نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری مهندسی بیوسیستم، مهندسی بیوسیستم، دانشگاه ارومیه، ارومیه، ایران.
2 گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Physiological disorder of citrus granulation is one of the qualitative issues in consumer markets that is not visually detectable. One of the applicable methods for non-destructive study of internal tissue of agricultural products is visible and near-infrared spectroscopy technique. In this research, the VIS/NIR signals in the wavelength range of 200-1100 nanometers were recorded for each sample. Spectroscopy was performed on the samples With contact Then, the amount of reflectance was calculated in the interaction mode, and absorption spectra were normalized using the Min-Max method. Subsequently, the recorded spectra were smoothed by the moving average (MA) method, and the Stavisky-Golay algorithm was applied to each spectrum, and finally, 7 statistical features were extracted from each spectrum. In order to show the intensity and amount of granulation in the internal tissue of oranges, five levels were defined, which included levels A, B, C, D and E. The degree of granulation of each sample was defined based on the dryness and appearance of the dried area using a destructive method. Support vector regression (SVR) and support vector machine (SVM) were used to estimate the moisture content of oranges and to detect the levels of granulation in oranges, respectively. The results showed that with the increase and development of the intensity of granulation, the juice bags become harder, drier and bigger. And their water content decreased, the highest moisture content of oranges was 90.97% in the state without granulated lesions and the lowest was 83.36% in the state where more than 75% of its tissue was granulated. Also, the examination of ViS-NIR spectra showed that with the development of granulation in oranges, the absorption in the 400-950 nanometer range significantly decreased, and the difference in absorption intensity for different levels of granulation was maximum in the 570-850 nanometer range. Granulation level detection results showed that the overall accuracy of support vector machine for linear, polynomial and Gaussian radial kernels was 92.50%, 96.50% and 95.00%, respectively. The sensitivity of SVM with polynomial kernel in detecting the levels A, B, C, D and E granulation were equal to 98.0%, 91.40%, 97.30%, 96.80% and 95.70%, respectively.
کلیدواژه‌ها [English]
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