通过紫外线可见光谱学与化学计量学相结合,快速量化佩里拉油混合物的单成分油
Yao Wang1, Zihan Li1, Wenqiang Wang1
1School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, PR China.
概括
使用紫外线可见光 (UV-vis) 光谱和化学方法解决了昂贵的香油的改问题. 该研究开发了一个强大的校准框架,用于准确量化里拉油混合物的成分,确保质量控制.
科学领域:
- 分析化学 分析化学
- 食品科学 食品科学 食品科学
- 频谱学是一种光谱学.
背景情况:
- 佩里拉油富含α-烯酸,是一种有价值的非常规油.
- 高昂的成本使得佩里拉油易受改,需要可靠的检测方法.
- 现有的方法可能不足以进行复杂混合物的定量分析.
研究的目的:
- 开发和验证对油混合物的定量分析框架.
- 为了比较各种化学测量模型的疗效,用于光谱数据分析.
- 优化预处理和变量选择技术,以提高预测准确度.
主要方法:
- 使用紫外线可见光 (UV-vis) 光谱学对佩里拉油混合物中单个成分油的定量分析.
- 化学测量方法的应用和比较:部分最小平方 (PLS),逆向传播-人工神经网络 (BP-ANN),支持向量回归 (SVR) 和极端学习机器 (ELM).
- 研究十种预处理方法和五种变量选择方法,包括连续波纹转换 (CWT) 和鱼优化算法 (WOA).
主要成果:
- 部分最小平方 (PLS) 在二进制和三进制危险油混合物中表现出卓越的性能.
- 对于二进制混合,PLS在没有预处理或变量选择的情况下实现了0.99以上的预测相关系数 (Rp).
- 对于三元混合物,优化的CWT-WOA-PLS模型产生了超过0.97的Rp值,显示出显著的改善.
结论:
- 紫外线光谱与化学测量方法相结合,为分析油混合物提供了可靠的框架.
- 该研究强调了PLS的有效性,特别是当通过CWT预处理和复杂混合物的WOA变量选择来增强时.
- 这种方法提供了一个有前途的解决方案,用于工业质量控制的perilla油和检测伪造.
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