Rapid classification and quality assessment of citrus essential oils via machine-learning-assisted Raman spectroscopy

Citrus essential oils (EOs) require accurate identification and assessment to ensure authenticity and consistency. However, conventional techniques such as gas chromatography (GC) and mass spectrometry (MS) are time-consuming and expensive, highlighting the need for novel analytical methods. This study proposes an approach for EOs detection using Raman spectroscopy (RS) combined with machine learning (ML) algorithms. Six citrus EOs underwent an evaporation experiment, with Raman spectra collected at five time points and GC-MS was used to analyze compositional changes at the starting and ending points of evaporation as a standard reference. Five ML algorithms were developed to identify differences among EOs and monitor their temporal changes. The predictive performance was evaluated using multiple quantitative metrics. The results show that the support vector machine (SVM) consistently achieves the best performance across all prediction tasks, and the interpretability algorithm identified key components in the Raman spectra of EOs. Taken together, RS-SVM is proven to be an accurate and cost-effective analytical technique for the rapid identification and quality assessment of citrus EOs. • The volatilization rates of six citrus EOs were measured over twenty-eight days. • Components of six citrus EOs were characterized via GC-MS as a reference. • The Raman spectra of six citrus EOs were collected,visualized, and characterized. • Machine models were built for the identification and assessment of six citrus EOs. • Model interpretability found key components in the Raman spectra of six citrus EOs.