Kosma Ioanna S, Kontominas Michael G, Badeka Anastasia V
Laboratory of Food Chemistry, Department of Chemistry, University of Ioannina, 45110 Ioannina, Greece.
Foods. 2020 Nov 15;9(11):1672. doi: 10.3390/foods9111672.
In the present study, volatile compound analysis of olive oil samples belonging to ten Greek cultivars was carried out. A total of 167 olive oil samples collected from two consecutive harvest years were analyzed by Head Space-Solid Phase Microextraction-Gas Chromatography/Mass Spectrometry (HS-SPME-GC/MS). Volatile compound data were combined with chemometric methods (Multivariate Analysis of Variance (MANOVA) and Linear Discriminant Analysis (LDA)) with the aim not only to differentiate olive oils but also to identify characteristic volatile compounds that would enable differentiation of botanical origin (marker compounds). The application of Stepwise LDA (SLDA) effectively reduced the large number of statistically significant volatile compounds involved in the differentiation process, and thus, led to a set of parameters, the majority of which belong to compounds that are highly dependent on variety. In addition, the use of these marker compounds resulted in an increased correct classification rate (85.6%) using the cross-validation method indicating the validity of the model developed despite the use of a large number of dependent variables (cultivars).
在本研究中，对属于十个希腊品种的橄榄油样品进行了挥发性化合物分析。通过顶空-固相微萃取-气相色谱/质谱联用仪（HS-SPME-GC/MS）对连续两个收获年份采集的总共167个橄榄油样品进行了分析。挥发性化合物数据与化学计量学方法（多变量方差分析（MANOVA）和线性判别分析（LDA））相结合，目的不仅是区分橄榄油，而且是识别能够区分植物来源的特征挥发性化合物（标记化合物）。逐步线性判别分析（SLDA）的应用有效地减少了参与区分过程的大量具有统计学意义的挥发性化合物，从而得到了一组参数，其中大多数属于高度依赖品种的化合物。此外，使用这些标记化合物通过交叉验证方法使正确分类率提高到85.6%，这表明尽管使用了大量相关变量（品种），所开发模型仍然有效。
