برآورد حجم حفره بذر میوه خربزه با استفاده از روش مقطع‌نگاری رایانه‌ای پرتو ایکس

علوم باغبانی

مقاله 1، دوره 34، شماره 4 - شماره پیاپی 48، اسفند 1399، صفحه 535-545   

نوع مقاله: مقالات پژوهشی

شناسه دیجیتال (DOI): 10.22067/jhorts4.v34i4.78294

نویسندگان

مهدی آرین¹؛ مهدی خجسته پور^{* 2}؛ محمودرضا گلزاریان³

¹دانشجوی دکتری گروه مهندسی بیوسیستم، دانشگاه فردوسی مشهد

²استاد گروه مهندسی بیوسیستم، دانشگاه فردوسی مشهد

³دانشیار گروه مهندسی بیوسیستم، دانشگاه فردوسی مشهد

چکیده

آگاهی نسبت به روند تغییرات حجم حفره بذر میوه خربزه می‌تواند کمک شایانی به برنامه‌ریزی مناسب جهت انجام اموری چون کود‌دهی، آبیاری و غیره در طی فصل رشد میوه نماید. به منظور برآورد حجم حفره بذر میوه خربزه، Cucumis melo L.، دو توده محلی ارزشمند قصری و خاتونی انتخاب شد و تغییرات حفره بذر آن­ها در طی فصل رشد، مورد مطالعه قرار گرفت. از روش مقطع­نگاری رایانه‌ای پرتو ایکس به عنوان ابزاری دقیق و غیر مخرب در تعیین اندازه حجم حفره بذر در هر مرحله از آزمایش استفاده شد. اندازه‌گیری حجم حفره بذر در کنار عوامل درصد مواد جامد محلول، وزن مخصوص، درصد رطوبت گوشت میوه و همچنین مقدار pH در طی سه بازه زمانی 15 روز و 30 روز پس از گلدهی و مرحله رسیدگی کامل میوه انجام شد. نتایج پژوهش همبستگی بین عامل درصد حجم حفره بذر توده قصری با سایر عوامل را نشان داد. از طرفی کلیه عوامل یاد شده تغییرات معنی‌داری در طی سه بازه زمانی برداشت داشتند، ضمن اینکه این تغییرات تحت تاثیر نوع توده نیز قرار ­گرفتند، اما رابطه معنی‌داری بین برهم­کنش دو عامل توده و مرحله برداشت مشاهده نشد.

کلیدواژه‌ها

سی‌تی اسکن؛ غیر مخرب؛ گوشت میوه

مراجع

1- Altuntas E., and Bayram M. 2013. The physical, chemical and mechanical properties of medlar (Mespilus germanica L.) during physiological maturity and ripening period 2. Gaziosmanpaşa Üniversitesi Ziraat Fakültesi Dergisi 30: 33-40.

2- AOAC. 1984. Official Methods of Analysis. Association of Official Analytical Chemists Press. Washington. DC.

3- Barcelon, E.G., Tojo, S., and Watanabe, K. 1999. X-ray CT imaging and quality detection of peach at physiological maturity. Transactions of the ASAE, 42, p. 435-441.

4- Beaulieu J.C., and Lea J.M. 2007. Quality changes in cantaloupe during growth, maturation, and in stored fresh-cut cubes prepared from fruit harvested at various maturities. Journal of American Society for Horticultural Science 132(5): 720-728.

5- Bianchi T., Guerrero L., Gratacós-Cubarsí M., Claret A., Argyris J., Gracia-Mas J., and Hortós M. 2016. Textural properties of different melon (Cucumis melo L.) fruit types: Sensory and physical-chemical evaluation. Scientia Horticulture 201: 46-56.

6- Cubero S., Aleixos N., Moltó E., Gómez-Sanchis J., and Blasco J. 2010. Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food Bioprocess Technology 4: 487–504.

7- Diels E., van Dael M., Keresztes J., Vanmaercke S., Verboven P., Nicolai B., Saeys W., Ramon H., and Smeets B. 2017. Assessment of bruise volumes in apples using X-ray computed tomography. Postharvest Biology and Technology 128. 24-32.

8- Donis-Gonzaleza I.R., Guyera D.E., Peaseb A., and Fulbright D.W. 2012. Relation of computerized tomography Hounsfield unit measurements and internal components of fresh chestnuts (Castanea spp.). Postharvest Biology and Technology 64: 74-82.

9- Donis-Gonzaleza I.R., Guyera D.E., Peaseb A., and Barthel F. 2014. Internal characterization of fresh agricultural products using traditional and ultrafast electron beam X-ray computed tomography imaging. Biosystem Engineering 117: 104-113.

10- Els H., Verboven P., Bongaers E., Estrade P., Bert E., Verlinden M., and Bart M.N. 2013. Characterization of ‘Braeburn’ browning disorder by means of X-ray micro-CT. Postharvest Biology and Technology 75: 114–124.

11- FAOSTAT. 2016. FAO Statistical Databases. http://www.fao.org /faostat /en /#data/QC.

12- Franco G., Cartagena V., Correa L., and Lobo A. 2013. Physical characterization of Gulupa fruits (Passiflora edulis Sims) during ripening and postharvest. Agronomía 21: 48-62.

13- Genard M., and Souty M. 1996. Modeling the peach sugar contents in relation to fruit growth. Journal of the American Society for Horticultural Science 121: 1122–1131.

14- Ghanbarian D., Shojaei Z.A., Ebrahimi A., and Yuneji S. 2007. Physical Properties and Compositional Changes of two Cultivars of Cantaloupe Fruit during Various Maturity Stages. Iran Agricultural Research 25(2) and 26(1-2).

15- Haff R.P., Jackson E.S., Moscetti R., and Massantini R. 2015. Detection of Fruit-fly Infestation in Olives using X-Ray Imaging: Algorithm Development and Prospects. American Journal of Agricultural Science and Technology 4(1): 33-40.

16- Karunakaran C., Jayas D.S., and White N.D.G. 2004. Mass determination of wheat kernels from X-ray images. ASAE annual international meeting, Paper number: 043120.

17- Kotwaliwale N., Weckler P.R., Brusewitz G.H., Kranzler G.A., and Maness N.O. 2007. Non-destructive quality determination of pecans using soft X-rays. Postharvest Biology and Technology 45: 372-380.

18- Koubala B.B., Bassang G., Yapo B.M., and Raihanatou R. 2016. Morphological and Biochemical Changes during Muskmelon (Cucumis melo var. Tibish) Fruit Maturation. Journal of Food and Nutrition Sciences 4(1): 18-28.

19- Kumar P.A., and Bal S. 2007. Automatic unhulled rice grain crack detection by X-ray imaging. Transactions of the ASABE 50(5): 1907-1911.

20- Liu L., Kakihara F., and Kato M. 2004. Characterization of six varieties of Cucumis melo L. based on morphological and physiological characters, including shelf-life of fruit. Euphytica 135: 305–313.

21- Long R.L. 2005. Improving Fruit Soluble Solids Content in Melon (Cucumis melo L.) in the Australian Production System. Queensland University, 25- Rock Hampton, Australia.

22- Lorente D., Aleixos N., Gómez-Sanchis J., Cubero S., García-Navarrete O.L., and Blasco J. 2011. Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food Bioprocess Technology 5: 1121–1142.

23-Mairhofer S., Pridmore T., Johnson S., Johnson J., Wells D.M., Bennett M.J., Mooney S.J., and Sturrock C.J. 2017. X-Ray Computed Tomography of Crop Plant Root Systems Grown in Soil. Current Protocols in Plant Biology 2: 270–286.

24- Milczarek R.R., Saltveit M.E., Garvey T.C., and McCarthy M.J. 2009. Assessment of tomato pericarp mechanical damage using multivariate analysis of magnetic resonance images. Postharvest Biology Technology 52: 189–195.

25- Mirzaee E., Rafiee S., and Keyhani A. 2010. Evaluation and selection of thin-layer models for drying kinetics of apricot (cv. NASIRY). CIGR Journal 12(2): 111-116.

26- Nascimento L.M., Garcia L.G.C., Ogata T., Brandao D.C., Silva-Neto C.M., and Seleguini A. 2017. Physical and chemical characteristics and productivity of persimmons (Diospyros kaki L.) cultivated in the Brazilian savannah. Australian Journal of Crop Science AJCS 11(02): 234-240.

27- Orina I., Manley M., Kucheryavskiy S., and Williams P.J. 2018. Application of Image Texture Analysis for Evaluation of X-Ray Images of Fungal-Infected Maize Kernels. Food Analytical Methods 11(10): 2799–2815.

28- Parveen S., Ali MA,. Asghar M., Khan AR., and Salam A. 2012. Physico-chemical changes in muskmelon (Cucumis melo L.) as affected by harvest maturity stage. Journal of Agricultural Research 50: 249-260.

29- Renu R., and Chidanand D.V. 2013. Internal Quality Classification of agricultural produce using Non-destructive Image Processing Technologies. International Journal of Latest Trends in Engineering and Technology 2(4): 535-543.

30- Robertson R.W., and Decker-Walters D.S. 1999. Cucurbits. CAB International, NewYork.

31-Tanakaa F., Nashirob K., Obatakeb W., Tanakaa F., and Uchino F. 2018. Observation and analysis of internal structure of cucumber fruit during storage using X-ray computed tomography. Engineering in Agriculture, Environment and Food 11(2): 51-56

32- Thomas P., Kannan A., Degwekar V.H., and Ramamurthy M.S. 1995. Non-destructive detection of seed weevil infested mango fruits by X-ray imaging. Postharvest Biology and Technology 5: 161-165.

33- Van Dael M., Verboven P., Zanella A., Sijbers J., and Nicolai B. 2018. Combination of shape and X-ray inspection for apple internal quality control: in silico analysis of the methodology based on X-ray computed tomography. Postharvest Biology and Technology.

34- Villanueva M., Tenorio M., Esteban M., and Mendoza M. 2004. Compositional changes during ripening of two cultivars of Muskmelon fruits. Food Chemistry 87: 179-185.