Dondurarak ve vakumla kurutulmuş mürdüm eriklerinin rehidrasyon davranışının geliştirilmiş bir Chebyshev ağı ile modellenmesi

Bu çalışmanın amacı, dondurarak ve vakumla kurutulmuş mürdüm eriklerinin (Prunus insititia) üç farklı sıcaklıkta (25, 45 ve 60°C) rehidrasyon özelliklerini incelemektir. İlk olarak, kinetik modeller (Weibull, Peleg, Üstel ve Birinci derece) matematiksel modeller oluşturmak ve rehidrasyon kinetiğini analiz etmek için tasarlanmıştır. İkinci olarak, yapay bir Chebsyhev ağı, modelleme kabiliyetini geliştirmek için yeni bir aşırı öğrenme makinesi tabanlı özellik çıkarma katmanı önerilecek şekilde rehidrasyon kinetiğinin modellenmesi için tasarlanmıştır. Deneysel veriler ve yapay modeller, rastgele seçilen veri setleri dikkate alınarak analiz edilmiş ve modellerin doğruluğunu karşılaştırmak için hataların kök ortalama kareleri (RMSE) hesaplanmıştır. Diklik ve öznitelik çıkarımı nedeniyle önerilen geliştirilmiş Chebyshev ağı, mürdüm eriklerinin rehidrasyon davranışını açıklamak için en düşük RMSE değerleri ile test edilen modeller arasında en iyi yaklaşım modeli olarak elde edilmiştir. Kinetik modelleri için yüzde RMSE değerleri ~%3.1 ve 4.8 aralığında değişirken, Chebyshev ağları için maksimum ve minimum yüzde değerleri sırasıyla %2.32 ve %0.51'dir. Önerilen Chebyshev ağının, rehidrasyon ve kurutma makinelerinin gömülü tasarımında cimri bir model olarak kullanılabileceği ve böylece rehidrasyon ve kurutma özelliklerinin önceden doğru bir şekilde tanımlanabileceği sonucuna varılmıştır.

Anahtar Kelimeler:

Rehidrasyon, Öznitelik çıkarımı, Yapay Chebyshev ağı, Dondurarak kurutma, Mürdüm eriği

Modeling of rehydration behavior of freeze- and vacuum-dried damson plums by an enhanced Chebyshev network

The aim of this paper is to investigate the rehydration properties of freeze- and vacuum-dried damson plums (Prunus insititia) at three different temperatures (25, 45 and 60°C). First, kinetic models (Weibull, Peleg, Exponential and First-order) were designed to construct mathematical models and analyze the rehydration kinetics. Second, an artificial Chebsyhev network was designed for modeling of the rehydration kinetics such that a novel extreme learning machine-based feature extraction layer is proposed to improve its modeling capability. The experimental data and artificial models were analyzed considering the randomly selected data sets, and the root mean squared errors (RMSE) were computed to compare accuracy of the models. Due to orthogonality and feature extraction, the proposed enhanced Chebyshev network was obtained as the best approximator model among tested models with the the lowest RMSE values to explain the rehydration behavior of damson plums. While the percentage RMSE values for kinetic models vary in the range of ~3.1 and 4.8%, the maximum and minimum percentage values for Chebyshev networks are 2.32% and 0.51%, respectively. It is concluded that the proposed Chebyshev network can be used as a parsimonious model in the embedded design of the rehydration and drying machines so that predefined rehydration and drying characteristics can be accurately defined.

Keywords:

Rehydration, Feature extraction, Artificial chebyshev network, Freeze drying, Damson plum,

PDF

___

[1] Sagar VR, Kumar S. “Recent advances in drying and dehydration of fruits and vegetables: A review”. Journal of. Food Science and Technology 47(1), 15-26, 2010.
[2] Górnicki K, Kaleta A, Trajer J. “Modelling of dried apple rehydration indices using ANN”. International Agrophysics, 33(3), 285-296, 2019.
[3] Moreira R, Chenlo F, Chaguri L, Fernandes C. “Water absorption, texture and color kinetics of air-dried chestnuts during rehydration”. Journal of Food Engineering, 86(4), 584-594, 2008.
[4] Link JV, Tribuzi G, Laurindo JB. “Improving quality of dried fruits: A comparison between conductive multi-flash and traditional drying methods”. LWT-Food Science and Technology, 84, 717-725, 2017.
[5] Zhou C, Feng Y, Zhang L, Yagoub AEA, Wahia H, Ma H, Sun Y, Yu X. “Rehydration characteristics of vacuum freeze-and hot air-dried garlic slices”. LWT-Food Science and Technology, 143, 1-8, 2021.
[6] Mousa N, Farid M. “Microwave vacuum drying of banana slices”. Drying. Technology, 20(10), 2055-2066, 2002.
[7] Tekin ZH, Baslar M. “The effect of ultrasound-assisted vacuum drying on the drying rate and quality of red peppers”. Journal of Thermal Analysis and Calorimetry, 132, 1131-1143, 2018.
[8] Fauster T, Giancaterino M, Pittia P, Jaeger H. “Effect of pulsed electric field pretreatment on shrinkage, rehydration capacity and texture of freeze-dried plant materials”. LWT-Food Science and Technology, 121, 1-7, 2020.
[9] Ratti C. “Hot air and freeze-drying of high-value foods: a review”. Journal of Food Engineering, 49(4), 311-319, 2001.
[10] Kaymak-Ertekin F. “Drying and rehydrating kinetics of green and red peppers”. Journal of Food Science, 67(1), 168-175, 2002.
[11] Dadalı G, Demirhan E, Özbek B. “Effect of drying conditions on rehydration kinetics of microwave dried spinach”. Food Bioproducts Processing, 86(4), 235-241, 2008.
[12] Cunningham S, Mcminn W, Magee T, Richardson P. “Modelling water absorption of pasta during soaking”. Journal of Food Engineering, 82, 600-607, 2007.
[13] Demiray E, Tulek Y. “Effect of temperature on water diffusion during rehydration of sun-dried red pepper (Capsicum annuum L.)”. Heat Mass Transfer, 53(5), 1829-1834, 2017.
[14] Rojas ML, Silveira I, Augusto PED. “Ultrasound and ethanol pre-treatments to improve convective drying: Drying, rehydration and carotenoid content of pumpkin”. Food Bioproducts Processing, 119, 20-30, 2020.
[15] García-Pascual, P., Sanjuán, N., Melis, R., & Mulet, A. “Morchella esculenta (morel) rehydration process modelling”. Journal of Food Engineering, 72(4), 346-353, 2006.
[16] García-Segovia P, Andrés-Bello A, Martínez-Monzó J. “Rehydration of air-dried Shiitake mushroom (Lentinus edodes) caps: Comparison of conventional and vacuum water immersion processes”. LWT-Food Science and Technology, 44(2), 480-488, 2011.
[17] Vega-Gálvez A, Notte-Cuello E, Lemus-Mondaca R, Zura L, Miranda M. “Mathematical modelling of mass transfer during rehydration process of Aloe vera (Aloe barbadensis Miller)”. Food Bioprod. Process, 87(4), 254-260, 2009.
[18] Lopez-Quiroga E, Prosapio V, Fryer PJ, Norton IT, Bakalis S. “A model-based study of rehydration kinetics in freezedried tomatoes”. Energy Procedia, 161, 75-82, 2019.
[19] Benseddik A, Azzi A, Zidoune MN, Khanniche R, Besombes C. “Empirical and diffusion models of rehydration process of differently dried pumpkin slices”. Journal of the Saudi Society of Agricultural Sciences, 18(4), 401-410, 2019.
[20] Beyhan S, Alci M. “An orthogonal ARX network for identification and control of nonlinear systems”. XXII International Symposium on Information, Communication and Automation Technologies, Sarajevo, Bosnia and Herzegovina, 29-30 October, 2009.
[21] Çetin M, Bahtiyar B, Beyhan S. “Adaptive uncertainty compensation-based nonlinear model predictive control with real-time applications”. Neural Computing and Applications, 31(2), 1029-1043, 2019.
[22] Lee KT, Farid M, Nguang SK. “The mathematical modelling of the rehydration characteristics of fruits”. Journal of Food Engineering, 72(1), 16-23, 2006.
[23] Ochoa-Martínez CI, Ramaswamy HS, Ayala-Aponte AA. “Artificial neural network modeling of osmotic dehydration mass transfer kinetics of fruits”. Drying. Technology, 25(1), 85-95, 2007.
[24] Lertworasirikul S, Saetan S. “Artificial neural network modeling of mass transfer during osmotic dehydration of kaffir lime peel”. Journal of Food Engineering, 98(2), 214-223, 2010.
[25] Jiao A, Xu X, Jin Z. “Modelling of dehydration–rehydration of instant rice in combined microwave-hot air drying”. Food Bioproducts Processing, 92(3), 259-265, 2014.
[26] Isleroglu H, Beyhan S. “Intelligent models based nonlinear modeling for infrared drying of mahaleb puree”. Journal of Food Process Engineering, 41(8), e12912, 1-7, 2018.
[27] AOAC. Official Methods for Analysis, Association of Official Analytical Chemists; 15th ed. Arlington, VA 1990.
[28] Yang SS, Tseng CS. “An orthogonal neural network for function approximation”. IEEE Transactions on Systems, Man and Cybernetics: Systems 26(5), 779-785, 1996.
[29] Sher CF, Tseng CS, Chen CS. “Properties and performance of orthogonal neural network in function approximation”. International Journal of Intelligent Systems, 16(12), 1377-1392, 2001.
[30] Purwar S, Kar IN, Jha AN. “On-line system identification of complex systems using Chebyshev neural networks”. Applied Soft Computing, 7(1), 364-372, 2007.
[31] Beyhan S, İtik M. “Adaptive fuzzy-Chebyshev network control of a conducting polymer actuator”. Journal of Intelligent Material Systems and Structures, 27(8), 1019-1029, 2016.
[32] Huang GB, Zhu QY, Siew CK. “Extreme learning machine: theory and applications”. Neurocomputing, 70(1-3), 489-501, 2006.
[33] Weinberger KQ, Tesauro G. “Metric learning for kernel regression”. In Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, San Juan, Puerto Rico, 21-24 March 2007.
[34] Isleroglu H. “Freeze drying and moisture adsorption kinetics of kefir powder”. Italian Journal of Food Science 31(3), 514-530, 2019.
[35] Maldonado S, Arnau E, Bertuzzi MA. “Effect of temperature and pretreatment on water diffusion during rehydration of dehydrated mangoes”. Journal of Food Engineering, 96(3), 333-341, 2010.