使用激光诱导分解光谱学 (LIBS) 结合长期短期记忆 (LSTM) 来测量奶粉的能量值
Yu Ding1,2, Meiling Zhao1,2, Yan Shu1,2
1Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China. dingyuaoi@163.com.
Analytical methods : advancing methods and applications
|September 7, 2023
概括
精确测量奶粉的使用情况.
科学领域:
- 分析化学 分析化学
- 食品科学 食品科学 食品科学
- 机器学习 机器学习
背景情况:
- 婴儿营养依赖于奶粉的能量价值.
- 精确的能量值测量对于婴儿健康至关重要.
- 传统的能源价值评估方法可能耗时.
研究的目的:
- 开发一种快速而准确的方法来量化奶粉的能量价值.
- 为此分析,比较不同机器学习模型的性能.
主要方法:
- 激光诱导分解光谱学 (LIBS) 用于光谱分析.
- 使用了三种机器学习模型:深度信念网络 (DBN),反向传播 (BP) 神经网络和长短期记忆 (LSTM).
- 对32个奶粉样本进行了能量价值的定量分析.
主要成果:
- 该LSTM模型表现出卓越的准确性,平均相对误差 (MREp) 为1.0029%.
- LSTM显著提高了确定系数 (Rp2) 到 0.9984 ,并将根平均平方误差 (RMSEp) 降低到 0.2140.
- LSTM的表现优于DBN和BP模型,MREp的减少分别为73.03%和69.53%.
结论:
- LIBS和LSTM的组合为商业测量奶粉能量价值提供了一种有效和可行的方法.
- 这种方法提供了准确可靠的能量值确定,支持婴儿营养健康.
- 该研究强调了先进的光谱分析和机器学习在食品质量评估中的潜力.
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