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    A preliminary study for determination of the possibility of simultaneous selection for oil content and grain yield in maize
    (Maydica-Ist Sper Cerealicoltur, 2015) Kahrıman, Fatih; Egesel, Cem O.; Onac, Iskender
    We investigated the genetic potential of six maize families for simultaneous selection of oil content and grain yield. Six generations of six families were generated in 2011 and 2012. These genotypes were tested in a field experiment, conducted in Dardanos Research and Application Center, Canakkale, Turkey, in 2013. Data were collected on oil content and grain yield per plant and then analyzed by using Generation Mean Analysis method to determine the appropriate families for simultanous selection studies. We also calculated genetic effect estimations for these traits in the investigated genotypes. Results showed that there were significant differences among the families and generations for oil content and grain yield. The variation in oil content in the families was mainly controlled by additive gene actions. Simultaneous selection did not seem feasible in the tested families, though there was a good chance for considerable enhancement in some genotypes if the investigated traits were taken into account singlehandedly. Three families (A680x-IHO, IHOxB73 and IHOxHYA) showed potential for selection to enhance oil content, while two others (IHOxMo17 and Mo17xIHO) were promising for grain yield. Estimated genetic gains were in the range of 0.4% to 4.1% per cycle for oil content, and 17.7 g to 60.7 g per cycle for grain yield.
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    Analysis of secondary biochemical components in maize flour samples by NIR (near infrared reflectance) spectroscopy
    (Springer, 2020) Kahrıman, Fatih; Onac, Iskender; Oner, Fatih; Mert, Figen; Egesel, Cem Omer
    This study was carried out to determine whether it is possible to detect secondary biochemical components in maize flour samples by near infrared reflectance (NIR) spectroscopy. Two hundred fifty maize samples were used as the material. Calibration models were developed for six different secondary biochemical components, namely amylose, amylopectin, lysine, tryptophan, zein, and phytic acid. The robustness of the calibration models (n = 200) was tested by external validation (n = 50). Results showed that NIR spectroscopy could be used to detect secondary quality components in maize. The most successful prediction model was for amylose content (SEP: 1.784%, RPD: 3.09, r = 0.963). Models for the other traits (amylopectin, zein, lysine, tryptophan, phytic acid) gave acceptable results (RPD > 2) for material screening purposes. Target traits subjected to calibration studies were found to be related to the different overtone regions of C-H, N-H and S-H bond vibrations in scanning the spectral region. It seems that it is necessary to improve the prediction performance of the models using different approaches, such as broadening the spectral area and/or using chemometric technique combinations.
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    Öğe
    Determination of carotenoid and tocopherol content in maize flour and oil samples using near-infrared spectroscopy
    (Taylor & Francis Inc, 2019) Kahrıman, Fatih; Onac, Iskender; Turk, Figen Mert; Oner, Fatih; Egesel, Cem Omer
    Secondary metabolites are important components in terms of nutrition and health. Carotenoids and tocopherols, two groups of the fat-soluble components, are also included in this category. There is an increasing interest in the detection of secondary metabolites with near-infrared spectroscopy. However, the number of scientific studies for the detection of these components, especially for tocopherols in corn flour or oil samples by near-infrared reflectance spectroscopy is rather limited. This study was carried out to determine the amount of carotenoids and tocopherols in flour and oil samples of 250 different maize genotypes by near-infrared reflectance spectroscopy using the partial least squares regression modeling method. Liquid chromatography mass spectrophotometry was used as a reference method in order to determine the contents of five carotenoids and four tocopherol subcomponents. The estimation models were created by using the spectral data collected from ground samples, and oil samples extracted from the same flour; along with the results of the reference analysis. The reliability of these models was tested by external validation (n?=?50). The prediction models generated by the spectra taken from corn flour yielded more successful results than the models created with the spectra taken from the oil samples. Among the models compared, the one developed with the spectra taken from flour samples for lutein was the most successful. It is seen that the estimation models generated from flour samples can be used for screening purposes, though different approaches are needed to increase the success of models.