Discriminative power of DNA-based, volatilome, near infrared spectroscopy, elements and stable isotopes methods for the origin authentication of typical Italian mountain cheese using sPLS-DA modeling.

Cheese authentication Element analysis Geographical origin Near Infrared Spectroscopy Shotgun metagenomics Stable isotopes

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:
Feb 2024
Historique:
received: 20 10 2023
revised: 29 12 2023
accepted: 03 01 2024
medline: 4 2 2024
pubmed: 4 2 2024
entrez: 3 2 2024
Statut: ppublish

Résumé

Origin authentication methods are pivotal in counteracting frauds and provide evidence for certification systems. For these reasons, geographical origin authentication methods are used to ensure product origin. This study focused on the origin authentication (i.e. at the producer level) of a typical mountain cheese origin using various approaches, including shotgun metagenomics, volatilome, near infrared spectroscopy, stable isotopes, and elemental analyses. DNA-based analysis revealed that viral communities achieved a higher classification accuracy rate (97.4 ± 2.6 %) than bacterial communities (96.1 ± 4.0 %). Non-starter lactic acid bacteria and phages specific to each origin were identified. Volatile organic compounds exhibited potential clusters according to cheese origin, with a classification accuracy rate of 90.0 ± 11.1 %. Near-infrared spectroscopy showed lower discriminative power for cheese authentication, yielding only a 76.0 ± 31.6 % classification accuracy rate. Model performances were influenced by specific regions of the infrared spectrum, possibly associated with fat content, lipid profile and protein characteristics. Furthermore, we analyzed the elemental composition of mountain Caciotta cheese and identified significant differences in elements related to dairy equipment, macronutrients, and rare earth elements among different origins. The combination of elements and isotopes showed a decrease in authentication performance (97.0 ± 3.1 %) compared to the original element models, which were found to achieve the best classification accuracy rate (99.0 ± 0.01 %). Overall, our findings emphasize the potential of multi-omics techniques in cheese origin authentication and highlight the complexity of factors influencing cheese composition and hence typicity.

Identifiants

pubmed: 38309918
pii: S0963-9969(24)00045-0
doi: 10.1016/j.foodres.2024.113975
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

113975

Informations de copyright

Copyright © 2024 The Author(s). Published by 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

Marco Cardin (M)

Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020, Legnaro, PD, Italy; Univ Brest, INRAE, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, F-29280 Plouzané, France.

Jérôme Mounier (J)

Univ Brest, INRAE, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, F-29280 Plouzané, France.

Emmanuel Coton (E)

Univ Brest, INRAE, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, F-29280 Plouzané, France.

Barbara Cardazzo (B)

Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020, Legnaro, PD, Italy.

Matteo Perini (M)

Centro Trasferimento Tecnologico, Fondazione Edmund Mach, Via E. Mach, 1, 38098 San Michele all'Adige, Italy.

Daniela Bertoldi (D)

Centro Trasferimento Tecnologico, Fondazione Edmund Mach, Via E. Mach, 1, 38098 San Michele all'Adige, Italy.

Silvia Pianezze (S)

Centro Trasferimento Tecnologico, Fondazione Edmund Mach, Via E. Mach, 1, 38098 San Michele all'Adige, Italy.

Severino Segato (S)

Department of Animal Medicine, Production and Health, University of Padova, Viale Università 16, 35020 Legnaro, PD, Italy.

Barbara Di Camillo (B)

Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020, Legnaro, PD, Italy; Department of Information Engineering, University of Padova, Via Gradenigo 6/b, 35131 Padova, Italy.

Marco Cappellato (M)

Department of Information Engineering, University of Padova, Via Gradenigo 6/b, 35131 Padova, Italy.

Monika Coton (M)

Univ Brest, INRAE, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, F-29280 Plouzané, France.

Lisa Carraro (L)

Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020, Legnaro, PD, Italy.

Sarah Currò (S)

Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020, Legnaro, PD, Italy.

Rosaria Lucchini (R)

Italian Health Authority and Research Organization for Animal Health and Food Safety (Istituto zooprofilattico sperimentale delle Venezie), Viale Università 10, 35020 Legnaro, PD, Italy.

Hooriyeh Mohammadpour (H)

Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020, Legnaro, PD, Italy.

Enrico Novelli (E)

Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020, Legnaro, PD, Italy. Electronic address: enrico.novelli@unipd.it.

Classifications MeSH