IntroductionOlive oil, derived from the olive tree (Olea europaea L.), is used in cooking, cosmetics, and soap production. Due to its high value, some producers adulterate olive oil with cheaper edible oils or fraudulently mislabel oils as olive to increase profitability. Adulterated products can cause allergic reactions in sensitive individuals and can lack compounds which contribute to the perceived health benefits of olive oil, and its corresponding premium price.ObjectiveThere is a need for robust methods to rapidly authenticate olive oils. By utilising machine learning models trained on the nuclear magnetic resonance (NMR) spectra of known olive oil and edible oils, samples can be classified as olive and authenticated. While high‐field NMRs are commonly used for their superior resolution and sensitivity, they are generally prohibitively expensive to purchase and operate for routine screening purposes. Low‐field benchtop NMR presents an affordable alternative.MethodsWe compared the predictive performance of partial least squares discrimination analysis (PLS‐DA) models trained on low‐field 60 MHz benchtop proton (1H) NMR and high‐field 400 MHz 1H NMR spectra. The data were acquired from a sample set consisting of 49 extra virgin olive oils (EVOOs) and 45 other edible oils.ResultsWe demonstrate that PLS‐DA models trained on low‐field NMR spectra are highly predictive when classifying EVOOs from other oils and perform comparably to those trained on high‐field spectra. We demonstrated that variance was primarily driven by regions of the spectra arising from olefinic protons and ester protons from unsaturated fatty acids in models derived from data at both field strengths.

中文翻译：

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区分特级初榨橄榄油和普通食用油：在低场和高场 1H NMR 数据上训练的 PLS-DA 模型的性能比较

简介橄榄油，源自橄榄树（油橄榄L.)，用于烹饪、化妆品和肥皂生产。由于其高价值，一些生产商将橄榄油掺入更便宜的食用油中，或者将油错误地贴上橄榄的标签，以增加利润。掺假产品可能会引起敏感个体的过敏反应，并且可能缺乏有助于橄榄油健康益处及其相应溢价的化合物。 目的需要一种可靠的方法来快速鉴定橄榄油。通过利用对已知橄榄油和食用油的核磁共振 (NMR) 光谱进行训练的机器学习模型，可以将样品分类为橄榄并进行验证。虽然高场核磁共振因其卓越的分辨率和灵敏度而被广泛使用，但出于常规筛查目的，购买和操作它们的成本通常过高。低场台式 NMR 提供了一种经济实惠的替代方案。方法我们比较了在低场 60 MHz 台式质子上训练的偏最小二乘判别分析 (PLS-DA) 模型的预测性能（1H) NMR 和高场 400 MHz1核磁共振氢谱。数据是从由 49 种特级初榨橄榄油 (EVOO) 和 45 种其他食用油组成的样本集中获取的。结果我们证明，在低场 NMR 光谱上训练的 PLS-DA 模型在将 EVOO 与其他油进行分类时具有高度预测性，并且表现相当那些受过高场光谱训练的人。我们证明，方差主要是由来自两个场强数据的模型中不饱和脂肪酸的烯质子和酯质子产生的光谱区域驱动的。