Application of principal component analysis in assessment of relation between the parameters of technological quality of wheat grains treated with inert dusts against rice weevil (Sitophilus oryzae L.)

• Published in: Pesticidi i Fitomedicina Vol. 26; no. 4; pp. 385 - 390
• Main Authors: Bodroza-Solarov, Marija, Kljajic, Petar, Andric, Goran, Filipcev, Bojana, Simurina, Olivera, Prazic-Golic, Marijana, Adamovic, Milan
• Format: Journal Article
• Language: English
• Published: Institute of Pesticides and Environmental Protection 2011
• Subjects: BLA; CALIDAD DE PROCESAMIENTO; DIATAMITA; DIATOMITE; http://www.fao.org/aos/agrovoc#c_15928; http://www.fao.org/aos/agrovoc#c_2419; http://www.fao.org/aos/agrovoc#c_26682; http://www.fao.org/aos/agrovoc#c_29772; http://www.fao.org/aos/agrovoc#c_32658; http://www.fao.org/aos/agrovoc#c_3887; http://www.fao.org/aos/agrovoc#c_8373; Inert dusts; INSECTICIDAS; INSECTICIDE; INSECTICIDES; Low and high vitreous wheat; PCA; POLVOS (FORMULACIONES); POUDRE; POWDERS; PROCESSING QUALITY; QUALITA TECHNOLOGIQUE; Quality parameters; SITOPHILUS ORYZAE; TRIGO; WHEATS; ZAOLITE; ZEOLITAS; ZEOLITES
• ISSN: 1820-3949; 2406-1026
• DOI: 10.2298/PIF1104385B

Quality parameters of several wheat grain lots (low vitreous and high vitreous grains, non-infested and infested with rice weevils, (Sitophilus oryzae L.) treated with inert dusts (natural zeolite, two diatomaceous earths originating from Serbia and a commercial product Protect-It(R)) were investigated. Principal component analysis (PCA) was used to investigate the classification of treated grain lots and to assess how attributes of technological quality contribute to this classification. This research showed that vitreousness (0.95) and test weight (0.93) contributed most to the first principal component whereas extensigraph area (-0.76) contributed to the second component. The determined accountability of the total variability by the first component was around 55%, while with the second it was 18%, which means that those two dimensions together account for around 70% of total variability of the observed set of variables. Principal component analysis (PCA) of data set was able to distinguish among the various treatments of wheat lots. It was revealed that inert dust treatments produce different effects depending on the degree of endosperm vitreousness.