Olive oil sensory defects classification with data fusion of instrumental techniques and multivariate analysis (PLS-DA)
Olive oil sensory defects classification with data fusion of instrumental techniques and multivariate analysis (PLS-DA)
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DOI：
10.1016/j.foodchem.2016.02.038
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发表时间：
2016-07-15
期刊：
影响因子：
8.8
通讯作者：
Busto, Olga
中科院分区：
文献类型：
--
作者：
Borras, Eva;Ferre, Joan;Busto, Olga
三种仪器技术，顶空质谱法（HS-MS），中红外光谱法（MIR）和紫外可见分光光度法（UV-vis），已被结合起来的基础上存在或不存在的感官缺陷的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。不同的数据融合策略进行了研究，以提高歧视能力相比，单独使用每一种仪器技术。一个通用的模型被用来区分高质量的无缺陷橄榄油（特级初榨）和最低质量的橄榄油被认为是不可食用的（lampante）。还研究了关键异味的具体鉴定，例如发霉、酒味、霉味和腐臭。三种方法的数据融合在大多数情况下改善了分类结果。低水平的数据融合是最好的策略来区分发霉，葡萄酒和发霉的缺陷，使用HS-MS，MIR和UV-vis，和腐臭的缺陷，仅使用HS-MS和MIR。使用偏最小二乘判别分析（PLS-DA）分数的中级数据融合方法被认为是有缺陷与无缺陷和食用油与非食用油歧视的最佳策略。然而，数据融合并没有充分改善由单一技术（HS-MS）分类无缺陷类所获得的结果。这些结果表明，仪器数据融合可以用于识别初榨橄榄油中的感官缺陷。(c)2016爱思唯尔有限公司版权所有
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. (c) 2016 Elsevier Ltd. All rights reserved.