References
- Abbasi-tarighat, M., Shahbazi, E., & Niknam, K. (2013). Simultaneous determination of Mn2+ and Fe3+ as complexes in some foods, vegetable and water samples by artificial neural networks. Food Chemistry, 138(2–3), 991–997. http://doi.org/10.1016/j.foodchem.2012.09.099
- Ahmed, J., Prabhu, S. T., Raghavan, G. S. V., & Ngadi, M. (2007). Physico-chemical, rheological, calorimetric and dielectric behavior of selected Indian honey. Journal of Food Engineering, 79, 1207–1213. http://doi.org/10.1016/j.jfoodeng.2006.04.048
- Ajala, S. O., & Betiku, E. (2015). Yellow Oleander Seed Oil Extraction Modeling and Process Parameters Optimization: Performance Evaluation of Artificial Neural Network and Response Surface Methodology. Journal of Food Processing and Preservation, 39, 1466–1474. http://doi.org/10.1111/jfpp.12366
- Al-Mahasneh, M. A., Rababah, T. M., & Ma’Abreh, A. S. (2013). Evaluating the combined effect of temperature, shear rate and water content on wild-flower honey viscosity using adaptive neural fuzzy inference system and artificial neural networks. Journal of Food Process Engineering, 36, 510–520. http://doi.org/10.1111/jfpe.12014
- Çelekli, A., Birecikligil, S. S., Geyik, F., & Bozkurt, H. (2012). Prediction of removal efficiency of Lanaset Red G on walnut husk using artificial neural network model. Bioresource Technology, 103, 64–70. http://doi.org/10.1016/j.biortech.2011.09.106
- Chen, Y. W., Lin, C. H., Wu, F. Y., & Chen, H. H. (2009). Rheological properties of crystallized honey prepared by a new type of nuclei. Journal of Food Process Engineering, 32(2009), 512–527. http://doi.org/10.1111/j.1745-4530.2007.00227.x
- Dahmoune, F., Remini, H., Dairi, S., Aoun, O., Moussi, K., Bouaoudia-Madi, N., … Madani, K. (2015). Ultrasound assisted extraction of phenolic compounds from P. lentiscus L. leaves: Comparative study of artificial neural network (ANN) versus degree of experiment for prediction ability of phenolic compounds recovery. Industrial Crops and Products, 77, 251–261. http://doi.org/10.1016/j.indcrop.2015.08.062
- Dobre, I., Georgescu, L. A., Alexe, P., Escuredo, O., & Seijo, M. C. (2012). Rheological behavior of different honey types from Romania. Food Research International, 49(1), 126–132. http://doi.org/10.1016/j.foodres.2012.08.009
- Faria, S., Alberto, C., Anjos, R., Nunes, R., Maria, R. (2015). Evaluation of extra virgin olive oil stability by artificial neural network. Food Chemistry, 179, 35–43. http://doi.org/10.1016/j.foodchem.2015.01.100
- Goktepe, A. B., Altun, S., Altintas, G., & Tan, O. (2008). Shear strength estimation of plastic clays with statistical and neural approaches. Building and Environment, 43, 849–860. http://doi.org/10.1016/j.buildenv.2007.01.022
- Herrmann, J., Brito, A., Trembley, J., & Grupa, U. (2013). Development of a rheological prediction model for food suspensions and emulsions. Journal of Food Engineering, 115(4), 481–485. http://doi.org/10.1016/j.jfoodeng.2012.05.034
- Karasu, S., Toker, S. O., Yilmaz, M. T., Karaman, S., Dertli, E. (2015). Thermal loop test to determine structural changes and thermal stability of creamed honey: Rheological characterization. Journal of Food Engineering, 150(150), 90–98. http://doi.org/10.1016/j.jfoodeng.2014.10.0040260-8774/?
- Kayacier, A., Yüksel, F., & Karaman, S. (2014). Dynamic mechanical spectra of selected Turkish honeys: Construction of predictive models for complex viscosity using two different nonlinear modeling techniques. International Journal of Food Properties, 17(June 2011), 93–110. http://doi.org/10.1080/10942912.2011.614985
- Khanlari, G. R., Heidari, M., Momeni, A. A., & Abdilor, Y. (2012). Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods. Engineering Geology, 131–132, 11–18. http://doi.org/10.1016/j.enggeo.2011.12.006
- Linder, R., & Po, S. J. (2003). A new neural network approach classifies olfactory signals with high accuracy. Food Quality and Preference, 14, 435–440. http://doi.org/10.1016/S0950-3293(03)00016-8
- Llave, Y. A., Hagiwara, T., & Sakiyama, T. (2012). Artificial neural network model for prediction of cold spot temperature in retort sterilization of starch-based foods. Journal of Food Engineering, 109(3), 553–560. http://doi.org/10.1016/j.jfoodeng.2011.10.024
- Mohanraj, M., Jayaraj, S., & Muraleedharan, C. (2012). Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems - A review. Renewable and Sustainable Energy Reviews, 16(2), 1340–1358. http://doi.org/10.1016/j.rser.2011.10.015
- Nayik, G. A., Dar, B. N., & Nanda, V. (2015). Physico-chemical, rheological and sugar profile of different honeys from Kashmir Valley of India. Arabian Journal of Chemistry, Avaible on. http://doi.org/10.1016/j.arabjc.2015.08.017
- Oroian, M. (2012). Physicochemical and rheological properties of Romanian honeys. Food Biophysics, 7, 296–307. http://doi.org/10.1007/s11483-012-9268-x
- Oroian, M. (2015). Influence of temperature, frequency and moisture content on honey viscoelastic parameters - Neural networks and adaptive neuro-fuzzy inference system prediction. LWT - Food Science and Technology, 63(2), 1309–1316. http://doi.org/10.1016/j.lwt.2015.04.051
- Phimolsiripol, Y., Siripatrawan, U., & Cleland, D. J. (2011). Weight loss of frozen bread dough under isothermal and fluctuating temperature storage conditions. Journal of Food Engineering, 106(2), 134–143. http://doi.org/10.1016/j.jfoodeng.2011.04.020
- Rahman, M. S., Rashid, M. M., & Hussain, M. A. (2012). Thermal conductivity prediction of foods by Neural Network and Fuzzy (ANFIS) modeling techniques. Food and Bioproducts Processing, 90(2), 333–340. http://doi.org/10.1016/j.fbp.2011.07.001
- Rai, P., Majumdar, G. C., & Dasgupta, S. (2005). Prediction of the viscosity of clarified fruit juice using artificial neural network: a combined effect of concentration and temperature. Journal of Food Engineering, 68, 527–533. http://doi.org/10.1016/j.jfoodeng.2004.07.003
- Ramzi, M., Kashaninejad, M., Salehi, F., Sadeghi Mahoonak, A. R., Ali Razavi, S. M. (2015). Modeling of rheological behavior of honey using genetic algorithm-artificial neural network and adaptive neuro-fuzzy inference system. Food Bioscience, 9, 60–67. http://doi.org/10.1016/j.fbio.2014.12.001
- Razmi-Rad, E., Ghanbarzadeh, B., Mousavi, S. M., Emam-djomeh, Z., Khazaei, J. (2007). Prediction of rheological properties of Iranian bread dough from chemical composition of wheat flour by using artificial neural networks. Journal of Food Engineering, 81, 728–734. http://doi.org/10.1016/j.jfoodeng.2007.01.009
- Rossi, F., Velázquez, D., Monedero, I., & Biscarri, F. (2014). Artificial neural networks and physical modeling for determination of baseline consumption of CHP plants. Expert Systems with Applications, 41(10), 4658–4669. http://doi.org/10.1016/j.eswa.2014.02.001
- Silva, V. M. Da, Torres Filho, R. D. A., & Resende, J. V. De. (2018). Rheological properties of selected Brazilian honeys as a function of temperature and soluble solid concentration. International Journal of Food Properties, 20(00), S2481–S2494. http://doi.org/10.1080/10942912.2017.1370599
- Silva, V. M. da, de Carvalho, L. A., de Oliveira, N. L., Torres Filho, R. de A., de Resende, J. V. (2016). Rheological and thermal properties of selected Brazilian honeys from various floral origins. Journal of Texture Studies, 47, 208–219. http://doi.org/10.1111/jtxs.12174
- Soltani, M., Omid, M., & Alimardani, R. (2014). Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network. Journal of Food Science and Technology, 52(May), 3065–3071. http://doi.org/10.1007/s13197-014-1350-6
- Xi, J., Xue, Y., Xu, Y., & Shen, Y. (2013). Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols. Food Chemistry, 141(1), 320–326. http://doi.org/10.1016/j.foodchem.2013.02.084
- Yanniotis, S., Skaltsi, S., & Karaburnioti, S. (2006). Effect of moisture content on the viscosity of honey at different temperatures. Journal of Food Engineering, 72, 372–377. http://doi.org/10.1016/j.jfoodeng.2004.12.017
- Zuluaga-Domínguez, C. M., Nieto-Veloza, A., & Quicazán-de-Cuenca, M. (2017). Classification of Colombian honeys by electronic nose and physical-chemical parameters, using neural networks and genetic algorithms. Journal of Apicultural Research, 57(1), 145–152. http://doi.org/10.1080/00218839.2017.1339521