title: Learning to Classify Organic and Conventional Wheat - A Machine Learning Driven Approach Using the MeltDB 2.0 Metabolomics Analysis Platform.
abstract: We present results of our machine learning approach to the problem of classifying GC-MS data originating from wheat grains of different farming systems. The aim is to investigate the potential of learning algorithms to classify GC-MS data to be either from conventionally grown or from organically grown samples and considering different cultivars. The motivation of our work is rather obvious nowadays: increased demand for organic food in post-industrialized societies and the necessity to prove organic food authenticity. The background of our data set is given by up to 11 wheat cultivars that have been cultivated in both farming systems, organic and conventional, throughout 3 years. More than 300 GC-MS measurements were recorded and subsequently processed and analyzed in the MeltDB 2.0 metabolomics analysis platform, being briefly outlined in this paper. We further describe how unsupervised (t-SNE, PCA) and supervised (SVM) methods can be applied for sample visualization and classification. Our results clearly show that years have most and wheat cultivars have second-most influence on the metabolic composition of a sample. We can also show that for a given year and cultivar, organic and conventional cultivation can be distinguished by machine-learning algorithms.
authors: Biodata Mining Group, Faculty of Technology, Bielefeld University , Bielefeld , Germany ; Bioinformatics Resource Facility, Center for Biotechnology, Bielefeld University , Bielefeld , Germany.
authors: Nikolas
authors: N
authors: Kessler
authors: Department of Safety and Quality of Cereals, Max Rubner-Institut , Detmold , Germany.
authors: Anja
authors: A
authors: Bonte
authors: Bioinformatics Resource Facility, Center for Biotechnology, Bielefeld University , Bielefeld , Germany.
authors: Stefan P
authors: SP
authors: Albaum
authors: Department of Soil Sciences, Research Institute of Organic Agriculture (FiBL) , Frick , Switzerland.
authors: Paul
authors: P
authors: Mäder
authors: Department of Crop Sciences, Research Institute of Organic Agriculture (FiBL) , Frick , Switzerland.
authors: Monika
authors: M
authors: Messmer
authors: Bioinformatics and Systems Biology, Justus-Liebig-University Gießen , Gießen , Germany.
authors: Alexander
authors: A
authors: Goesmann
authors: Department of Proteome and Metabolome Research, Faculty of Biology, Center for Biotechnology, Bielefeld University , Bielefeld , Germany.
authors: Karsten
authors: K
authors: Niehaus
authors: Department of Safety and Quality of Cereals, Max Rubner-Institut , Detmold , Germany.
authors: Georg
authors: G
authors: Langenkämper
authors: Biodata Mining Group, Faculty of Technology, Bielefeld University , Bielefeld , Germany.
authors: Tim W
authors: TW
authors: Nattkemper
keywords: computational metabolomics; food authentication; machine learning; metabolome informatics; metabolomics; organic farming; statistics