Yazar "Buyukcan, M. Burak" seçeneğine göre listele
Listeleniyor 1 - 5 / 5
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Analysis of Fatty Acids in Kernel, Flour, and Oil Samples of Maize by NIR Spectroscopy Using Conventional Regression Methods(Wiley, 2016) Egesel, Cem Omer; Kahrıman, Fatih; Ekinci, Neslihan; Kavdir, Ismail; Buyukcan, M. BurakHigh cost and painstaking procedures associated with fatty acid analyses of maize kernel necessitate the use of alternative methods. NIR spectroscopy offers advantages in this respect for a variety of areas such as plant breeding, food and feed industries, and biofuel production, in which different forms of maize kernel (e.g., intact kernel, flour, or oil) are used as material. We investigated the possibility of estimating maize oil quality traits by using different samples (intact kernel, flour, and oil) and conventional regression methods (multiple linear regression [MLR] and partial least squares regression [PLSR]) applied to their NIR spectra. MLR and PLSR calibration models were developed for oleic acid, linoleic acid, oleic/linoleic acid ratios, total monounsaturated fatty acid, total polyunsaturated fatty acid (PUFA), and total saturated fatty acid by analyzing 120 maize samples. Robustness in terms of prediction accuracy of the models developed here was tested with a reserved set of samples (n = 30). The results suggested that fatty acids could be possibly estimated by calibrations developed from flour and oil samples with a high degree of accuracy, whereas intact samples did not offer satisfactory results. PLSR and MLR methods gave better results in flour and oil samples, respectively. PUFA was the trait that was most successfully estimated from both flour (for the PLSR model, standard error of the estimate [SEP] of 1.78%, relative performance to deviation [RPD] of 3.09, R-2 = 0.93) and oil (for the MLR model, SEP of 0.85%, RPD of 6.52, R-2 = 0.98) samples. We concluded that sample type and chemometric method should be handled as important factors in calibration development, and the effects of these factors may vary depending on the trait being analyzed.Öğe Classification of olives using FT-NIR spectroscopy, neural networks and statistical classifiers(Springer, 2018) Kavdir, Ismail; Buyukcan, M. Burak; Kurtulmus, FerhatGreen olives (Olea europaea L. cv. Ayvalik') were classified based on their surface features such as existence of bruise and fly-defect using two NIR spectrometer readings of reflectance and transmittance, and classifiers such as artificial neural networks (ANN) and statistical (Ident and Cluster). Spectral readings were performed in the ranges of 780-2500 and 800-1725nm for reflectance and transmittance modes, respectively. Original spectral readings were used as input features to the classifiers. Diameter correction was applied on reflectance spectra used in ANN classifier expecting improved classification results. ANN classifier performed better in general compared to statistical classifiers. Classification performance in detecting bruised olives using diameter corrected reflectance features and ANN classifier was 99% while it was 98% for Ident and Cluster classification approaches using regular reflectance features. Classification between solid and fly-defected olives was performed with success rates of 93% using reflectance features and 58% using transmittance features with ANN classifier while statistical classifiers of Ident and Cluster performed between 52 and 78% success rates using the same spectral readings. ANN classifier resulted 92% classification success for the classification application considering three output classes of solid, bruised and fly-defected olives using reflectance features while it performed 57.3% success rate using transmittance features.Öğe Prediction of olive quality using FT-NIR spectroscopy in reflectance and transmittance modes(Academic Press Inc Elsevier Science, 2009) Kavdir, Ismail; Buyukcan, M. Burak; Lu, Renfu; Kocabiyik, Habib; Seker, MuratQuality features, including firmness, oil content and colour (chroma, hue), of two olive (Olea europaea L.) varieties ('Ayvalik' and 'Gemlik') were predicted using Fourier transform near infrared (FT-NIR) spectroscopy. Spectral measurements of the intact olives were performed for the wavelength range of 780-2500 nm in reflectance and for 800-1725 nm in transmittance. Measurements of olive firmness, oil content and colour were performed, following the spectral measurements, using standard methods. Calibration models for prediction of olive quality features were developed using the partial least squares method, and they were validated by leave-one-out cross validation. Better prediction results were obtained for Magness-Taylor (MT) maximum force (firmness) for both varieties in transmission mode, with the coefficient of determination (R(2)) of 0.77 and the root mean squared error of cross validation (RMSECV) of 1.36 for 'Ayvalik'. Reflectance mode, on the other hand, had a lower R(2) value of 0.65 (RMSCV=1.82) for 'Ayvalik'. Similar results were obtained for MT maximum force prediction for 'Gemlik' olives. Oil content prediction for each olive variety was poor, due to the relatively homogenous samples. However, better oil content prediction was achieved for the pooled data with the R(2) value of 0.64 (RMSECV=0.05) in reflectance and 0.61 (RMSECV=0.05) in transmittance. Both FT-NIR reflectance and transmittance measurements gave good prediction of olive colour, with the R(2) values for chroma ranging between 0.83 and 0.88 in reflectance and between 0.85 and 0.92 in transmittance. Similar results for hue prediction were also obtained. These results demonstrated that FT-NIR spectroscopy is potentially useful for assessing internal and external quality attributes of olives. (C) 2009 IAgrE. Published by Elsevier Ltd. All rights reserved.Öğe Prediction of some internal quality parameters of apricot using FT-NIR spectroscopy(Springer, 2017) Buyukcan, M. Burak; Kavdir, IsmailThe characteristics of internal quality attributes (firmness, soluble solids content and color values) of the Tokaloglu apricot cultivar (Prunus armeniaca L.) were predicted nondestructively using Fourier Transform-Near Infrared (FT-NIR) spectroscopy. Calibration methods were developed between the physical parameters, which were measured using standard methods, and the spectral measurements (in reflectance mode between 780 and 2500 nm) using Partial Least Squares method (PLS). Good correlations were obtained in calibration and validation procedures for Magness-Taylor (MT) maximum force, with a coefficient of determination (R-2) of 0.82 (RMSEE = 4.45) in calibration and 0.80 (RMSECV = 4.68) in validation for multiple-harvest (MH) apricot group. The coefficient of determination (R-2) for predicting MT slope was 0.79 (RMSEE = 0.83) in calibration and 0.77 (RMSECV = 0.88) in validation for the MH apricot group while it was 0.56 (RMSEE = 0.69) in calibration and 0.47 (RMSECV = 0.80) in validation for single-harvest (SH) apricot group. Good correlations were obtained for MT area with the coefficient of determination (R-2) of 0.75 (RMSEE = 20.1) in calibration and R-2 = 0.71 (RMSECV = 21) in validation for MH group. Good prediction values were obtained for soluble solids content for both applications (MH and SH) using FT-NIR spectroscopy: the best coefficient of determination was obtained for MH application with 0.77 (RMSEE = 1.45) in calibration and 0.75 (RMSECV = 1.51) in validation. Correlation values for prediction of chroma and hue were low for MH application, with R-2 = 0.55 (RMSECV = 3.38) for chroma and with R-2 = 0.16 (RMSECV = 0.49) for hue. The results showed that NIR spectroscopy has a good potential to predict internal quality of apricots non-destructively, however it has a limited ability to predict color features.Öğe The effects of middle infrared radiation intensity on the quality of dried tomato products(Wiley, 2014) Kocabiyik, Habib; Yilmaz, Nese; Tuncel, N. Baris; Sumer, Sarp K.; Buyukcan, M. Burak[Anstract Not Available]
