Non-destructive egg breed separation using advanced VOC analytical techniques HSSE-GC-MS, PTR-TOF-MS, and SIFT-MS: Assessment of performance and systems' complementarity.

Egg breed discrimination HSSE-GC-MS Hatching eggs Mass spectrometry PTR-TOF-MS SIFT-MS VOC 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:
Jan 2024
Historique:
received: 18 09 2023
revised: 24 11 2023
accepted: 02 12 2023
medline: 2 1 2024
pubmed: 2 1 2024
entrez: 1 1 2024
Statut: ppublish

Résumé

Over the past decade, advanced analytical techniques have been utilized to examine volatile organic compounds (VOCs) in eggs. These VOCs offer valuable insights into factors such as freshness, fertility, the presence of cracks, embryo sex, and breed. In our study, we assessed three mass spectrometry-based systems (headspace sorptive extraction gas chromatography-mass spectrometry; HSSE-GC-MS, proton transfer reaction time-of-flight-mass spectrometry; PTR-TOF-MS; and selected ion flow tube mass spectrometry; SIFT-MS) to analyze and identify VOCs present in intact hatching eggs from three distinct breeds (Dekalb white layer, Shaver brown layer, and Ross 308 broiler). The eggs were sampled on incubation days 2 and 8, to identify VOCs that distinguish breeds irrespective of incubation day. VOC measurements were conducted on 15 eggs per breed by placing them together with PDMS-coated stir bars inside inert Teflon® air sampling bags. After an accumulation period of 2 h, the headspace was analyzed using PTR-TOF-MS and SIFT-MS, while the VOCs adsorbed onto the stir bars were analyzed using GC-MS for additional compound identification. Partial least squares discriminant analysis (PLS-DA) models were constructed for breed differentiation, and variable selection was performed. As a result, 111 VOCs were identified using HSSE-GC-MS, with alcohols and esters being the most abundant. The PLS-DA models demonstrated the efficacy of breed discrimination, with the HSSE-GC-MS and the PTR-TOF-MS exhibiting the highest balanced accuracy of 95.5 % using a reduced set of 11 VOCs and 5 product ions, respectively. The SIFT-MS model had a balanced accuracy of 92.8 % with a reduced set of 11 product ions. Furthermore, complementarity was observed between HSSE-GC-MS, which primarily selected higher molecular weight VOCs, and PTR-TOF-MS and SIFT-MS. A higher correlation was found for compound abundances between the HSSE-GC-MS and the PTR-TOF-MS relative to the SIFT-MS, indicating that the PTR-TOF-MS was better suited to quantify specific compounds identified by the HSSE-GC-MS. Finally, the findings support the presence of VOCs originating from both synthetic and natural sources, highlighting the ability of the VOC analysis systems to non-destructively perform quality control and reveal differences in management practices or biological information encoded in eggs.

Identifiants

pubmed: 38163682
pii: S0963-9969(23)01350-9
doi: 10.1016/j.foodres.2023.113802
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

113802

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

Matthias Corion (M)

KU Leuven, BIOSYST-MeBioS Biosensors Group, Department of Biosystems, Leuven, Belgium.

Miguel Portillo-Estrada (M)

University of Antwerp, Research Group Pleco, Department of Biology, Wilrijk, Belgium.

Simão Santos (S)

KU Leuven, BIOSYST-MeBioS Biosensors Group, Department of Biosystems, Leuven, Belgium.

Jeroen Lammertyn (J)

KU Leuven, BIOSYST-MeBioS Biosensors Group, Department of Biosystems, Leuven, Belgium.

Bart De Ketelaere (B)

KU Leuven, BIOSYST-MeBioS Biostatistics Group, Department of Biosystems, Leuven, Belgium.

Maarten Hertog (M)

KU Leuven, BIOSYST-MeBioS Postharvest Group, Department of Biosystems, Leuven, Belgium. Electronic address: maarten.hertog@kuleuven.be.

Classifications MeSH