State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, People's Republic of China.
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, People's Republic of China.
Food Chem. 2019 Sep 30;293:348-357. doi: 10.1016/j.foodchem.2019.04.109. Epub 2019 Apr 29.
Camellia oil is a high quality oil mainly produced in southern China. It is common that unscrupulous merchants attempt to make huge profits by adulterating camellia oil with other cheaper or lower-quality vegetable oils. Therefore, this paper proposed excitation-emission matrix fluorescence spectroscopy combined with chemometric methods for the rapid identification and quantification of camellia oil adulteration with other cheaper vegetable oils. A five-component parallel factor analysis (PARAFAC) model roughly completed spectral characterization of oil samples, and obtained chemically meaningful information. Four advanced chemometrics methods were used for the classification of camellia oil and other vegetable oils (model 1) and the classification of camellia oil and adulterated camellia oil (models 2 and 3), respectively. Two-directional two-dimensional linear discriminant analysis ((2D)LDA) was used for chemical data for the first time and showed huge potential. Furthermore, the developed N-PLS regression model used for the prediction of adulteration level in camellia oil showed satisfactory accuracy.
茶油是一种高质量的油，主要在中国南方生产。不良商人常试图通过将茶油与其他更便宜或更低质量的植物油混合来牟取暴利。因此，本文提出了利用激发-发射矩阵荧光光谱结合化学计量学方法快速识别和定量茶油与其他廉价植物油掺假的方法。一个五组分平行因子分析（PARAFAC）模型大致完成了油样的光谱特征化，并获得了有意义的化学信息。四种先进的化学计量学方法分别用于茶油和其他植物油的分类（模型 1）以及茶油和掺假茶油的分类（模型 2 和 3）。双向二维线性判别分析（2D-LDA）首次用于化学数据，显示出巨大的潜力。此外，所建立的用于预测茶油掺假水平的 N-PLS 回归模型具有令人满意的准确性。
