with the collaboration of Iranian Scientific Association for Landscape (ISAL)

Document Type : Research Article

Authors

1 Ph.D. Student, , Department of Biosystems Engineering, Ferdowsi University of Mashhad

2 Professor Department of Biosystems Engineering, Ferdowsi University of Mashhad

3 Associate Professor, Department of Biosystems Engineering, Ferdowsi University of Mashhad

Abstract

Introduction: Cucumis melo L. is a plant from the Cucurbitaceae family. Many researchers consider the early habitat of this plant to be African, which has gradually become domesticated in Iran and Egypt. Melon fruit has undergone many physical and chemical changes during the growing season from the flowering to the maturity. Since these changes have a direct impact on the fruitfulness of fruit juices, the knowledge of process of these changes can be helpful in farm management include fertilizers, irrigation, and so on. The use of X-ray radiation in study of fruits quality and other agricultural products has always been of interest to researchers. The purpose of this study is to provide a method that is able to inspect melon fruits in non-destructive and accurate.
Materials and Methods: In this research, two local populations of melon, ‘Ghasri’ and ‘Khatooni’, were used. In summer of 2017, the specimens were collected from two adjacent farms from Abrvan village with a geographical position of 59° 58′ 40″ and 36° 4′ 34″ and an approximate height of 930 m above sea level. With the advent of flowers, a number of them were plated and harvested in 15, 30 and 45 days after flowering. In the ‘Khatooni’ population, as the fruit reaches its full maturity earlier than the ‘Ghasri’, the harvesting time was considered 40 days after flowering. In this research, the Optima-CT540, CT-Scan made by GE Healthcare was used. This device can display up to 16 cuts per round. All selected melons are in the direction of longitudinal diameter, which is the largest diameter of the fruit and entered into the device on the head. The images were loaded in the Microview medical software (v. 21), and the volume of seed cavity and the total volume of melon fruit were calculated. In this research, the quantitative change factor was a percentage of cavity volume percentage (Cavity) along with factors such as moisture content percentage, soluble solids, volumetric weight, and pH, with simultaneously changing two qualitative variables of "Melon variety " (with two levels of ‘Ghasri’ and ‘Khatooni’). The nominal variable "harvesting stage" (with three levels) was performed using multivariate analysis of variance (MANOVA) and combined with Duncan's multiple range test using SPSS (v. 24) software. Normality of data was also evaluated by the SPSS software. Data was analyzed at 1% of probability level.
Results and Discussion: The results of this study showed that as the fruit grows, the volume of seed cavity increases, but the rate of this increase is lower than the increase in total fruit volume. This causes the percentage of seed cavity to decline during the growing season. Meanwhile, there is a decrease in the amount of ‘Ghasri’, which has a larger lateral mass compared to the ‘Khatooni’. By decreasing the volume of seed cavity during the growth period, the percentage of moisture content and specific gravity of fruit is reduced. The moisture content decreased from 94.04 to 90.61% in ‘Ghasri’ and from 93.52 to 89.12% in ‘Khatooni’, which showed a significant difference (p ≤ 0.01) at each stage. The percentage of soluble solids increased from a minimum of 4.97% in the type of ‘Khatooni’ to a maximum of 12.74% in the ‘Ghasri’ type during the growing season, and the pH value in this experiment was from a minimum of 4.53 in the type of ‘Khatooni’ to the maximum value of 5.98 has changed in ‘Ghasri’.
Conclusion: The results of this study showed correlation between the dependent variables of moisture content, the percentage of soluble solids, volumetric weight and pH with independent variable of seed cavity volume in ‘Ghasri’ population. In addition, the apparent impact of two harvesting stages and the variety on other factors was evident. In the meantime, the use of X-Ray Computed Tomography Techniques as a non-destructive method to observe the internal variation of the fruit has helped to study the intrinsic properties of melon fruit.

Keywords

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.
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