Process Optimization of Fluidized Bed Drying for Water Spinach: Evaluating the Effect of Blanching Through RSM and ANN Models DOI Creative Commons
Mir Tuhin Billah,

Noor E Zannat,

Md Akram Hossain

et al.

Food Science & Nutrition, Journal Year: 2025, Volume and Issue: 13(4)

Published: March 24, 2025

ABSTRACT The quality of the dried leafy vegetables, such as water spinach ( Ipomoea aquatica ), has been found to be significantly affected by drying process in terms moisture content and retention important nutrients, namely vitamin C β‐carotene. There is great potential for fluidized bed applied vegetables optimizing parameters maximum nutrient since it not researched. This work investigated effect temperature, time, thickness on nutritional blanched unblanched samples. In present study, designed optimized using a Central Composite Design (CCD) Response Surface Methodology (RSM). For this both RSM artificial neural network (ANN) predictive models are developed further comparison. Using multiobjective desirability function, best‐optimized response was given from experimental model responses content, C, β‐carotene retention. Appropriate statistical metrics applied, example, AARD (Absolute Average Relative Deviation), MRD (Mean MSE Squared Error), R 2 (Coefficient Determination), which helped comparison during study. It observed experiment that all variables thickness. Variation samples > 16% attainment compared samples, exhibited variation more than 25% due changes contrary. shown better performance ANN its precision prediction power. conditions came out 60°C 7.19 min 5.12 cm thickness, resulted 2.95% 5.99 mg/100 g 139.16 μg/g close alignment between predicted values confirms suitability industrial‐scale vegetables.

Language: Английский

Process Optimization of Fluidized Bed Drying for Water Spinach: Evaluating the Effect of Blanching Through RSM and ANN Models DOI Creative Commons
Mir Tuhin Billah,

Noor E Zannat,

Md Akram Hossain

et al.

Food Science & Nutrition, Journal Year: 2025, Volume and Issue: 13(4)

Published: March 24, 2025

ABSTRACT The quality of the dried leafy vegetables, such as water spinach ( Ipomoea aquatica ), has been found to be significantly affected by drying process in terms moisture content and retention important nutrients, namely vitamin C β‐carotene. There is great potential for fluidized bed applied vegetables optimizing parameters maximum nutrient since it not researched. This work investigated effect temperature, time, thickness on nutritional blanched unblanched samples. In present study, designed optimized using a Central Composite Design (CCD) Response Surface Methodology (RSM). For this both RSM artificial neural network (ANN) predictive models are developed further comparison. Using multiobjective desirability function, best‐optimized response was given from experimental model responses content, C, β‐carotene retention. Appropriate statistical metrics applied, example, AARD (Absolute Average Relative Deviation), MRD (Mean MSE Squared Error), R 2 (Coefficient Determination), which helped comparison during study. It observed experiment that all variables thickness. Variation samples > 16% attainment compared samples, exhibited variation more than 25% due changes contrary. shown better performance ANN its precision prediction power. conditions came out 60°C 7.19 min 5.12 cm thickness, resulted 2.95% 5.99 mg/100 g 139.16 μg/g close alignment between predicted values confirms suitability industrial‐scale vegetables.

Language: Английский

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