Affiliation:
1. Department of Petroleum Engineering College of Engineering, Knowledge University Erbil Iraq
2. Department of Agricultural Machinery and Technologies Engineering, Faculty of Agriculture Ankara University Ankara Turkey
3. Department of Civil Engineering Cihan University‐Erbil Erbil Iraq
4. Department of Biosystems Engineering College of Agriculture and Natural Resources, University of Mohaghegh Ardabili Ardabil Iran
5. Scientific Research Center Erbil Polytechnic University Erbil Iraq
6. Department of Mechanical Engineering of Biosystems, Faculty of Agriculture Urmia University Urmia Iran
Abstract
AbstractDrying plays a crucial role in preserving the quality of agricultural products. Nevertheless, suboptimal conditions in drying systems have an adverse effect on drying characteristics and energy efficiency. Machine learning approaches are innovative and reliable that have been successfully used to solve such challenges and achieve optimization in drying processes. In this study, five machine learning approaches (multilayer perceptron [MLP], gaussian processes [GP], support vector regression [SVR], k‐nearest neighbors [kN], and random forest [RF]) were used to estimate moisture content and moisture ratio of apricot in five various dryers (convective [CV], microwave [MW], infrared [IR], microwave‐convective [MW‐CV], and infrared‐convective [IR‐CV]). Also, the values of specific energy consumption (SEC) and effective moisture diffusivity (Deff) were calculated in these dryers. Accordingly, the best result of the Deff (3.14 × 10−10 m2/s) and the minimum value of the drying time (130 min) and SEC (18.67 MJ/kg) were obtained using MW‐CV hybrid dryer. While the lowest values of Deff (2.09 × 10−11 m2/s) and highest drying time (18.5 h) and SEC (209.34 MJ/kg) were detected in CV dryer at 50°C. The best correlation coefficients (R) for the estimation of moisture content were gained using RF technique for k‐fold cross validation and train‐test split with the values of 0.9908 and 0.9912, respectively. Moreover, moisture ratio results showed that the MLP achieved the highest R value over 0.9985 for both validation methodologies. In the discrimination of the drying methods, the MLP had the greatest accuracy as 82.00% and 86.00% for k‐fold cross validation and train‐test split, respectively. The results showed that the RF and ML models could potentially be used for estimation and discrimination for drying applications.Practical ApplicationsRecently, there has been an increased interest in healthy food choices such as foodstuffs, snacks, and dried products. This trend has captured the attention of both dietitians and conscious consumers. Apricots are a prime example of a valuable dried product that can be dry in various conditions. Machine learning techniques can be used for rapid and non‐destructive determination of drying characteristics and such techniques yield objective and accurate results. Present findings revealed that texture machine learning models could be used as an effective and reliable discrimination tool for dried products.
Subject
General Chemical Engineering,Food Science
Reference86 articles.
1. Optimization of process parameters of soybean seeds dried in a constant‐bed dryer using response surface methodology;Abbasi S. A.;Journal of Agricultural Science and Technology,2010
2. Prediction of heat capacity of amine solutions using artificial neural network and thermodynamic models for CO2 capture processes
3. Random forests and decision trees;Ali J.;International Journal of Computer Science Issues (IJCSI),2012
4. Detection of Mulberry Ripeness Stages Using Deep Learning Models
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