Wheat Flour Milling Yield Estimation Based on Wheat Kernel Physical Properties Using Artificial Neural Networks
AbstractWheat is a stable food raw material for the majority of people around the world as wheat-based products provide an important part of the daily energy intake in many countries. Wheat is generally milled into flour prior to use in the bakery industry. Flour yield is one of the major quality criteria in wheat milling. Flout yield detection requires large amount of sample, costly machinery, longer time of tempering and milling practices requiring substantial workload. In this study, artificial neural network (ANN) approach has been employed to predict flour milling yield. The ANN was designed in the MATLAB software using such wheat physical properties as hectoliter weight, thousand-kernel weight, kernel size distribution, and grain hardness. Flour yields and four different kernel physical features (hectoliter weight, thousand-kernel weight, kernel size distribution, and grain hardness) were first collected from 2400 wheat samples through the conventional methods. The ANN was trained using 85% of 2400 flour yield data and tested with 15% of the remaining data. In the training of the ANN, various models have been investigated to find the best ANN structure. Additionally, two data sets with and without grain hardness have been employed to determine the effect of grain hardness on the prediction performance of the ANN model. It was found that grain hardness which reduced the MAE values from 2.3333 to 2.2611 and RMSE values from 3.0775 to 2.9146 gave better result. The results proved that the developed ANN model can be used to estimate flour yield using wheat kernel physical properties.
R. H. Liu, "Whole grain phytochemicals and health," Journal of Cereal Science, vol. 46, no. 3, pp. 207-219, 2007.
N. Geographic. (2015). Available: https://media.nationalgeographic.org
A.-C. Eliasson and K. Larsson, Cereals in breadmaking: a molecular colloidal approach (no. 664.6). Marcel Dekker, 1993.
J. A. Delcour and R. C. Hoseney, Principles of Cereal Science and Technology. AACC International, 2010.
R. W. Summers and P. I. Payne, "International Wheat Quality Conference," in Proceedings, International Wheat Quality Conference: May 18-22, 1997, Holiday Inn-Holidome, Manhattan, Kansas, USA, 1997, p. 185: Grain Industry Alliance.
T. Botwright, A. Condon, G. Rebetzke, and R. Richards, "Field evaluation of early vigour for genetic improvement of grain yield in wheat," Australian Journal of Agricultural Research, vol. 53, no. 10, pp. 1137-1145, 2002.
D. Marshall, F. Ellison, and D. Mares, "Effects of grain shape and size on milling yields in wheat. I. Theoretical analysis based on simple geometric models," Australian journal of agricultural research, vol. 35, no. 5, pp. 619-630, 1984.
D. Marshall, D. Mares, H. Moss, and F. Ellison, "Effects of grain shape and size on milling yields in wheat. II. Experimental studies," Australian Journal of Agricultural Research, vol. 37, no. 4, pp. 331-342, 1986.
R. Cheng et al., "Mapping QTLs controlling kernel dimensions in a wheat inter-varietal RIL mapping population," Theoretical and Applied Genetics, vol. 130, no. 7, pp. 1405-1414, 2017.
R. Alvarez, "Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach," European Journal of Agronomy, vol. 30, no. 2, pp. 70-77, 2009.
S. E. Jørgensen and G. Bendoricchio, Fundamentals of ecological modelling. Elsevier, 2001.
N. A. Abdullah and A. M. Quteishat, "Wheat seeds classification using multi-layer perceptron artificial neural network," International Journal of Electronics Communication and Computer Engineering, vol. 6, no. 2, pp. 306-309, 2015.
A. Yasar, E. Kaya, and I. Saritas, "Classification of Wheat Types by Artificial Neural Network," International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. 1, pp. 12-15, 2016.
M. K. A. Kadir, M. Z. Ayob, and N. Miniappan, "Wheat yield prediction: Artificial neural network based approach," in Engineering Technology and Technopreneuship (ICE2T), 2014 4th International Conference on, 2014, pp. 161-165: IEEE.
Y. Çakır, M. Kırcı, and E. O. Güneş, "Yield prediction of wheat in south-east region of Turkey by using artificial neural networks," in Agro-geoinformatics (Agro-geoinformatics 2014), Third International Conference on, 2014, pp. 1-4: IEEE.
K. Kulp, Handbook of Cereal Science and Technology, revised and expanded. CRC Press, 2000.
G. Zhang and C. Li, Genetics and improvement of barley malt quality. Springer Science & Business Media, 2010.
A. Elgun, Z. Ertugay, M. Certel, and H. Kotancılar, "Tahıl ve Ürünlerinde Analitik Kalite Kontrolü ve Laboratuar Uygulama Kılavuzu.(3. baskı) Atatürk Üniversitesi Yayın No: 867, Ziraat Fakültesi Yayın No: 335, Ders Kitapları Serisi No: 82," Erzurum. s, vol. 245, 2002.
N. Stenvert, "Grinding resistance. A simple measure of wheat hardness," Journal of Flour and Animal Feed Milling, 1974.
Y. Wu and T. Nelsen, "A simple, rapid method to measure wheat hardness by grinding time and speed reduction in a micro hammer-cutter mill," Cereal chemistry, 1991.
A. A. o. C. C. A. M. Committee, Approved Methods of the American Association of Cereal Chemists (no. 1-2. c.ler). AACC, 2000.
E. Öztemel, Yapay Sinir Ağlari. PapatyaYayincilik, Istanbul, 2003.
K. Sabanci, A. Kayabasi, and A. Toktas, "Computer vision‐based method for classification of wheat grains using artificial neural network," Journal of the Science of Food and Agriculture, vol. 97, no. 8, pp. 2588-2593, 2017.
Copyright (c) 2020 Kadir Sabancı, Dr., Nevzat Aydın, Prof. Dr., Abdulvahit Sayaslan, Prof. Dr., Mesut Ersin Sönmez, Mr., Muhammet Fatih ASLAN, Lutfu Demir, Cemal Sermet
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.