Doğrusal Regresyon Modelleri Kullanılarak Fiziksel Özelliklere Göre Karayemiş Genotiplerinin Kütle Tahmini

Yıl 2021, Cilt: 38 Sayı: 2, 87 - 94, 31.08.2021

Ebubekir Altuntaş , Manoj Kumar Mahawar

https://doi.org/10.13002/jafag4765

Cited By: 3

Öz

Bu araştırma, 55 K 07 ve 61 K 04 karayemiş genotiplerin boyut, geometrik ortalama çap (Dg), birinci, ikinci, üçüncü izdüşüm alanları (PA1, PA2, PA3), kriter alan (CAE) ve oblate ve elipsoid şekilli hacimlere (Vob, Vell) kütle tahminini belirlemek için yürütüldü. Analiz, seçilen bu genotipler için 57 doğrusal regresyon modeli kullanılarak gerçekleştirildi. İstatistiksel sonuçlar, için meyve boyutlarına göre kütle tahmini için büyük, orta ve küçük çapa (a, b, c) dayalı olarak üç değişkenli kütle modeli için sırasıyla k R2 = 0.876, and R2 = 0.798 önerilebilir. Projeksiyon alanlarına göre, kütleleri tahmin etmek için 55 K 07 ve 61 K 04 genotipleri için PA1+ PA2+ PA3 (R2 = 0.881) ve PA1+ PA2 (R2 = 0.803) projeksiyon alanlarına dayalı kütle modelleri önerilmiştir. Ek olarak, 55 K 07 ve 61 K 04 genotipleri için kütleleri tahmin etmek için sırasıyla Vell (R2 = 0.877) ve Vob+ Vell (R2 = 0.791) kütle modelleri kullanılabilir. Karma genotipler için, sırasıyla a + b + c'ye (R2 = 0.964) dayalı üç değişkenli kütle modeli, CAE'ye dayalı tek değişkenli kütle modeli (R2 = 0.964) ve Vell'e dayalı tek değişkenli kütle modeli (R2 = 0.959) önerilebilir. Bu modeller, karayemiş meyveleri için boyutlandırma makinelerinin tasarımında ve geliştirilmesinde kullanılabilir.

Anahtar Kelimeler

Karayemiş, kütle modelleri, fiziksel özellikler, kriter alanı

Mass Prediction of Cherry Laurel Genotypes Based on Physical Attributes Using Linear Regression Models

Öz

This investigation was inducted to predict the mass models of cherry laurel genotypes (55 K 07, 61 K 04 and mixed) based on some physical characteristics such as dimension, geometric mean diameter (Dg), the first, second, third projection areas (PA1, PA2, PA3), the criteria area (CAE) and oblate and ellipsoid shaped volumes (Vob, Vell). The analysis was executed using 57 linear regression models for the selected genotypes. The statistical results substantiated that three variables mass model based on major, intermediate and minor diameter (a, b, c) as R2 = 0.876, and R2 = 0.798 can be recommended for mass estimation according to fruit sizes for 55 K 07 and 61 K 04 genotypes, respectively. According to the projection areas, mass models based on the projection areas PA1 + PA2 + PA3 (R2 = 0.881) and PA1 + PA2 (R2 = 0.803) for the 55 K 07 and 61 K 04 genotypes were proposed to estimate the masses. In addition, the mass models based on the Vell (R2 = 0.877) and Vob + Vell (R2 = 0.791) for the 55 K 07 and 61 K 04 genotypes can be used to estimate the masses, respectively. For mixed genotypes, three variables mass model based on a + b + c (R2= 0.964), single variable mass model based on CAE (R2 =0.964), and single variable mass model based on Vell (R2= 0.959) can be recommended, respectively. These models can be used in the design and development of sizing machines for cherry laurel fruits.

Anahtar Kelimeler

Cherry laurel, criteria area, mass models, physical characteristics, Cherry laurel, criteria area, mass models, physical characteristics

Kaynakça

• Ansin Z and Ozkan ZC (1993). Prunus laurocerasus L. In: Spermatophyta, Karadeniz Technical University, Faculty of Forestry, Publication No: 19, 512 p, Trabzon, Turkey
• Ayaz FA, Kadioglu A, Reunanen M, Var M (1997). Sugar composition in fruits of Laurocerasus officinalis Roem. and its three cultivars. Journal of Food Composition. Analysis, 10, 82–86.
• Berberoglu E, Altuntas E, Dulger E (2014). Development of adequate mathematical models to predict the mass of potato varieties from their some physical attributes. Journal of Agricultural Faculty of Gaziosmanpasa University, 31(3), 1-9.
• Bostan SZ, Islam A (2003). Trabzon'da (Merkez ilçe) yetiştirilen mahalli karayemiş (Prunus laurocerasus L.) tiplerinin pomolojik ve fenolojik özellikleri. Journal of Ondokuz Mayis University 18(1), 27-31.
• Jaliliantabar F, Lorestani AN (2014). Mass modeling of kumquat fruit (cv. Nagami) with some physical attributes. International Journal of Biosciences, 5(1): 82-88.
• Keramat Jahromi M, Jafari A, Rafiee S, Keyhani AR, Mirasheh R, Mohtasebi SS (2008). Mass modeling of date fruit (cv. Zahedi) with some physical characteristics. American-Eurasian Journal of Agriculture and Environment Science 3(1), 127-131.
• Khezri SL, Rashidi M, Gholami M (2012). Modeling of peach mass based on geometrical attributes using linear regression models. American-Eurasian Journal of Agriculture and Environmental Science. 12 (7), 991-995.
• Khodabakhshian R, Emadi B (2016). Mass model of date fruit (cv. Mazafati) based on its physiological properties. International Food Research Journal 23(5), 2070-2075
• Mahawar MK, Bibwe B, Jalgaonkar K, Ghodki BM (2019). Mass modeling of kinnow mandarin based on some physical attributes. Journal of Food Process Engineering 42(5), DOI: 10.1111/jfpe.13079.
• Pathak SS, Pradhan RC, Mishra S (2019). Physical characterization and mass modeling of dried Terminalia chebula fruit. J Food Process Eng. 2019; e12992
• Rashidi M, Seyfi K (2007). Classification of fruit shape in cantaloupe using the analysis of geometrical attributes. World Applied Sciences Journal, 3, 735-740.
• Rashidi M, Seyfi K (2008a). Determination of kiwifruit volume using image processing. World Applied Sciences Journal, 3, 184-190.
• Rashidi M, Seyfi K (2008b). Modeling of kiwifruit mass based on outer dimensions and projected areas. American-Eurasian Journal of Agricultural and Environmental Sciences, 3, 14-17.
• Rashidi, M, Gholami M (2011). Prediction of egg mass based on geometrical attributes. Agriculture and Biology Journal of North America, 2 (4), 638-644.
• Sadrnia H, Rajabipour A, Jafary A, Javadi A, Mostofi Y (2007). Classification and analysis of fruit shapes in long type watermelon using image processing. International Journal of Agriculture and Biology, 9, 68-70.
• Sasikumar R, Vivek K, Chakkaravarthi S, Deka SC (2020). Physicochemical characterization and mass modeling of blood Fruit (Haematocarpus Validus) – An Underutilized Fruit of Northeastern India, International Journal of Fruit Science, DOI: 10.1080/15538362.2020.1848752
• Shahbazi F, Rahmati S (2013). Mass modeling of fig (Ficuscarica L.) fruit with some physical characteristics. Food Science and Nutrition 1(2), 125-129
• Sulusoglu M (2011). The cherry laurel (Prunus laurocerasus L.) tree selection. African Journal of Agricultural Research, 6, 3574-3582.
• Tabatabaeefar A, Rajabipour A (2005). Modeling the mass of apples by geometrical attributes. Scientia Horticulture, 105, 373–382.
• Vivek K, Mıshra S, Pradhan RC (2018). Physicochemical characterization and mass modelling of Sohiong (Prunusnepalensis L.) fruit. Journal of Food Measurement and Characterization. 12, 923–936.
• Wilhelm LR, Suter DA and Brusewitz GH (2005). Physical properties of food materials. Food and Process Engineering Technology. ASAE, St. Joseph, Michigan, USA.
• Zainal A’Bidin, FN, Shamsudin R, Mohd Basri MS and Mohd Dom Z (2020). Mass modelling and effects of fruit positionon firmness and adhesiveness of banana variety Nipah. International Journal of Food Engineering, https://doi.org/10.1515/ijfe-2019-0199.