Combining parallel factor analysis and machine learning for the classification of dissolved organic …

Highlights • Machine-learning applied to 1029 PARAFAC-modeled EEMs to classify 24 DOM sources. • Classification accuracy: 97% river vs leachate; 93% leachate by species; 87% by river. • Some machine learning algorithms achieved higher classification accuracies. • Accuracy similar to NPLS-DA, but faster and with simultaneous multiclass comparison. • Extending # components past cross-validated PARAFAC model improved accuracy. Abstract Parallel factor (PARAFAC) analysis of dissolved organic matter (DOM) fluorescence has facilitated a surge of investigation into its biogeochemical cycling. However, rigorous, PARAFAC-based methods for holistically distinguishing DOM sources are lacking. This study classified 1029 PARAFAC-analyzed excitation-emission matrices (EEMs) measured using DOM isolated from 24 different leaf leachates, rivers, and organic matter standards using four machine learning methods (MLM). EEMs were also divided into subsets to assess the impact of experimental…


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