Surface enhanced Raman scattering (SERS) microspectra give biochemical information from nanoscopic volumes in a heterogeneous biomaterial. With the help of machine learning, molecular structure and interactions can be inferred based on SERS data.

For the untargeted analysis of the metabolome of biological samples with liquid chromatography–mass spectrometry (LC-MS), high-dimensional data sets containing many different metabolites are obtained. Since the utilization of these complex data is challenging, different machine learning approaches have been developed. Those methods are usually applied as black box classification tools, and detailed information about class differences that result from the complex interplay of the metabolites are not obtained. Here, we demonstrate that this information is accessible by the application of random forest (RF) approaches and especially by surrogate minimal depth (SMD) that is applied to metabolomics data for the first time. We show this by the selection of important features and the evaluation of their mutual impact on the multi-level classification of white asparagus regarding provenance and biological identity. SMD enables the identification of multiple features from the same metabolites and reveals meaningful biological relations, proving its high potential for the comprehensive utilization of high-dimensional metabolomics data.

The importance of animal welfare and the organic production of chicken eggs has increased in the European Union in recent years. Legal regulation for organic husbandry makes the production of organic chicken eggs more expensive compared to conventional husbandry and thus increases the risk of food fraud. Therefore, the aim of this study was to develop a non-targeted lipidomic LC-ESI-IM-qToF-MS method based on 270 egg samples, which achieved a classification accuracy of 96.3%. Subsequently, surrogate minimal depth (SMD) was applied to select important variables identified as carotenoids and lipids based on their MS/MS spectra. The LC-MS results were compared with FT-NIR spectroscopy analysis as a low-resolution screening method and achieved 80.0% accuracy. Here, SMD selected parts of the spectrum which are associated with lipids and proteins. Furthermore, we used SMD for low-level data fusion to analyze relations between the variables of the LC-MS and the FT-NIR spectroscopy datasets. Thereby, lipid-associated bands of the FT-NIR spectrum were related to the identified lipids from the LC-MS analysis, demonstrating that FT-NIR spectroscopy partially provides similar information about the lipidome. In future applications, eggs can therefore be analyzed with FT-NIR spectroscopy to identify conspicuous samples that can subsequently be counter-tested by mass spectrometry.

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.