Recently, the olive oil industry has been the subject of harsh criticism for false labeling and even adulterating olive oils. This situation in which both the industry and the population are affected leads to an urgent need to increase controls to avoid fraudulent activities around this precious product. The aim of this work is to propose a new analytical platform by coupling electrospray ionization (ESI), differential mobility analysis (DMA), and mass spectrometry (MS) for the analysis of olive oils based on the information obtained from the chemical fingerprint (nontargeted analyses). Regarding the sample preparation, two approaches were proposed: (i) sample dilution and (ii) liquid-liquid extraction (LLE). To demonstrate the feasibility of the method, 30 olive oil samples in 3 different categories were analyzed, using 21 of them to elaborate the classification model and the remaining 9 to test it (blind samples). To develop the prediction model, principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used. The overall success rate of the classification to differentiate among extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO) was 89% for the LLE samples and 67% for the diluted samples. However, combining both methods, the ability to differentiate EVOO from lower-quality oils (VOO and LOO) and the edible oils (EVOO and VOO) from nonedible oil (LOO) was 100%. The results show that ESI-DMA-MS can become an effective tool for the olive oil sector.

中文翻译：

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电喷雾电离-差动分析-质谱法对橄榄油的化学指纹图谱：食品真伪测试的新选择。

最近，橄榄油行业因其标签错误甚至掺假橄榄油而受到严厉批评。行业和人口都受到影响的这种情况导致迫切需要加强控制，以避免围绕这种珍贵产品进行欺诈活动。这项工作的目的是基于化学指纹学获得的信息（非针对性分析）。关于样品制备，提出了两种方法：（i）样品稀释和（ii）液-液萃取（LLE）。为了证明该方法的可行性，对3个不同类别的30个橄榄油样品进行了分析，使用其中的21项来详细说明分类模型，其余的9项来进行测试（盲样本）。为了建立预测模型，使用了主成分分析（PCA）和正交偏最小二乘判别分析（OPLS-DA）。对于LLE样品，区分特级初榨橄榄油（EVOO），初榨橄榄油（VOO）和Lampante橄榄油（LOO）的分类总体成功率为89％，而稀释样品为67％。但是，结合这两种方法，将EVOO与劣质油（VOO和VOO）以及食用油（EVOO和VOO）与非食用油（LOO）区分的能力为100％。结果表明，ESI-DMA-MS可以成为橄榄油行业的有效工具。使用主成分分析（PCA）和正交偏最小二乘判别分析（OPLS-DA）。对于LLE样品，区分特级初榨橄榄油（EVOO），初榨橄榄油（VOO）和Lampante橄榄油（LOO）的分类总体成功率为89％，而稀释样品为67％。但是，结合这两种方法，将EVOO与劣质油（VOO和VOO）以及食用油（EVOO和VOO）与非食用油（LOO）区分的能力为100％。结果表明，ESI-DMA-MS可以成为橄榄油行业的有效工具。使用主成分分析（PCA）和正交偏最小二乘判别分析（OPLS-DA）。对于LLE样品，区分特级初榨橄榄油（EVOO），初榨橄榄油（VOO）和Lampante橄榄油（LOO）的分类总体成功率为89％，而稀释样品为67％。但是，结合这两种方法，将EVOO与劣质油（VOO和VOO）以及食用油（EVOO和VOO）与非食用油（LOO）区分的能力为100％。结果表明，ESI-DMA-MS可以成为橄榄油行业的有效工具。LLE样品的兰潘特橄榄油（LOO）为89％，稀释样品为67％。但是，结合这两种方法，将EVOO与劣质油（VOO和VOO）以及食用油（EVOO和VOO）与非食用油（LOO）区分的能力为100％。结果表明，ESI-DMA-MS可以成为橄榄油行业的有效工具。LLE样品的兰潘特橄榄油（LOO）为89％，稀释样品为67％。但是，结合这两种方法，将EVOO与劣质油（VOO和VOO）以及食用油（EVOO和VOO）与非食用油（LOO）区分的能力为100％。结果表明，ESI-DMA-MS可以成为橄榄油行业的有效工具。