Non-destructive monitoring of qualitative properties of salted cabbage using hyperspectral image analysis

Abstract

The feasibility of using VIS-NIR and SWIR hyperspectral imaging (HSI) to determine the salinity, soluble solids, and water content in salted kimchi cabbages during the salting process was assessed. Hyperspectral images of kimchi cabbages acquired at different salt concentrations and periods of the salting process were developed as prediction and discriminant models for the qualitative properties. Principal component analysis showed that the distribution according to the spectral characteristics of kimchi cabbages could be grouped, and data reduction for optimal model developing was attempted based on the key wavelengths selected in the loading plot. The optimal partial least squares regression models for the prediction of salinity and soluble solids had best performance by achieving R2p values of over 0.86 and 0.90 in the VIS-NIR and SWIR regions, respectively. The accuracy and specificity of the optimal PLS-DA model for salinity level have improved efficiency to over 0.93. Visualization of the spectrum information in each pixel of the hyperspectral image using the PCA model displayed the salinity level in kimchi cabbages under different salting conditions. The prediction models were sufficiently accurate to consider HSI as a useful tool for controlling and optimizing the salting process.