Journal of Food Measurement and Characterization

, Volume 13, Issue 4, pp 2961–2969 | Cite as

A rapid dicrimination of wheat, walnut and hazelnut flour samples using chemometric algorithms on GC/MS data

  • Kristian PastorEmail author
  • Marijana Ačanski
  • Djura Vujić
  • Predrag Kojić
Original Paper


There is a worldwide growing trend in developing methods for determining authenticity and detecting adulteration in food products. In this study an approach utilizing gas chromatography—mass spectrometry (GS/MS) combined with chemometric multivariate data analysis was proposed in order to determine discrimination and classification possibilities of flour samples produced from 16 genotypes of wheat, 9 genotypes of hazelnut, and 8 genotypes of walnut, grown in the Vojvodina region, Republic of Serbia. Plant samples were milled into flour, lipid fraction was extracted with n-hexane and derivatized using a 0.2 M TMSH solution and analyzed on a GC/MS device. Molecular ions of eluting lipid components were selected, isolated from total ion current chromatograms and their peaks employed in further data processing. Unsupervised exploratory data analysis techniques: principal component analysis (PCA), expression heat mapping, hierarchical cluster analysis (HCA) and principal coordinate analysis (PCoA) were used to select the most important variables, and to explore their potential in discrimination of investigated flour samples according to belonging botanical origin. PCA and heat maps demonstrated that molecular ions of methyl esters of four fatty acids: 270 (palmitic), 294 (linoleic), 296 (oleic), and 298 (stearic), were most discriminative variables, HCA and PCoA showed a clear and strong separations between groups of analyzed samples. A support vector machine (SVM) algorithm was employed in order to classify samples in three groups. The performance of the classification SVM model was excellent, achieving high coefficient of determination of 98.6, with only 1 value being misclassified.


Hazelnut flour Walnut flour GC/MS Chemometrics Support vector machines Authenticity 



The authors gratefully acknowledge the financial support from the Ministry of Education, Science and Technological Development of the Republic of Serbia, as well as COST Actions CA16233 Drylands facing change: interdisciplinary research on climate change, food insecurity, political instability, and CA18101 Sourdugh biotechnology network towards novel, healthier and sustainable food and bioprocesses.


Funding was provided by Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja (Grant No. TR31066).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

11694_2019_216_MOESM1_ESM.docx (168 kb)
Electronic supplementary material 1 (DOCX 168 kb)


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of TechnologyUniversity of Novi SadNovi SadSerbia

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