PT  - JOURNAL ARTICLE
AU  - Kapil Nichani
AU  - Steffen Uhlig
AU  - Bertrand Colson
AU  - Karina Hettwer
AU  - Kirsten Simon
AU  - Josephine Bönick
AU  - Carsten Uhlig
AU  - Harshadrai M. Rawel
AU  - Manfred Stoyke
AU  - Petra Gowik
AU  - Gerd Huschek
TI  - AI-based identification of grain cultivars via non-target mass spectrometry
AID  - 10.1101/2020.05.07.082065
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.05.07.082065
4099  - http://biorxiv.org/content/early/2020/05/08/2020.05.07.082065.short
4100  - http://biorxiv.org/content/early/2020/05/08/2020.05.07.082065.full
AB  - 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 StatementThe authors have declared no competing interest.