Geographical classification and authentication of lentils (Lens culinaris Medik.) was attempted by discriminant and modelling pattern-recognition methods applied to multi-elemental composition determined by means of inductively coupled plasma optical emission spectrometry (ICP-OES). After microwave-assisted digestion, the content of 15 elements was determined in 89 Italian lentil samples produced in three relatively close areas of the Central Apennines (Castelluccio di Norcia, Colfiorito and Santo Stefano di Sessanio) and 20 samples imported from Canada. Preliminary exploration of the ICP-OES data revealed a visible effect of the production year on the mineral composition. A good geographical classification of the lentil samples was obtained by discriminant approaches. Class models generated by Soft Independent Model Class Analogy presented high sensitivity (all the calibration and external samples were correctly accepted by the target classes) and good specificity since most of non-compliant samples were refused by each of the four modelled classes.

Geographical discrimination and authentication of lentils (Lens culinaris Medik.) by ICP-OES elemental analysis and chemometrics

Foschi, Martina;D'Archivio, Angelo Antonio;Rossi, Leucio
2020

Abstract

Geographical classification and authentication of lentils (Lens culinaris Medik.) was attempted by discriminant and modelling pattern-recognition methods applied to multi-elemental composition determined by means of inductively coupled plasma optical emission spectrometry (ICP-OES). After microwave-assisted digestion, the content of 15 elements was determined in 89 Italian lentil samples produced in three relatively close areas of the Central Apennines (Castelluccio di Norcia, Colfiorito and Santo Stefano di Sessanio) and 20 samples imported from Canada. Preliminary exploration of the ICP-OES data revealed a visible effect of the production year on the mineral composition. A good geographical classification of the lentil samples was obtained by discriminant approaches. Class models generated by Soft Independent Model Class Analogy presented high sensitivity (all the calibration and external samples were correctly accepted by the target classes) and good specificity since most of non-compliant samples were refused by each of the four modelled classes.
File in questo prodotto:
File Dimensione Formato  
FOOD_CONTROL_LENTILS.pdf

non disponibili

Tipologia: Documento in Versione Editoriale
Licenza: Dominio pubblico
Dimensione 1.8 MB
Formato Adobe PDF
1.8 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: http://hdl.handle.net/11697/152964
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 6
  • ???jsp.display-item.citation.isi??? 5
social impact