Drying Kinetics and Chemical Properties of Mango

Area of Science:

• Food Science and Technology
• Agricultural Engineering
• Materials Science

Background:

• Optimizing drying processes for fruits like mangoes is crucial for preservation and extending shelf life.
• Understanding the impact of drying parameters on mango quality attributes is essential for industrial applications.
• Thin-layer drying models are vital for predicting and controlling the drying kinetics of agricultural products.

Purpose of the Study:

• To investigate the thin-layer drying characteristics of four mango varieties (Kent, Keitt, Haden, Palmer) under different mechanical dryer temperatures.
• To determine the effect of drying temperature on moisture content, moisture ratio, effective moisture diffusivity, and Vitamin C content.
• To evaluate the performance of twelve mathematical models in describing the thin-layer drying behavior of mango slices.

Main Methods:

• Four mango varieties were sliced and dried in a mechanical dryer at 55°C, 65°C, and 75°C.
• Moisture content (MC) and moisture ratio were measured over time for each variety and temperature.
• Effective moisture diffusivity and activation energy were calculated, and Vitamin C content was analyzed.
• Twelve thin-layer drying models were fitted to the experimental data to predict drying curves.

Main Results:

• Moisture content decreased with increased drying time for all mango varieties and temperatures.
• Palmer and Haden varieties exhibited the lowest final moisture content at 65°C.
• Effective moisture diffusivity values ranged from 5.90 × 10⁻⁷ to 7.33 × 10⁻⁷ m²/s, with Palmer showing the highest value.
• The Midilli, Page, Wang and Singh, and Logarithmic models best represented the drying behavior of mango slices.

Conclusions:

• Mechanical drying effectively reduces moisture content in mango slices, with optimal conditions varying by variety.
• Drying temperature significantly influences drying kinetics and effective moisture diffusivity.
• Selected mathematical models provide reliable predictions for mango slice drying, aiding in process optimization.