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

Öz

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

Abstract

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

Kaynakça

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