Department of Analytical Chemistry, Faculty of Sciences, University of Granada, C/ Fuentenueva s/n, E-18071, Granada, Spain.
Department of Organic Chemistry, Faculty of Sciences, University of Granada, C/ Fuentenueva s/n, E-18071, Granada, Spain.
Talanta. 2019 Apr 1;195:69-76. doi: 10.1016/j.talanta.2018.11.033. Epub 2018 Nov 12.
Second-order data acquired using liquid chromatography coupled to a diode array detector were used to classify extra virgin olive oils samples according to their cultivars. The chromatographic fingerprints from the epoxidised fraction were obtained using normal-phase liquid chromatography. To reduce the data matrices two strategies were employed: (1) multivariate curve resolution-alternating least squares (MCR-ALS) and (2) a new strategy proposed in this work based on the fusion of the mean data profiles in both spectral and time domains. Several conventional chemometric tools were then applied to both raw and reduced data: principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA), soft independent modelling of class analogies (SIMCA) and n-way partial least-squares-discriminant analysis (NPLS-DA). Furthermore, an emergent multivariate classification method known as random forest (RF) has been first applied to second-order data. It was shown that RF is more efficient than conventional tools. Indeed, the obtained sensibility, specificity and accuracy are 1.00, 0.92 and 0.95 respectively; these performance metrics are significantly better than the values found for the other methods.
利用液相色谱与二极管阵列检测器获得的二阶数据，根据品种对特级初榨橄榄油样品进行分类。采用正相液相色谱法得到氧化部分的色谱指纹图谱。为了减少数据矩阵，采用了两种策略：（1）多元曲线分辨交替最小二乘法（MCR-ALS）和（2）本工作提出的基于在光谱和时间域中融合均值数据谱的新策略。然后将几种常规化学计量学工具应用于原始和简化数据：主成分分析（PCA）、偏最小二乘判别分析（PLS-DA）、类相似性的软独立建模（SIMCA）和 N 路偏最小二乘判别分析（NPLS-DA）。此外，首次将一种称为随机森林（RF）的新兴多元分类方法应用于二阶数据。结果表明，RF 比常规工具更有效。实际上，获得的灵敏度、特异性和准确性分别为 1.00、0.92 和 0.95；这些性能指标明显优于其他方法的结果。
