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).
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
