Multivariate versus machine learning-based classification of rapid evaporative Ionisation mass spectrometry spectra towards industry based large-scale fish speciation. (15th March 2023) - De Graeve, Marilyn
Graphical abstract: Highlights: iKnife-REIMS is a suitable technique for large-scale rapid in situ analysis to combat food fraud. A total of 1736 samples comprising 17 fish species were analysed using iKnife-REIMS. PCA-LDA and (O)PLS-DA generated accuracies from 93 to 100%, RF and SVM from 89 to 96% Real-time PCA-LDA outperformed all other classification strategies for all, white and pink fish speciation. Real-time PCA-LDA with an external batch (n = 432) yielded a correct speciation of 90–100 % 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 analysed 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 recognition 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.