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

Simple item page
dc.contributor.authorPastor, Kristian
dc.contributor.authorIlić, Marko
dc.contributor.authorKojić, Jovana
dc.contributor.authorAčanski, Marijana
dc.contributor.authorVujić, Đura
dc.date.accessioned2023-08-23T13:53:04Z
dc.date.available2023-08-23T13:53:04Z
dc.date.issued2022
dc.description.abstractAn 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.identifier.doihttps://doi.org/10.1080/00032719.2022.2050921
dc.identifier.urihttp://oa.fins.uns.ac.rs/handle/123456789/396
dc.identifier.wos000771245800001
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.relationinfo:eu-repo/grantAgreement/MESTD/inst-2020/200222/RS// info:eu-repo/grantAgreement/MESTD/inst-2020/200134/RS//
dc.rightsrestrictedAccess
dc.subjectAutomated machine learningen_US
dc.subjectcereal flouren_US
dc.subjectgas chromatography – mass spectrometry (GC-MS)en_US
dc.titleClassification of cereal flour by gas ghromatography – mass spectrometry (GC-MS) liposoluble fingerprints and automated machine learningen_US
dc.typeArticleen_US
dc.typeinfo:eu-repo/semantics/article

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Pastor et al. 2022 (1).pdf
Size:
1.18 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
22.51 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Scientific papers