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AI-based identification of grain cultivars via non-target mass spectrometry

Kapil Nichani, View ORCID ProfileSteffen Uhlig, Bertrand Colson, Karina Hettwer, Kirsten Simon, Josephine Bönick, Carsten Uhlig, Harshadrai M. Rawel, Manfred Stoyke, Petra Gowik, Gerd Huschek
doi: https://doi.org/10.1101/2020.05.07.082065
Kapil Nichani
1QuoData GmbH, Prellerstr. 14, D-01309 Dresden, Germany
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  • For correspondence: kapil.nichani@quodata.de
Steffen Uhlig
2QuoData GmbH, Fabeckstr. 43, D-14195 Berlin, Germany
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  • ORCID record for Steffen Uhlig
Bertrand Colson
1QuoData GmbH, Prellerstr. 14, D-01309 Dresden, Germany
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Karina Hettwer
1QuoData GmbH, Prellerstr. 14, D-01309 Dresden, Germany
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Kirsten Simon
2QuoData GmbH, Fabeckstr. 43, D-14195 Berlin, Germany
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Josephine Bönick
6IGV-Institut für Getreideverarbeitung GmbH, Arthur-Scheunert-Allee 40/41, D-14558 Nuthetal OT Bergholz Rehbrücke, Germany
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Carsten Uhlig
3Akees GmbH, Ansbacher Str. 11, D-10787 Berlin, Germany
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Harshadrai M. Rawel
4University of Potsdam, Institute of Nutritional Science, Arthur-Scheunert-Allee 114-116, D-14558 Nuthetal OT Berg-holz-Rehbrücke, Germany
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Manfred Stoyke
5Bundesamt für Verbraucherschutz und Lebensmittelsicherheit, Diedersdorfer Weg 1, D-12277, Berlin, Germany
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Petra Gowik
5Bundesamt für Verbraucherschutz und Lebensmittelsicherheit, Diedersdorfer Weg 1, D-12277, Berlin, Germany
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Gerd Huschek
6IGV-Institut für Getreideverarbeitung GmbH, Arthur-Scheunert-Allee 40/41, D-14558 Nuthetal OT Bergholz Rehbrücke, Germany
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ABSTRACT

Detection of food fraud and geographical traceability of ingredients is a continually sought goal for government institutions, producers, and consumers. Herein we explore the use of non-target high-resolution mass spectrometry approaches and demonstrate its utility through a particularly challenging case study – to distinguish wheat and spelt cultivars. By employing a data-independent acquisition (DIA) approach for sample measurement, the spectra are of considerable size and complexity. We utilize artificial intelligence (AI) algorithms (artificial neural networks) to evaluate the extensive proteomic footprint of several wheat and spelt cultivars. The AI model thus obtained is used to classify newer varieties of spelt, processed flour, and bread samples. Additionally, we discuss the validation of such a method coupling DIA and AI approaches. The novel framework for method validation enables calculation of precision parameters for facile comparison of the discriminatory power of the method and in the development of a reliable decision rule.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted May 08, 2020.
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AI-based identification of grain cultivars via non-target mass spectrometry
Kapil Nichani, Steffen Uhlig, Bertrand Colson, Karina Hettwer, Kirsten Simon, Josephine Bönick, Carsten Uhlig, Harshadrai M. Rawel, Manfred Stoyke, Petra Gowik, Gerd Huschek
bioRxiv 2020.05.07.082065; doi: https://doi.org/10.1101/2020.05.07.082065
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AI-based identification of grain cultivars via non-target mass spectrometry
Kapil Nichani, Steffen Uhlig, Bertrand Colson, Karina Hettwer, Kirsten Simon, Josephine Bönick, Carsten Uhlig, Harshadrai M. Rawel, Manfred Stoyke, Petra Gowik, Gerd Huschek
bioRxiv 2020.05.07.082065; doi: https://doi.org/10.1101/2020.05.07.082065

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