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Castor seed quality model with some geometric properties

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https://www.eduzhai.net/ International Journal of A griculture and Forestry 2012, 2(5): 235-238 DOI: 10.5923/j.ijaf.20120205.06 Mass Modeling of Castor Seed (Ricinus Communis) with Some Geometrical Attributes Ali Nejat Lorestani*, Akbar Kazemi M echanical Engineering of A gricultural M achinery Department, Razi University, Kermanshah, 6715685438, Iran Abstract Hort icultural crops with the similar weight and uniform shape are in high demand in terms of marketing value. Therefore, an awareness of grading fruits and vegetables based on weight is crucial. A part of this research was aimed to present some physical properties of Castor seed. In addition, in this study the mass of Castor seed variety was pred icted with using different physical characteristics in four models includes: Linear, Quadratic, S-curve, and Power. According to the results, all properties considered in the current study were found to be statistically significant at the 1% probability level and the best and the worst models for prediction the mass of Castor seed were based on third projected area and first projected area of the Castor seed with determination coeffic ients of 0.82 and 0.757, respectively. At last, mass model based on third projected area fro m economical standpoint is recommended. Keywords Mass, modeling, Castor seed, physical characteristics 1. Introduction Ricinus communis seed, commonly known as “higuerilla”, “ricine” or “mamona”, is a member of the Euphorbiaceae family and it is native fro m tropical climates although it has been adapted to a wide range of sub-tropical and temperate climates. The R. communis plant has been cultivated since antiqu ity not only as a garden o rnament for its striking foliage and interesting flowers but also because their seeds were used as a medicinal p lant. The annual world production is around one million tons and nowadays it is used mainly for the production of non-edible o il, as these seeds are poisonous to humans and animals, because they contain ricin, a protein with cytoto xic act iv ity that inhib its p rotein synthesis at ribosome levels[1]. R. communis plant is considered as an important source of oil because of their seeds which contain about 35– 55% o f o il by weight . The o il, also kno wn as “castor oil”, enjoys a tremendous annual demand worldwide, which is estimated in about 220,000 tons[1]. It contains a high concentration (over 85–90%) o f ricino leic acid (12hydroxy-oleate), wh ich has mu ltiple nonfood applications in the p roduct ion o f d ifferent produ cts such as p aints and v arn ish es, ny lo n -typ e synth et ic po ly mers , fun g icides , medicat ions, cosmet ics, hydraulic flu ids and high quality lubricants. As additive, the ricinoleic acid has been found usefu l to rep lace su lfu r b ased lub ricit y co mponents in petroleum diesel, helping to reduce sulfur emissions, among * Corresponding author: ali.lorestani@gmail.com (Ali Nejat Lorestani) Published online at https://www.eduzhai.net Copyright © 2012 Scientific & Academic Publishing. All Rights Reserved others Applications[1]. To design and optimization a machine for handling, cleaning, conveying and storing, the physical attributes and their relat ionships must be known. As an instance, grading of fruits by their size can be replaced with grading by their weight because it may be more econo mical. Grading fru it based on weight is important in packing and handling. In nearly all cases raw product grades are based on weight[2]. Size and shape determine how many fru it can be placed in containers of a given size. Volu me and surface area could be beneficial in proper pred iction drying rates and hence drying time in the dryer. On the other hand, volume and its relationship with packing coefficient are very important because having any information about Packing coefficient of fruits could result in efficient control of fru it quality during storage. Physical characteristics of agricultural products are the most important parameters to determine the proper standards of design of grading, conveying, processing and packaging systems[3]. Among these physical characteristics, mass, volume, projected area are the most important ones in determining sizing systems[4, 5]. Many researches have been conducted to find physical properties of various types of agricultural products. Mass grading of fruit can reduce packaging and transportation costs, and also may provide an optimu m packaging configuration[6]. Determining relat ionships between mass and dimensions and projected areas may be useful and applicable[7, 8]. Tabatabaeefar predicted apple mass by models that were based upon apple physical properties[3]. A l-Maiman studied the physical properties of pomegranate and found models of predicting fruit mass while emp loying dimensions, volume and surface areas[9]. 236 Ali Nejat Lorestani et al.: M ass M odeling of Castor Seed (Ricinus Communis) with Some Geometrical Attributes Keramat investigated some physical properties of date (cv. Lasht). They determined dimensions and projected areas by using image processing technique[10]. Lorestani concluded that the linear regression models of kiwi fru it have higher R2 than nonlinear models for them, and are economical models for application. A mong the linear regression dimensions models, the model that is based on width and among the linear p rojected area models, the model that is based on third projected area, and a mong the other models, the model that is based on measured volume, had h igher R2, that are recommended for sizing of kiwi fru it[11]. A lso Tabatabaeefar determined a total of 11 regression models in the three different categories for two different varieties of apple fruits[3]. Lorestani concluded that the best model for prediction the mass of Fava bean among the d imensional models was Linear as: M = −1.607 + 0.264 W ; R2 = 0.733 and the best model for pred iction the mass of Fava bean was based on third projected area which perpendicular to L direct ion of Fava bean and it was Power form as M = 0.006 PA31.071, R2 = 0.819, and the worst was based on first projected area of Fava bean and it was Linear form as M = 1.686 + 0.006 PA1, R2 = 0.152,[12]. No detailed studies concerning mass modeling of Castor seed (Ricinus Communis) have yet been performed. The aims of this study were to determine the most suitable model for predicting Castor seed mass by its physical attributes and study some physical properties of Iranian Castor seed to form an important database for other investigators. 2. Materials and Methods The common Castor seed was obtained fro m farms located in the west of Iran in June2011. 100 Castor seed samples collected fro m cultivations growing in Iranian farms were used for measurement in the Biophysical laboratory and Biological laboratory of Razi Un iversity of Kermanshah, Iran. The samples were weighted and dried in an oven at 105℃ for 24 h[13] and then weight loss on drying to final content weight was recorded as mo isture content. The remain ing material was kept in the desiccators until use. Castor seed mass (M) was determined with an electronic balance with 0.01 g sensitivity. To determine the average size of the samples, three linear d imensions namely as length, width and thickness were measured by using a digital caliper with 0.01 mm sensitivity. Vo lu me (V) was determined by the water d isplacement method[14]. The geo metric mean diameter (Dg) and surface areas (S) were determined by using following formu las[14], respectively: ???????????????? = (????????????????????????)1�3 (1) 2 ???????? = ????????�???????????????? � (2) Where: L is length of Castor seed (mm), W is width of Castor seed (mm); T is thickness of Castor seed (mm), S is surface area (mm2) and Dg is geometric mean d iameter (mm). Then, projected areas (PA1, PA2 and PA3) in three perpendicular d irections of the Castor seed were measured by a ΔT area-meter, MK2 model device with 0.1 cm2 accuracy and criteria projected area (CPA) is defined as follow[14]: ???????????????????????? = (????????????????1 + ????????????????2 + ????????????????3)⁄3 (3) Where: PA1, PA2 and PA3 are first, second and third projected area (mm2), respectively. In order to estimate mass models of Castor seeds, the following models were co ns id ered : 1. Single variable regression of Castor seed mass based on Castor seed dimensional characteristics: length (L), width (W), thickness (T), and geometric mean d iameter (Dg). 2. Single variable regressions of Castor seed mass based on Castor seed projected areas and criteria pro jected area. 3. Single variable regression of Castor seed mass based on measured volu me. 4. Single variable regression of Castor seed mass based on surface area. In all cases, the results which were obtained from experiments were fitted to Linear, Quadratic, S-curve, and Power models which are presented as following equations, res p ectiv ely : ???????? = ????????0 + ????????1???????? (4) ???????? = ????????0 + ????????1???????? + ????????2????????2 (5) ????????????????(????????) = ????????0 + ????????1⁄???????? (6) ???????? = ????????0????????????????1 (7) Where M is mass (g), X is the value o f a parameter(independent parameter) that we want to find its relationship with mass, and b0, b1, and b2 are curve fitting parameters wh ich are d ifferent in each equation. One evaluation of the goodness of fit is the value of the coefficient of determination. For regression equations in general, the nearer R2 is to 1.00, the better the fit[7]. SPSS 15, software was used to analyse data and determine regression models a mong the physical attributes. 3. Results and Discussion A summary of the physical properties of Castor seed is shown in Table 1. These properties were found at specific mo isture contents about 85.82% wet basis. Table 1. Some physical propert ies of Cast or seed Physical Properties L (mm) W (mm) T (mm) M (g) V (ml) Dg (mm) S (mm2) PA1 (mm2) PA2 (mm2) PA3 (mm2) CPA (mm2) Max 15.01 14.26 14.89 1.54 0.25 14.31 643.94 159.2 163.5 155.80 145.90 Castor seed* Min 10.06 11.39 10.36 0.89 0.12 11.34 404.07 123.7 102.6 127.00 117.80 Average 13.52 13.39 13.38 1.29 0.17 13.42 566.62 143.80 117.50 143.10 134.8 * Significant level: P <0.01 International Journal of A griculture and Forestry 2012, 2(5): 235-238 237 Table 2. The best models for predict ion the mass of Cast or seed with some physical Characterist ics Dependent Parameter M(g) M(g) M(g) M(g) M(g) M(g) M(g) M(g) M(g) M(g) Independent Parameters L(mm) W(mm) T (mm) V(ml) Dg(mm) S(mm2) P A1 (mm2 ) P A2 (mm2 ) P A3 (mm2 ) CPA (mm2) The best model Quadrat ic Quadrat ic S-curve Linear Quadrat ic Quadrat ic Linear Quadrat ic Quadrat ic Quadrat ic Const ant Values of model b0 b1 b2 5.648 -0.805 0.036 15.844 -2.290 0.090 1.481 -16.457 - 1.367 -0.466 - 3.171 1.000 -0.470 -0.001 0.025 3.1*10-6 -0.262 0.011 - -5.165 0.096 0.000 -7.899 0.116 0.000 -11.685 0.179 -0.001 R2 0.771 0.547 0.701 0.409 0.741 0.742 0.757 0.820 0.780 0.870 As seen in Table 1, all properties wh ich were considered in the current study were found to be statistically significant at 1% probability level. According to the results, the mean values of properties which were studied in this research (length, width, thickness, geometric mean d iameter, Vo lu me, surface area, mass and projected areas) are 13.52 mm, 13.39 mm, 13.38 mm, 13.42 mm, 0.17 cm3, 566.62 mm2, 1.29 g, 143.8 mm2 , 117.5 mm2 and 143.1 mm2, respectively. Mass models and coefficient of determination (R2) that obtained fro m the data for Castor seed are shown in table 2. All of the models coefficients were analysed with F-test and t-test in SPSS 15 Soft ware, where, all o f them were significant at α= 1%. Nonlinear models were used only for comparison with linear regression models. Lorestani reported that the linear regression models have higher R2 than the other models, and are economical models for application. A mong the linear regression dimensions models, the model that is based on the smallest diameter (T), and among the linear projected area models, the model that is based on projected area normal to the smallest diameter; (PA3), and among the other linear regression models, the model that is based on measured volume (V), had higher R2 that are recommended for sizing of Oak[15]. For mass modeling based on dimensional characteristic including length, width and thickness, the best model was Quadratic with R2 = 0.771: ???????? = 5.648 − 0.805???????? + 0.771 ????????2, ????????2 = 0.771 Whereas this model can pred ict the relationships between the mass with thickness and width with R2 of 0.701 and 0.547, respectively. Tabatabaeefar reported that among systems that sort oranges based on one dimension, the system that applies intermediate diameter is suited with nonlinear relatio n s h ip [1 6] . For prediction of the mass of Castor seed based on volume the best model was Linear with R2 = 0.409. ???????? = 1.307 − 0.466 ???????? ????????2 = 0.409 According to the results, for pred iction of the mass of the Castor seed based on geometric mean diameter, Quadratic model was the best models with R2 = 0.741. ???????? = 3.171 − 0.470 ???????????????? + 0.025 ????????????????2 , ????????2 = 0.741 This model is not economical because for calculating the geometric mean diameter(Dg) we must measure three dimensions of Castor seeds and it is time consuming and costly. For mass modeling of Castor seed based on projected areas including PA1, PA2 , PA3 and CPA, the best model was Quadratic with R2 = 0.870. ???????? = −11.685 + 0.179 ???????????????????????? − 0.001 ????????????????????????2, ????????2 = 0.870 For predict ion of the mass of the Castor seed based on surface area the best model was Quadratic with R2 = 0.742. ???????? = 1 − 0.001 ???????? + 3.1 ∗ 10−6????????2, ????????2 = 0.742 According to the results the Quadratic model could pred ict the relationships among the mass and some physical properties of Castor seed with proper value fo r determination coefficient. So we suggest the Quadratic model based on projected area for prediction the mass of Castor seed because we need one camera and it is applicable and economical method. 4. Conclusions Some physical properties and their relat ionships of mass of Castor seed are presented in this study. Fro m this study it can be concluded that: 1. A ll properties considered in the current study were found to be statistically significant at the 1% probability level. 2. The best model for predict ion the mass of Castor seed among the dimensional models was Quadratic as: ???????? = 5.648 − 0.805 ???????? + 0.036 ????????2 , ????????2 = 0.771 3. The best model for predict ion the mass of Castor seed was based on second projected area which perpendicular to W direction of Castor seed and it was Quadratic form as ???????? = −5.165 + 0.096 ????????????????2 + 0.000 ????????????????22 , ????????2 = 0.82 , and the worst was based on first projected area o f Castor seed and it was Linear form as ???????? = −0.262 + 0.011 ????????????????1, ????????2 = 0.757. 4. At last, mass model based on second projected area fro m economical standpoint is recommended. This information can be used in the design and development of sizing mechanis ms and other post harvest processing machines. At the end, it is recommended that 238 Ali Nejat Lorestani et al.: M ass M odeling of Castor Seed (Ricinus Communis) with Some Geometrical Attributes other properties of Castor seed such as thermal, mechanical, and nutritional characteristics are to be studied and changes of these properties are to be examined as a function of mo isture content and ripening phases. Nome nclat ure M= fruit mass, g; V= fruit Vo lu me, cm3; Dg = geomet ric mean diameter, mm; S= surface area, mm2; L= length of fruits, mm; W= width of fru it, mm; T= thickness of fruit, mm; PA1 = first projected area, mm2; PA2 = second projected area, mm2; PA3 = third pro jected area, mm2; CPA= criteria projected area, mm2; b0,b1,b2 = curve fitting parameters; X= independent parameter. ACKNOWLEDGEMENTS The authors would like to express their gratitude and their sincere appreciat ion to the Deputy of Agronomy Depart ments for their cooperation and laboratory support at the Razi University of Kermanshah. [6] Peleg, K. (1985). Produce Handling, Packaging and Distribution. The AVI Publishing Company. Inc. Westport, Connecticut, pp. 20-90. [7] Stroshine, R. (1998). Physical Properties of Agricultural M aterials and Food Products. Course manual. Purdue Univ. USA. [8] M arvin, J.P., Hyde, G.M ., & Cavalieri, R.P. (1987). M odeling potato tuber mass with tuber dimensions. Transactions of the ASAE 30, 1154-1159. [9] Al-M aiman, S., & Ahmad, D. (2002). Changes in physical and chemical properties during pomegranate (Punica granatum L.) fruit maturation. Journal of Food Chemistry. 76(4), 437-441. [10] Cárcel, L.M ., Bon, J., Acuña, L., Nevares, I., del Álamo, M ., & Crespo, R. (2012). M oisture dependence on mechanical properties of pine nuts from Pinus pinea L. Journal of Food Engineering.110, 294-297. [11] Lorestani, A.N., Tabatabaeefar, A. (2006). M odeling the mass of kiwi fruit by geometrical attributes. International Agrophysics. 20(2), 135-139. 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M ass M odeling of Fava bean (vicia faba L.) with Some Physical Characteristics. Scientia Horticulturae. 133(6), 6-9. [13] Suthar, S.H., & Das, S.K. (1996). Some physical properties of karingda[Citrus lanatus (thumb) mansf] grains. Journal of Agricultural Engineering Research, 65(1), 15–22. [14] M ohsenin, N.N. (1986). Physical properties of Plant and Animal M aterials. Second revised. Gordon and Breach Sci. Publ., New York. [15] Lorestani, A.N., Azami, H., & Amirian, Y. (2011). Some Physical Properties of Oak (Quercus Persica) and M odeling the M ass of Oak by Physical Attributes. Proceedings of the 6th CIGR Section VI International Symposium “Towards a Sustainable Food Chain” Food Process, Bioprocessing and Food Quality M anagement, Nantes, France - April 18-20, 2011. [16] Tabatabaeefar, A., Vefagh-Nematolahee, A., & Rajabipour, A. (2000). M odeling of orange mass based on dimensions. Journal of Agricultural Science and Technology, 2(4), 299-305.

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