پیش‌بینی برخی ویژگی‌های کیفی میوۀ انار با استفاده از روش غیرزیانبار پردازش تصویر

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی سابق کارشناسی ارشد، دانشگاه علوم کشاورزی و منابع طبیعی ساری

2 استادیار، دانشگاه علوم کشاورزی و منابع طبیعی ساری

چکیده

روش­های غیرزیانبار پایدار اهمیت زیادی دارند، چراکه محصول مورد بررسی به چرخۀ عرضه و مصرف برگشته و نیز مشکلات روش­های زیانبار مانند وقت­گیری و پرهزینه بودن را ندارند. از سوی دیگر تنوع و فراوانی ویژگی­های کیفی محصولات کشاورزی، از دیگر دلایل توسعۀ روش­های غیرزیانبار به‌شمار می­آید. بنابراین در این پژوهش توانایی روش‌های پردازش تصویر به­منظور پیش­بینی ویژگی‌های کیفی مانند حجم، وزن، مواد جامد محلول کل، اسید قابل عیارسنجی (تیتراسیون)، فنل کل و فعالیت پاداکسندگی (آنتی­اکسیدانی) میوۀ سه رقم انار شامل زاغ یزدی، ملس یزدی و ملس اصفهان در طول فصل رشدی سال 1392 ارزیابی شد. میوه­ها در چهار مرحلۀ 50، 80، 110 و 140 روز پس از گلدهی گردآوری‌شده و از لحاظ ویژگی‌های رنگی و شیمیایی ارزیابی شدند. درنهایت مدل‌های واسنجی (کالیبراسیون) مربوط به داده‌های رنگی و اندازه‌گیری‌های شیمیایی تدوین شدند. نتایج به‌دست‌آمده نشان از اعتبارسنجی مدل­ها داشت، نسبت انحراف معیار در رقم ملس اصفهان برای وزن 3/2، حجم 52/2، مواد جامد محلول 8/1 و فعالیت پاداکسندگی 95/2 بود. همچنین مشخص شد که روند تغییرپذیری اسید قابل عیارسنجی و فنل کل با رنگ میوۀ انار در هیچ‌کدام از رقم‌های مورد بررسی ضریب همبستگی معنی­داری نداشت و توسط روش پردازش تصویر قابل پیش‌بینی نبود. درمجموع می­توان نتیجه گرفت که روش پردازش تصویر، روشی کارا و قابلیت بسیار قوی در تشخیص همزمان و سریع مرحله‌های بلوغ و همچنین وضعیت ویژگی­های کیفی میوۀ انار رقم ملس اصفهان دارد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Prediction of some pomegranate fruit quality characteristics by non-destructive image processing technique

نویسندگان [English]

  • Ali Mirhosseini 1
  • Hossein Sadeghi 2
  • Hossein Moradi 2
1 Foermer M.Sc. Student, Department of Horticulture, Sari University of Agricultural Sciences and Natural Resources, Iran
2 Assistant Professor, Department of Horticulture, Sari University of Agricultural Sciences and Natural Resources, Iran
چکیده [English]

Non-destructive methods are very important in agriculture because the tested product can return to the market process, and also they don’t have the problems of destructive methods such as time-consuming and cost. On the other hand, diversity and abundance of quality characteristics of agricultural products are considered as the other reasons for the development of non-destructive methods. Therefore, in this study the ability of the image processing method in order to predict the quality characteristics such as volume, weight, total soluble solids, titrable acid, total phenolic and antioxidant activity of three Zaghe Yazdi, Malase Yazdi and Malase Esfahan cultivars of pomegranate fruit during 2013 growth season, was investigated. Samples were collected at four stages of 50, 80, 110 and 140 days after full bloom and then the color and chemical characteristics were evaluated. Finally, the calibration models related to colorimetric data and chemical measurements were prepared.Results of validation of models showed that in Malase Esfahan cultivar, the standard deviation ratio was 2.3, 2.52, 1.8 and 2.95 for weight, volume, total soluble solids and antioxidant activity, respectively. Moreover, it was found that the changes of titratable acid and total phenolic had no significant correlation coefficient with the color of pomegranate fruit and there were not predictable by image processing technique.  Overall, it can be concluded that the image processing technique is an efficient method and has a very strong potential for simultaneous and rapid detection of maturity stages and also to detect the status of qualitative characteristics in pomegranate fruit cv. Malase Esfahan.

کلیدواژه‌ها [English]

  • Antioxidant activity
  • color changes
  • Malase Esfahan
  • ripening
  • standard deviation
  1. Afshari-Jouybari, H. & Farahnaky, A. (2011). Evaluation of Photoshop software potential for food colourimetry. Journal of Food Engineering, 106, 170-175.
  2. Amos, O. F. & Opara, U. L. (2013). Developmental changes in maturity indices of pomegranate fruit. Scientia Horticulture, 159, 152-161.
  3. Azadshahraki, F., Kalantari, S., Mostofi, Y., Jamshidi, B., Masudi, R. & Najafi, S. (2015). Predict some fruit quality parameters of grape cv. red beans using nondestructive spectrometry near-infrared. Iranian Journal of Biosystem Engineering, 46(4), 371-378. (in Farsi)
  4. Brand-Williams, W., Cuvelier, M. E. & Berset, C. (1995). Use of a free radical method to evaluate. Food Science and Technology, 28, 25-30.
  5. Brosnan, T. & Sun, D. W. (2003). Improving quality inspection food products by computer vision: A review. Journal of Food Engineering, 61, 3-16.
  6. Carlini, P., Massantini, R. & Mencarelli, F. (2000). Vis-NIR measurement of soluble solids in cherry andApricot by PLS regression and wavelength selection. Journal of Agricultural and Food Chemistry, 48, 236-242.
  7. Clark, C. J., McGlone, V. A., Requejo, C., White, A. & Woolf, A. B. (2003). Dry matter determination in ‘Hass’ avocado by NIR spectroscopy. Postharvest Biology and Technology, 29, 300-307.
  8. Fadavi, A., Barzegar, M. & Azizi, M. (2005). Determination of fatty acid and total lipid content in oilseed of 25 pomegranate varieties grown in Iran. Journal of Food Composition and Analysis, 19, 676-680.
  9. Fadock, M. (2011). Non-destructive vis-NIR reflectance spectrometry for red wine grape analysis. M.Sc. thesis. Faculty of Graduate Studies, University of Guelph, Ontario, Canada.
  10. Fan, G., Zha, J., Du, R. & Gao, L. (2009). Determination of soluble solids and firmness of apples by Vis/NIR transmittance. Journal of Food Engineering, 93, 416-420.
  11. Giovenzana, V., Beghi, R., Mena, A., Civelli, R., Guidetti, R., Best, S. & Leon, G. L. F. (2013). Quick quality evaluation of Chilean grape by a portable vis/NIR device. Acta Horticulture, 978, 93-100.
  12. Khodabakhshian, R. (2015). Quality testing methods Non-destructive agricultural products from principles to implementation. Tehran Agricultural Extension and Education Publications. (In Farsi).
  13. Khodabakhshian, R., Emadi, B., khojastehpoor, M. R. & Sazgari, A. (2015). Quick polls quality seeds using spectroscopy visible / near-infrared. Journal of Food technologies, 8, 103-114. (in Farsi)
  14. Khoshnam F., Tabatabaeefar A., Ghasemi Varnamkhasti M. & Borghei A. (2007). Mass modeling of pomegranate (Punica granatum L.) fruit with some physical characteristics. Scientia Horticulturae, 114, 21-26.
  15. Lu, R., Guyer, D. & Beaudry, R. M. (2000). Determination of firmness and sugar content of apple using NIR diffuse reflectance. Journal of Texture Studies, 31, 615-630.
  16. Nicolai, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I., Karen, I. T. & Lammertyn, J. (2007). Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology, 46, 99-118.
  17. Nikbakht, A. M., Tavakoli Hashjin, T., Malekfar, R. & Ghobadian, B. (2010). Application of raman spectroscopy for non-destructive determination of qualitative parameters of tomato. Journal of Agricultural Science and Technology, 7(4), 25-33. (in Farsi)
  18. Salahvarzi, Y. & Tehranifar, A. (2013). Effect of essential oil of some medicinal plants and polyethylene packaging on quality and pomegranate shelf life (cv. Shishehkap). Journal of Horticultural Science, 27(3), 318-325. (in Farsi)
  19. Salmanizadeh, F., Nasiri, M., Rahemi, M. & Jafari, A. (2013).  Feasibility of using X-ray absorption as a non-destructive method to determine some qualitative indicators pomegranate. Journal of horticulture science, 27, 335-341. (in Farsi)
  20. Shao, Y., He, Y., Gomez, A. H., Pereir, A. G., Qiu, Z. & Zhag, Y. (2007). Visible/near infrared spectrometric technique for nondestructive assessment of tomato ‘Heatwave’ (Lycopersicum esculentum) quality characteristics. Journal of Food Engineering, 81, 672-678.
  21. Shao, Y. H., He, Y., Bao, Y. D. & Mao, J. Y. (2009). Near-infrared spectroscopy for classification of oranges and prediction of the sugar content. International Journal of Food Properties, 12, 644-658.
  22. Singleton, V. L. & Rossi, J. L. (1965). Colorimetry of total phenolics with phosphomolybdic–phosphotungstic acid reagents. American Journal of Enology and Viticulture, 16(3), 144-158.
  23. Tabatabaeefar, A. & Rajabipour, A. (2005). Modeling the mass of apples by geometrical attributes. Scientia Horticulturae, 105, 373-382.
  24. Tabatabaeefar, A. (2002). Size and shape of potato tubers. International Agrophysics, 16(4), 301-305.
  25. Taghadomi-Saberi, S., Omid, M., Emam-Djomeh, Z. & Faraji-Mahyari, KH. (2015). Determination of cherry color parameters during ripening by artificial neural network assisted image processing technique. Journal of Agricultural Science and Technology, 17, 589-600.
  26. Tarkosova, J. & Copikova, J. (2000). Determination of carbohydrate content in bananas during ripening and storage by near infrared spectroscopy. Journal of Near Infrared Spectroscopy, 8, 21–26.
  27. Ying, Y. B., Liu, Y. D., Wang, J. P., Fu, X. P. & Li, Y. B. (2005). Fourier transforms near-infrared determination of total soluble solids and available acid in intact peaches. American Society of Agricultural Engineers, 48, 229-234.
  28. Zhang, L. & McCarthy, M. J. (2013). Assessment of pomegranate postharvest quality using nuclear magnetic resonance. Postharvest Biology and Technology, 77, 59-66.
  29. Zhao, X., Yuan, Z., Yin, Y. & Feng, L. (2015). Patterns of pigment changes in pomegranate (punica granatum L.) peel during fruit ripening. Acta Horticulturae, 1089, 83-89.