拉曼光谱和多变量分析用于废物和食用植物油的分类
Matteo Poddighe1, Alberto Mannu2, Giacomo Luigi Petretto3
1Laboratory of Materials Science and Nanotechnology (LMNT), Department of Chemical, Physics, Mathematics and Natural Science, University of Sassari, Sassari, Italy.
Natural product research
|October 12, 2024
概括
拉曼光谱和统计分析可以区分废食用油和食用油. 该方法可以识别独特的化学指纹,用于检测工业应用中的废油造.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 化学测量 化学测量 化学测量
背景情况:
- 废食用油 (WCO) 给环境和经济带来了挑战.
- 区分WCO与食用油对于工业应用至关重要.
- 拉曼光谱为化学分析提供了一种非破坏性的方法.
研究的目的:
- 探索拉曼光谱和多变量分析的潜力,以对食用和废弃油进行分类.
- 为了确定不同油样品的特征化学指纹.
- 评估在工业植物油中检测WCO改的可行性.
主要方法:
- 用四种油炸方法制备十二个废食用油样本.
- 使用拉曼光谱法对食用油和废油样品进行表征.
- 在拉曼光谱上应用统计多变量分析,包括主要成分分析 (PCA) 和部分最小平方差异分析 (PLS-DA).
- 使用稀疏部分最小平方差分分析 (S-PLS-DA) 通过投影变量重要性 (VIP) 图表来确定样本特定的化学指纹.
主要成果:
- 拉曼光谱揭示了食用油和废油之间的光谱差异最小.
- 多变量统计分析 (PCA和PLS-DA) 成功地区分了食用植物油和废植物油.
- S-PLS-DA确定了为分类做出贡献的关键光谱变量,创造了独特的化学指纹.
- 该方法在区分石油类型方面表现出前景,尽管样本规模有限.
结论:
- 拉曼光谱与多变量分析相结合是一种可行的方法,用于分类食用和废弃的油.
- 开发的方法可以识别每个油样品的特征化学指纹.
- 这种技术有可能检测用于非食品工业用途的植物油中废油改的情况.
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