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.

Background: Honey is one of the most adulterated foods worldwide, and several analytical methods have been developed over the last decade to detect syrup additions to honey. These include approaches based on stable isotopes and the specific detection of individual marker compounds or foreign enzymes. Proton nuclear magnetic resonance (¹H-NMR) spectroscopy is applied as a rapid and comprehensive screening method, which also enables the detection of quality parameters and the analysis of the geographical and botanical origin. However, especially for the detection of foreign sugars, ¹H-NMR has insufficient sensitivity. Methods: Since untargeted liquid chromatography–mass spectrometry (LC-MS) is more sensitive, we used this approach for the detection of positive and negative ions in combination with a recently developed data processing workflow for routine laboratories based on bucketing and random forest for the detection of rice, beet and high-fructose corn syrup in honey. Results: We show that the distinction between pure and adulterated honey is possible for all three syrups, with classification accuracies ranging from 98 to 100%, while the accuracy of the syrup content estimation depends on the respective syrup. For rice and beet syrup, the deviations from the true proportion were in the single-digit percentage range, while for high-fructose corn syrup they were much higher, in some cases exceeding 20%. Conclusions: The approach presented here is very promising for the robust and sensitive detection of syrup in honey applied in routine laboratories.

Apples are one of the most popular fruits in Germany, valued for their regional availability and health benefits. When deciding which apple to buy, several characteristics are important to consumers, including the taxonomic variety, organic cultivation and regional production. To verify that these characteristics are correctly declared, powerful analytical methods are required. In this study, ultra-high performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-Q-ToF-MS) is applied in combination with random forest to 193 apple samples for the analysis of various authentication issues. Accuracies of 93.3, 85.5, 85.6 and 90% were achieved for distinguishing between German and non-German, North and South German, organic and conventional apples and for six different taxonomic varieties. Since the classification models largely use different parts of the data, which is shown by variable selection, this method is very well suited to answer different authentication issues with one analytical approach.