Akademik Veri Yönetim Sistemi
JOURNAL OF FOOD PROCESSING AND PRESERVATION, ss.1-11, 2022 (SCI-Expanded, Scopus)
In order to improve the drying characteristics and to optimization of drying conditions machine learning (ML) and response surface methodology (RSM) was applied in air-convective drying of orange slices (Washington Navel and Valencia cultivars). Interactions of temperature (T, 50-60°C), sample thickness (ST, 5-9 mm) and drying time (DT, 8-10 h) like independent variables with specific moisture extraction rate, effective moisture diffusivity, energy efficiency and energy consumption like dependent variables were determined. In addition, five machine learning algorithms (random forest-RF; artificial neural network-ANN; gaussian processes-GP support vector regression-SVR and k-nearest neighbors-kNN) were used to predict moisture ratio and drying rate. In Washington Navel and Valencia cultivars, the greatest correlation coefficients (R) for prediction of moisture ratio were obtained k-NN algorithm with values of 0.9944 and 0.9898, respectively. Also, drying rate prediction results showed that k-NN achieved higher R with values of 1.0000 and 0.9954, respectively. Experimental findings were adapted by a second-degree polynomial model through variance analysis to identify model fitness and optimal drying conditions. Combined desirability value was calculated as 0.8812 for Valencia and 0.8564 for Washington. Increasing energy consumption were encountered with increasing drying time and sample thickness. Besides, energy consumption had a decreasing trend at higher temperatures.
In order to improve the drying characteristics and to optimization of drying conditions machine learning (ML) and response surface methodology (RSM) was applied in air-convective drying of orange slices (Washington Navel and Valencia cultivars). Interactions of temperature (T, 50-60°C), sample thickness (ST, 5-9 mm) and drying time (DT, 8-10 h) like independent variables with specific moisture extraction rate, effective moisture diffusivity, energy efficiency and energy consumption like dependent variables were determined. In addition, five machine learning algorithms (random forest-RF; artificial neural network-ANN; gaussian processes-GP support vector regression-SVR and k-nearest neighbors-kNN) were used to predict moisture ratio and drying rate. In Washington Navel and Valencia cultivars, the greatest correlation coefficients (R) for prediction of moisture ratio were obtained k-NN algorithm with values of 0.9944 and 0.9898, respectively. Also, drying rate prediction results showed that k-NN achieved higher R with values of 1.0000 and 0.9954, respectively. Experimental findings were adapted by a second-degree polynomial model through variance analysis to identify model fitness and optimal drying conditions. Combined desirability value was calculated as 0.8812 for Valencia and 0.8564 for Washington. Increasing energy consumption were encountered with increasing drying time and sample thickness. Besides, energy consumption had a decreasing trend at higher temperatures.