| تعداد نشریات | 286 |
| تعداد نشریات سازمان | 84 |
| تعداد شماره‌ها | 3,012 |
| تعداد مقالات | 33,667 |
| تعداد مشاهده مقاله | 44,678,345 |
| تعداد دریافت فایل اصل مقاله | 36,120,109 |
طبقه‌بندی محصول مرزه (Satureja hortensis L.) براساس سطح کود اوره مصرفی به کمک ماشین بویایی | ||
| تحقیقات گیاهان دارویی و معطر ایران | ||
| مقاله 8، دوره 35، شماره 5 - شماره پیاپی 97، آذر و دی 1398، صفحه 789-801 اصل مقاله (1.01 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijmapr.2019.125506.2525 | ||
| نویسندگان | ||
| فرانه خدامرادی1؛ اسماعیل میرزایی قلعه* 2؛ محمد جعفر دالوند3؛ روح الله شریفی4 | ||
| 1دانشجوی کارشناسی ارشد، گروه مهندسی مکانیک بیوسیستم، دانشگاه رازی، کرمانشاه، ایران | ||
| 2استادیار، گروه مهندسی مکانیک بیوسیستم، دانشگاه رازی، کرمانشاه، ایران | ||
| 3دانش‌آموخته مقطع دکتری، رشته مهندسی مکانیک بیوسیستم، دانشگاه تهران، تهران، ایران | ||
| 4استادیار، گروه گیاه‌پزشکی، دانشگاه رازی، کرمانشاه، ایران | ||
| چکیده | ||
| شناسه دیجیتال (DOR): 98.1000/1735-0905.1398.35.789.97.5.1591.1610 مرزه (Satureja hortensisL.) گیاهی معطر و از خانواده نعناعیان است که دارای خواص دارویی بسیار زیادی می‌باشد. امروزه در پرورش این گیاه از کود اوره استفاده می‌شود که تأثیر زیادی بر عملکرد آن دارد. اما استفاده بی‌رویه از کود اوره سلامت مصرف‌کننده را به شدت تهدید می‌کند. ازاین‌رو در این تحقیق، طبقه‌بندی محصول مرزه برداشت شده براساس میزان اوره مصرفی به کمک سامانه ماشین بویایی بررسی شد. اوره در چهار سطح (صفر، 50، 100 و 150 کیلوگرم در هکتار) به خاک اضافه شد. مرزه در کرت‌های آزمایشی و در شرایط گلخانه‌ای پرورش داده شد. رایحه نمونه‌ها به کمک سامانه بینی الکترونیک ارزیابی شد. به‌منظور بهبود سیگنال‌های خروجی بینی الکترونیک، داده‌ها به روش کسری نرمال شدند. برای ترکیب ویژگی‌ها از روش تحلیل مؤلفه‌های اصلی (PCA) و برای طبقه‌بندی آنها از شبکه عصبی مصنوعی (ANN) استفاده شد. براساس نتایج، حسگر TGS822 بهترین پاسخ را در محدوده تغییرات کود اوره مورد آزمایش از خود نشان داد. براساس نتایج تحلیل مؤلفه‌های اصلی، PC1 و PC2 93% واریانس داده‌ها را مورد پوشش قرار دادند. همچنین ماتریس اغتشاش بهترین ساختار ANN، تفکیک 100% را نشان داد. | ||
| کلیدواژه‌ها | ||
| تحلیل مؤلفه‌های اصلی؛ شبکه عصبی مصنوعی؛ کود اوره؛ ماشین بویایی؛ مرزه (Satureja hortensis L.) | ||
| عنوان مقاله [English] | ||
| Classification of savory (Satureja hortensis L.) based on the level of used urea fertilizer consumed using an olfactory machine | ||
| نویسندگان [English] | ||
| F. Khodamoradi1؛ E. Mirzaee- Ghaleh2؛ M.J. Dalvand3؛ R. Sharifi4 | ||
| 1M.Sc. student, Mechanical Engineering of Biosystem Department, Razi University, Kermanshah, Iran | ||
| 2Mechanical Engineering of Biosystem Department, Razi University, Kermanshah, Iran | ||
| 3Ph.D. graduate of Mechanical Engineering of Biosystems, University of Tehran, Tehran, Iran | ||
| 4Plant Protection Department, Razi University, Kermanshah, Iran | ||
| چکیده [English] | ||
| Summer savory (Satureja hortensisL.) is a fragrant herb in the Lamiaceae family with many medicinal properties. Today, urea fertilizer is used for growing this plant which has a great impact on its performance. However, the unnecessary use of urea fertilizer threatens consumer health. Therefore, in this research, the harvested savory plants were classified based on the amount of urea consumed by an olfactory machine. The urea was added to soil at four levels (0, 50, 100 and 150 kg ha-1). Plants were grown in experimental plots under greenhouse conditions. The fragrance of the samples was evaluated using the electronic nose system. In order to optimize the output signals of the electronic nose, the data were normalized by the fractional method. Principal component analysis (PCA) and artificial neural network (ANN) were used respectively to combine the features and classifying them. Based on the results, TGS822 sensorshowed the best response in the range of urea fertilizer tested. Based on the results of PCA, PC1 and PC2 accounted for 93% of the variance. Also, the confusion matrix showed a 100% correct classification rate by the best ANN structure. | ||
| کلیدواژه‌ها [English] | ||
| Principal component analysis, artificial neural network, urea fertilizer, olfactory machine, summer savory (Satureja hortensis L.) | ||
| مراجع | ||
|
- Al-mansour, B., Kalaivanan, D., A-Suryanarayana, M., Umesha, K. and K Nair, A., 2018. Influence of organic and inorganic fertilizers on yield and quality of sweet basil (Ocimum basilicum L.). Journal of Spices and Aromatic Crops, 27(1): 38-44. - Amerian, M., Ali-Mohamadian, L. and Malekhosini, A., 2018. Evaluation the reasons of inattention and ignorance farmers of adverse effects the chemical fertilizers (especially nitrogen fertilizer) the using focus group discussion. Journal of Environmental Science and Technology, 19(4): 36-47. - Arshak, K., Moore, E., Lyons, G.M., Harris, J. and Clifford, S., 2004. A review of gas sensors employed in electronic nose applications. Sensor Review, 24: 181-198. - Ayari, F., 2018. Development and implementation of an electronic nose system for detection of cow ghee from adulterated samples. Faculty of Agriculture Department of Mechanical Engineering of Biosystems, Razi University. - Ayari, F., Mirzaee-Ghaleh, E., Taheri Gravandi, A. and Heideri, S., 2018. Using of olfactory system for detecting the freshness of chicken meat. 11th Congress on Mechanical Engineering of Bio-System and Mechanization of Iran, Hamedan, Iran, 3-5 September. - Baby, R., Cabezas, M., Castro, E., Filip, R. and Walsöe de Reca, N.E., 2005. Quality control of medicinal plants with an electronic nose. Sensors and Actuators B: Chemical, 106: 24-28. - Benedetti, S., Spinardi, A., Mignani, I. and Buratti, S., 2010. Non-destructive evaluation of sweet cherry (Prunus avium L.) ripeness using an electronic nose. Italian Journal of Food Science, 22: 298-304. - Chezgi, M., Chalavi, V. and Akbarpour, V., 2018. The effect of organic and chemical nitrogen fertilizers on the yield and qualitative characteristics of two basil cultivars. Journal of Crop Production and Processing, 8(1):29-44. - Cui, S., Wang, J., Yang, L., Wu, J. and Wang, X., 2015. Qualitative and quantitative analysis on aromacharacteristics of ginseng at different ages using E-nose and GC-MS combined with chemometrics. Journal of Pharmaceutical and Biomedical Analysis, 102: 64-77. - Fayyaz, P., Muthashi, S., Jafari, A. and Masoudi, A., 2019. Development and evaluation of an electronic system based on semiconductor metal oxide sensors for the detection and separation of lemon essential oils. Two Agricultural Machinery Weekly, Ferdowsi University of Mashhad, 9(2): 253-263. - Ghasemi-Varnamkhasti, M., Mohtasebi, S.S., Siadat, M.J., Lozano, H., Ahmadi, S.H. and Dicko, A., 2011. Aging fingerprint characterization of beer using electronic nose. Sensors and Actuators B: Chemical, 159: 51-59. - Gorji Chakespari, A., Mohammad Nikbakht, A., Sefidkon, F. and Ghasemi-Varnamkhasti, M., 2017. Investigation of electronic nose system in classification of Rosa damascena Mill. essential oil by artificial neural network. Iranian Journal of Medicinal and Aromatic Plants Research, 33(3): 339-349. - Hajinezhad, M., Mohtasebi, S., Ghasemi-varnamkhasti, M. and Aghbashlo, M., 2017. Detecting adulteration in lotus honey using a machine olfactory system. Journal of Agricultural Machinery, 7(2): 439-450. - Heidarbeigi, K., 2014. Implementation, fabrication and evaluation of a system for detecting original saffron from adulteration based on electronic tongue and electronic nose technology. Faculty of Agricultural Engineering and Technology Department of Agricultural Machinery Engineering, University of Tehran. - Heidarbeigi, K., Mohtasebi, S.S., Foroughirad, A., Ghasemi-Varnamkhasti, M., Rafiee, Sh. and Rezaei, K., 2015. Detection of adulteration in saffron samples using electronic nose. International Journal of Food Properties, 18: 1391-1401. - Hosseinzadeh-Samani, B., Lorigooini, Z. and Fasihzadeh, Sh., 2017. Evaluation of the effect of different microwave dryer power on the qualitative and quantitative properties of essential oil of Bakhtiari savory. Iranian Journal of Food Science and Technology, 66(14): 145-155. - Kasbe, M.S., Mujawar, T.H., Mule, S.S., Prabhakar, P., Shaligram, A.D. and Deshmukh, L.P., 2018. An advanced electronic nose (EN) system: application to classification of tomato and - Khoshkhooiy, M., Rouhani, A., Sheibani, B. and. Tafazoli, A., 2005. New Principles of Gardening. Shiraz University Press, 604p. - Laureati, M., Buratti, S., Bassoli, A., Borgonovo, G. and Pagliarini, E., 2010. Discrimination and characterization of three cultivars of Perilla frutescens by means of sensory descriptors and electronic nose and tongue analysis. Food Research International, 43: 959-964. - Li, C., Heinemann, P. and Sherry, R., 2007. Neural network and Bayesian network fusion models to fuse electronic nose and surface acoustic wave sensor data for apple defect detection. Sensors and Actuators B: Chemical, 125: 301-310. - Lotfi, M. and Mokhtari, F., 2015. Comparison of methods for drying with microwave, oven and drying naturally on plant characteristics Savory. International Conference on Research in Engineering, Science and Technology, Istanbul Turkey, July 2015: 1-21. - Mahmoudi, E., 2009. Electronic nose technology and its applications. Sensors & Transducers, 107(8): 17-25. - Makizadeh Tafti, M., Chaichi, M.R., Nasrollahzadeh, P. and Khavazi K., 2013. The effect of different nitrogen resources on the quantitative and qualitative characteristics of Satureja hortensis L. Iranian Journal of Medicinal and Aromatic Plants Research, 28(2): 330-341. - Moradi, P. and Omidi Najaf Abadi, W., 2012. The barriers to the application of the standard for suitable agricultural in the agricultural sector of Iran. Agricultural Extension and Education Research, 4(1): 27-39. - Naijii, M. and Souri, M.K., 2015. Evaluation of growth and yield of savory (Satureja hortensis) under organic and biological fertilizers toward organic production. The Plant Production (Agricultural Science Magazine), 38(3): 93-103. - Rahman, S., Usmani, T. and Saeed, S.H., 2013. Review of electronic nose and application. International Journal of Computing and Corporate Research, 3(2): 1-9. - Russo, M., Serra, D., Suraci, F., Di Sanzo, R., Fuda, S. and Postorino, S., 2014. The potential of e-nose aroma profiling for identifying the geographical origin of licorice (Glycyrrhiza glabra L.) roots. Food Chemistry, 165: 467-474. - Sanaeifar, A., Mohtasebi, S.S., Ghasemi-Varnamkhasti, M. and Jafari, A., 2013. Application of machine olfaction system (electronic nose) as anondestructive tool in the classification ripening stages of banana. Eighth National Congress, Agricultural Machinery (Biological Systems) and Mechanization, 29 January, Mashahd, Iran: 1-9. - Sepahvand, M. and Omidvari, S., 2011. Effect of urea fertilizer excessive use on nitrate accumulation in vegetables and risk of public health. First congress on fertility challenges in Iran, Tehran, Iran, 1-3 March. - Torri, L., Sinell, N. and Limbo, S., 2010. Shelf life evaluation of fresh-cut pineapple by using an electronic nose. Postharvest Biology and Technology, 56(3): 239-245. - Towhidi, M., Ghasemi-Varnamkhasti, M., Ghaffariya, W., Muthasbi, S. and Baniadian, M., 2016. Construction and development of a smelter machine system in combination with pattern recognition methods for detecting formalin cheating on raw milk. Biotechnology Engineering of Iran, 47(4): 770-761. - Wan, T., Zheng, L., Dong, S., Gong, Z., Sang, M., Long, X., Luo, M. and Peng, H., 2019. Rapid detection and classification of citrus fruits infestation by Bactrocera dorsalis (Hendel) based on electronic nose. Postharvest Biology and Technology, 174: 156-165. - Zakaria, A., Shakaff, A.Y.M., Masnan, M.J., Saad, F.S.A., Adom, A.H., Ahmad, M.N., Jaafar, M.N., Abdullah, A.H. and Kamarudin, L.M., 2012. Improved maturity and ripeness classifications of Magnifera indica cv. harumanis mangoes through sensor fusion of an electronic nose and acoustic sensor. Sensors, 12(5): 6023-6048. - Zare, Sh., Sirus Mehr, A.S.R., Ghanbari, A. and Tabatabai, J., 2014. The effect of different rates of municipal compost and N fertilizer on the essential oil and some vegetative characteristics of summer savoury (Satureja hortensis L.). Iranian Journal of Agricultural Research, 11(1): 191-199. - Zhang, B., Huang, Y., Zhang, Q., Liu, X., Li, F. and Chen, K., 2014. Fragrance discrimination of Chinese Cymbidium species and cultivars using an electronic nose. Scientia Horticulturae, 172: 271-277. - Zohora, S.E., Khan, A.M., Srivastava, A.K. and Hundewale, N., 2013. Electronic noses application to food analysis using metal oxide sensors: a review. International Journal of Soft Computing and Engineering, 3(5): 199-205. | ||
|
آمار تعداد مشاهده مقاله: 567 تعداد دریافت فایل اصل مقاله: 449 |
||