Modeling the Moisture Content and Drying Rate of Zucchini (Cucurbita pepo L.) in a Solar Hybrid Dryer Using ANN and ANFIS Methods
Küçük Resim Yok
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
College of Agriculture and Food Science, University of the Philippines Los Banos
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Estimating product drying kinetics is critical to obtain the best drying process without compromising product quality and necessitates the development of numerical drying models. This research aims to compare the prediction models developed using artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS), two popular machine learning approaches in the recent years. Zucchini slices were chosen as samples and dried in a solar-assisted microwave belt dryer at 0.245 m/min belt speed and microwave powers of 0.7, 1, and 1.4 kW. On the data set obtained by computing the moisture content and drying rate values, prediction models were developed using ANN and ANFIS approaches. These models were evaluated using the coefficient of determination, mean absolute percent error, and root mean square error data. The ANFIS-based prediction model outperformed the ANN model in terms of drying rate performance, but the ANN model outperformed the ANFIS model in terms of moisture content values. Results showed that both methods established can be utilized to estimate zucchini slices. © 2023, College of Agriculture and Food Science, University of the Philippines Los Banos. All rights reserved.
Açıklama
Anahtar Kelimeler
ANFIS, ANN, conveyor, microwave drying, zucchini
Kaynak
Philippine Agricultural Scientist
WoS Q Değeri
Scopus Q Değeri
Q4
Cilt
106
Sayı
3
