Mass modeling of fig (<i><scp>F</scp>icus carica </i><scp>L</scp>.) fruit with some physical characteristics

Shahbazi, Feizollah; Rahmati, S Ghaed

doi:10.1002/fsn3.20

Cited by 24 publications

(11 citation statements)

References 9 publications

(13 reference statements)

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“…Thus, numerous models have been prepared to predict physical attributes from the measurement of other attributes. Models for predicting fruit mass based on the measurement of physical features have been previously published [10][11][12][13][14][15][16][17][18][19][20]. All of the available models were developed based on a combination of different physical attributes in different forms using statistical approaches, such as regression analysis.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural network and support vector regression in predicting mass of ber fruits (Ziziphus mauritiana Lamk.) based on fruit axial dimensions

2021

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Fruit quality attributes are important factors for designing a market for agricultural goods and commodities. Support vector regression (SVR), MLR, and ANN models were established to predict the mass of ber fruits (Ziziphus mauritiana Lamk.) based on the axial dimensions of the fruit from manual measurements of fruit length, minor fruit diameter, and maximum fruit diameter of four ber cultivars. The precision and accuracy of the established models were assessed given their predicted values. The results revealed that using the validation dataset, the developed ANN (R2 = 0.9771; root mean square error [RMSE] = 1.8479 g) and SVR (R2 = 0.9947; RMSE = 1.8814 g) models produced better results when predicting ber fruit mass than those obtained by the MLR model (R2 = 0.4614; RMSE = 11.3742 g). In estimating ber fruit mass, the established SVR and ANN models produced more precise prediction values than those produced by the MLR model; however, the performance differences between the SVR and ANN models were not clear.

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Section: Introductionmentioning

confidence: 99%

Application of artificial neural network and support vector regression in predicting mass of ber fruits (Ziziphus mauritiana Lamk.) based on fruit axial dimensions

2021

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“…The volume of the fruit was also determined in the terms of the ellipsoid, oblate spheroid, and prolate spheroid volume by using the formulae given by Shahbazi and Rahmati ().

V_{ellip} = \frac{4 π}{3} \times ([], \frac{LA}{2}) \times ([], \frac{IA}{2}) \times ([], \frac{TA}{2})

where V ellip is volume of an ellipsoid (mm 3 ), LA is longitudinal axis (mm), IA is intermediate axis (mm), and TA is transverse axis (mm).

V_{pro} = \frac{4 π}{3} \times {[\frac{LA}{2}]}^{2} \times ([], \frac{IA}{2})

where V pro is volume of prolate spheroid volume (mm 3 ), LA is longitudinal axis (mm), IA is intermediate axis (mm).

V_{osp} = \frac{4 π}{3} \times ([], \frac{LA}{2}) \times {[\frac{IA}{2}]}^{2}

where V osp is volume of an oblate spheroid (mm 3 ), LA is longitudinal axis (mm), and IA is intermediate axis (mm).…”

Section: Methodsmentioning

confidence: 99%

“…length (longitudinal axis), width (intermediate axis) and thickness (transverse axis) and D a , D e , D g . The single or multiple regression of T. chebula fruit based on the projected areas viz. CPA, criteria projected area mean of the projected areas, projected area perpendicular to the longitudinal axis ( P LA) , intermediate axis ( P IA ), and transverse axis ( P TA ), respectively. The single regression of T. chebula fruit based on the measured volume of the fruit ( V ) and calculated volume as ( V ellip ) ellipsoid, ( V osp ) oblate spheroid, and ( V pro) prolate spheroid volume (Naderi‐Boldaji, Fattahi, Ghasemi‐Varnamkhasti, Tabatabaeefar, & Jannatizadeh, ; Shahbazi & Rahmati, ).…”

Section: Methodsmentioning

confidence: 99%

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Physical characterization and mass modeling of dried Terminalia chebula fruit

Pathak

Pradhan

Mishra

2019

J Food Process Engineering

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The research was inducted to investigate the physical properties and to determine the mass model for Terminalia chebula fruits depending on its dimensional attributes. The investigation revealed that principle dimensions of the whole fruit T. chebula viz. average longitudinal axis (length), intermediate axis (width), and transverse axis (thickness) were 31.076 mm, 17.404 mm, and 16.073 mm, respectively. The thousand fruit mass was found to be 3.890 kg while the fruit has a mass distribution over the range of 1.730–9.794 g. The dimensional properties such as arithmetic mean diameter, equivalent mean diameter, and geometric mean diameter were 21.518 mm, 20.553 mm, and 20.538 mm, respectively. The aspect ratio, sphericity, eccentricity, and fruit part fraction were calculated. The surface area and volume were noted to be 1,302.351 mm2 and 10,054.28 mm3, respectively. The frictional properties such as coefficient of friction and angle of friction were measured. Mass modeling was done by using four different models viz. linear, power, quadratic, and s‐curve relating to the mass and dimensional property of the fruit which will be applicable in machine vision for grading systems. The quadratic and linear models showed the best relations between the mass and dimensional parameters of the fruit. Practical applications The fruit of Terminalia chebula is a minor forest produce but has a wide application in the traditional medicines and food industries. The determination of the physiological properties such as dimensional, gravimetric, and frictional properties will help researchers, industrial process designers to design the process handling equipment, postharvest machinery, storage structures, grading, sorting equipment, etc., in the processing and manufacturing industries. The fruits can be separated on the basis of mass rather than its shape and size. The mass modeling of the fruit will be beneficial to segregate the fruits on industrial scale with minimal labor and more accuracy by using the relationship for mass of fruit with its physical properties.

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“…To estimate the mass, M of pineapple fruits Linear, Quadratic, S-curve, and Power were used and fitted with the data from the trials. These models are presented in Equations ( 10), ( 11), ( 12) and ( 13) respectively (Shahbazi & Rahmati, 2012;Azman et al, 2020):…”

Section: Mass Modellingmentioning

confidence: 99%

Some Engineering Properties of Pineapple Fruit

SHAMSUDIN,

CHE-MAN,

ARIFFIN

et al. 2023

EPSTEM

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Pineapple is a tropical fruit that is highly relished for its unique aroma and sweet taste. In contrast to other tropical fruits, pineapples typically feature medium-sized fruits with yellow flesh. Based on the physical properties of pineapple fruit variety MD2 such as dimensions, geometric mean diameter (Dg), arithmetic mean diameter arithmetic mean diameter (Da), surface area, volume, sphericity, aspect ratio, and projected area and the best-fit mass models have been determined. From the result obtained, the physical properties such as length, thickness, width, Dg, Da and circumference were found to be 217.8mm, 132.7mm, 132.9mm, 156.5mm, 161.2mm, 394.3mm respectively. Meanwhile for the aspect ratio, mass, volume surface area, sphericity and projected area perpendicular to dimension namely PAL, PAT, AND PAW were found to be 0.61, 1730.4g, 1420 cm3, 77107.4 mm2, 0.72, 22746.0 mm2, 13942.9 mm2, and 16866.3 mm2 respectively. For all physical properties except volume, the best fit mass model to predict mass of pineapple fruits was the quadratic model. Additionally, the findings demonstrated that, in comparison to other attributes, the mass model based on actual volume was more appropriate, with the highest determination coefficient (R2) for Quadratic and S-curve model. For developing and optimizing machinery for handling, maintenance, distribution, and storage, the mass model of pineapple fruits according to the actual volume in the outcomes is relatively important.

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Mass modeling of fig (Ficus carica L.) fruit with some physical characteristics

Cited by 24 publications

References 9 publications

Application of artificial neural network and support vector regression in predicting mass of ber fruits (Ziziphus mauritiana Lamk.) based on fruit axial dimensions

Application of artificial neural network and support vector regression in predicting mass of ber fruits (Ziziphus mauritiana Lamk.) based on fruit axial dimensions

Physical characterization and mass modeling of dried Terminalia chebula fruit

Some Engineering Properties of Pineapple Fruit

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