件说明：
移动端阅览
万仕文，冯耀泽，舒国强，等.基于高光谱和多头注意力机制的草鱼鲜味强度检测［J］.华中农业大学学报，2025，44（5）：280‐287.

WAN Shiwen,FENG Yaoze,SHU Guoqiang,et al.Detection of umami intensity in grass carp based on hyper-spectrum and multi-attention mechanisms[J].Journal of Huazhong Agricultural University,2025,44(05):280-287.
万仕文，冯耀泽，舒国强，等.基于高光谱和多头注意力机制的草鱼鲜味强度检测［J］.华中农业大学学报，2025，44（5）：280‐287. DOI： 10.13300/j.cnki.hnlkxb.2025.05.028.

WAN Shiwen,FENG Yaoze,SHU Guoqiang,et al.Detection of umami intensity in grass carp based on hyper-spectrum and multi-attention mechanisms[J].Journal of Huazhong Agricultural University,2025,44(05):280-287. DOI： 10.13300/j.cnki.hnlkxb.2025.05.028.

摘要

针对现有鲜味强度检测方法主观性强、耗时长和样本破坏性等问题，使用深度学习和机器学习算法结合高光谱成像技术构建草鱼鲜味强度快速无损检测方法。采集草鱼高光谱数据后，使用竞争性自适应重加权抽样法选取光谱特征波长，开发高斯加权多头注意力网络（gaussian-weighted multi-head attention network，GMANet）并应用支持向量机回归（support vector machine regression，SVR）、偏最小二乘回归（partial least squares regression，PLSR）、随机森林（random forest，RF）、1D-ResNet等传统算法建立和优化草鱼鲜味检测模型。结果显示，GMANet网络的预测均方根误差RMSEP和预测决定系数（

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3.64066648

3.38666677

）分别为0.008 2和0.884 4，优于传统算法中的最优建模方法SVR，其RMSEP和

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分别为0.007 7和0.818 8。

Abstract

Deep learning and machine learning algorithms combined with hyperspectral imaging technology were used to establish a fast and nondestructive method for detecting umami intensity in grass carp to solve the prob

lems of strong subjectivity，long time-consumption and sample destructiveness in the current detection methods of umami intensity.The competitive adaptive reweighted sampling method was used to select the feature wavelengths of spectrum after collecting the hyperspectral data in grass carp.The Gaussian-weighted multi-head attention network （GMANet） was developed.The model of detecting umami intensity in grass carp was established and optimized with the support vector machine regression （SVR），partial least squares regression （PLSR） random forest （RF），1D-ResNet and other algorithms.The results showed that the root mean square error of prediction （

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13.03866673

2.87866688

） and the coefficient of determination of prediction （

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4.14866638

3.97933316

） of GMANet network was 0.008 2 and 0.884 4，better than the RMSEP and

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4.14866638

3.97933316

of 0.007 7 and 0.818 8 in the optimal modeling method SVR in traditional algorithms.It is indicated that hyperspectral technology has a large application prospect in the detection of umami intensity and the GMANet network can make full use of the spatial image and spectral information of the samples.It will provide a new method for the further application of detection with hyperspectral image.

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references

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