Quantitative analysis of blended oils by matrix-assisted laser desorption/ionization mass spectrometry and partial least squares regression

Suying Li, Tsz Tsun Ng, Zhong Ping Yao (Corresponding Author)

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Quantitative labeling of oil compositions has become a trend to ensure the quality and safety of blended oils in the market. However, methods for rapid and reliable quantitation of blended oils are still not available. In this study, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) was used to profile triacylglycerols in blended oils, and partial least squares regression (PLS-R) was applied to establish quantitative models based on the acquired MALDI-MS spectra. We demonstrated that this new method allowed simultaneous quantitation of multiple compositions, and provided good quantitative results of binary, ternary and quaternary blended oils, enabling good limits of detection (e.g., detectability of 1.5% olive oil in sunflower seed oil). Compared with the conventional GC–FID method, this new method could allow direct analysis of blended oils, analysis of one blended oil sample within minutes, and accurate quantitation of low-abundance oil compositions and blended oils with similar fatty acid contents.

Original languageEnglish
Article number127601
JournalFood Chemistry
Volume334
DOIs
Publication statusPublished - 1 Jan 2021

Keywords

  • Blended oils
  • Mass spectrometry
  • Matrix-assisted laser desorption/ionization
  • Partial least squares regression
  • Quantitation

ASJC Scopus subject areas

  • Analytical Chemistry
  • Food Science

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