The application of machine-learning and Raman spectroscopy for the rapid detection of edible oils type and adulteration

Hefei Zhao, Yinglun Zhan, Zheng Xu, Joshua John Nduwamungu, Yuzhen Zhou, Robert Powers, Changmou Xu

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Raman spectroscopy is an emerging technique for the rapid detection of oil qualities. But the spectral analysis is time-consuming and low-throughput, which has limited the broad adoption. To address this issue, nine supervised machine learning (ML) algorithms were integrated into a Raman spectroscopy protocol for achieving the rapid analysis. Raman spectra were obtained for ten commercial edible oils from a variety of brands and the resulting spectral dataset was analyzed with supervised ML algorithms and compared against a principal component analysis (PCA) model. A ML-derived model obtained an accuracy of 96.7% in detecting oil type and an adulteration prediction of 0.984 (R2). Several ML algorithms also were superior than PCA in classifying edible oils based on fatty acid compositions by gas chromatography, with a faster readout and 100% accuracy. This study provided an exemplar for combining conventional Raman spectroscopy or gas chromatography with ML for the rapid food analysis.

Original languageEnglish (US)
Article number131471
JournalFood Chemistry
Volume373
DOIs
StatePublished - Mar 30 2022

Keywords

  • Edible oil quality
  • Food adulteration
  • Machine learning
  • Raman spectroscopy

ASJC Scopus subject areas

  • Analytical Chemistry
  • Food Science

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