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Food Science and Human Wellness 2022, 11(2): 427-435 https://doi.org/10.1016/j.fshw.2021.11.024
Research Article | Open Access | Issue | Published: 25 November 2021

Effects of different processing methods on the lipid composition of hazelnut oil: A lipidomics analysis

Show Author's Information Jiayang Suna,  Xiaonuo Fenga,  Chunmao Lyua( ),  Shuang Zhoub,  Zixuan Liua
College of Food Science, Shenyang Agricultural University, Shenyang 110866, China
College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang 110866, China
Keywords:
Lipidomics, Hazelnut oil, Processing method, Lipid composition
Cite this article:
Sun J, Feng X, Lyu C, et al. Effects of different processing methods on the lipid composition of hazelnut oil: A lipidomics analysis. Food Science and Human Wellness, 2022, 11(2): 427-435. https://doi.org/10.1016/j.fshw.2021.11.024
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Abstract Full text About this article

Although hazelnut oil is rich in nutrients, its quality is greatly affected by how it is processed. However, no studies to date have comprehensively analyzed the lipid composition of hazelnut oil using different processing methods. Here, we conducted a lipidomics analysis using UPLC-QTOF-MS to characterize the lipid composition of cold-pressed hazelnut oil (CPO), ultrasonic-assisted hexane hazelnut oil (UHO) and enzyme-assisted aqueous hazelnut oil (EAO). A total of 10 subclasses of 98 lipids were identified, including 35 glycerolipids (GLs), 56 glycerophospholipids (GPs) and 7 sphingolipids (SPs). The total lipid and GL content were the highest in CPO, GP content was the highest in UHO and the ceramide content in SPs was most abundant in EAO. Multivariate statistical analysis showed that the lipid profiles of hazelnut oil prepared with different processing methods varied. Twelve significantly different lipids (TAG 54:3, TAG 52:2, TAG 54:4, TAG 54:2, TAG 52:3, TAG 54:5, DAG 36:2, DAG 36:4, DAG 36:3, PC 36:2, PA 36:2 and PE 36:3) were identified, and these lipids could potentially be used as biomarkers to distinguish between hazelnut oil subjected to different processing methods. Our results provide useful information for hazelnut oil applications and new insight into the effects of edible oil processing.


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About this article

Effects of different processing methods on the lipid composition of hazelnut oil: A lipidomics analysis

Show Author's information Jiayang Suna, Xiaonuo Fenga, Chunmao Lyua( ), Shuang Zhoub, Zixuan Liua
College of Food Science, Shenyang Agricultural University, Shenyang 110866, China
College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang 110866, China

Abstract

Although hazelnut oil is rich in nutrients, its quality is greatly affected by how it is processed. However, no studies to date have comprehensively analyzed the lipid composition of hazelnut oil using different processing methods. Here, we conducted a lipidomics analysis using UPLC-QTOF-MS to characterize the lipid composition of cold-pressed hazelnut oil (CPO), ultrasonic-assisted hexane hazelnut oil (UHO) and enzyme-assisted aqueous hazelnut oil (EAO). A total of 10 subclasses of 98 lipids were identified, including 35 glycerolipids (GLs), 56 glycerophospholipids (GPs) and 7 sphingolipids (SPs). The total lipid and GL content were the highest in CPO, GP content was the highest in UHO and the ceramide content in SPs was most abundant in EAO. Multivariate statistical analysis showed that the lipid profiles of hazelnut oil prepared with different processing methods varied. Twelve significantly different lipids (TAG 54:3, TAG 52:2, TAG 54:4, TAG 54:2, TAG 52:3, TAG 54:5, DAG 36:2, DAG 36:4, DAG 36:3, PC 36:2, PA 36:2 and PE 36:3) were identified, and these lipids could potentially be used as biomarkers to distinguish between hazelnut oil subjected to different processing methods. Our results provide useful information for hazelnut oil applications and new insight into the effects of edible oil processing.

Keywords: Lipidomics, Hazelnut oil, Processing method, Lipid composition

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Publication history
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Publication history

Received: 09 March 2021
Revised: 18 June 2021
Accepted: 20 June 2021
Published: 25 November 2021
Issue date: March 2022

Copyright

© 2022 Beijing Academy of Food Sciences. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd.

Acknowledgements

This work was supported by Key R & D Project of Liaoning Province, under Grant Research and Demonstration of Key Technologies for Deep Processing and Comprehensive Utilization of Northeast Hazelnuts (2020JH2/10200037); Service Local Project of Liaoning Province, under Grant Demonstration and Promotion of new deep-processing technology for comprehensive utilization of Northeast Hazelnuts (LSNFW201903) and horizontal subject, under Grant Demonstration and Promotion of key technologies for transformation and deep processing of wild hazelnut forest in northwestern Liaoning (H2019388).

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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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