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Keywords:

  • Chromatographic fingerprint;
  • Partial least square regression;
  • Principal component analysis;
  • Quality control;
  • White apricot almond oil

A new chromatographic fingerprinting method was established for quality control of white apricot almond (WAA) oil. Fifteen WAA oil samples from different batches were analyzed by GC–MS, among which 13 were selected to establish a fatty acid fingerprint of WAA oil according to the results of clustering analysis (CA). Spectral correlative chromatogram was adopted to identify common fatty acid and 18 “common fatty acids” were obtained in 13 WAA oil samples, accounting for 97.3% of the total content. The method of fingerprint analysis was then validated based on the relative retention time and relative peak area of the common peaks, sample stability, and similarity analysis. The similarities of the 13 WAA oil samples were more than 0.98, indicating that the samples from different batches were consistent to some extent. The developed chromatographic fingerprint was successfully used to differentiate WAA oil from frauds and other kinds of oil by similarity comparison, principal-component analysis (PCA), and partial least square regression (PLSR). The fatty acid fingerprint of WAA oil established by supercritical carbon dioxide extraction (SCDE) coupled with GC–MS proved to be suitable for identifying and differentiating samples and can be used for quality control.