Mir Tuhin Billah, Noor E Zannat, Md Akram Hossain, Ishmam Haque Sachcha, Sabina Yasmin, Md. Sazzat Hossain Sarker
{"title":"菠菜流化床干燥工艺优化:用RSM和ANN模型评价漂烫效果","authors":"Mir Tuhin Billah, Noor E Zannat, Md Akram Hossain, Ishmam Haque Sachcha, Sabina Yasmin, Md. Sazzat Hossain Sarker","doi":"10.1002/fsn3.70114","DOIUrl":null,"url":null,"abstract":"<p>The quality of the dried leafy vegetables, such as water spinach (<i>Ipomoea aquatica</i>), has been found to be significantly affected by the drying process in terms of moisture content and retention of important nutrients, namely vitamin C and β-carotene. There is great potential for fluidized bed drying to be applied for leafy vegetables in optimizing process parameters for maximum nutrient retention since it has not been researched. This work investigated the effect of temperature, drying time, and bed thickness on the nutritional quality of blanched and unblanched water spinach samples. In the present study, the fluidized bed drying process has been designed and optimized using a Central Composite Design (CCD) and Response Surface Methodology (RSM). For this study, both RSM and artificial neural network (ANN) predictive models are developed for further comparison. Using a multiobjective desirability function, the best-optimized response was given from the experimental model for the responses of moisture content, vitamin C, and β-carotene retention. Appropriate statistical metrics are applied, for example, AARD (Absolute Average Relative Deviation), MRD (Mean Relative Deviation), MSE (Mean Squared Error), and <i>R</i><sup>2</sup> (Coefficient of Determination), which helped in model comparison during the study. It was observed from the experiment that all the response variables are significantly affected by drying temperature, time, and bed thickness. Variation of bed thickness in the blanched samples affected > 16% in moisture content attainment compared to unblanched samples, and vitamin C content exhibited a variation of more than 25% due to changes in bed thickness for blanched samples on the contrary. RSM has shown a better performance than ANN in its precision and prediction power. The optimized drying conditions came out to be 60°C as the drying temperature, 7.19 min as the drying time, and 5.12 cm as the bed thickness, which resulted in 2.95% of moisture content, 5.99 mg/100 g vitamin C, and 139.16 μg/g of β-carotene. The close alignment between predicted and experimental values confirms the suitability of the optimized conditions for industrial-scale drying of leafy vegetables.</p>","PeriodicalId":12418,"journal":{"name":"Food Science & Nutrition","volume":"13 4","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/fsn3.70114","citationCount":"0","resultStr":"{\"title\":\"Process Optimization of Fluidized Bed Drying for Water Spinach: Evaluating the Effect of Blanching Through RSM and ANN Models\",\"authors\":\"Mir Tuhin Billah, Noor E Zannat, Md Akram Hossain, Ishmam Haque Sachcha, Sabina Yasmin, Md. Sazzat Hossain Sarker\",\"doi\":\"10.1002/fsn3.70114\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The quality of the dried leafy vegetables, such as water spinach (<i>Ipomoea aquatica</i>), has been found to be significantly affected by the drying process in terms of moisture content and retention of important nutrients, namely vitamin C and β-carotene. There is great potential for fluidized bed drying to be applied for leafy vegetables in optimizing process parameters for maximum nutrient retention since it has not been researched. This work investigated the effect of temperature, drying time, and bed thickness on the nutritional quality of blanched and unblanched water spinach samples. In the present study, the fluidized bed drying process has been designed and optimized using a Central Composite Design (CCD) and Response Surface Methodology (RSM). For this study, both RSM and artificial neural network (ANN) predictive models are developed for further comparison. Using a multiobjective desirability function, the best-optimized response was given from the experimental model for the responses of moisture content, vitamin C, and β-carotene retention. Appropriate statistical metrics are applied, for example, AARD (Absolute Average Relative Deviation), MRD (Mean Relative Deviation), MSE (Mean Squared Error), and <i>R</i><sup>2</sup> (Coefficient of Determination), which helped in model comparison during the study. It was observed from the experiment that all the response variables are significantly affected by drying temperature, time, and bed thickness. Variation of bed thickness in the blanched samples affected > 16% in moisture content attainment compared to unblanched samples, and vitamin C content exhibited a variation of more than 25% due to changes in bed thickness for blanched samples on the contrary. RSM has shown a better performance than ANN in its precision and prediction power. The optimized drying conditions came out to be 60°C as the drying temperature, 7.19 min as the drying time, and 5.12 cm as the bed thickness, which resulted in 2.95% of moisture content, 5.99 mg/100 g vitamin C, and 139.16 μg/g of β-carotene. 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Process Optimization of Fluidized Bed Drying for Water Spinach: Evaluating the Effect of Blanching Through RSM and ANN Models
The quality of the dried leafy vegetables, such as water spinach (Ipomoea aquatica), has been found to be significantly affected by the drying process in terms of moisture content and retention of important nutrients, namely vitamin C and β-carotene. There is great potential for fluidized bed drying to be applied for leafy vegetables in optimizing process parameters for maximum nutrient retention since it has not been researched. This work investigated the effect of temperature, drying time, and bed thickness on the nutritional quality of blanched and unblanched water spinach samples. In the present study, the fluidized bed drying process has been designed and optimized using a Central Composite Design (CCD) and Response Surface Methodology (RSM). For this study, both RSM and artificial neural network (ANN) predictive models are developed for further comparison. Using a multiobjective desirability function, the best-optimized response was given from the experimental model for the responses of moisture content, vitamin C, and β-carotene retention. Appropriate statistical metrics are applied, for example, AARD (Absolute Average Relative Deviation), MRD (Mean Relative Deviation), MSE (Mean Squared Error), and R2 (Coefficient of Determination), which helped in model comparison during the study. It was observed from the experiment that all the response variables are significantly affected by drying temperature, time, and bed thickness. Variation of bed thickness in the blanched samples affected > 16% in moisture content attainment compared to unblanched samples, and vitamin C content exhibited a variation of more than 25% due to changes in bed thickness for blanched samples on the contrary. RSM has shown a better performance than ANN in its precision and prediction power. The optimized drying conditions came out to be 60°C as the drying temperature, 7.19 min as the drying time, and 5.12 cm as the bed thickness, which resulted in 2.95% of moisture content, 5.99 mg/100 g vitamin C, and 139.16 μg/g of β-carotene. The close alignment between predicted and experimental values confirms the suitability of the optimized conditions for industrial-scale drying of leafy vegetables.
期刊介绍:
Food Science & Nutrition is the peer-reviewed journal for rapid dissemination of research in all areas of food science and nutrition. The Journal will consider submissions of quality papers describing the results of fundamental and applied research related to all aspects of human food and nutrition, as well as interdisciplinary research that spans these two fields.