Obstetrics, Gynaecology, Paediatrics, Preventive Medicine and Public Health, Toxicology and Legal Medicine. Faculty of Medicine, University of La Laguna. Campus de Ofra s/n, 38071 La Laguna, Tenerife, Spain.
Talanta. 2012 Aug 15;97:325-30. doi: 10.1016/j.talanta.2012.04.038. Epub 2012 Apr 26.
Mangoes of uniform genetics (Lippens variety) cultivated in the Gomera Island (Canary Islands) by conventional and organic farming were used to analyze the mineral content in order to differentiate crops cultivated in the same geographic area by the cultivation practices. Farming differences as well as soil differences may be reflected in the mineral content of the mangoes cultivated in these extensions. Concentration metal profiles consisting of the content of Ca, Co, Cu, Fe, K, Mg, Mn, Na, Ni and Zn in mangoes were obtained by using atomic absorption spectrometry (AAS). Pattern recognition classification procedures were applied for discriminating purposes. Linear discriminant analysis (LDA) allows to a classification performance of about 73% and support vector machines (SVM) found up to a 93% of prediction ability. The classification success when applying support vector machines techniques is due to their ability for modeling non-linear class boundaries.
采用常规种植和有机种植方式在戈梅拉岛（加那利群岛）种植的遗传均匀的芒果（利彭斯品种）被用于分析其矿物质含量，以区分在同一地理区域内采用不同种植方式种植的作物。种植差异以及土壤差异可能会反映在这些区域种植的芒果的矿物质含量中。通过原子吸收光谱法（AAS）获得由 Ca、Co、Cu、Fe、K、Mg、Mn、Na、Ni 和 Zn 含量组成的金属浓度分布图谱。为了进行区分目的，应用了模式识别分类程序。线性判别分析（LDA）允许进行约 73%的分类性能，而支持向量机（SVM）发现高达 93%的预测能力。当应用支持向量机技术进行分类时，其成功率归因于它们对非线性分类边界进行建模的能力。
