Optimization of microwave vacuum drying of pineapples: A three-stage approach
The present study aims to optimize the microwave vacuum drying (MVD) of pineapples using three analytical stages: One-way analysis of variance (ANOVA), the Taguchi method, and process adjustment. Key performance indicators, including drying curve, rehydration rate, color, texture, energy consumption...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025003147 |
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| Summary: | The present study aims to optimize the microwave vacuum drying (MVD) of pineapples using three analytical stages: One-way analysis of variance (ANOVA), the Taguchi method, and process adjustment. Key performance indicators, including drying curve, rehydration rate, color, texture, energy consumption, and sensory evaluation, were assessed to determine optimal settings. Initial ANOVA identified critical factors, including temperature control, microwave power density, vacuum pressure, and carousel speed. Higher microwave power densities significantly reduced drying time, and temperature control ensured product quality by balancing drying efficiency and minimizing overheating risks. Vacuum pressure contributed to enhanced moisture removal and improved color preservation, while turntable speed had a minimal impact on drying efficiency but ensured uniform temperature distribution. The Taguchi method further optimized these parameters, comprising a microwave power density of 7 W/g, a vacuum degree of 20 kPa, temperature control at 55 °C, and a turntable speed of 4 rpm. Subsequent process adjustments refined the settings to improve drying quality and stability, achieving comprehensive scores of 7.7 and 7.5 for the optimized configurations of 50 °C with microwave power densities of 6 W/g and 7 W/g, respectively. This three-stage approach significantly improves MVD efficiency and product quality, offering practical insights for industrial-scale applications. |
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| ISSN: | 2590-1230 |