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Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
Published on: March 13, 2020
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Learning to Classify Organic and Conventional Wheat - A Machine Learning Driven Approach Using the MeltDB 2.0
Nikolas Kessler1, Anja Bonte2, Stefan P Albaum3
1Biodata Mining Group, Faculty of Technology, Bielefeld University , Bielefeld , Germany ; Bioinformatics Resource Facility, Center for Biotechnology, Bielefeld University , Bielefeld , Germany.
Frontiers in Bioengineering and Biotechnology
|April 9, 2015
Summary
Machine learning can distinguish organic from conventional wheat based on GC-MS data. Farming system, year, and cultivar significantly impact metabolic profiles, enabling authenticity verification for organic food.
Area of Science:
- Agricultural Science
- Analytical Chemistry
- Computational Biology
Background:
- Growing demand for organic food necessitates methods to verify authenticity.
- Wheat grains from different farming systems (organic vs. conventional) and cultivars were analyzed.
- Metabolomic data provides a basis for distinguishing cultivation practices.
Purpose of the Study:
- To apply machine learning for classifying wheat GC-MS data.
- To investigate the potential of algorithms in differentiating organic and conventional farming systems.
- To assess the influence of year and cultivar on metabolic profiles.
Main Methods:
- Gas Chromatography-Mass Spectrometry (GC-MS) for data acquisition.
- MeltDB 2.0 metabolomics analysis platform for data processing.
- Unsupervised (t-SNE, PCA) and supervised (SVM) machine learning algorithms for classification and visualization.
Main Results:
- Year of cultivation had the most significant influence on metabolic composition.
- Wheat cultivar showed the second-highest influence on metabolic profiles.
- Machine learning algorithms successfully distinguished between organic and conventional cultivation for specific years and cultivars.
Conclusions:
- Machine learning effectively classifies wheat based on GC-MS data, differentiating farming systems.
- Metabolic profiling can serve as a tool for verifying organic food authenticity.
- Environmental and genetic factors (year, cultivar) are key determinants of wheat's metabolic signature.
