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Multivariate versus machine learning-based classification of rapid evaporative Ionisation mass spectrometry spectra towards industry based large-scale fish speciation
De Graeve, M.; Birse, N.; Hong, Y.H.; Elliott, C.T.; Hemeryck, L.Y.; Vanhaecke, L. (2023). Multivariate versus machine learning-based classification of rapid evaporative Ionisation mass spectrometry spectra towards industry based large-scale fish speciation. Food Chemistry 404(Part B): 134632. https://dx.doi.org/10.1016/j.foodchem.2022.134632
In: Food Chemistry. Elsevier: London. ISSN 0308-8146; e-ISSN 1873-7072, meer
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| Author keywords |
Ambient Ionisation Mass Spectrometry; Multivariate Chemometric Modelling; Machine Learning; Fish Speciation; Real-time Prediction; Metabolomics |
| Auteurs | | Top |
- De Graeve, M., meer
- Birse, N.
- Hong, Y.H.
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- Elliott, C.T.
- Hemeryck, L.Y., meer
- Vanhaecke, L., meer
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| Abstract |
Detection and prevention of fish food fraud are of ever-increasing importance, prompting the need for rapid, high-throughput fish speciation techniques. Rapid Evaporative Ionisation Mass Spectrometry (REIMS) has quickly established itself as a powerful technique for the instant in situ analysis of foodstuffs. In the current study, a total of 1736 samples (2015-2021) -comprising 17 different commercially valuable fish species -were ana-lysed using iKnife-REIMS, followed by classification with various multivariate and machine learning strategies. The results demonstrated that multivariate models, i.e. PCA-LDA and (O)PLS-DA, delivered accuracies from 92.5 to 100.0%, while RF and SVM-based classification generated accuracies from 88.7 to 96.3%. Real-time recog-nition on a separate test set of 432 samples (2022) generated correct speciation between 89.6 and 99.5% for the multivariate models, while the ML models underperformed (22.3-95.1%), in particular for the white fish species. As such, we propose a real-time validated modelling strategy using directly amenable PCA-LDA for rapid industry-proof large-scale fish speciation. |
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