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Several researchers have investigated the relationships among different physical attributes of the fruits. For proper design and operation of grading systems, important relationships among the mass and other properties of fruits such as length, width, thickness, arithmetic mean diameter, geometric mean diameter, sphericity, surface area, volume, projected area, shape index, aspect ratio and elongations must be known. Recent researches have focused on artificial neural network (ANN) approaches to predict hard-to-find attributes of the fruits from easily-determined and readily available values. In this study, Modular Neural Network (MNN) and Radial Basis Neural Network (RBNN) structures of Artificial Neural Network (ANN) were employed to predict walnut mass from the physical attributes of the walnuts. Root mean square errors (RMSE) of MNN structure ranged from 0.60 to 0.89, while RMSE of RBNN structure were found to be very low (0.0002) in all of walnut varieties. These results showed that RBNN structures of Artificial Neural Network could potentially be used to estimate mass of walnuts and various physical attributes of walnuts were sufficient to predict the mass characteristics of a walnut. [ABSTRACT FROM AUTHOR]

Copyright of Yuzuncu Yil Universitesi Journal of Agricultural Sciences (YYU J Agr Sci) is the property of Yuzuncu Yil University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Faculty of Gaziosmanpasa University / Gazi Osman Pasa Üniversitesi Ziraat Fakültesi Dergisi is the property of Gaziosmanpasa University, Agricultural Faculty and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study was conducted to determine the total oil contents and fatty acid compositions of some commercial almond cultivars. The total oil contents changed significantly (p<0.05) by year in all cultivars with the exception of cultivar Ferrastar. Total oil contents were changed from 50.90% (Picantili) to 62.01% (Supernova) in 2008 and from 52.44% (Lauranne) to 63.18% (Cristomorto) in 2009. While predominant unsaturated fatty acids were oleic and linoleic acids, predominant saturated fatty acid was palmitic acid. The highest amount of oleic acid was obtained in Glorieta in both 2008 (83.35%) and 2009 (72.74%). Linoleic acid content varied by year and the highest content was recorded in Picantili (26.08%) in 2008 and Yaltinski (30.01%) in 2009. The highest amount of palmitic acid was detected in cultivar Sonora in both years, i.e. as 7.76% in 2008 and 10.11% in 2009. The mean UFA:SFA ratio was 11.73 in 2008 but 7.59 in 2009. Principal component (PC) analysis indicated that palmitic acid, palmitoleic acid, stearic acid, oleic acid, arachidic acid, unsaturated fatty acid (UFA), saturated fatty acid (SFA) and UFA:SFA ratio were primarily responsible for the separation on PC1. [ABSTRACT FROM AUTHOR]