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Discrimination of the Sicilian Prickly Pear ( Opuntia Ficus‐Indica L., CV. Muscaredda) According to the Provenance by Testing Unsupervised and Supervised Chemometrics
Author(s) -
Albergamo Ambrogina,
Mottese Antonio F.,
Bua Giuseppe D.,
Caridi Francesco,
Sabatino Giuseppe,
Barrega Luna,
Costa Rosaria,
Dugo Giacomo
Publication year - 2018
Publication title -
journal of food science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 150
eISSN - 1750-3841
pISSN - 0022-1147
DOI - 10.1111/1750-3841.14382
Subject(s) - chemometrics , principal component analysis , linear discriminant analysis , hierarchical clustering , provenance , partial least squares regression , geographical indication , pear , mathematics , artificial intelligence , botany , horticulture , cluster analysis , pattern recognition (psychology) , biology , computer science , statistics , geography , machine learning , paleontology , regional science
Different multivariate techniques were tested in an attempt to build up a statistical model for predicting the origin of prickly pears ( Opuntia ficus‐indica L., cv. Muscaredda) from several localities within the Sicilian region. Specifically, two areas known for producing fruits marked respectively by TAP (traditional agri‐food product) and PDO (protected designation of origin) brands, and three sites producing non‐branded fruits, were considered. A validated inductively coupled plasma mass spectrometry (ICP‐MS) method allowed to obtain elemental fingerprints of prickly pears, which were subsequently elaborated by unsupervised tools, such as hierarchical clustering analysis (HCA) and principal component analysis (PCA), and supervised techniques, such as stepwise‐canonical discriminant analysis (CDA) and partial least squares—discriminant analysis (PLS‐DA). With the exception of HCA, which was not enough powerful to correctly cluster all selected samples, PCA successfully investigated the effect of subregional provenance on prickly pears, thus, differentiating labeled products from the non‐labeled counterpart. Also, stepwise CDA and PLS‐DA allowed to build up reliable models able to correctly classify 100% of fruits on the basis of the production areas, by exploiting a restricted pool of metals. Both statistical models, including unsupervised (PCA) and supervised techniques (stepwise CDA or PLS‐DA), may guarantee the provenance of prickly pears protected by quality labels and safeguard producers and consumers. Practical Application Based on elemental analysis and chemometrics, the reliable traceability models herein proposed, could be applied to commercial Sicilian prickly pears protected by TAP and PDO logos to guarantee their provenance and, at the same time, to safeguard producers and consumers.