Microwave Drying Modelling of Stevia rebaudiana Leaves Using Artificial Neural Network and Its Effect on Color and Biochemical Attributes
Stevia rebaudiana has grown in popularity and consumption across the world as an excellent natural sweetener due to its 300 times sweetness than sugar. Since Stevia leaves are often used in their dried state, the drying process has an inevitable effect on the attributes of finished product. In this...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Food Quality |
| Online Access: | http://dx.doi.org/10.1155/2023/2811491 |
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| author | Baldev Singh Kalsi Sandhya Singh Mohammed Shafiq Alam Surekha Bhatia |
| author_facet | Baldev Singh Kalsi Sandhya Singh Mohammed Shafiq Alam Surekha Bhatia |
| author_sort | Baldev Singh Kalsi |
| collection | DOAJ |
| description | Stevia rebaudiana has grown in popularity and consumption across the world as an excellent natural sweetener due to its 300 times sweetness than sugar. Since Stevia leaves are often used in their dried state, the drying process has an inevitable effect on the attributes of finished product. In this study, Stevia leaves were microwave dried at five different levels of powers ranging from 180 to 900 W to evaluate the influence of power levels on moisture ratio (MR), drying rate and time, effective moisture diffusivity, specific energy consumption (SEC), color, and biochemical characteristics. Among the five selected thin layer models for evaluating the drying behavior, the semiempirical page model described the drying kinetics very well with R2 > 0.997. The effective diffusivity increased from 3.834×10−11 to 1.997×10−10 m2/s with increasing microwave power, while SEC first increased till 320 W to a value of 9.77 MJ/kg and then followed a decreasing trend. Furthermore, multilayer feed forward (MLF) artificial neural network (ANN) using backpropagation algorithm was used to predict the moisture ratio of Stevia leaves during microwave drying. The result showed that the ANN model with 15 neurons in 1 hidden layer could predict the MR with a high R2 value (0.999). Thus, ANN modelling can successfully be used as an effective tool for predicting drying kinetics of samples. Furthermore, the color properties showed significant differences between fresh and dried samples except for the hue angle, and the variation in their values was not affected by the microwave dryer’s power output. At 720 W power level, the highest content of stevioside (11.84 mg/g) and rebaudioside A (7.11 mg/g) along with maximum retention of ascorbic acid (∼86%) was observed, while the highest total phenol content (56.98 mg GAE/g) and antioxidant capacity (74.22%) was reported in microwave dried samples at 900 W. |
| format | Article |
| id | doaj-art-37550283cf184cec8560244835d2f461 |
| institution | Kabale University |
| issn | 1745-4557 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Food Quality |
| spelling | doaj-art-37550283cf184cec8560244835d2f4612025-02-03T06:47:17ZengWileyJournal of Food Quality1745-45572023-01-01202310.1155/2023/2811491Microwave Drying Modelling of Stevia rebaudiana Leaves Using Artificial Neural Network and Its Effect on Color and Biochemical AttributesBaldev Singh Kalsi0Sandhya Singh1Mohammed Shafiq Alam2Surekha Bhatia3Department of Processing & Food EngineeringDepartment of Processing & Food EngineeringDepartment of Processing & Food EngineeringDepartment of Processing & Food EngineeringStevia rebaudiana has grown in popularity and consumption across the world as an excellent natural sweetener due to its 300 times sweetness than sugar. Since Stevia leaves are often used in their dried state, the drying process has an inevitable effect on the attributes of finished product. In this study, Stevia leaves were microwave dried at five different levels of powers ranging from 180 to 900 W to evaluate the influence of power levels on moisture ratio (MR), drying rate and time, effective moisture diffusivity, specific energy consumption (SEC), color, and biochemical characteristics. Among the five selected thin layer models for evaluating the drying behavior, the semiempirical page model described the drying kinetics very well with R2 > 0.997. The effective diffusivity increased from 3.834×10−11 to 1.997×10−10 m2/s with increasing microwave power, while SEC first increased till 320 W to a value of 9.77 MJ/kg and then followed a decreasing trend. Furthermore, multilayer feed forward (MLF) artificial neural network (ANN) using backpropagation algorithm was used to predict the moisture ratio of Stevia leaves during microwave drying. The result showed that the ANN model with 15 neurons in 1 hidden layer could predict the MR with a high R2 value (0.999). Thus, ANN modelling can successfully be used as an effective tool for predicting drying kinetics of samples. Furthermore, the color properties showed significant differences between fresh and dried samples except for the hue angle, and the variation in their values was not affected by the microwave dryer’s power output. At 720 W power level, the highest content of stevioside (11.84 mg/g) and rebaudioside A (7.11 mg/g) along with maximum retention of ascorbic acid (∼86%) was observed, while the highest total phenol content (56.98 mg GAE/g) and antioxidant capacity (74.22%) was reported in microwave dried samples at 900 W.http://dx.doi.org/10.1155/2023/2811491 |
| spellingShingle | Baldev Singh Kalsi Sandhya Singh Mohammed Shafiq Alam Surekha Bhatia Microwave Drying Modelling of Stevia rebaudiana Leaves Using Artificial Neural Network and Its Effect on Color and Biochemical Attributes Journal of Food Quality |
| title | Microwave Drying Modelling of Stevia rebaudiana Leaves Using Artificial Neural Network and Its Effect on Color and Biochemical Attributes |
| title_full | Microwave Drying Modelling of Stevia rebaudiana Leaves Using Artificial Neural Network and Its Effect on Color and Biochemical Attributes |
| title_fullStr | Microwave Drying Modelling of Stevia rebaudiana Leaves Using Artificial Neural Network and Its Effect on Color and Biochemical Attributes |
| title_full_unstemmed | Microwave Drying Modelling of Stevia rebaudiana Leaves Using Artificial Neural Network and Its Effect on Color and Biochemical Attributes |
| title_short | Microwave Drying Modelling of Stevia rebaudiana Leaves Using Artificial Neural Network and Its Effect on Color and Biochemical Attributes |
| title_sort | microwave drying modelling of stevia rebaudiana leaves using artificial neural network and its effect on color and biochemical attributes |
| url | http://dx.doi.org/10.1155/2023/2811491 |
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