Vis-NIR hyperspectral imaging in visualizing moisture distribution of mango slices during microwave-vacuum drying

Area of Science:

• Food Science and Technology
• Agricultural Engineering
• Spectroscopy

Background:

• Microwave-vacuum drying is an efficient method for preserving food quality.
• Accurate monitoring of moisture content is crucial for optimizing drying processes and ensuring product quality.
• Hyperspectral imaging (HSI) offers potential for non-destructive analysis of food properties.

Purpose of the Study:

• To evaluate the suitability of hyperspectral imaging (HSI) for monitoring moisture content during microwave-vacuum drying of mango slices.
• To develop and validate predictive models for moisture content using HSI data.
• To visualize the moisture distribution within mango slices during the drying process.

Main Methods:

• Mango slices were dried using a domestic microwave oven with a vacuum desiccator.
• Two lab-scale hyperspectral imaging (HSI) systems were utilized for data acquisition.
• Thin-layer drying models (Page and Two-term) were applied to describe the drying kinetics.
• Partial Least Squares (PLS) regression was employed for spectral-moisture correlation, with waveband selection strategies used to optimize models.
• The best performing model (RC-PLS-2) was used for moisture visualization.

Main Results:

• The Page and Two-term models accurately described the drying process (R²=0.978).
• The optimized RC-PLS-2 model achieved high prediction accuracy (Rp²=0.972, RMSEP=4.611%) for moisture content.
• Moisture distribution maps revealed lower moisture content in the central regions of the mango slices compared to peripheral areas.

Conclusions:

• Hyperspectral imaging (HSI) is a valuable, non-destructive tool for real-time moisture monitoring in mango slices during drying.
• HSI enables rapid and accurate visualization of moisture distribution, aiding in process optimization.
• The study validates HSI as a powerful technique for quality assessment in food drying applications.