Mass Modeling Based on The Physical Properties of Marfona and Hermes Potatoes With Different Shape Index

Öz

The aim of this study was to determine the mass modeling of Marfona, Hermes potato varieties with different shape indexes according to their geometric properties, projection areas, and volume properties. As different classifications in models for mass estimation; classification according to dimensions, 1st classification, 2nd classification according to projection area, and 3rd classification according to volume properties. For this purpose, the mass estimations were made according to geometric dimension characteristics as the length (l), width (w), thickness (t) dimensions, geometric mean diameter (Gmd), projected area characteristics as the first, second, and third projection areas (FPA, SPA, TPA), and criteria area (Cae), volume characteristics as the oblate spheroid (Vobsp) and ellipsoid spheroid (Vellsp) of Marfona and Hermes and mixed potato varieties with shape index. Potatoes were characterized as round (100-160) and oval (161-240) according to the shape index (SI). For mass estimation, a general total of 114 linear regression models were used in the analyzes, and models were proposed considering the maximum coefficient of determination (R2), minimum regression standard error (RSE), and root mean square error (RMSE) values. The model gave a maximum coefficient of determination (R2), minimum regression standard error (RSE), and root mean square error (RMSE) values among all models, without taking into account shape index, mass estimation with ellipsoid sphere volume for round (100-160) shape index as m=2.830 + 1.169Vellsp (R2=0.940, RSE=5.112, RMSE=5.035) in Hermes variety, and it can be recommended. Suggested models for mass estimation can be used in the design, project, and development of machines and systems for classification, cleaning, and sizing operations for post-harvest use of potato tubers.

Anahtar Kelimeler

• Potato
• Dimension
• Projected Area
• Oblate Spheroid Volume
• Shape Index
• Mass Modeling

Farklı Şekil İndeksine Sahip Marfona ve Hermes Patateslerin Fiziksel Özelliklerine Göre Kütle Modellemesi

Öz

Bu çalışmanın amacı, farklı şekil indeksine sahip Marfona ve Hermes patates çeşitlerinin geometrik özellikler, projeksiyon alanları ve hacim özelliklerine göre kütle modellemelerinin belirlenmesidir. Kütle tahmini için modellemelerde farklı sınıflandırmalar olarak; boyutlara göre 1. sınıflandırma, projeksiyon alanına göre 2. sınıflandırma ve hacim özelliklerine göre 3. sınıflandırma olarak dikkate alınmıştır. Bu amaçla, şekil indeksine göre Marfona ve Hermes ve karışık patates çeşitlerinin geometrik boyut özellikleri olarak; uzunluk (l), genişlik (w), kalınlık (t) boyutları, geometrik ortalama çap (Gmd); projeksiyon alanları olarak, birinci, ikinci ve üçüncü projeksyon alanları (FPA, SPA, TPA) ve kriter alanı (Cae) ile hacim karakteristikleri olarak oblate sferoid (Vobsp) ve elipsoid spheroid (Vellsp) hacimlerine göre kütle tahminlemesi yapılmıştır. Şekil indeksi (SI)'ne göre patatesler yuvarlak (100-160) ve oval (161-240) olarak karakterize edilmiştir. Kütle tahmini için analizlerde genel toplamda 114 lineer regresyon modeli kullanılmıştır ve maksimum belirtme katsayısı (R2), minimum regresyon standart hatası (RSE) ve kök ortalama karesel hata (RMSE) değerleri dikkate alınarak modeller önerilmiştir. Patates çeşitleri arasında yuvarlak şekil indeksine göre boyutlar, projeksiyon alanları ve hacimler açısından patates yumrularının kütlesini tahmin etmede geliştirilen model Hermes çeşidinde belirlenirken, oval şekil indeksine göre ise Marfona çeşidinde belirlenmiştir. Tüm modeller içinde şekil indeksi dikkate alınmadan maksimum belirtme katsayısı (R2), minimum regresyon standart hatası (RSE) ve kök ortalama karesel hata (RMSE) değerleri veren model, elipsoid küre hacmiyle kütle tahmini yuvarlak (100-160) şekil indeksinde m=2.830 + 1.169Vellsp (R2=0.940, RSE=5.112, RMSE=5.035) ile Hermes çeşidinde belirlenmiş olup bu model önerilmiştir. Kütle tahminlerinde önerilen bu modeller; patates yumrularının hasat sonrası kullanımına yönelik olarak sınıflandırma, temizleme ve boyutlandırma işlemlerine ilişkin makine ve sistemlerin tasarım, projeleme ve geliştirilmesinde kullanılabilir.

Anahtar Kelimeler

• Patates
• Boyut
• Projeksiyon alanı
• Basık küre hacim
• Şekil İndeksi
• Kütle Modeli

Kaynakça

1. Abd Elhay, YB. (2017). Determination of some physical and mechanical properties of potato tubers related to design of sorting, cleaning and grading machine. Misr Journal of Agricultural Engineering, 34 (3), 1375-1388.
2. Anonymous. (2005). Netherlands Potato Consultative Foundation (NIVAP), Netherlands, 2005. Available at: http://europotato.org/datasource.
3. Anonymous. (2021, Şubat 01). https://www.tarimorman.gov.tr.
4. Altuntaş, E. (2021). Japon elmasının fiziksel özelliklerine göre doğrusal regresyon modelleri ile kütle tahmini. Gaziosmanpaşa Bilimsel Araştırma Dergisi 10 (1), 153-161.
5. Altuntas, E., Mahawar, M.K. (2021). Mass prediction of cherry laurel genotypes based on physical attributes using linear regression models. Journal of Agricultural Faculty of Gaziosmanpasa University, 38(1), 87-94.
6. Altuntas, E., Yilmaz, G., Karan, Y.B., Dulger, E. (2013). Assessment of the physico-mechanical, chemical and colour characteristics of potatoes depending on tuber size and cultivar. International Journal of Food Engineering, 9(4), 487-497.
7. 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.
8. Boydas, M.G., Sayinci, B., Gozlekci, S., Oztürk, I., Ercisli, S. (2012). Basic physical properties of fruits in loquat (Eriobotrya Japonica (Thunb. Lindl.) cultivars and genotypes determined by both classical method and digital image processing. African Journal of Agricultural Research, 7(29), 4171-4181.

9. Demir, B., Sayıncı, B., Çetin, N., Yaman, M., Çömlek, R. (2019). Shape discrimination of almond cultivars by elliptic fourier descriptors. Erwerbs-Obstbau, 61(3), 245-256.
10. Er, O., Cetişli, B., Sofu, M.M., Kayacan, M.C. (2013). Gerçek zamanlı otomatik elma tasnifleme. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 17(2), 31-38.
11. Golmohammadi, A., Purrahimi G. (2009). Physical Properties of Three Potato Varieties During Storage Period. 10. International Agricultural Engineering Conference, Asian Assocation for Agricultural Engineering, 7-10 December, 2009.
12. Gorji Chakespari, A., Rajabipour, A., Mobli, H. (2010). Mass modeling of two apple varieties by geometrical attributes. Australian Jurnal of Agricultural Engineering, 1(3), 112-118.
13. Henriksen, C.B., Molgaard, J.P., Rasmussen, J. (2006). The effect of autumn ridging and inter-row subsoiling on potato tuber yield and quality on a sandy soil in Denmark. Soil & Tillage Research, 93: 309–315.
14. Jahromi, M.K., Jafari, A., Rafiee, S., Mirasheh, R., Mohtasebi, S.S. (2008). Mass modeling of date fruit (cv. Zahedi) with some physical characteristics. American-Eurasian Journal of Agricultural & Environmental Sciences, 3(1), 127-131.
15. Jahromi, M.K., Rafiee, S., Mirasheh, R., Jafari, A., Mohtasebi, S.S., Ghasemi Varnamkhasti M. (2007). Mass and Surface Area Modeling of Bergamot (Citrus medica) Fruit with Some Physical Attributes. Agricultural Engineering International: the CIGR Ejournal. Manuscript FP 07 029. Vol. IX. October, 2007.
16. Khezri, S.L., Rashidi, M., Gholami, M. (2012). Modeling of Peach Mass Based on Geometrical Attributes Using Linear Regression Models. American-Eurasian Journal of Agricultural and Environmental Sciences, 12 (7), 991-995.
17. Lorestani, A.N., Tabatabaeefar, A. (2006). Modelling the mass of kiwifruit by geometrical attributes. Int. Agrophysics, 20, 135-139.
18. Mahawar, M.K., Bibwe, B., Jalgaonkar, K., Ghodki, B.M. (2019). Mass modeling of kinnow mandarin based on some physical attributes. Journal of Food Process Engineering, 42 (5), https://doi.org/ 10.1111/jfpe.13079.
19. Rashidi, M., Seyfi, K. (2008). Modeling of kiwifruit mass based on outer dimensions and projected areas. American-Eurasian Journal of Agricultural and Environmental Sciences, 3, 14-17.
20. Rashidi, M., Gholami, M. (2008). Classification of Fruit Shape in Kiwifruit Using the Analysis of Geometrical Attributes. American-Eurasian Journal of Agricultural and Environmental Sciences, 3(2), 258-263.
21. Saraçoğlu, T. (2017). Mathematical Models for Estimating the Mass of Plum Fruit by Selected Physical Properties. Journal of Agricultural Faculty of Gaziosmanpasa University, 34 (3), 82-90.
22. Saraçoğlu, T., Özarslan, C. (2015). Kiraz Domatesi Meyvesinin Kütle ve Hacminin Matematiksel Modellemesi. Adnan Menderes Üniversitesi Ziraat Fakültesi Dergisi, 12(1), 103-108.
23. Sasikumar, R., Vivek, K., Chakkaravarthi, S., Deka, S.C. (2020). Physicochemical Characterization and Mass Modeling of Blood Fruit (Haematocarpus Validus) – An Underutilized Fruit of Northeastern India, International Journal of Fruit Science, https://doi.org/10.1080/15538362.2020.1848752
24. Sayinci, B., Ercisli, S., Ozturk, I., Eryilmaz, Z., Demir, B. (2012). Determination of size and shape in the ‘Moro’ blood orange and ‘Valencia’ sweet orange cultivar and its mutants using image processing. Notulae Botanicae Horti Agrobotanici Cluj-Napuca, 40(1), 234-242.
25. Sharifi, M., Rafiee, S., Keyhani, A., Jafari, A., Mobli, H., Rajabipour A., Akram A. (2007). Some physical properties of orange (var. Tompson). International Agrophysics, 21, 391-397.
26. Singh, S., Kumar, D., Singh B.P. (2004). Short note quantification of tuber shape in Indian potato cultivars. Potato Journal, 31(3-4), 205-207.
27. Tabatabaeefar, A. (2002). Size and shape of potato tubers. International Agrophysics, 16: 301–305.
28. Vivek, K., Mishra, S., Pradhan R.C., 2018. Physicochemical characterization and mass modelling of Sohiong (Prunusnepalensis L.) fruit. Journal of Food Measurement and Characterization, 12, 923–936.
29. Vursavuş, K.K., Kesilmiş, Z. (2016). Hasarsız çarpma tekniği kullanılarak domates meyvesinin kütle tahmini için farklı model yaklaşımlarının geliştirilmesi ve değerlendirilmesi Anadolu Tarım Bilimleri Dergisi, 31, 385-392.
30. Zainal A’Bidin, F.N., Shamsudin, R., Mohd Basri, M.S., Mohd Dom, Z. (2020). Mass Modelling and Effects of Fruit Position on Firmness and Adhesiveness of Banana Variety Nipah. International Journal of Food Engineering, e2019019.