Rapid and non-destructive classification of salinity levels in brined kimchi cabbage using hyperspectral imaging.

In this study, we demonstrate the potential of a non-destructive hyperspectral imaging processing method in the near-infrared (NIR) region (874-1734 nm) for classifying the quality of brined kimchi cabbage. The salinity level of brined kimchi cabbage is an important indicator of consumer preference and the quality of kimchi. Hence, we compared the water content and salinity of brined kimchi cabbage via hyperspectral data. We extracted the optimal wavelengths from the hyperspectral image dataset to classify the salinity level of the predicted brined kimchi cabbage, and thus, established a novel approach for classifying kimchi samples into quality-unacceptable and quality-acceptable groups. Standard normal variate and multiplicative scatter correction (MSC) were used for pathlength correction. The Savitzky-Golay first and second derivatives were used for the deconvolution of the raw spectral data. The experimental results confirmed that the decision tree model combined with MSC pathlength correction and Savitzky-Golay first derivative preprocessing was the best classification model. The proposed hyperspectral image-NIR system can be applied to the detection of salinity during industrial kimchi manufacturing.