通过基于ICNAFS的拉曼光谱法识别液体改
Cancan Yi1, Zhenyu Zhang1, Tao Huang1
1Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan 430081, China.
使用改进凸非负矩阵因子化 (ICNAFS) 和k-means集群的改进方法可以准确地识别酒精改. 这种拉曼光谱技术达到98.67%的精度,提高了消费者安全.
科学领域:
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
- 频谱学是一种光谱学.
背景情况:
- 由于酒精改,消费者健康面临风险.
- 目前用于检测酒精造的方法的准确性,可靠性和复杂程序都很低.
研究的目的:
- 开发一种快速而准确的方法,使用拉曼光谱来识别液体造.
- 改进无监督的特征提取和维度减小,以提高伪造检测.
主要方法:
- 提出了一种改进的凸非负矩阵因子化 (ICNAFS),包括回归和NMF.
- 使用主要组件分析 (PCA),顺序投影算法 (SPA),凸非负矩阵因数分解与自适应图约束 (CNAFS) 和ICNAFS应用的维度缩小.
- 使用k-means集群来分析缩小维度数据.
主要成果:
- ICNAFS辅助的k-means模型实现了98.67%的集群精度.
- 这比现有的CNAFS方法改进了4%.
- 该模型在分析五个类别的150组污染性饮料数据时表现出高准确度.
结论:
- 拟议的ICNAFS辅助k-means模型提供了一个强大而准确的解决方案,用于检测液体改.
- 拉曼光谱与ICNAFS和k-means集群结合,提供了一种可靠的方法来确保液体的安全性.
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