Data fusion of elemental and metabolic fingerprints of asparagus with random forest approaches

Various analytical methods such as liquid chromatography-mass spectrometry (LC-MS) and inductively coupled plasma-mass spectrometry (ICP-MS) are used for the characterisation and authentication of foods. These two analytical techniques target very different parts of the complex composition of the samples and therefore fusion of the data promises better performance of the corresponding models.

ICP-MS and LC-MS data were fused for the classification of the geographical origin of 220 asparagus samples with random forest. The results show that the combination of elemental and metabolomic fingerprints leads to an improvement of the accuracy from approximately 88 % to 92.3 %. In particular, the fusion improves the classification of small groups, which is reflected by an increase in the Cohen's Kappa value from around 0.7 to 0.8. Furthermore, we applied surrogate minimal depth (SMD) to elemental fingerprints and fused data of elemental and metabolomic fingerprints for the first time. This made it possible to select relevant features and evaluate their mutual impact on the classification model, illustrating the interplay of the elemental and metabolic variables in the fused random forest model.

Using the classification of the geographical origin of asparagus, we show that the fusion of LC-MS and ICP-MS data is useful for improving the performance of food authentication. Furthermore, we show that SMD can be applied to analyse the mutual impact of features of single data sets but also across multiple data sets in the context of data fusion.