Department of Analytical Chemistry, University of Granada, c/Fuentenueva s.n., E-18071 Granada, Spain.
J Chromatogr B Analyt Technol Biomed Life Sci. 2012 Dec 1;910:71-7. doi: 10.1016/j.jchromb.2012.01.026. Epub 2012 Feb 9.
A reliable procedure for the identification and quantification of the adulteration of olive oils in terms of blending with other vegetable oils (sunflower, corn, seeds, sesame and soya) has been developed. From the analytical viewpoint, the whole procedure relies only on the results of the determination of the triacylglycerol profile of the oils by high temperature gas chromatography-mass spectrometry. The chromatographic profiles were pre-treated (baseline correction, peak alignment using iCoshift algorithm and mean centering) before building the models. At first, a class-modeling approach, Soft Independent Modeling of Class Analogy (SIMCA) was used to identify the vegetable oil used blending. Successively, a separate calibration model for each kind of blending was built using Partial Least Square (PLS). The correlation coefficients of actual versus predicted concentrations resulting from multivariate calibration models were between 0.95 and 0.99. In addition, Genetic algorithms (GA-PLS), were used, as variable selection method, to improve the models which yielded R(2) values higher than 0.90 for calibration set. This model had a better predictive ability than the PLS without feature selection. The results obtained showed the potential of this method and allowed quantification of blends of olive oil in the vegetable oils tested containing at least 10% of olive oil.
已经开发出一种可靠的程序，用于根据与其他植物油（向日葵、玉米、种子、芝麻和大豆）混合的情况来识别和量化橄榄油的掺假。从分析的角度来看，整个过程仅依赖于高温气相色谱-质谱法测定油的三酰基甘油谱的结果。在建立模型之前，对色谱图谱进行预处理（基线校正、使用 iCoshift 算法进行峰对齐和均值中心化）。首先，使用软独立建模分类分析（SIMCA）对用于混合的植物油进行分类建模。然后，使用偏最小二乘法（PLS）为每种混合分别建立校准模型。多元校准模型得出的实际浓度与预测浓度之间的相关系数在 0.95 到 0.99 之间。此外，还使用遗传算法（GA-PLS）作为变量选择方法来改进模型，使校准集的 R²值高于 0.90。与没有特征选择的 PLS 相比，该模型具有更好的预测能力。所得结果表明了该方法的潜力，并允许对测试中包含至少 10%橄榄油的植物油进行橄榄油混合的定量。
