Sixty samples of chickpea (Cicer arietinum L.) harvested in the Italian territories of Cicerale (Campania), Valentano (Lazio) and Navelli (Abruzzo) in 2019 were characterized by determination of the content of ten elements (Ca, K, P, Mg, Mo, Cu, Fe, Mn, Zn and Sr) with Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). Classification of the samples was performed using both discriminant (Linear Discriminant Analysis, LDA) and class-modelling (Soft Independent Modelling of Class Analogies, SIMCA) methods, after the application of Analysis of Variance (ANOVA) to assess the significance of the detected elements. Both discriminant and class models were calibrated on 33 samples and eventually applied on a prediction set of 27 samples to evaluate the classification ability and class-modelling efficiency, respectively. LDA led to 100% classification rate on the external set, whereas the class models developed using SIMCA exhibited good sensitivity (external samples accepted by the respective classes were 88% for Cicerale, 90% for Valentano and 100% for Navelli) and 100% specificity (all the extraneous samples were correctly rejected by each class-model).

Characterization of high value Italian chickpeas (Cicer arietinum L.) by means of ICP-OES multi-elemental analysis coupled with chemometrics

Di Donato F.
;
Biancolillo A.;Rossi L.;D'Archivio A. A.
2022

Abstract

Sixty samples of chickpea (Cicer arietinum L.) harvested in the Italian territories of Cicerale (Campania), Valentano (Lazio) and Navelli (Abruzzo) in 2019 were characterized by determination of the content of ten elements (Ca, K, P, Mg, Mo, Cu, Fe, Mn, Zn and Sr) with Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). Classification of the samples was performed using both discriminant (Linear Discriminant Analysis, LDA) and class-modelling (Soft Independent Modelling of Class Analogies, SIMCA) methods, after the application of Analysis of Variance (ANOVA) to assess the significance of the detected elements. Both discriminant and class models were calibrated on 33 samples and eventually applied on a prediction set of 27 samples to evaluate the classification ability and class-modelling efficiency, respectively. LDA led to 100% classification rate on the external set, whereas the class models developed using SIMCA exhibited good sensitivity (external samples accepted by the respective classes were 88% for Cicerale, 90% for Valentano and 100% for Navelli) and 100% specificity (all the extraneous samples were correctly rejected by each class-model).
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/169512
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