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.

Autor: 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., Mounier J; Univ Brest, INRAE, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, F-29280 Plouzané, France., Coton E; Univ Brest, INRAE, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, F-29280 Plouzané, France., Cardazzo B; Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020, Legnaro, PD, Italy., Perini M; Centro Trasferimento Tecnologico, Fondazione Edmund Mach, Via E. Mach, 1, 38098 San Michele all'Adige, Italy., Bertoldi D; Centro Trasferimento Tecnologico, Fondazione Edmund Mach, Via E. Mach, 1, 38098 San Michele all'Adige, Italy., Pianezze S; Centro Trasferimento Tecnologico, Fondazione Edmund Mach, Via E. Mach, 1, 38098 San Michele all'Adige, Italy., Segato S; Department of Animal Medicine, Production and Health, University of Padova, Viale Università 16, 35020 Legnaro, PD, Italy., 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., Cappellato M; Department of Information Engineering, University of Padova, Via Gradenigo 6/b, 35131 Padova, Italy., Coton M; Univ Brest, INRAE, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, F-29280 Plouzané, France., Carraro L; Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020, Legnaro, PD, Italy., Currò S; Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020, Legnaro, PD, Italy., 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., Mohammadpour H; Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020, Legnaro, PD, Italy., 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.
Jazyk: angličtina
Zdroj: Food research international (Ottawa, Ont.) [Food Res Int] 2024 Feb; Vol. 178, pp. 113975. Date of Electronic Publication: 2024 Jan 05.
DOI: 10.1016/j.foodres.2024.113975
Abstrakt: 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.
Competing Interests: 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.
(Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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