Foam mat drying of papaya using microwaves: Machine learning modeling

Abstract

The aim of this article is to study the microwave-assisted foam mat drying of papaya to form papaya powder. The process of foam mat drying of papaya using microwaves was modeled by machine learning approaches like artificial neural network (ANN), support vector regression (SVR), and Gaussian process regression (GPR). Effect of microwave power (480-640 W), inlet air temperature (40-50 C), and thickness of foam (2-4 mm) on the rate of drying were studied. The performance of the models was evaluated on the basis of different performance matrices including root mean square error (RMSE), coefficient of determination (R 2 ), model predictive error, and Chi-square (χ 2 ). The microwave heating of the papaya foam reduced the drying time manifold. All three machine learning approaches were able to predict the drying process efficiently.SVR showed the best performance (R 2 = 0.96; RMSE = 0.03) followed by GPR (R 2 = 0.92; RMSE = 0.04) and ANN (R 2 = 0.91; RMSE = 0.04). SVR-based model was simulated to predict the effect of power, temperature, and thickness on drying rate.Machine learning approaches can be efficiently used for modeling and microwaveassisted foam mat drying. SVR-based model proves to be a good alternative of ANN.Practical Applications: Foam mat drying is the method of dehydrating for heatsensitive and viscous materials which cannot be dried by other conventional methods. It is cost-effective, simple, and provides high product quality. The use of microwave-assisted drying decreases the drying time manifold. Modeling of the drying process is significant for its scale-up to industrial scale. Machine learning techniques have the capability of learning the hidden factors involved in the process and thus provide better predictions as compared with statistical regression methods. In this study, three machine learning methods (artificial neural network [ANN], support vector regression [SVR], and Gaussian process regression [GPR]) were compared for their efficiency in modeling the foam-mat drying of papaya pulp using microwaves.SVR showed the best performance as compared with ANN and GPR.

Mi̇krodalga Koşullarinda Muzun Köpük Kurutma Özelli̇kleri̇ni̇n Modellenmesi̇

“…Drying rate was increased 727.62% when increasing power intensity from 100 to 600W. Similar findings were reported by several authors for other foods as foam-mat drying of guava pulp (Qadri and Srivastava, 2017), blue honeysuckle berry (Sun et al, 2020) and papaya (Qadri et al, 2020). The experimental results indicate that there was no constant rate period and drying of banana foam occurred only at falling rate period with internal liquid diffusion.…”

Encapsulation of coffee silverskin extracts by foam mat drying and comparison with powders obtained by spray drying and freeze‐drying

“…This minimal restriction was adopted since MD and GA, individually, are not able to form foam, being more used as foam stabilizers and also contribute to increasing the viscosity of the medium. On the other hand, EA is a good foaming agent (Hardy & Jideani, 2017; Qadri et al., 2020; Sangamithra et al., 2015). These agents are commonly used in FMD and can also act as excellent wall materials for encapsulating bioactive compounds (Araújo et al., 2022; Arzeni et al., 2015; Ballesteros et al., 2017).…”

“…In turn, these foam properties also influence the drying process and the quality of the powder obtained. Therefore, these properties must be carefully determined (Hardy & Jideani, 2017; Qadri et al., 2020).…”

Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying in an infrared‐assisted continuous hybrid solar dryer

“…The predicted MR values of the developed models (mathematical, ANN and ANFIS) were compared to the experimental MR data based on three statistical criteria, namely R 2 , RMSE and χ 2 which were calculated using the Equations (13–15) (Qadri et al., 2020):…”