A geographical origin assessment of Italian hazelnuts: Gas chromatography-ion mobility spectrometry coupled with multivariate statistical analysis and data fusion approach.

Data fusion Food authenticity Gas chromatography-ion mobility system Hazelnut (Corylus avellana) Multivariate statistical analysis Sensory analysis

Journal

Food research international (Ottawa, Ont.)
ISSN: 1873-7145
Titre abrégé: Food Res Int
Pays: Canada
ID NLM: 9210143

Informations de publication

Date de publication:
Sep 2023
Historique:
received: 15 02 2023
revised: 31 05 2023
accepted: 02 06 2023
medline: 19 6 2023
pubmed: 18 6 2023
entrez: 18 6 2023
Statut: ppublish

Résumé

Hazelnut is a commodity that has gained interest in the food science community concerning its authenticity. The quality of the Italian hazelnuts is guaranteed by Protected Designation of Origin and Protected Geographical Indication certificates. However, due to their modest availability and the high price, fraudulent producers/suppliers blend, or even substitute, Italian hazelnuts with others from different countries, having a lower price, and often a lower quality. To contrast or prevent these illegal activities, the present work investigated the application of the Gas Chromatography-Ion mobility spectrometry (GC-IMS) technique on the hazelnut chain (fresh, roasted, and paste of hazelnuts). The raw data obtained were handled and elaborated using two different ways, software for statistical analysis, and a programming language. In both cases, Principal Component Analysis and Partial Least Squares-Discriminant Analysis models were exploited, to study how the Volatile Organic Profiles of Italian, Turkish, Georgian, and Azerbaijani products differ. A prediction set was extrapolated from the training set, for a preliminary models' evaluation, then an external validation set, containing blended samples, was analysed. Both approaches highlighted an interesting class separation and good model parameters (accuracy, precision, sensitivity, specificity, F1-score). Moreover, a data fusion approach with a complementary methodology, sensory analysis, was achieved, to estimate the performance enhancement of the statistical models, considering more discriminant variables and integrating at the same time further information correlated to quality aspects. GC-IMS could be a key player as a rapid, direct, cost-effective strategy to face authenticity issues regarding the hazelnut chain.

Identifiants

pubmed: 37330839
pii: S0963-9969(23)00630-0
doi: 10.1016/j.foodres.2023.113085
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

113085

Informations de copyright

Copyright © 2023 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Giuseppe Sammarco (G)

Sensory and Analytical Food Science, Barilla G. e R. Fratelli S.p.A., Parma, Italy; Department of Food and Drug, University of Parma, Parma, Italy.

Daniele Bardin (D)

Sensory and Analytical Food Science, Barilla G. e R. Fratelli S.p.A., Parma, Italy.

Federica Quaini (F)

Sensory and Analytical Food Science, Barilla G. e R. Fratelli S.p.A., Parma, Italy.

Chiara Dall'Asta (C)

Department of Food and Drug, University of Parma, Parma, Italy.

Joscha Christmann (J)

Institute of Analytics and Bioanalytics, Faculty of Biotechnology, Mannheim University of Applied Sciences, Mannheim, Germany.

Philipp Weller (P)

Institute of Analytics and Bioanalytics, Faculty of Biotechnology, Mannheim University of Applied Sciences, Mannheim, Germany.

Michele Suman (M)

Sensory and Analytical Food Science, Barilla G. e R. Fratelli S.p.A., Parma, Italy; Department for Sustainable Food Process, Catholic University Sacred Heart, Piacenza, Italy.

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