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Lu, Y, Zhang, C, Feng, K, Luan, J, Cao, Y, Rahman, K, Ba, J, Han, T and Su, J (2024) Characterization of saffron from different origins by HS-GC-IMS and authenticity identification combined with deep learning. Food Chemistry: X, 24. ISSN 2590-1575
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Characterization of saffron from different origins by HS-GC-IMS and authenticity identification combined with deep learning.pdf - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (7MB) | Preview |
Abstract
With the rising demand of saffron, it is essential to standardize the confirmation of its origin and identify any adulteration to maintain a good quality led market product. However, a rapid and reliable strategy for identifying the adulteration saffron is still lacks. Herein, a combination of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and convolutional neural network (CNN) was developed. Sixty-nine volatile compounds (VOCs) including 7 groups of isomers were detected rapidly and directly. A CNN prediction model based on GC-IMS data was proposed. With the merit of minimal data prepossessing and automatic feature extraction capability, GC-IMS images were directly input to the CNN model. The origin prediction results were output with the average accuracy about 90 %, which was higher than traditional methods like PCA (61 %) and SVM (71 %). This established CNN also showed ability in identifying counterfeit saffron with a high accuracy of 98 %, which can be used to authenticate saffron.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Convolutional neural networks; HS-GC-IMS; Origins; Saffron; Volatile components; Bioengineering; Machine Learning and Artificial Intelligence |
| Subjects: | Q Science > QH Natural history > QH301 Biology R Medicine > R Medicine (General) R Medicine > RV Botanic, Thomsonian, and eclectic medicine |
| Divisions: | Pharmacy and Biomolecular Sciences |
| Publisher: | Elsevier |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 31 Jan 2025 17:09 |
| Last Modified: | 31 Jan 2025 17:15 |
| DOI or ID number: | 10.1016/j.fochx.2024.101981 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/25521 |
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