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

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 com

SUBMITTER: Zhao H 

PROVIDER: S-EPMC10304463 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature