Sixty-five samples of red garlic (Allium sativum L.) coming from four different production territories of Italy were analysed by means of inductively coupled plasma optical emission spectrometry. The garlic samples were discriminated according to the geographical origin using the content of seven elements (Ba, Ca, Fe, Mg, Mn, Na and Sr). Both classification and class modelling methods by using linear discriminant analysis (LDA) and soft independent model class analogy (SIMCA), respectively, were applied. Classification ability and modelling efficiency were evaluated on an external prediction set (21 garlic samples) designed by application of duplex Kennard-Stone algorithm. All the calibration and prediction samples were correctly classified by means of LDA. The class models developed using SIMCA exhibited high sensitivity (almost all the calibration and external samples were accepted by the respective classes) and good specificity (the majority of extraneous samples were refused by each class model).

65个红大蒜样品（大蒜）通过电感耦合等离子体发射光谱法分析了来自意大利四个不同生产地区的L.）。大蒜样品根据地理来源使用7种元素（Ba，Ca，Fe，Mg，Mn，Na和Sr）的含量进行区分。分别采用了通过线性判别分析（LDA）和软件独立模型类比（SIMCA）进行分类和类建模的方法。在应用双工Kennard-Stone算法设计的外部预测集（21个大蒜样本）上评估了分类能力和建模效率。通过LDA对所有校准和预测样品进行了正确分类。