Naslov
Baking quality evaluation of winter wheat cultivars with the application of the chemometric technique principal component analysis (PCA)

Autori
Lukinac, Jasmina ; Filipović, Marija ; Koči, Matea ; Jukić, Marko

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, prošireni sažetak, znanstveni

Izvornik
86 International scientific conference of young scientist and students "Youth scientific achievements to the 21st century nutrition problem solution", Book of abstract. Part 1. NUFT, Kyiv. / - Kijev : National University of Food Technologies (NUFT), 2020, 8-8

Skup
85 International scientific conference of young scientists and students Youth Scientific Achievements to the 21st Century Nutrition Problem Solution

Mjesto i datum
Kijev, Ukrajina, 11.04.2019. - 12.04.2019

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
baking quality ; wheat ; chemometric ; principal component analysis

Sažetak
In human diet wheat (Triticum aestivum L.) holds major part up to 44%, that makes it higher rank than other cereals. Numerous factors like, protein content, hardness, cultivar type, season, and growing conditions are linked to the functionality of flour obtained from wheat varieties. The aim of this study was to investigate the correlation of quality parameters of different wheat cultivars using the principal components analysis method (PCA) and to identify the most important parameters for wheat quality assessment. The variations in physicochemical properties of grain and flour, and rheological properties of flour were studied for 54 winter wheat cultivars harvested in 2017 in the locations of Osijek and Zagreb. To obtain an overview of the main properties of the wheat samples PCA was used, where each principal component is a linear combination of the original variables, and so it is often possible to ascribe the meaning to what the components represent. PCA method seems to be an efficient tool for selection of wheat varieties with specific properties. Reduction of several analyzed wheat characteristics to some PCAs makes it possible to explain about 78% of the total input data variability. Physicochemical and extensographic wheat quality properties have the greatest impact on the first component F1, which accounts for about 62% of sample variability. The second component contributes with 16.02% clarification of variance in the observed data set, dominated by extensographic wheat quality parameters (O and O/R).

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Poljoprivreda (agronomija), Prehrambena tehnologija

POVEZANOST RADA