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Updated: Jan 29, 2026

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Fruit Volatile Analysis Using an Electronic Nose
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基于电子鼻子的黄色鱼的总挥发性基本检测方法与AIE-LSTM回归模型相结合
Lei Ren1,2, Pei Li1,2, Wei Chen1,2
1School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, China.
Journal of the science of food and agriculture
|January 28, 2026
概括
这项研究引入了一种改进的注意力编码器长期短期记忆 (AIE-LSTM) 模型,用于预测鱼类的总挥发性基本 (TVB-N). 这种新方法增强了电子鼻子数据分析,以准确检测鱼的新鲜度.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 机器学习 机器学习
背景情况:
- 电子鼻子提供快速的食品质量监测,但产生复杂的时间序列数据.
- 传统模型难以处理非线性信号,导致特征提取和预测准确性差.
研究的目的:
- 开发一种使用电子鼻子数据预测总挥发性基本 (TVB-N) 的新方法.
- 提高鱼类新鲜度检测的准确性和可靠性.
主要方法:
- 提出了一种注意力改进的编码器长短期内存 (AIE-LSTM) 混合网络,具有双流特征融合.
- 采用了改进的告知器编码器,具有多头注意力和时间向下采样层.
- 集成的原始电压序列和物理特征使用双向LSTM和多级特征融合.
主要成果:
- AIE-LSTM模型在9个电子鼻子数据集中实现了最佳的TVB-N预测性能 (平均R2为0.960).
- 证明了R2的最小相对标准偏差,根平均平方误差 (RMSE) 和平均绝对误差 (MAE).
- 在表现最好的批次中,R2达到0.979,RMSE为0.988,MAE为0.589.
结论:
- AIE-LSTM模型显著提高了鱼类TVB-N含量的预测准确性.
- 提供可靠的技术解决方案,用于快速,非破坏性和准确的鱼类新鲜度检测.
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