Laser light backscattering image to predict moisture content of mango slices with different ripeness during drying process

Abstract

AbstractMoisture content is an essential parameter in investigating the quality of agricultural products during drying. This study aimed to evaluate the potential of using laser light backscatter imaging (LLBI) to monitor the moisture content of mangoes at various stages of ripeness during drying. Backscattered images of mango slices were collected using laser diodes with different wavelengths (450, 520, and 635 nm) during drying. The effects of different drying temperatures (60°C, 70°C, and 80°C) and maturity levels (Ripeness 1, Ripeness 2, Ripeness 3) on the moisture content of mango slices were studied, and the backscattered images of mango slices were analyzed by Gaussian amplitude function. The results showed that drying temperatures and stage of ripeness had a significant effect on the drying characteristics of mango (p < .05). Increasing the drying temperature led to a considerable reduction in the moisture ratio, and increasing the maturity resulted in a considerable decrease in the total drying time. Additionally, there were differences in the laser transmission properties of the materials with varying levels of moisture. Variations in the structure of the tissue during the drying process may result in different degrees of backscattering. The model for predicting moisture content at 635 nm was the most accurate, with the maximum determination coefficient R2 of 0.9247, and the minimum root mean square error (RMSE) at this time is 0.0771. Therefore, this study has shown that LLBI can better predict the moisture content of mangoes with different maturity during drying.Practical ApplicationsLaser light backscatter imaging (LLBI) is widely used in agricultural products processing. Using LLBI to predict the moisture content of agricultural products during drying is a good method. It can predict the moisture content of agricultural products whose mature state is unknown, and at the same time express the physical characteristics of agricultural products and their interactions through mathematical models. Furthermore, this research provides ideas for the prediction of moisture content of other agricultural products and the improvement of the quality of dried agricultural products.