RT Journal Article
SR Electronic
T1 AI-based identification of grain cultivars via non-target mass spectrometry
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.05.07.082065
DO 10.1101/2020.05.07.082065
A1 Kapil Nichani
A1 Steffen Uhlig
A1 Bertrand Colson
A1 Karina Hettwer
A1 Kirsten Simon
A1 Josephine Bönick
A1 Carsten Uhlig
A1 Harshadrai M. Rawel
A1 Manfred Stoyke
A1 Petra Gowik
A1 Gerd Huschek
YR 2020
UL http://biorxiv.org/content/early/2020/05/08/2020.05.07.082065.abstract
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.