Projects

The problem of adulteration of strawberries has been known for some time, as it is increasingly observed that German strawberries are stretched with foreign goods. Due to the large price differences, a re-declaration offers a high financial incentive, but besides the consumers, also the honest German strawberry farmers are massively harmed.

Currently, there are no analytical methods available to the strawberry industry that allow reliable proof of origin. The commonly used stable isotope analysis does not always provide unambiguous results and can only be carried out by very specialized research facilities, which is why a broad application is not possible.

In order to protect both honest strawberry producers and consumers from fraud, alternative analytical methods for origin determination based on objective data collection are being developed. Two complementary approaches (LC-MS/MS and ICP-MS) will be used, as it is not yet clear which technology is better suited for provenance detection. In any case, chemometric fusion of the different data sets will provide a higher resolution chemical fingerprint of the respective strawberries of different origins, which may be invaluable, for example in areas that are located on the border with neighboring countries.

After successful completion of the project, the developed methods can be directly implemented in quality assurance laboratories of SMEs, in commercial laboratories and in the laboratories of governmental investigation agencies. The project is designed so that implementation can be done with typical laboratory equipment or requires little additional investment. Alternatively, companies have the option of outsourcing the analyses to contract laboratories.

This project is funded by the German Federal Ministry of Economic Affairs and Climate Action (via AiF) through the Research Association of the German Food Industry (FEI) under project number AiF 22909 N and is being worked on by Kim Brettschneider in collaboration with Markus Fischer's research group.

In modern day science Omics technologies gain in importance for characterization and classification of various biological samples, including food. In contrast to genomics and transcriptomics, metabolomics is strongly influenced by the environment of the sample under study. Especially the metabolome of honey products is strongly affected by environmental influences, because honey is produced by bees who gather nectar and pollen from their immediate surroundings. In order to analyze the metabolome, a liquid chromatograph coupled to a mass spectrometer (LCMS) is highly suitable due to its high throughput, optional soft ionization and good coverage of certain compound classes. The goal of this project is to enable authenticity analysis of honey with appropriate bioinformatics analysis of LCMS data.

This project is divided into three parts. In the first part, a workflow is established to utilize LCMS data from different devices. Subsequently, chemometric approaches like principle component analysis (PCA), Soft Independent Modelling of Class Analogy (SIMCA) and machine learning methods, for instance random forest (RF), are applied to discriminate honey samples. In addition to the application of RFs for classification, RF‑based approaches are also utilized to characterize the honey based on their LCMS data. This is accomplished by the selection of important variables that characterize the variation between samples and and thus can be used to interpret specific environmental influences. Furthermore, the complex data is investigated by the relation analysis provided by Surrogate Minimal Depth (SMD). SMD enables the identification of co-occurring metabolites or metabolic pathways that can be associated with specific environmental influences and therefore with the geographical origin of honey.

In the second part of this project, honey adulteration by sugar syrups is revealed. Therefore, multivariate regression models are generated, e.g. by partial least squares (PLS) regression and RF to determine the proportion of syrup added to honey. Also in this context variable selection methods will be used to identify specific markers of the sugar syrups.

The approaches developed in the first two parts of the project will be applied to other food authentication issues, such as fruit juice adulteration, in the third part of the project

This projekt is worked on by  Jule Hansen.

The analysis of the metabolome is getting more and more important for characterization and classification of various biological samples including food. The metabolome comprises several very different molecule classes which is why different analytical techniques have been developed for metabolomics analysis. Most of them are based on NMR and mass spectrometry. For food analysis, metabolomics is used to detect fraud, e.g. by the determination of the geographic origin and taxonomic characteristics.

In this project, bioinformatic approaches for the comprehensive analysis of various metabolomics data for food profiling and authentication are developed. This is achieved by the application of established methods like principle component analysis (PCA) and machine learning methods, e.g. random forests (RF). In addition to the application of RF for classification, RF-based approaches are utilized to characterize the investigated food samples. This is accomplished by the selection of important variables that are exploited for the interpretation of differences between the classes and, hence, specific influences on the metabolome of foods. In this context, various variable selection methods are applied and compared. Furthermore, the complex properties of the food metabolome are investigated by the relation analysis provided by Surrogate Minimal Depth (SMD) that also enables the identification of co-occurring metabolites or metabolomics pathways that can be associated with specific environmental influences of food. In order to directly include this external knowledge about functional relationships into the modeling process, pathway-guided RF approaches are also applied in this project.

This projekt is worked on by  Sören Wenck.

This project is part of the excellence cluster "Understanding Written Artefacts" and is worked on by Lucas Voges. Further information about the project can be found here.

Surface-enhanced Raman scattering (SERS) in the absence of labels, tags or reporters is a powerful method to obtain comprehensive and diverse information about the composition and structure of biomolecular samples. Because of the nature of SERS that probes the varying interaction of the molecules with a nanostructured metal substrate and the proximity of specific functional groups, it is difficult to exploit the highly complex data that are generated. Specific challenges are posed by biological samples that are characterized by many different biomolecules and changing conditions, influencing the obtained SERS spectra. This is why efficient and unbiased approaches for the utilization of SERS data are needed.

In this project, random forest (RF) based methods, which have been shown to be very robust when applied to real SERS data, will be adapted and used for the analysis of model data obtained under well-defined experimental conditions. The complexity of the systems that are analyzed here will be increased step-wise from individual molecular components such as building blocks of lipid membranes or a drug molecule, over the combination of two components, to the complex environment of an endolysosomal vesicle inside cultured cells. The RF analysis will be established in such a way that it can serve three different purposes: (i) the selection of important spectral features for direct structural interpretation, (ii) the identification of co-occurring spectral features to analyze the interaction of different molecules, and (iii) the integration of a priori knowledge from previous experiments, including those conducted with respective other models in this project. The development of such a multi-purpose framework will rely on the different degrees of complexity of the SERS experiments as well as on a set of experimental conditions that are defined and modified in a systematic way. As a further aspect, simulated data that are specifically generated to imitate the impact of a particular experimental condition will be compared with actual experiments in an iterative fashion. The successful completion of this project will mark an important step towards utilizing SERS for the structural characterization of well-defined biophysical models as well as of molecules in complex biological systems.

This project is funded by the DFG under project number 511107129 and is worked on by Florian Gärber in cooperation with the research group of Janina Kneipp at the Humboldt University Berlin.