Volatile fraction analysis by HS-SPME/GC-MS and chemometric modeling for traceability of apples cultivated in the Northeast Italy. (August 2017)

• Record Type: Journal Article
• Authors: Giannetti, Vanessa; Boccacci Mariani, Maurizio; Mannino, Paola; Marini, Federico
• Is Part Of: Food control. Volume 78(2017:Aug.)
• Page Start: 215
• Page End: 221
• Publication Date: 2017-08
• Subjects: Apples -- Volatiles -- Ancient cultivars -- Organic farming -- HS- SPME/GC-MS -- Multivariate analysis -- Chemometrics; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2017.02.036
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: The present study aimed at characterising the flavour composition of apple cultivars grown in the Northeast Italy through different cultivation methods, by combining Head Space-Solid Phase Micro Extraction/Gas Chromatography Mass Spectrometry (HS-SPME/GC-MS) analysis of volatile fraction with chemometric tools for class modeling. In order to represent the overall production in the target area, the investigation included 42 apples varieties consisting of ancient, non-native and new hybrid cultivars grown in Friuli Venezia Giulia and Alto Adige-South Tyrol, respectively. Moreover, apple samples from both conventional and organic agricultural practices were considered. Overall 118 volatile compounds were identified in the samples and Partial Least Squares-Discriminant Analysis (PLS-DA) was used to classify apples based on their different geographical origin or growing conditions. Models highlighted good classification results both in calibration (over 91%) and cross-validation (over 87%), enabling to obtain a good separation between apple categories with high prediction accuracy (over 90%). In addition, the Variable Importance in Projection (VIP) scores of the PLS-DA models were calculated, allowing to identify a reduced number of volatiles (e.g., ethanol, ethyl acetate, isobutyl acetate, propyl propanoate, 1-hexanol, d -limonene, (Z)-2-hexen-1-ol acetate and others) which are relevant for the discrimination of different apple groups. The proposed approach may represent a powerful tool for fruit traceability. Highlights: HS/SPME-GC/MS enabled to detect 118 volatiles in apples from North-East Italy. 42 cultivars of apples, including ancient endangered species, were studied. PLS-DA used to classify apples based on geographical origin or growing conditions. VIP scores of PLS-DA models to identify volatiles relevant in apples discrimination. A set of 25 volatiles in apple pulp offers a fingerprint for fruit traceability.