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Classification of cereal flour by gas ghromatography – mass spectrometry (GC-MS) liposoluble fingerprints and automated machine learning

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dc.contributor.author Pastor, Kristian
dc.contributor.author Ilić, Marko
dc.contributor.author Kojić, Jovana
dc.contributor.author Ačanski, Marijana
dc.contributor.author Vujić, Đura
dc.date.accessioned 2023-08-23T13:53:04Z
dc.date.available 2023-08-23T13:53:04Z
dc.date.issued 2022
dc.identifier.uri http://oa.fins.uns.ac.rs/handle/123456789/396
dc.description.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. en_US
dc.language.iso en en_US
dc.publisher Taylor & Francis en_US
dc.relation info:eu-repo/grantAgreement/MESTD/inst-2020/200222/RS// info:eu-repo/grantAgreement/MESTD/inst-2020/200134/RS//
dc.rights restrictedAccess
dc.subject Automated machine learning en_US
dc.subject cereal flour en_US
dc.subject gas chromatography – mass spectrometry (GC-MS) en_US
dc.title Classification of cereal flour by gas ghromatography – mass spectrometry (GC-MS) liposoluble fingerprints and automated machine learning en_US
dc.type Article en_US
dc.type info:eu-repo/semantics/article
dc.identifier.wos 000771245800001
dc.identifier.doi https://doi.org/10.1080/00032719.2022.2050921


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