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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 Apple grading using fuzzy logic(TUBITAK, 2003) Kavdir, Ismail; Guyer, Daniel E.Classification is vital for the evaluation of agricultural produce. However, the high costs, subjectivity, tediousness and inconsistency associated with manual sorting have been forcing the post harvest industry to apply automation in sorting operations. Fuzzy logic (FL) was applied as a decision making support to grade apples in this study. Quality features such as the color, size and defects of apples were measured through different equipment. The same set of apples was graded by both a human expert and a FL system designed for this purpose. Grading results obtained from FL showed 89% general agreement with the results from the human expert, providing good flexibility in reflecting the expert's expectations and grading standards into the results. This application of apple grading can be fully automated by measuring the required features by means of high-tech sensors or machine vision and making the grading decision using FL.Öğe Classification of chestnuts according to moisture levels using impact sound analysis and machine learning(Springer, 2018) Kurtulmus, Ferhat; Oztufekci, Sencer; Kavdir, IsmailIn this study, a prototype system was designed, built and tested to classify chestnuts using impact sound signals and machine learning methods according to moisture contents. Briefly, the system consisted of a shotgun microphone, a sliding platform, an impact surface, a triggering system, a sound device and a computer. Sound signal data were acquired from 2028 chestnut samples with three different moisture levels. Acoustic signals from chestnut samples were filtered to alleviate negative effects of unwanted noise. Four machine learning classifiers using three different feature sets obtained from two feature groups applying feature reduction methods were trained and tested to classify pairs of chestnut moisture group categories as 35% versus 45%, 35% versus 55%, 45% versus 55% (classification with two outputs) and 35% versus 45% versus 55% (classification with three outputs), respectively. The highest classification success (88%) was achieved for the classification application category of 35 versus 55%.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 Classification of pepper seeds using machine vision based on neural network(Chinese Acad Agricultural Engineering, 2016) Kurtulmus, Ferhat; Alibas, Ilknur; Kavdir, IsmailPepper is widely planted and used all over the world as fresh vegetable and spice. Genetic and morphological information of pepper are stored through seeds. Determination of seed variety is crucial for correctly identifying genetic materials. Pepper varieties cannot be easily classified even by an expert eye due to the very small size of seeds and visual similarities. Hence, more advanced technologies are required to determine the variety of a pepper seed. A classification method was proposed to discriminate pepper seed based on neural networks and computer vision. Image acquisition was conducted using an office scanner at a resolution of 1200 dpi. Image features representing color, shape, and texture were extracted and used to classify pepper seeds. By calculating features from different color components, a feature database was constructed. Effective features were selected using sequential feature selection with different criterion functions. As a result of the feature selection procedure, the number of the features was significantly reduced from 257 to 10. Cross validation rules were applied to obtain a reliable classification model by preventing overfitting. Different numbers of neurons in the hidden layer and various training algorithms were investigated to determine the best multilayer perceptron model. The best classification performance was obtained using 30 neurons in the hidden layer of the network. With this network, an accuracy rate of 84.94% was achieved using the sequential feature selection and the training algorithm of resilient back propagation in classifying eight pepper seed varieties.Öğe Detecting corn tassels using computer vision and support vector machines(Pergamon-Elsevier Science Ltd, 2014) Kurtulmus, Ferhat; Kavdir, IsmailAn automated solution for maize detasseling is very important for maize growers who want to reduce production costs. Quality assurance of maize requires constantly monitoring production fields to ensure that only hybrid seed is produced. To achieve this cross-pollination, tassels of female plants have to be removed for ensuring all the pollen for producing the seed crop comes from the male rows. This removal process is called detasseling. Computer vision methods could help positioning the cutting locations of tassels to achieve a more precise detasseling process in a row. In this study, a computer vision algorithm was developed to detect cutting locations of corn tassels in natural outdoor maize canopy using conventional color images and computer vision with a minimum number of false positives. Proposed algorithm used color informations with a support vector classifier for image binarization. A number of morphological operations were implemented to determine potential tassel locations. Shape and texture features were used to reduce false positives. A hierarchical clustering method was utilized to merge multiple detections for the same tassel and to determine the final locations of tassels. Proposed algorithm performed with a correct detection rate of 81.6% for the test set. Detection of maize tassels in natural canopy images is a quite difficult task due to various backgrounds, different illuminations, occlusions, shadowed regions, and color similarities. The results of the study indicated that detecting cut location of corn tassels is feasible using regular color images. (C) 2014 Elsevier Ltd. All rights reserved.Öğe Development and applicability of an agarose-based tart cherry phantom for computer tomography imaging(Springer, 2015) Donis-Gonzalez, Irwin R.; Guyer, Daniel E.; Kavdir, Ismail; Shahriari, Dena; Pease, AnthonyComputer tomography (CT) imaging is an effective method for in vivo characterization of object internal attributes including fresh agro-food product quality. Limitations to move CT technology forward into the development of an inline system include the lack of standardized tools (phantoms) for image quality analysis, cross-sharing, and consistent evaluation. The objective of this study was to develop a set of agarose phantoms suitable for detection of pit and pit fragments using CT imaging. Efficiently sorting out these undesirable features during handling and processing will be extremely beneficial to the tart cherry industry. These phantoms can be used on several CT devices (including ultra-fast CT systems) to quantify CT performance, reproducibility, and applicability. This article describes how the phantoms were created, using agarose, a broadly available and inexpensive material. Developed phantoms allow for the measurement of CT image parameters that are relevant to detect fresh cherry pits and/or pit fragments and helps in the development of inline CT equipment. Measured phantom CT image parameters include simulated flesh and embedded pit X-ray CT attenuation properties (HU-values), which are statistically similar (p = 0.05) to fresh tart cherries. In addition, using CT images, pit and pit fragment size can be inferred with a high accuracy rate (R = 0.99, p value <0.01).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.
