Prediction of Tangerine Mass Based on Geometrical Properties Using Linear Regression Models

Similar documents
Modeling of Apricot Mass Based on Geometrical Properties

Modeling of Peach Mass Based on Geometrical Attributes Using Linear Regression Models

Modeling of Nectarine Mass Based on Geometrical Attributes

INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCES Volume 1, No 4, Copyright 2010 All rights reserved Integrated Publishing services

Modelling the mass of kiwi fruit by geometrical attributes

Mass and volume modelling of tangerine (Citrus reticulate) fruit with some physical attributes

Modeling the Main Physical Properties of Banana Fruit Based on Geometrical Attributes

Some physical and mechanical properties of caper

Prediction of Apple Mass Based on Some Geometrical Properties Using Linear Regression Models

Physical and mechanical properties of Oak (Quercus Persica) fruits

Size and shape of potato tubers

Mass modelling by dimension attributes for Mango (Mangifera indica cv. Zebdia) relevant to post-harvest and food plants engineering

Mass and Surface Area Modeling of Bergamot (Citrus medica) Fruit with Some Physical Attributes

MODELING OF SOIL CATION EXCHANGE CAPACITY BASED ON SOME SOIL PHYSICAL AND CHEMICAL PROPERTIES

Some Physical and Chemical Properties of four Apple Varieties of North-West Iran

NON-DESTRUCTIVE METHOD FOR INTERNAL QUALITY DETERMINATION OF BELGIAN ENDIVE (CICHORIUM INTYBUS L.) V. Quenon, J. De Baerdemaeker

Journal of American Science, 2011; 7(6)

Modeling physical properties of lemon fruits for separation and classification

Airflow Resistance in Walnuts

NIR Spectroscopy Identification of Persimmon Varieties Based on PCA-SVM

Research Article Theoretical Research on Performance of the Corn Length Grading System

Approximate Solution of Convection- Diffusion Equation by the Homotopy Perturbation Method

Some physical characteristics of pomegranate, seeds and arils

Physical and mechanical properties of wheat straw as influenced by moisture content

Effects of Temperature and Loading Characteristics on Mechanical and Stress Relaxation Properties of Sea Buckthorn Berries. Part 2. Puncture Tests.

FARSI CHARACTER RECOGNITION USING NEW HYBRID FEATURE EXTRACTION METHODS

QUALITY AND DRYING BEHAVIOUR OF ORGANIC FRUIT PRODUCTS

Physical properties of apricot to characterize best post harvesting options

Mass modeling of two varieties of apricot (prunus armenaica L.) with some physical characteristics

INVESTIGATING YIELD AND YIELD COMPONENT OF WINTER RAPESEED CULTIVARS AT BOJNORD-IRAN

Plum-tasting using near infra-red (NIR) technology**

Rheological behavior of sesame paste/honey blend with different concentration of honey

Short Term Load Forecasting Using Multi Layer Perceptron

MHealthy Approved Menu Items

*** (RASP1) ( - ***

Statistical Modeling for Citrus Yield in Pakistan

Press Release Consumer Price Index October 2017

Desertification Hazard Zonation by Means of ICD Method in Kouhdasht Watershed

Design for limit stresses of orange fruits (Citrus sinensis) under axial and radial compression as related to transportation and storage design

ELEMENTARY SCHOOL SCIENCE CURRICULUM KINDERGARTEN

The Prospect of Electrical Impedance Spectroscopy as Nondestructive Evaluation of Citrus Fruits Acidity

An Introduction to Bayesian Networks. Alberto Tonda, PhD Researcher at Team MALICES

Effects of Cold Chain Break on Quality of Summer Squash and Lettuce UNIVERSITY OF FLORIDA HORTICULTURAL SCIENCES TIAN GONG

Using radial basis elements for classification of food products by spectrophotometric data

Press Release Consumer Price Index December 2014

The Analysis of Uncertainty of Climate Change by Means of SDSM Model Case Study: Kermanshah

Research and Reviews: Journal of Botanical Sciences

EXPERIMENTAL CHECK OF THE PROCESS OF CRUSHING SEED FRUIT OF MELON CULTURES

Vibration Signals Analysis and Condition Monitoring of Centrifugal Pump

Press Release Consumer Price Index March 2018

Press Release Consumer Price Index April 2018

Respiration. Internal & Environmental Factors. Respiration is tied to many metabolic process within a cell

Introduction. Molecules, Light and Natural Dyes. Experiment

SMTX1014- PROBABILITY AND STATISTICS UNIT V ANALYSIS OF VARIANCE

COMPARISON OF PIXEL-BASED AND OBJECT-BASED CLASSIFICATION METHODS FOR SEPARATION OF CROP PATTERNS

Department of Agriculture, Zahedan Branch, Islamic Azad University, Zahedan, Iran. Corresponding author: Hamidreza Mobasser

Modeling the Mechanical Behavior of Fruits: A Review

Nondestructive Determination of Tomato Fruit Quality Characteristics Using Vis/NIR Spectroscopy Technique

Investigation of Compressional Creep Behaviour of Persian Hand-woven Carpet During Simulated Storage under Different Environmental Conditions

Comparison of Four Nondestructive Sensors for Firmness Assessment of Apples

Moisture Sorption Isotherms of Rosemary (Rosmarinus officinalis L.) Flowers at Three Temperatures

All About Plants. What are plants?

FOR 373: Forest Sampling Methods. Simple Random Sampling: What is it. Simple Random Sampling: What is it

Study of Genetic Diversity in Some Newly Developed Rice Genotypes

Department of Computer Science, University of Waikato, Hamilton, New Zealand. 2

CONSUMER ACCEPTANCE OF GROUND B E E F

AN INTEGRATED METHOD FOR FOREST CANOPY COVER MAPPING USING LANDSAT ETM+ IMAGERY INTRODUCTION

Citrus Degreening. Mary Lu Arpaia University of California, Riverside. What is degreening?

Effect of Allelopathic weeds on Characteristics seed Growth in maize (Zea mays L. cv. KSC 704)

CptS 570 Machine Learning School of EECS Washington State University. CptS Machine Learning 1

Self-Guided Tour. Plants and the Plant Life Cycle. K to 2nd Grade

LadyLibertyBrands.com

Megaman Horticulture Lighting

Building Kinetic Models for Determining Vitamin C Content in Fresh Jujube and Predicting Its Shelf Life Based on Near-Infrared Spectroscopy

APPLICATION OF NEAR INFRARED REFLECTANCE SPECTROSCOPY (NIRS) FOR MACRONUTRIENTS ANALYSIS IN ALFALFA. (Medicago sativa L.) A. Morón and D. Cozzolino.

Base Menu Spreadsheet

Press Release Consumer Price Index December 2018

Citrus Degreening. Ethylene Degreening What do we degreen? What is degreening?

Geographic Information Systems (GIS) and inland fishery management

Prediction of corn and lentil moisture content using dielectric properties

Automatic Classification of Sunspot Groups Using SOHO/MDI Magnetogram and White Light Images

Citrus Degreening. What is degreening? The process of exposing green citrus fruit with low levels of ethylene to enhance coloration.

Acidity of Beverages Lab

Context-based Reasoning in Ambient Intelligence - CoReAmI -

THE APPLICATION OF SIMPLE STATISTICS IN GRAINS RESEARCH

Assessment of Qualitative, Quantitative and Visual Flower Quality Parameters of Certain Commercial Jasmine Varieties during Peak Flowering Season

Deoxynivalenol, sometimes called DON or vomitoxin,

Multiple Linear Regression estimation, testing and checking assumptions

An optimization model for designing acceptance sampling plan based on cumulative count of conforming run length using minimum angle method

Online Evaluation of Yellow Peach Quality by Visible and Near-Infrared Spectroscopy

MODELING ON RHEOLOGICAL PROPERTIES OF TROPICAL FRUIT JUICES

Chapter 5: Matter Properties and Changes M R. P O L A R D P H Y S I C A L S C I E N C E

Investigation of Correlation and Board Sense Heritability in Tritipyrum Lines under Normal and Drought Stress Conditions

Moisture Sorption Isotherm Characteristics of Taro Flour

Estimating soil specific surface area using the summation of the number of spherical particles and geometric mean particle-size diameter

Reviews of related Literatures. Dwarf Bunt

PY.34/PR.104. Pigments and Preparations. Integrated Colour Solutions. Committed to Colour Worldwide. Your Idea. Our Solution. dominioncolour.

Solubility Modeling of Diamines in Supercritical Carbon Dioxide Using Artificial Neural Network

ABASTRACT. Determination of nitrate content and ascorbic acid in intact pineapple by Vis-NIR spectroscopy Student. Thesis

Pennsylvania Core and Academic Standards Science Grade: 3 - Adopted: Biological Sciences. Organisms and Cells

Transcription:

American-Eurasian J. Agric. & Environ. Sci., 14 (1): 17-4, 014 ISSN 1818-6769 IDOSI Publications, 014 DOI: 10.589/idosi.aejaes.014.14.01.175 Prediction of Tangerine Mass Based on Geometrical Properties Using Linear Regression Models Majid Rashidi and Mahmood Fayyazi Department of Agricultural Machinery, Takestan Branch, Islamic Azad University, Takestan, Iran Abstract: In this study, nine linear regression models for predicting tangerine mass from some geometrical properties of tangerine such as length (L), diameter (D), geometrical mean diameter (GMD), first projected area (PA 1), second projected area (PA ), criteria area (CAE) and estimated volume based on an oblate spheroid assumed shape (V Sp) was suggested. Models were divided into three main classes, i.e. first class (outer dimensions), second class (projected areas) and third class (estimated volume). The statistical results of the study indicated that in order to predict tangerine mass based on outer dimensions, the mass model based on GMD as M = - 150.5 +43.94 GMD with R = 0.89 can be recommended. In addition, to predict tangerine mass based on projected areas, the mass model based on CAE as M = - 6.08 + 4.84 CAE with R = 0.90 can be suggested. Moreover, to predict tangerine mass based on estimated volume, the mass model based on V Sp as M = 16.00 + 0.88 V Sp with R = 0.88 can be used. These models can also be utilized to design tangerine sizing machines equipped with an image processing system. Key words: Tangerine Mass Prediction Geometrical properties Linear regression models INTRODUCTION The tangerine (Citrus tangerina) is an orangecolored citrus fruit (Fig. 1) which is closely related to the Mandarin orange (Citrus reticulata). They are smaller than most oranges and are usually much easier to peel and to split into segments. Tangerines have been found in many shapes and sizes, from as small as a small walnut, to larger than an average orange. The number of seeds in each segment varies greatly. The taste is often less sour, or tart, than that of an orange. While less tart, tangerines are also sweeter than oranges. Peak tangerine season lasts from October to April in the Northern Hemisphere. Tangerines are most commonly peeled and eaten out of hand. The fresh fruit is also used in salads, desserts and Fig. 1: Tangerine (Citrus tangerina) main dishes. The peel is dried and used in some foods. Fresh tangerine juice and frozen juice concentrate are suitable packaging []. Similar to other fruits, tangerine commonly available. Moreover, tangerines are a good size is one of the most important quality parameters for source of vitamin C, folate and beta-carotene. They also evaluation by consumer preference. Consumers prefer contain potassium, magnesium and vitamins B 1, B and B3 fruits of equal size and shape [3, 4]. Sorting can increase [1]. uniformity in size and shape, reduce packaging and Iran products 3.5 million tones of citrus and is ranked transportation costs and also may provide an optimum nd in the world. But, Iranian tangerines are not exported packaging configuration [5]. Moreover, sorting is because of variability in size and shape and lack of important in meeting quality standards, increasing market Corresponding Author: Dr. Majid Rashidi, Ph.D., Department of Agricultural Machinery, Takestan Branch, Islamic Azad University, Takestan, Iran. 17

Am-Euras. J. Agric. & Environ. Sci., 14 (1): 17-4, 014 value and marketing operations [6]. Sorting manually is laboratory and held at 5±1 C and 90±5% relative humidity associated with high labor costs in addition to until experimental procedure. In order to obtain required subjectivity, tediousness and inconsistency which lower parameters for determining mass models, the mass of each the quality of sorting [7]. However, replacing human with tangerine was measured to 1.0 g accuracy on a digital a machine may still be questionable where the labor cost balance. By assuming the shape of tangerine as an oblate is comparable with the sorting equipment [8]. Studies on spheroid, the outer dimensions of each tangerine, i.e. sorting in recent years have focused on automated length (L) and diameter (D) was measured to 0.1 cm sorting strategies and eliminating human efforts to accuracy by a digital caliper. The geometric mean provide more efficient and accurate sorting systems which diameter (GMD) of each tangerine was then calculated improve the classification success or speed up the by equation 1. classification process [9, 10]. Physical and geometrical characteristics of products 1/3 GMD = (LD ) (1) are the most important parameters in design of sorting systems. Among these characteristics, mass, outer Two projected areas of each tangerine, i.e. first dimensions, projected areas and volume are the most projected area (PA 1) and second projected area (PA ) was important ones in sizing systems [11]. The size of produce also calculated by using equation and 3, respectively. is frequently represented by its mass because it is The average projected area known as criteria area (CAE) relatively simple to measure. However, sorting based on of each tangerine was then determined from equation 4. some geometrical properties may provide a more efficient method than mass sorting. Moreover, the mass of PA 1 = D /4 () produce can be easily estimated from geometrical properties if the mass model of the produce in known [1- PA = LD/4 (3) 18]. For that reason, modeling of tangerine mass based on some geometrical properties may be useful and applicable. CAE = (PA 1+PA )/3 (4) Therefore, the main objectives of this research were to determine suitable mass model(s) based on some In addition, the estimated volume of each tangerine geometrical properties of tangerine and to verify selected by assuming the shape of tangerine as an oblate spheroid mass model(s) by comparing its (their) results with those (V Sp) was calculated by using equation 5. of the measuring method. V sp = LD /6 (5) MATERIALS AND METHODS Table 1 shows physical and geometrical properties of Experimental Procedure: One of the most common the tangerines used to determine mass models. Also, in commercial cultivars of tangerine in Iran, i.e. Clementine order to verify mass models, physical and geometrical was considered for this study. One hundred randomly properties of twenty randomly selected tangerines of selected tangerines of various sizes were purchased from various sizes were determined as described early. Table a local market. Tangerines were selected for freedom from shows physical and geometrical properties of the defects by careful visual inspection, transferred to the tangerines used to verify mass models. Table 1: The mean values, standard deviation (S.D.) and coefficient of variation (C.V.) of physical and geometrical properties of the 100 randomly selected tangerines used to determine mass models Parameter Minimum Maximum Mean S.D. C.V. (%) Mass (M), g 4.00 193.0 101. 33.00 3.61 Length (L), cm 3.300 8.600 5.059 0.76 15.06 Diameter (D), cm 4.600 7.900 6.104 0.757 1.40 Geometrical mean diameter (GMD), cm 4.118 7.36 5.78 0.706 1.33 First projected area (PA 1), cm 16.6 49.0 9.71 7.37 4.67 Second projected area (PA ), cm 11.9 41.0 4.58 6.183 5.15 Criteria area (CAE), cm 13.49 4.40 6.9 6.44 4.43 3 Estimated volume (V Sp), cm 36.56 05.9 10.8 37.4 36.39 18

Table : Am-Euras. J. Agric. & Environ. Sci., 14 (1): 17-4, 014 The mean values, standard deviation (S.D.) and coefficient of variation (C.V.) of physical and geometrical properties of the twenty randomly selected tangerines used to verify mass models Parameter Minimum Maximum Mean S.D. C.V. (%) Mass (M), g 39.00 194.0 103.5 39.78 38.44 Length (L), cm 3.700 6.400 5.030 0.671 13.33 Diameter (D), cm 4.300 8.00 6.5 0.958 15.39 Geometrical mean diameter (GMD), cm 4.090 7.389 5.796 0.837 14.44 First projected area (PA 1), cm 14.5 5.81 31.1 9.413 30.5 Second projected area (PA ), cm 1.50 38.64 5.03 6.889 7.5 Criteria area (CAE), cm 13.17 43.36 7.06 7.690 8.4 3 Estimated volume (V Sp), cm 35.8 11. 108.0 45.09 41.75 Table 3: Nine linear regression mass models and their relations in three classes Class Model No. Model Relation Outer dimensions 1 M = k 0 + k 1 L M = -68.3 + 33.49 L M = k 0 + k 1 D M = -147.9 + 40.81 D 3 M = k 0 + k 1 GMD M = -150.5 + 43.94 GMD 4 M = k 0 + k 1 L + k D M = -15.8 + 8.384 L + 34.66 D Projected areas 5 M = k 0 + k 1 PA1 M = -4.06 + 4.16 PA1 6 M = k 0 + k 1 PA M = -18.71 + 4.878 PA 7 M = k 0 + k 1 CAE M = -6.08 + 4.84 CAE 8 M = k 0 + k 1 PA 1 + k PA M = -7.5 +.733 PA 1 + 1.933 PA Estimated volume 9 M = k 0 + k 1 VSp M = 16.00 + 0.88 VSp Regression Models: A typical multiple-variable linear RESULTS regression model is shown in equation 6: Y = k 0+ k1x 1+ kx + + knx n (6) The p-value of the independent variable(s) and coefficient of determination (R ) of all the linear regression mass models are shown in Table 4. Where: Y = Dependent variable, for example mass of tangerine First Class Models (Outer Dimensions): In this class X 1, X,, X n = Independent variables, for example tangerine mass can be predicted using single variable geometrical properties of tangerine linear regressions of length (L), diameter (D) and k 0, k 1, k,, k n= Regression coefficients geometrical mean diameter (GMD) of tangerine, or In order to estimate tangerine mass from geometrical multiple-variable linear regression of tangerine outer properties, nine linear regression mass models were dimensions (length and diameter). As indicated in suggested and all the data were subjected to linear Table 4, among the first class models (models No. 1-4), regression analysis using the Microsoft Excel 007. model No. 3 had the highest R values (0.89). Also, the p- Models were divided into three main classes (Table 3), i.e. value of independent variable (GMD) was the lowest first class (outer dimensions), second class (projected (1.06E-47). Based on the statistical results model No. 3 areas) and third class (estimated volume). was selected as the best model of first class. Model No. 3 is given in equation 7. Statistical Analysis: A paired samples t-test and the mean difference confidence interval approach were used M = - 150.5 + 43.94 GMD (7) to compare the tangerine mass values predicted using models with the tangerine mass values measured by Second Class Models (Projected Areas): In this class digital balance. The Bland-Altman approach [19] was also tangerine mass can be predicted using single variable used to plot the agreement between the tangerine mass linear regressions of first projected area (PA 1), second values measured by digital balance with the tangerine projected area (PA ) and criteria area (CAE) of tangerine, mass values predicted using models. The statistical or multiple-variable linear regression of tangerine analyses were also performed using Microsoft Excel 007. projected areas. As showed in Table 4, among the second 19

Am-Euras. J. Agric. & Environ. Sci., 14 (1): 17-4, 014 Table 4: Mass models, p-value of model variable(s) and coefficient of determination (R ) p-value --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Model No. L D GMD PA1 PA CAE VSp R 1 4.34E-1 --- --- --- --- --- --- 0.60 --- 3.80E-46 --- --- --- --- --- 0.88 3 --- --- 1.06E-47 --- --- --- --- 0.89 4 0.00019 1.1E-9 --- --- --- --- --- 0.88 5 --- --- ---.86E-46 --- --- --- 0.88 6 --- --- --- --- 3.74E-40 --- --- 0.83 7 --- --- --- --- ---.13E-48 --- 0.90 8 --- --- --- 6.08E-1 9.54E-06 --- --- 0.89 9 --- --- --- --- --- ---.53E-47 0.88 class models (models No. 5-8), model No. 7 had the highest R values (0.90). Moreover, the p-value of independent variable (CAE) was the lowest (.13E-48). Again, based on the statistical results model No. 7 was chosen as the best model of second class. Model No. 7 is given in equation 8. M = - 6.08 + 4.84 CAE (8) Third Class Model (Estimated Volume): In this class tangerine mass can be predicted using single variable linear regression of estimated volume of tangerine (V Sp). As indicated in Table 4, model No. 9 had high R value (0.88). In addition, the p-value of independent variable (V Sp) was (.53E-47). Once more, based on the statistical results model No. 9 was chosen as a suitable model. Fig. : Measured mass of tangerine and predicted mass Model No. 9 is given in equation 9. of tangerine using model No. 3 with the line of equality (1.0: 1.0) M = 16.00 + 0.88 V Sp (9) digital balance and are shown in Table 5. A plot of the DISCUSSION tangerine mass values determined by model No. 3 and digital balance with the line of equality (1.0: 1.0) is Among the linear regression models (models No. 1-9), shown in Fig.. The mean tangerine mass difference models No. 3, 7 and 9 were chosen based on the statistical between two methods was 0.73 g (95% confidence results. A paired samples t-test and the mean difference interval: -3.6 g and 5.07 g; P = 0.7305). The standard interval approach were used to compare the tangerine deviation of the tangerine mass difference was 9.8 g mass values predicted using models No. 3, 7 and 9 with (Table 6). The paired samples t-test results showed the tangerine mass values measured by digital balance. that the tangerine mass values predicted by model No. 3 The Bland-Altman approach [19] was also used to plot the were not significantly different than that measured by agreement between the tangerine mass values measured digital balance. The tangerine mass values difference by digital balance with the tangerine mass values between two methods were normally distributed and predicted using the selected mass models. 95% of these differences were expected to lie between µ-1.96 and µ+1.96, known as 95% limits of agreement. Comparison of Model No. 3 with Measuring Method: The 95% limits of agreement for comparison of the The tangerine mass values predicted by model No. 3 were tangerine mass values determined by digital balance and compared with the tangerine mass values measured by model No. 3 was calculated at -17.45 g and 18.90 g (Fig. 3). 0

Am-Euras. J. Agric. & Environ. Sci., 14 (1): 17-4, 014 Table 5: Geometrical properties of the twenty tangerines used in evaluating selected mass models Tangerine mass (g) Geometrical properties of tangerine ------------------------------------------------------------------------------------------------------ -------------------------------------------------------------- Measured by Predicted by Predicted by Predicted by 3 Sample No. GMD (cm) CAE (cm ) V Sp(cm ) digital balance model No. 3 model No. 7 model No. 9 1 4.1 13. 35.80 39.00 9.0 37.70 45.70 4.5 15.6 46.30 53.00 45.0 49.60 54.30 3 4.9 18.6 59.90 57.00 6.80 63.80 65.60 4 5. 1.4 73.90 68.00 78.30 77.60 77.0 5 5. 1.4 73.90 76.00 78.30 77.60 77.0 6 5.3 1.9 76.60 80.00 81.00 80.10 79.40 7 5.4.8 81.00 85.00 85.40 84.0 83.10 8 5.6 4.8 9.40 91.00 95.90 94.10 9.50 9 5.7 5.4 96.10 94.00 99.0 97.10 95.60 10 5.9 7. 106.0 97.00 107.5 105.7 103.8 11 5.8 6.6 10.6 100.0 104.8 10.8 101.0 1 6. 30.5 14.6 10.0 11.8 11.7 119. 13 6.1 9.0 116.3 108.0 115.6 114.5 11.3 14 5.9 7.6 108.7 110.0 109.7 107.5 106.0 15 6.0 8.5 113.7 113.0 113.6 111.8 110.1 16 6.4 3.4 138.0 131.0 131. 130.8 130.3 17 6.7 35.4 156. 149.0 143.1 145.1 145.4 18 7.0 38.4 178.6 155.0 156.5 159.9 163.9 19 6.8 37.1 167.9 168.0 150. 153.6 155.0 0 7.4 43.4 11. 194.0 174. 183.9 190.9 Table 6: Paired samples t-test analyses on comparing tangerine mass determination methods 95% confidence intervals for Determination methods Average difference (g) Standard deviation of difference (g) p-value the difference in means (g) Model No. 3 vs. measuring 0.73 9.8 0.7305-3.6, 5.07 Model No. 7 vs. measuring 1.46 7.6 0.3814-1.94, 4.85 Model No. 9 vs. measuring 1.88 6.5 0.19-1.17, 4.93 Thus, the tangerine mass values predicted by model No. paired samples t-test results showed that the tangerine 3 may be 18. g lower or higher than the tangerine mass mass values predicted by model No. 7 were not values measured by digital balance. The average significantly different than that measured by digital percentage difference for the tangerine mass values balance. The 95% limits of agreement for comparison of predicted by model No. 3 and measured by digital balance the tangerine mass values determined by digital balance was 7.5%. and model No. 7 was calculated at -1.78 g and 15.69 g (Fig. 5). Therefore, the tangerine mass values predicted by Comparison of Model No. 7 with Measuring Method: The model No. 7 may be 14. g lower or higher than the tangerine mass values predicted by model No. 7 were tangerine mass values measured by digital balance. The compared with the tangerine mass values measured by average percentage difference for the tangerine mass digital balance and are shown in Table 5. A plot of the values predicted by model No. 7 and measured by digital tangerine mass values determined by model No. 7 and balance was 5.3%. digital balance with the line of equality (1.0: 1.0) is shown in Fig. 4. The mean tangerine mass difference between two Comparison of Model No. 9 with Measuring Method: methods was 1.46 g (95% confidence interval: -1.94 g and The tangerine mass values predicted by model 4.85 g; P = 0.3814). The standard deviation of the No. 9 were compared with the tangerine mass values tangerine mass difference was 7.6 g (Table 6). Again, the measured by digital balance and are shown in Table 5. 1

Am-Euras. J. Agric. & Environ. Sci., 14 (1): 17-4, 014 Fig. 3: Bland-Altman plot for the comparison of measured Fig. 5: Bland-Altman plot for the comparison of measured mass of tangerine and predicted mass of tangerine mass of tangerine and predicted mass of tangerine using model No. 3; the outer lines indicate the 95% using model No. 7; the outer lines indicate the 95% limits of agreement (-17.45, 18.90) and the center limits of agreement (-1.78, 15.69) and the center line shows the average difference (0.73) line shows the average difference (1.46) Fig. 4: Measured mass of tangerine and predicted mass of Fig. 6: Measured mass of tangerine and predicted mass of tangerine using model No. 7 with the line of tangerine using model No. 9 with the line of equality (1.0: 1.0) equality (1.0: 1.0) A plot of the tangerine mass values determined by model measured by digital balance. The 95% limits of agreement No. 9 and digital balance with the line of equality (1.0: 1.0) for comparison of the tangerine mass values determined is shown in Fig. 6. The mean tangerine mass difference by digital balance and model No. 9 was calculated at - between two methods was 1.88 g (95% confidence 10.90 g and 14.66 g (Fig. 7). As a result, the tangerine mass interval: -1.17 g and 4.93 g; P = 0.19). The standard values predicted by model No. 9 may be 1.8 g lower or deviation of the tangerine mass difference was 6.5 g higher than the tangerine mass values measured by digital (Table 6). Once more, the paired samples t-test results balance. The average percentage difference for the showed that the tangerine mass values predicted by tangerine mass values predicted by model No. 9 and model No. 9 were not significantly different than that measured by digital balance was 5.5%.

Am-Euras. J. Agric. & Environ. Sci., 14 (1): 17-4, 014 5. Sadrnia, H., A. Rajabipour, A. Jafary, A. Javadi and Y. Mostofi, 007. Classification and analysis of fruit shapes in long type watermelon using image processing. Int. J. Agric. Biol., 9: 68-70. 6. Wilhelm, L.R., D.A. Suter and G.H. Brusewitz, 005. Physical Properties of Food Materials. Food and Process Engineering Technology. ASAE, St. Joseph, Michigan, USA. 7. Wen, Z. and Y. Tao, 1999. Building a rule-based machine-vision system for defect inspection on apple sorting and packing lines. Expert Systems with Application, 16: 307-713. 8. Kavdir, I. and D.E. Guyer, 004. Comparison of artificial neural networks and statistical classifiers in apple sorting using textural features. Biosystems Fig. 7: Bland-Altman plot for the comparison of measured Engineering, 89: 331-344. mass of tangerine and predicted mass of tangerine 9. Kleynen, O., V. Leemans and M.F. Destain, 003. using model No. 9; the outer lines indicate the 95% Selection of the most effective wavelength bands for limits of agreement (-10.90, 14.66) and the center Jonagold apple sorting. Postharvest Biology and line shows the average difference (1.88) Technology, 30: 1-3. 10. Polder, G., G.W.A.M. van der Heijden and I.T. Young, CONCLUSIONS 003. Tomato sorting using independent component analysis on spectral images. Real-Time Imaging, To predict tangerine mass based on outer 9: 53-59. dimensions, the mass model based on GMD as M = - 150.5 11. Mohsenin, N.N., 1986. Physical Properties of Plant +43.94 GMD with R = 0.89 can be suggested. In addition, and Animal Materials. Gordon and Breach Science to predict tangerine mass based on projected areas, the Publishers. New York. USA. mass model based on CAE as M = - 6.08 + 4.84 CAE 1. Khanali, M., M. Ghasemi Varnamkhasti, with R = 0.90 can be recommended. Moreover, to predict A. Tabatabaeefar and H. Mobli, 007. Mass and tangerine mass based on estimated volume, the mass volume modeling of tangerine (Citrus reticulate) fruit model based on V Sp as M = 16.00 + 0.88 V Sp with R = 0.88 with some physical attributes. International can be utilized. These models can also be used to design Agrophysics, 1: 39-334. tangerine sizing machines equipped with an image 13. Rashidi, M. and K. Seyfi, 008. Modeling of kiwifruit processing system. mass based on outer dimensions and projected areas. Am-Euras. J. of Agric. and Environ. Sci., 3: 14-17. REFERENCES 14. Taheri-Garavand, A. and A. Nassiri, 010. Study on some morphological and physical characteristics of 1. Anonymous, 01. Tangerine. From Wikipedia, sweet lemon used in mass models. International the free encyclopedia. Available at http://en. Journal of Environmental Sciences, 1: 580-590. wikipedia.org/wiki/tangerine on 01.01.0. 15. Rashidi, M. and M. Gholami, 011. Modeling of. Sahraroo, A., A. Khadivi Khub, A.R. Yavari and apricot mass based on some geometrical attributes. M. Khanali, 008. Physical properties of tangerine. Middle-East J. Sci. Res., 7: 959-963. Am-Euras. J. of Agric. and Environ. Sci., 3: 16-0. 16. Rashidi, M. and M. Gholami, 011. Modeling of 3. Rashidi, M. and K. Seyfi, 007. Classification of fruit nectarine mass based on some geometrical shape in cantaloupe using the analysis of geometrical properties. Am-Euras. J. of Agric. and Environ. Sci., attributes. World Appl. Sci. J., 3: 735-740. 10: 61-65. 4. Rashidi, M. and M. Gholami, 008. Classification of 17. Keshavarzpour, F. and A.K.K. Achakzai, 013. fruit shape in kiwifruit using the analysis of Fruit shape classification in cantaloupe using the geometrical attributes. Am-Euras. J. of Agric. and analysis of geometrical attributes. World Eng. and Environ. Sci., 3: 58-63. Appl. Sci. J., 4(1): 1-5. 3

Am-Euras. J. Agric. & Environ. Sci., 14 (1): 17-4, 014 18. Keshavarzpour, F. and A.K.K. Achakzai, 013. 19. Bland, J.M. and D.G. Altman, 1999. Fruit shape classification in kiwifruit using the Measuring agreement in method comparison analysis of geometrical attributes. World Eng. and studies. Statistical Method in Medical Research, Appl. Sci. J., 4(1): 6-10. 8: 135-160. 4