E. Rahmati; F. Sharifian; M. Fattahi; Gh. Najafi
Abstract
Introduction Dracocephalum moldavica L. is an annual plant with blue or white flowers and fragrant leaves which belongs to the family of Lamiaceae with the height of up to 80 cm. This plant is native to Central Asia and is accepted in Central and Eastern Europe. In Iran, it is mainly grown in the province ...
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Introduction Dracocephalum moldavica L. is an annual plant with blue or white flowers and fragrant leaves which belongs to the family of Lamiaceae with the height of up to 80 cm. This plant is native to Central Asia and is accepted in Central and Eastern Europe. In Iran, it is mainly grown in the province of West Azarbaijan and the Alborz Mountains. The essential oils and extracts derived from the secondary metabolisms which are mainly used in the pharmaceutical industry, dietary, cosmetic, flavoring and also as tea and beverage with sugar or honey. The liquid extract of the herb contains a high percentage of water, which should evaporate to increase shelf-life, easy transport, handling and storage, the ease of standardization and preservation of the product quality. On the other hand, the active compounds of the extracts are affected by temperature, oxygen, light and enzymes. Therefore, because of the uses and benefits of herbal extracts, they need to be dried by a practical and effective method like spray drying. In literature still there are no studies taking into account to the comparisons between RSM and TOPSIS as two important optimization methods. So, as the main objective of the present work, the effects of moisture content, drying performance, total phenol content, total flavonoid content and antioxidant activity have been surveyed. Finally, the optimal point of each process variable was presented by two optimization methods. Materials and Methods Aerial parts of Moldavian balm plant were cleaned and drying of plant was carried out under shade and thin layer conditions. The extraction of Moldavian balm was obtained by maceration method using ethanol 50 % (v/v), plant to solvent ratio of 1/10 (w/v). After 48h, the extract was concentrated in a rotary evaporator (Buchi Rotavapor R-205, Switzerland) to obtain a solid concentration of 6%. The used carrier was: Maltodextrin and apple pectin. Different ratios of carrier were prepared, then the ratio was added to distilled water and stirred by a magnetic stirrer. Finally, the solution was mixed with extract. The drying of Moldavian Balm plant extract was performed using a spray-dryer (Büchi B-191, Switzerland) with co-current flow regime. The powders provided by the spray drying were stored in refrigerator until they were needed for the experiment. Results and Discussion The results of variance analysis showed that the Box-Behnken design with the second-order model has led to the meaningfulness of the model, insignificant of the Lack of Fit and the appropriate correlation coefficient for each of the responses. A total number of 15 experiments were conducted to investigate the effect of process variables such as inlet air temperature, compressed air flow rate and concentration of carriers on moisture content, drying performance, total phenolic content, total flavonoid content and antioxidant activity of Moldavian balm powder. Inlet air temperature and compressed air flow rate had the most significant effect on moisture content and drying performance, while Chemical properties of the powder affected by changing the concentration of carriers. Optimization parameters of the spray drying process was performed using surface response and TOPSIS methods. The optimum predicted conditions in the response surface method and TOPSIS method were obtained at inlet air temperature, compressed air flow rate and concentration of carrier (152.5-150°C), (8.046-7.5 lit min-1) and 20%, respectively. Conclusion By comparing two methods, it can be concluded that although they could provide the same optimum points, the RSM is more efficient. Because RSM offers a mathematical model that can be used at any desired point of variables to predict the output quantities as well as describing the process trend, while TOPSIS method is unable to predict the process trend and only provides the ranking of alternatives.
S. Babazadeh; P. Ahmadi Moghaddam; A. Sabatyan; F. Sharifian
Abstract
The overall objective of this research is to check the abilities of two non-destructive techniques, the digital imaging (DI) and laser light backscattering imaging (LLBI), on detection of α-solanine toxicant in potatoes. Potato samples were classified in healthy and toxic categories based on the ...
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The overall objective of this research is to check the abilities of two non-destructive techniques, the digital imaging (DI) and laser light backscattering imaging (LLBI), on detection of α-solanine toxicant in potatoes. Potato samples were classified in healthy and toxic categories based on the amount of α-solanine. For quantifying α-solanine in potato tubers, high-performance liquid chromatography (HPLC) has been used. The results of classification showed that single layer perceptron neural networks can classify potatoes with the accuracies of 94.28% and 98.66% by DI and LLBI systems (Donald cultivar), respectively. It can be said that LLBI systems might take precedent over DI systems due to their high accuracy, rapidity, and industrial capability.
F. Jannatdost; P. Ahmadi Moghaddam; F. Sharifian
Abstract
Introduction Fruits and vegetables play an important role in food supply and public health. This group of agricultural products due to high humidity are perishable and most of them (5 to 50 percent) waste during post-harvest operation. Decreasing and minimizing such waste as "hidden harvest" could be ...
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Introduction Fruits and vegetables play an important role in food supply and public health. This group of agricultural products due to high humidity are perishable and most of them (5 to 50 percent) waste during post-harvest operation. Decreasing and minimizing such waste as "hidden harvest" could be an effective way to save food and increase profitability. Despite the surplus of the fruit production in the country, our position in terms of exportation is not commensurate with production, so measurements and grading on the basis of qualitative parameters such as firmness, taste, color, and shape can influence the marketing and export of fruit. In this research, application of an acoustic test is considered to achieve an effective and economic technology in the field to determine the stiffness of kiwifruit in post-harvest step. The aim of this study is to investigate the stiffness index of kiwifruit and provide a classification algorithm in the post-harvest step by using the non-destructive method of processing impact acoustic signals. Materials and Method In this research, an acoustic-based intelligent system was developed and the possibility of using the acoustic response to classify kiwifruit into soft, semi-soft and stiff categories was studied. 150 samples of Hayward variety of Kiwifruit was used during the 18 days shelf life in controlled conditions of temperature and humidity. Analyses were done in 9 sets per two days. In each analysis, an acoustic test was done by 48 samples in both free fall condition and fall from a conveyor belt. The feature extraction of acoustic signals in both the time domain and frequency domain has done, then the classification of samples was done by using the Artificial Neural Network. After getting the impact signals of stiff, semi-soft and soft samples, stiffness of kiwifruits identification has done by using acoustic features. The stiffness of kiwifruit samples in this study was measured to be 15.9±4.9 (N) by using the Magnes- Taylor test. Finally, samples were classified into stiff, semi-soft and soft by comparison of maximum force and flux of signals amplitude. Results and Discussion The results showed that the features of CF and maximum amplitude in the time domain have high accuracy in kiwifruit classification. The frequency resonances as environmental noises or impact position are out of control in the time domain which causes a decrease in accuracy. So, the ANN by features of time domain has not the acceptable capability to identify the semi-soft samples. The identification of semi-soft samples is not easy because of having same properties of stiff and soft samples. Extracted features of frequency domain have the most capability of correct detection. The optimal network has five neurons in the hidden layer and 0.014782 of mean square error. The accuracy of correct detection of the optimal network was 93.3, 91.3 and 78.3 percent for stiff, semi-soft and soft samples, respectively. Because of using more features in the frequency domain, the classification of all categories was acceptable and identification of semi-soft samples was as good as stiff and soft samples. The results of combined features of time and frequency domain showed that the artificial neural network has less efficiency in comparison with the other two attitudes. The accuracy of identification and classification was decreased by adding the extracted features of the time domain. So achieving the most accuracy in classification is accomplishable just by using the features of the frequency domain. By comparing the results of both free fall and online tests, it is claimed that this research can be industrialized. Conclusion Comparison of all results shows that there was no significant difference in the capability of ANN for identification and classification of the sample in three categories. After all, we can use this method in online sorting of kiwifruits by controlling the vector and position of impaction.
