Abstract The aim of this study was to discriminate organic from conventional olive samples based on the levels of macro and trace elements, combined with chemometric techniques. Ten elements (Na, K, Ca, Fe, Mg, Cu, Zn, Se, S and P) were determined in organic (n = 30) and conventional (n = 30) olive samples by inductively coupled plasma optical emission spectrometry analysis (ICP-OES). The classification of samples was performed by using a well-known chemometric techniques, linear discriminant analysis (LDA), partial least square-discriminant analysis (PLS-DA), support vector machine-discriminant analysis (SVM-DA), k-nearest neighbors (k-NN) and random forest (RF). The k-NN technique showed the best performance in discriminating organic from conventional samples (Accuracy: 94%) using all chemical variables. After variable reduction, an accuracy of 83% was found by using only the elements K and P. The use of a fingerprint based on multielemental levels associated with classification chemometric techniques may be used as a simple method to authenticate organic olive samples.

中文翻译：

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基于元素数据化学计量学分析的有机橄榄分类

摘要 本研究的目的是根据宏观和微量元素的水平，结合化学计量学技术，区分有机橄榄样品和常规橄榄样品。通过电感耦合等离子体发射光谱分析法测定了有机 (n = 30) 和常规 (n = 30) 橄榄样品中的十种元素（Na、K、Ca、Fe、Mg、Cu、Zn、Se、S 和 P）（ ICP-OES）。通过使用众所周知的化学计量学技术、线性判别分析（LDA）、偏最小二乘判别分析（PLS-DA）、支持向量机判别分析（SVM-DA）、k-最近邻进行样本分类(k-NN) 和随机森林 (RF)。k-NN 技术在使用所有化学变量区分有机样品和常规样品方面表现出最佳性能（准确度：94%）。变量归约后，