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).

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

Angelo Antonio D'Archivio
;
Martina Foschi;Leucio Rossi;Fabrizio Ruggieri
2019-01-01

Abstract

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).
File in questo prodotto:
File Dimensione Formato  
AGLIO_ICP_OES_FOOD_CHEM_2019.pdf

solo utenti autorizzati

Tipologia: Documento in Versione Editoriale
Licenza: Dominio pubblico
Dimensione 1.03 MB
Formato Adobe PDF
1.03 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/127406
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 35
  • ???jsp.display-item.citation.isi??? 34
social impact