iSens Group, Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Campus Sescelades, 43007 Tarragona, Spain.
Chemometrics, Qualimetrics and Nanosensors Group, Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Campus Sescelades, 43007 Tarragona, Spain.
Food Chem. 2016 Jul 15;203:314-322. doi: 10.1016/j.foodchem.2016.02.038. Epub 2016 Feb 4.
Three instrumental techniques, headspace-mass spectrometry (HS-MS), mid-infrared spectroscopy (MIR) and UV-visible spectrophotometry (UV-vis), have been combined to classify virgin olive oil samples based on the presence or absence of sensory defects. The reference sensory values were provided by an official taste panel. Different data fusion strategies were studied to improve the discrimination capability compared to using each instrumental technique individually. A general model was applied to discriminate high-quality non-defective olive oils (extra-virgin) and the lowest-quality olive oils considered non-edible (lampante). A specific identification of key off-flavours, such as musty, winey, fusty and rancid, was also studied. The data fusion of the three techniques improved the classification results in most of the cases. Low-level data fusion was the best strategy to discriminate musty, winey and fusty defects, using HS-MS, MIR and UV-vis, and the rancid defect using only HS-MS and MIR. The mid-level data fusion approach using partial least squares-discriminant analysis (PLS-DA) scores was found to be the best strategy for defective vs non-defective and edible vs non-edible oil discrimination. However, the data fusion did not sufficiently improve the results obtained by a single technique (HS-MS) to classify non-defective classes. These results indicate that instrumental data fusion can be useful for the identification of sensory defects in virgin olive oils.
三种仪器技术，顶空-质谱（HS-MS）、中红外光谱（MIR）和紫外可见分光光度法（UV-vis），已结合使用，根据感官缺陷的存在与否对特级初榨橄榄油样品进行分类。参考感官值由官方品尝小组提供。研究了不同的数据融合策略，以提高与单独使用每种仪器技术相比的区分能力。应用通用模型来区分高质量无缺陷的橄榄油（特级初榨）和被认为不可食用的最低质量的橄榄油（灯油）。还研究了对关键异味（如霉味、酒味、陈腐味和酸败味）的特定识别。在大多数情况下，三种技术的数据融合提高了分类结果。在使用 HS-MS、MIR 和 UV-vis 来区分霉味、酒味和陈腐味缺陷，以及仅使用 HS-MS 和 MIR 来区分酸败味缺陷时，低水平数据融合是最佳策略。使用偏最小二乘判别分析（PLS-DA）得分的中级数据融合方法被发现是区分有缺陷和无缺陷以及可食用和不可食用油的最佳策略。然而，数据融合并没有充分提高单一技术（HS-MS）对无缺陷类别的分类结果。这些结果表明，仪器数据融合可用于鉴定特级初榨橄榄油中的感官缺陷。
