Classification of cereal flour by gas ghromatography – mass spectrometry (GC-MS) liposoluble fingerprints and automated machine learning
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Date
2022
Authors
Pastor, Kristian
Ilić, Marko
Kojić, Jovana 
Ačanski, Marijana
Vujić, Đura
Journal Title
Journal ISSN
Volume Title
Publisher
Taylor & Francis
Abstract
An innovative and rapid approach is described for classifying common types of gluten and non-gluten cereal flour (wheat, rye, triticale, barley, oats, and corn) into the groups defined by their botanical origin. Liposoluble compounds were extracted from flour samples, derivatized, and analyzed using gas chromatography – mass spectrometry
(GC-MS). Raw signals used for data processing consisted of mass spectra scans of full chromatograms. These represented unique fingerprints for each class. An automated machine learning framework was applied for classification. The algorithm automatically
explored each of the 39 classifiers provided by the software. Using 10-fold cross-validation, a simple logistic classifier was recommended to be optimal. The constructed model resulted in 85.71% correctly classification according to the botanical origin. Furthermore, it unequivocally discriminated samples of non-gluten corn flour. This
non-targeted strategy supports the use of artificial intelligence in developing methods for flour authentication.
Description
Keywords
Automated machine learning, cereal flour, gas chromatography – mass spectrometry (GC-MS)