The application of ¹H and ¹³C nuclear magnetic resonance (NMR) in conjunction with chemometric methods was applied for the discrimination and authentication of Maltese extra virgin olive oils (EVOOs). A total of 65 extra virgin olive oil samples, consisting of 30 Maltese and 35 foreign samples, were collected and analysed over four harvest seasons between 2013 and 2016. A preliminary examination of ¹H NMR spectra using unsupervised principle component analysis (PCA) models revealed no significant clustering reflecting the geographical origin. In comparison, PCA carried out on ¹³C NMR spectra revealed clustering approximating the geographical origin. The application of supervised methods, namely partial least squares discriminate analysis (PLS-DA) and artificial neural network (ANN), on ¹H and ¹³C NMR spectra proved to be effective in discriminating Maltese and non-Maltese EVOO samples. The application of variable selection methods significantly increased the effectiveness of the different classification models. The application of ¹³C NMR was found to be more effective in the discrimination of Maltese EVOOs when compared to ¹H NMR. Furthermore, results showed that different ¹H NMR pulse methods can greatly affect the discrimination of EVOOs. In the case of ¹H NMR, the Nuclear Overhauser Effect (NOESY) pulse sequence was more informative when compared to the zg30 pulse sequence, since the latter required extensive spectral manipulation for the models to reach a satisfactory level of discrimination.

中文翻译：

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1H和13C NMR指纹图谱作为鉴定马耳他特级初榨橄榄油的工具的应用。

中的应用¹ H和¹³与化学计量学方法结合C核磁共振（NMR）涂敷了用于马耳他特级初榨橄榄油（EVOOs）的鉴别和验证。在2013年至2016年的四个收获季节中，总共收集并分析了65个特级初榨橄榄油样品，其中包括30个马耳他样品和35个国外样品。使用无监督主成分分析（PCA）模型初步检查了¹ H NMR光谱没有反映地理起源的明显聚类。相比之下，PCA进行了^13次13 C NMR光谱显示出近似地理起源的聚类。在¹ H和¹³ C NMR光谱上应用监督方法，即偏最小二乘判别分析（PLS-DA）和人工神经网络（ANN），已证明可有效区分马耳他和非马耳他EVOO样品。变量选择方法的应用显着提高了不同分类模型的有效性。与¹ H NMR相比，发现¹³ C NMR的应用在区分马耳他EVOOs方面更为有效。此外，结果表明，不同的¹ H NMR脉冲方法可以极大地影响EVOOs的鉴别。在¹的情况下与zg30脉冲序列相比，1 H NMR的核过度劳累效应（NOESY）脉冲序列更具信息性，因为后者需要对模型进行广泛的光谱操作才能达到令人满意的鉴别水平。