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• Composition Analysis •     Next Articles

The effects of different oil production processes on the quality and flavor of soybean oil were evaluated based on Electronic nose, HS-SPME-GC-MS and HS-GC-IMS.

Si-Yu WUXuan XIE2, Yao Guan2,   

  • Received:2023-05-10 Revised:2023-11-09 Online:2024-02-25 Published:2024-03-06

Abstract: In order to explore the effects of different oil production processes (cold pressed, leached, cold pressed-leached) on the quality and flavor of tertiary soybean oil. The physicochemical properties and fatty acid composition of soybean oil were determined, and the volatile compounds in three soybean oils were identified and analyzed by electronic nose, headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS), and headspace-gas chromatography-ion mobility spectroscopy (HS-GC-IMS), and the volatile compounds’ data of the three soybean oils were analyzed by clustering heat map, principal component analysis (PCA) and partial orthogonal least squares discrimination analysis (OPLS-DA). The results showed that cold-pressed soybean oil had the lowest moisture content, and the peroxide value of leached soybean oil was significantly higher and the oil color was the darkest. Fragrant soybean oil has the most linoleic acid content and higher nutritional value; Among the volatile components detected, alcohols, aldehydes and pyrazine compounds mainly contributed to the formation of soybean oil flavor, and the reasons for the formation of some flavor compounds were clarified. Finally, 45 volatile compounds with large contributions were screened by OPLS-DA, and constructed a reliable model to identify fragrant soybean oil.In addition, a correlation was found between the quality and flavor of soybean oil.

Key words: soybean oil, physical and chemical properties, volatile compounds, headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS), headspace-gas chromatography-ion mobility spectroscopy (HS-GC-IMS), orthogonal partial least squares-discriminant analysis (OPLS-DA)

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