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

doi:10.1016/j.foodchem.2021.131471

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

Food Chem. 2022 Mar 30;373(Pt B):131471. doi: 10.1016/j.foodchem.2021.131471. Epub 2021 Oct 26.

Authors

Hefei Zhao¹, Yinglun Zhan², Zheng Xu³, Joshua John Nduwamungu¹, Yuzhen Zhou², Robert Powers⁴, Changmou Xu⁵

Affiliations

¹ Food Processing Center, Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln NE 68588, USA.
² Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE 68583, USA.
³ Department of Mathematics and Statistics, Wright State University, Dayton, OH 45435, USA.
⁴ Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA; Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
⁵ Food Processing Center, Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln NE 68588, USA. Electronic address: cxu13@unl.edu.

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.

Keywords: Edible oil quality; Food adulteration; Machine learning; Raman spectroscopy.

MeSH terms

Food Contamination / analysis
Machine Learning
Plant Oils*
Principal Component Analysis
Spectrum Analysis, Raman*

Substances

Plant Oils

Grants and funding

P20 GM113126/GM/NIGMS NIH HHS/United States