Processing, food industry and biochemistry
hamed bakhshi; Mohamad Abaspour; Mohammad Hossein Saeidirad; Mohammad Hossein Aghkhani; Roghayeh pourbagher
Abstract
Separation of stigma from petals is one of the required tasks in saffron production. The mechanical separation of saffron flowers may be performed through the following steps: I) singulation of the flowers ii) aligning the flowers iii) cutting the style (Konje) and iv) separating the stigma from the ...
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Separation of stigma from petals is one of the required tasks in saffron production. The mechanical separation of saffron flowers may be performed through the following steps: I) singulation of the flowers ii) aligning the flowers iii) cutting the style (Konje) and iv) separating the stigma from the petals. Because of the importance of saffron flower separation, a prototype device was constructed in this project for singulating and aligning of saffron flowers, prior to the cutting operation. This device can also be installed to work with other cutting and separating equipment and produce on an industrial scale. A pickup vacuum cylinder was used for singulating the flowers and an inclined V-shaped surface was employed for aligning of saffron flowers. The constructed apparatus was evaluated from the standpoint of singling efficiency and losses. For this purpose, three types of saffron flowers (buds, open short tail and long tail), three rotational speeds of pickup cylinder (6, 12 and 21.5 rpm), three levels of suction (30, 70 and 100 mm Hg), and four different inner diameters of finger’s nozzle (2, 3, 4 and 5 mm) were used with three replications. The results of analysis on the constructed device showed that the highest efficiency (75%) is observed for short tailopen saffron flowers with 5 mm inner diameter of sucking nozzles, suction of 100 mm Hg and the rotational speed of 6 rpm. To evaluate the performance of flower aligning, 100 saffron flowers with an average length of 5 cm were selected and tested with 5 replications. The average aligning with the correct direction obtained was equal to 84%.
Other subject about saffron
mehdi Bashiri; Ali Maroosi; Amir Salari; Mohammad Ghodoosi
Abstract
Yield prediction for agricultural crops plays an important role in export-import planning, purchase guarantees, pricing, secure profits and increasing in agricultural productivity. Crop yield is affected by several parameters especially climate. In this study, the saffron yield in the Khorasan-Razavi ...
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Yield prediction for agricultural crops plays an important role in export-import planning, purchase guarantees, pricing, secure profits and increasing in agricultural productivity. Crop yield is affected by several parameters especially climate. In this study, the saffron yield in the Khorasan-Razavi province was evaluated by different classification algorithms including artificial neural networks, regression models, local linear trees, decision trees, discriminant analysis, random forest, support vector machine and nearest neighbor analysis. These algorithms analyzed data for 20 years (1989-2009) including 11 climatological parameters. The results showed that a few numbers of climatological parameters affect the saffron yield. The minimum, mean and maximum of temperature, had the highest positive correlations and the relative humidity of 6.5h, sunny hours, relative humidity of 18.5h, evaporation, relative humidity of 12.5h and absolute humidity had the highest negative correlations with saffron cultivation areas, respectively. In addition, in classification of saffron cultivation areas, the discriminant analysis and support vector machine had higher accuracies. The correlation between saffron cultivation area and saffron yield values was relatively high (r=0.38). The nearest neighbor analysis had the best prediction accuracy for classification of cultivation areas. For this algorithm the coefficients of determination were 1 and 0.944 for training and testing stages, respectively. However, the algorithms accuracy for prediction of crop yield from climatological parameters was low (the average coefficients of determination equal to 0.48 and 0.05 for training and testing stages). The best algorithm i.e. nearest neighbor analysis had coefficients of determination equal to 1 and 0.177 for saffron yield prediction. Results showed that, using climatological parameters and data mining algorithms can classify cultivation areas. By this way it is possible to identify areas that have similar climate to prone areas and recognize suitable areas for cultivation.
