Geographical discrimination of red garlic (Allium sativum L.) produced in Italy by means of multivariate statistical analysis of ICP-OES data

• Published in: Food chemistry Vol. 275; pp. 333 - 338
• Main Authors: D'Archivio, Angelo Antonio, Foschi, Martina, Aloia, Rosaria, Maggi, Maria Anna, Rossi, Leucio, Ruggieri, Fabrizio
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.03.2019
• Subjects: Algorithms; Allium sativum; atomic absorption spectrometry; barium; calcium; Calibration; Class-modelling; Discriminant Analysis; Garlic; Garlic - chemistry; Geographical classification; ICP-OES; iron; Italy; magnesium; manganese; Mineral composition; Models, Statistical; Multivariate Analysis; prediction; provenance; sodium; Spectrum Analysis - methods; Spectrum Analysis - statistics & numerical data; strontium; Trace Elements - analysis; Italy; Class-modelling; Garlic; Mineral composition; Geographical classification; ICP-OES
• ISSN: 0308-8146; 1873-7072; 1873-7072
• DOI: 10.1016/j.foodchem.2018.09.088

•The major mineral elements of red garlic were determined using ICP-OES analysis.•Geographical classification of Italian red garlic was performed.•We built and validated efficient class models for four red garlic varieties. 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).