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一种基于气体传感器阵列和优化神经网络的果新鲜度预测的新方法
Wei Wang1, Weizhen Yang1, Maozhen Li1,2
1School of Information and Communication Engineering, North University of China, Taiyuan 030051, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了一种电子鼻子系统,通过分析排放的气体来预测果的新鲜度. 这种新的方法利用混乱序列优化的神经网络进行准确,非破坏性的新鲜度评估.
科学领域:
- 农业科学 农业科学
- 传感器技术 传感器技术
- 人工智能的人工智能
背景情况:
- 果新鲜度预测对于减少储存风险和中国重要的果行业经济损失至关重要.
- 乙烯,二氧化碳和乙醇等挥发性有机化合物 (VOC) 的变化是果降解的关键指标.
- 需要非破坏性方法来评估果的质量,而不会造成身体损伤.
研究的目的:
- 开发一种准确而非破坏性的方法来预测果的新鲜度.
- 设计一种电子鼻子系统,能够捕获果发出的气味信息.
- 创建一个优化的神经网络模型,以提高预测准确度.
主要方法:
- 设计了一种电子鼻子系统,包括气体传感器阵列和无线传输.
- 一个反向传播 (BP) 神经网络通过使用改进的搜索算法 (SSA) 和混乱的帐序列进行了优化.
- 预测系数和输入向量之间的关系被调整为建立一个准确度基准.
主要成果:
- 开发的系统实现了基于气味信息的精确果新鲜度预测.
- 与传统方法相比,SSA优化的BP模型显示出更好的预测准确性.
- 该系统被证明是操作简单,具有成本效益,可靠和移动的.
结论:
- 电子鼻子系统为果新鲜度预测提供了可行的非破坏性解决方案.
- 优化的神经网络模型显著提高了基于气味的新鲜度评估的准确性.
- 这项技术可以有效地减少果供应链中的存储风险和经济损失.
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