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
This is a preview of subscription content, log in to check access.
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.
B. Oliete, M. Gomez, V. Pando, E. Fernandez-Fernandez, P.A. Caballero, F. Ronda, Effect of nut paste enrichment on physical characteristics and consumer acceptability of bread. Food Sci. Technol. Int. 14(3), 259–269 (2008)CrossRefGoogle Scholar
D. Turan, E. Capanoglu, F. Altay, Investigating the effect of roasting on functional properties of defatted hazelnut flour by response surface methodology (RSM). LWT—Food Sci. Technol. 63, 758–765 (2015)CrossRefGoogle Scholar
D. Turan, F. Altay, E.C. Güven, The influence of thermal processing on emulsion properties of defatted hazelnut flour. Food Chem. 167, 100–106 (2015)CrossRefGoogle Scholar
M. Dervisoglu, Influence of hazelnut flour and skin addition on the physical, chemical and sensory properties of vanilla ice cream. Int. J. Food Sci. Technol. 41, 657–661 (2006)CrossRefGoogle Scholar
S. Yagci, F. Gogus, Selected physical properties of expanded extrudates from the blends of hazelnut flour-durum clear flour-rice. Int. J. Food Prop. 12, 405–413 (2009)CrossRefGoogle Scholar
I.M. López-Calleja, S. de la Cruz, I. González, T. García, R. Martín, Market analysis of food products for detection of allergenic walnut (Juglans regia) and pecan (Carya illinoinensis) by real-time PCR. Food Chem. 177, 111–119 (2015)CrossRefGoogle Scholar
V. Janská, L. Piknová, T. Kuchta, Relative quantification of walnuts and hazelnuts in bakery products using real-time polymerase chain reaction. Eur. Food Res. Technol. 232, 1057–1060 (2011)CrossRefGoogle Scholar
V. Janska, L. Piknova, T. Kuchta, Semi-quantitative estimation of the walnut content in fillings of bakery products using real-time polymerase chain reaction with internal standard material. Eur. Food Res. Technol. 235, 1033–1038 (2012)CrossRefGoogle Scholar
I.M. López, E. Trullols, M.P. Callao, I. Ruisánchez, Multivariate screening in food adulteration: untargeted versus targeted modeling. Food Chem. 147, 177–181 (2014)CrossRefGoogle Scholar
I.M. López-Calleja, S. de la Cruz, N. Pegels, I. González, T. García, R. Martín, High resolution TaqMan real-time PCR approach to detect hazelnut DNA encoding for ITS rDNA in foods. Food Chem. 141, 1872–1880 (2013)CrossRefGoogle Scholar
Detection of food fraud, I.M. López, N. Colomer, I. Ruisánchez, M.P. Callao, Validation of multivariate screening methodology. Case study. Anal. Chim. Acta 827, 28–33 (2014)CrossRefGoogle Scholar
M. Locatelli, J.D. Coïsson, F. Travaglia, E. Cereti, C. Garino, M. D’Andrea, A. Martelli, M. Arlorio, Chemotype and genotype chemometrical evaluation applied to authentication and traceability of “Tonda Gentile Trilobata” hazelnuts from Piedmont (Italy). Food Chem. 129, 1865–1873 (2011)CrossRefGoogle Scholar
A. Caligiani, J.D. Coisson, F. Travaglia, D. Acquotti, G. Palla, L. Palla, M. Arlorio, Application of 1 H NMR for the characterisation and authentication of ‘‘Tonda Gentile Trilobata’’ hazelnuts from Piedmont (Italy). Food Chem. 148, 77–85 (2014)CrossRefGoogle Scholar
M. Manfredi, E. Robotti, F. Quasso, E. Mazzuccoa, G. Calabrese, E. Marengo, Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics. Spectrochim. Acta A. 189, 427–435 (2018)CrossRefGoogle Scholar
G.P. Danezis, A.S. Tsagkaris, V. Brusic, C.A. Georgiou, Food authentication: state of the art and prospects. Curr. Opin. Food Sci. 10, 22–31 (2016)CrossRefGoogle Scholar
A.I. Ropodi, E.Z. Panagou, G.-J.E. Nychas, Data mining derived from food analyses using non-invasive/nondestructive analytical techniques; determination of food authenticity, quality & safety in tandem with computer science disciplines. Trends Food Sci. Technol. 50, 11–25 (2016)CrossRefGoogle Scholar
O. Hammer, D. Harper, P. Ryan, PAST: paleontological statistics software package for education and data analysis (Palaeontological Association, London, 2001)Google Scholar
S. Babicki, D. Arndt, A. Marcu, Y. Liang, J.R. Grant, A. Maciejewski, D.S. Wishart, Heatmapper: web-enabled heat mapping for all. Nucleic Acids Res. 44, 147–153 (2016)CrossRefGoogle Scholar
E. Byvatov, U. Fechner, J. Sadowski, G. Schneider, Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J. Chem. Inf. Comput. Sci. 43, 1882–1889 (2003)CrossRefGoogle Scholar
T.G. Dietterich, G. Bakiri, Solving multiclass learning problems via error-correcting output codes. J. Artif. Intell. Res. 2, 263–286 (1995)CrossRefGoogle Scholar
S. Escalera, O. Pujol, P. Radeva, On the decoding process in ternary error-correcting output codes. IEEE Trans. Pattern Anal. Mach. Intell. 32(7), 120–134 (2010)CrossRefGoogle Scholar
J. Fürnkranz, Round robin classification. J. Mach. Learn. Res. 2, 721–747 (2002)Google Scholar
O. Paliy, V. Shankar, Application of multivariate statistical techniques in microbial ecology. Mol. Ecol. 25, 1032–1057 (2016)CrossRefGoogle Scholar
H. Hotelling, Analysis of a complex of statistical variables into principal components. Br. J. Educ. Psychol. 24, 417–441 (1993)CrossRefGoogle Scholar
K. Pastor, M. Acanski, D. Vujic, D. Jovanovic, S. Wienkoop, Authentication of cereal flours by multivariate analysis of GC–MS data. Chromatographia 79, 1387–1393 (2016)CrossRefGoogle Scholar