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Food Chem

Title:		"Quality evaluation of table grapes during storage by using (1)H NMR, LC-HRMS, MS-eNose and multivariate statistical analysis"
Author(s):		Innamorato V; Longobardi F; Cervellieri S; Cefola M; Pace B; Capotorto I; Gallo V; Rizzuti A; Logrieco AF; Lippolis V;
Address:		"Dipartimento di Chimica, Universita di Bari 'Aldo Moro', Via Orabona 4, 70126 Bari, Italy; Consiglio Nazionale delle Ricerche (CNR), Istituto Scienze delle Produzioni Alimentari (ISPA), Via Amendola 122/O, 70126 Bari, Italy. Dipartimento di Chimica, Universita di Bari 'Aldo Moro', Via Orabona 4, 70126 Bari, Italy. Electronic address: francesco.longobardi@uniba.it. Consiglio Nazionale delle Ricerche (CNR), Istituto Scienze delle Produzioni Alimentari (ISPA), Via Amendola 122/O, 70126 Bari, Italy. Consiglio Nazionale delle Ricerche (CNR), Istituto Scienze delle Produzioni Alimentari (ISPA), c/o CS-DAT, Via Michele Protano, 71121 Foggia, Italy. Dipartimento di Ingegneria Civile, Ambientale, del Territorio, Edile e di Chimica (DICATECh), Politecnico di Bari, via Orabona 4, Bari I-70125, Italy"
Journal Title:		Food Chem
Year:		2020
Volume:		20200121
Issue:
Page Number:		126247 -
DOI:		10.1016/j.foodchem.2020.126247
ISSN/ISBN:		1873-7072 (Electronic) 0308-8146 (Linking)
Abstract:		"Three non-targeted methods, i.e. (1)H NMR, LC-HRMS, and HS-SPME/MS-eNose, combined with chemometrics, were used to classify two table grape cultivars (Italia and Victoria) based on five quality levels (5, 4, 3, 2, 1). Grapes at marketable quality levels (5, 4, 3) were also discriminated from non-marketable quality levels (2 and 1). PCA-LDA and PLS-DA were applied, and results showed that, the MS-eNose provided the best results. Specifically, with the Italia table grapes, mean prediction abilities ranging from 87% to 88% and from 98% to 99% were obtained for discrimination amongst the five quality levels and of marketability/non-marketability, respectively. For the cultivar Victoria, mean predictive abilities higher than 99% were achieved for both classifications. Good models were also obtained for both cultivars using NMR and HRMS data, but only for classification by marketability. Satisfying models were further validated by MCCV. Finally, the compounds that contributed the most to the discriminations were identified"
Keywords:		Electronic Nose/statistics & numerical data Food Analysis/*methods/statistics & numerical data Food Quality *Food Storage Least-Squares Analysis Mass Spectrometry/methods/statistics & numerical data Multivariate Analysis Principal Component Analysis Proto;
Notes:		"MedlineInnamorato, Valentina Longobardi, Francesco Cervellieri, Salvatore Cefola, Maria Pace, Bernardo Capotorto, Imperatrice Gallo, Vito Rizzuti, Antonino Logrieco, Antonio F Lippolis, Vincenzo eng Evaluation Study England 2020/02/02 Food Chem. 2020 Jun 15; 315:126247. doi: 10.1016/j.foodchem.2020.126247. Epub 2020 Jan 21"