通过形发射反射率差异结合深度学习算法来表征乙醇-水溶液的度
Xiaorong Sun1, Haoyue Zhang1, Cuiling Liu1
1School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|September 6, 2024
概括
本研究引入了一种斜发射反射率差异 (OIRD) 方法,用于精确检测乙醇-水溶液度. 这种通过深度学习增强的非破坏性技术为各种行业提供了准确可靠的结果.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 乙醇-水溶液度在各种工业,医疗和食品应用中至关重要.
- 准确而非破坏性的检测方法非常受欢迎.
- 现有的方法可能在速度,准确性或破坏性方面存在局限性.
研究的目的:
- 开发和验证一种用于检测乙醇-水溶液度的新方法.
- 探索深度学习算法的有效性与光学测量结合.
- 建立一个快速,非破坏性的,准确的检测系统.
主要方法:
- 采用了632.8nm激光与形发射反射率差异 (OIRD) 技术相结合.
- 获得的信号与溶液度和介电特性线性相关.
- 应用了各种深度学习模型,包括MLP,CNN,LSTM和混合CNN+BiLSTM+注意力模型.
- 在0-95%的体积度范围内测试了乙醇-水溶液.
主要成果:
- 通过OIRD方法,提供了含有介电常数信息的信号.
- 深度学习模型表现出高预测准确度:MLP (93.65%),CNN (96.54%),LSTM (97.12%).这些模型的预测准确度都很高.
- 在CNN+BiLSTM+注意力模型实现了最高的精度99.23%的检测.
- 实验结果证实了该方法在广泛度范围内的有效性.
结论:
- 与深度学习相结合的OIRD方法,可以快速而非破坏性地检测乙醇-水溶液.
- 混合CNN+BiLSTM+注意力模型为度预测提供了卓越的准确性.
- 这种方法为工业和其他需要精确监测乙醇度的应用提供了可靠和准确的解决方案.
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