Department of Chemistry, Faculty of Sciences, European Excellence Research Centre for Environmental Problems, Dunărea de Jos University of Galati, Romania.
Anal Chim Acta. 2010 Mar 17;663(1):91-7. doi: 10.1016/j.aca.2010.01.034. Epub 2010 Jan 25.
An electronic panel has been used to characterise the organoleptic characteristics of twenty-five extra virgin olive oils from varieties Hojiblanca, Picual and Arbequina, with different degree of bitterness. The method consists in the combination of three systems: electronic nose, electronic tongue and electronic eye. The Principal Component Analysis (PCA), where PC1, PC2 and PC3 explained 59% of the total variance between the samples, has demonstrated that the capability of discrimination of the combined system is superior to that obtained with the three instruments separately. This improvement is due to the increased information extracted from each sample. Partial Least Squares-Discriminant Analysis (PLS-DA) has allowed separation of the groups in function of olive variety with a root mean square error of prediction (RMSEP) lower than 0.099. Using PLS1 and PLS2 regression models, good correlations have been found between the signals obtained from the electronic tongue and the polyphenolic content (measured by chromatographic methods) or the bitterness index (scored by a panel of experts) with correlation coefficients higher than 0.9 in calibration and validation. These preliminary results indicate that the combination of an e-nose, an e-tongue and an e-eye can be a useful tool for the analysis of olive oil bitterness.
一个电子面板已被用于描述二十五种特级初榨橄榄油的感官特征，这些橄榄油来自不同苦味程度的霍约布兰卡（Hojiblanca）、皮夸尔（Picual）和阿贝基纳（Arbequina）品种。该方法结合了三个系统：电子鼻、电子舌和电子眼。主成分分析（PCA）表明，PC1、PC2 和 PC3 解释了样品之间 59%的总方差，组合系统的分辨能力优于三个仪器单独使用时的分辨能力。这种改进是由于从每个样品中提取了更多的信息。偏最小二乘判别分析（PLS-DA）允许根据橄榄品种对组进行分离，预测均方根误差（RMSEP）低于 0.099。使用 PLS1 和 PLS2 回归模型，在电子舌获得的信号与通过色谱方法测量的多酚含量或专家评分的苦味指数之间找到了很好的相关性，校准和验证的相关系数均高于 0.9。这些初步结果表明，电子鼻、电子舌和电子眼的组合可以成为分析橄榄油苦味的有用工具。
