Abstract

Due to their high economic value, essential oils (EOs) are increasingly subject to adulteration, posing significant challenges for quality assessment and control. Owing to its excellent molecular specificity, Raman spectroscopy has been widely employed for the analysis and evaluation of EO products. In this study, we collected a total of 2700 Raman spectra comprising rose essential oil (REO, n = 900), geranium essential oil (GEO, n = 900), and their mixtures (n = 900). We first constructed a conventional convolutional neural network to distinguish spectra corresponding to varying EO mixing ratios. Interpretability analysis revealed that spectral peaks at 800, 1000, 1002, 1028, 1200, 1378, and 1668 cm–1, which show notable intensity variations in the averaged spectra of REO and GEO, also played a critical role in model decision-making, indicating that these spectral features can serve as discriminative markers for different EO ratios. Subsequently, we developed a channel attention residual feature extraction network (CARFENet), which employs spectral capturing and spectral separation modules to deconstruct mixed EO spectra into their constituent pure components. CARFENet demonstrated robust performance on the validation set and yielded predicted spectra that closely resembled the true spectra in an external test set, with similarity indices exceeding 0.99. These findings indicate that CARFENet enables the effective quantitative analysis of pure EO components within mixed Raman spectra.

Document Type

Journal Article

Date of Publication

12-17-2025

Volume

5

Issue

6

Publication Title

ACS Measurement Science Au

Publisher

ACS

School

Centre for Precision Health / 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

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Comments

Tang, J., Hong, Y., Chen, J., Xie, Y., Ma, Z., Liu, Q., & Wang, L. (2025). Ratio quantification of geranium and rose essential oil mixtures via deep learning analysis of complex Raman spectra. ACS Measurement Science Au, 5(6), 823–832. https://doi.org/10.1021/acsmeasuresciau.5c00088

First Page

823

Last Page

832

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Link to publisher version (DOI)

10.1021/acsmeasuresciau.5c00088