Prediction of moisture ratio and drying rate of orange slices using machine learning approaches


Journal of Food Processing and Preservation, vol.46, no.11, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 46 Issue: 11
  • Publication Date: 2022
  • Doi Number: 10.1111/jfpp.17011
  • Journal Name: Journal of Food Processing and Preservation
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aquatic Science & Fisheries Abstracts (ASFA), Biotechnology Research Abstracts, Business Source Elite, Business Source Premier, CAB Abstracts, Compendex, Food Science & Technology Abstracts, INSPEC, Veterinary Science Database
  • Kayseri University Affiliated: No


© 2022 Wiley Periodicals LLC.In order to improve the drying characteristics and to optimization of drying conditions, machine learning (ML) and response surface methodology (RSM) were 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 was encountered with increasing drying time and sample thickness. Besides, energy consumption had a decreasing trend at higher temperatures. Practical applications: Machine learning models are novelty and rapid methods that have been successfully utilized to solve such challenges agricultural commodities. Drying is common process to preserve the food quality. This study provides optimum conditions for drying orange slices in single unit air-convective dryer and improves the effect of drying system on some drying characteristics energy aspects. In addition, this study can be able to present a technical knowledge for orange slice drying and related equipment design.