XVII th World Congress of the International Commission of Agricultural and Biosystems Engineering (CIGR) COMPARISON OF MECHANICAL PROPERTIES OF TWO APPLE VARIETIES UNDER COMPRESSION LOADING CSBE100603 -Presented at Section VI: Postharvest Technology and P

ABSTRACT In this study strength properties of two Iranian apple varieties (Golab Kohanz and Shafi Abadi), under compression loading, were considered and compared using standard methods. The properties such as rupture force and energy, failure stress and strain, Young's modulus, toughness and hardness were determined in 86% and 84% moisture content (w.b) for Golab Kohanz and Shafi Abadi, respectively. Mechanical properties of apples were evaluated using 20 cylindrical specimens of each variety, taken in horizontal and vertical direction by Universal Testing Machine. Average values of rupture force and energy, failure stress, failure strain, Young's modulus, toughness and hardness were determined, 57.82 N, 285.88 mJ, 0.37 MPa, 31.26%, 0.93 MPa, 0.07 J/cm3 and 9.14 N/mm for Shafi Abadi variety, respectively. The corresponding values for Golab Kohanz were obtained as 51.11 N. 157.51 mJ, 0.32 MPa, 23.36%, 0.81 MPa, 0.04 J/cm3 and 7.79 N/mm respectively. According to results, effect of the sampling orientation was not significant on the mechanical properties in any two varieties

Performance Comparison of Fuzzy ARTMAP and LDA in Qualitative Classification of Iranian Rosa damascena Essential Oils by an Electronic Nose

Quality control of essential oils is an important topic in industrial processing of medicinal and aromatic plants. In this paper, the performance of Fuzzy Adaptive Resonant Theory Map (ARTMAP) and linear discriminant analysis (LDA) algorithms are compared in the specific task of quality classification of Rosa damascene essential oil samples (one of the most famous and valuable essential oils in the world) using an electronic nose (EN) system based on seven metal oxide semiconductor (MOS) sensors. First, with the aid of a GC-MS analysis, samples of Rosa damascene essential oils were classified into three different categories (low, middle, and high quality, classes C1, C2, and C3, respectively) based on the total percent of the most crucial qualitative compounds. An ad-hoc electronic nose (EN) system was implemented to sense the samples and acquire signals. Forty-nine features were extracted from the EN sensor matrix (seven parameters to describe each sensor curve response). The extracted features were ordered in relevance by the intra/inter variance criterion (Vr), also known as the Fisher discriminant. A leave-one-out cross validation technique was implemented for estimating the classification accuracy reached by both algorithms. Success rates were calculated using 10, 20, 30, and the entire selected features from the response of the sensor array. The results revealed a maximum classification accuracy of 99% when applying the Fuzzy ARTMAP algorithm and 82% for LDA, using the first 10 features in both cases. Further classification results explained that sub-optimal performance is likely to occur when all the response features are applied. It was found that an electronic nose system employing a Fuzzy ARTMAP classifier could become an accurate, easy, and inexpensive alternative tool for qualitative control in the production of Rosa damascene essential oil