Author Identifier (ORCID)
Liang Wang: https://orcid.org/0000-0001-5339-7484
Abstract
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.
Document Type
Journal Article
Date of Publication
1-1-2026
Volume
12
Publication Title
Current Research in Food Science
Publisher
Elsevier
School
Nutrition and Health Innovation Research Institute / School of Medical and Health Sciences
Funders
Research Foundation for Advanced Talents of Guandong Provincial People’ s Hospital (KY012023293) / Australian Commonwealth Government / University of Western Australia
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Comments
Hong, Y., Tang, J., Chen, J., Xie, Y., Ma, Z., Liu, Q., & Wang, L. (2026). Rapid classification and quality assessment of citrus essential oils via machine-learning-assisted Raman spectroscopy. Current Research in Food Science, 12, 101321. https://doi.org/10.1016/j.crfs.2026.101321