Assessment of drying characteristics for whole and sliced red ginseng using short-wave infrared spectroscopy and mathematical model

Monitoring the moisture content is crucial for red ginseng quality control. In this study, we established a moisture content prediction model using partial least squares regression (PLSR) based on short-wave infrared (SWIR) data of different ginseng types (whole and sliced) at various drying temperatures and times. The model parameters, including the R2 values, ratio of prediction deviation, and range error ratio, showed that the developed model was robust. Sliced ginseng showed a constant rate period, followed by a falling rate period, whereas whole ginseng showed only a falling rate period. The falling-rate period coincided with the formation of a dry zone on the sample surface in the moisture distribution map, indicating that monitoring the moisture distribution map can predict the onset of the falling-rate period. These results suggest that the SWIR technique is a promising nondestructive method for evaluating the drying process of red ginseng.Practical applications: Monitoring the moisture content of red ginseng is essential to prevent microbial spoilage during transportation. Traditional monitoring of water content is destructive and time-consuming. Therefore, using hyperspectral imaging, we aimed to develop an efficient moisture prediction model for the drying process of red ginseng. We believe that our study makes a significant contribution to non-destructive and rapid quality evaluation in red ginseng production.