Learning to classify organic and conventional wheat - a machine-learning driven approach using the MeltDB 2.0 metabolomics analysis platform

Learning to classify organic and conventional wheat - a machine-learning driven approach using the MeltDB 2.0 metabolomics analysis platform

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 gr...

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Bibliographic Details
Main Authors: Nikolas eKessler, Anja eBonte, Stefan P Albaum, Paul eMäder, Monika eMessmer, Alexander eGoesmann, Karsten eNiehaus, Georg eLangenkämper, Tim W Nattkemper
Format: Article
Language:English
Published: Frontiers Media S.A. 2015-03-01
Series:Frontiers in Bioengineering and Biotechnology
Subjects:
Metabolomics
machine learning
statistics
food authentication
computational metabolomics
Organic farming
Online Access:http://journal.frontiersin.org/Journal/10.3389/fbioe.2015.00035/full

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