Spectroscopy

Quantum mechanics

Raman spectroscopy

Chemometrics

Biology

Chromatography

Principal component analysis

Computer science

Food science

Physics

Pattern recognition (psychology)

Optics

Gas chromatography

Artificial intelligence

Analytical Chemistry (journal)

Chemistry

Biological system

Machine learning

Edible oil

Food Chemistry

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.

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