Colour detection method of Korla fragrant pear based on dielectric spectroscopy technology.

Introduction: Accurate control of fruit quality determines the commercial value of Korla fragrant pear. The rapid and accurate detection of the colour of fragrant pear is crucial for improving its commercial value. Methods: In this study, a vector network analyser and coaxial probe were applied to detect the dielectric constant ϵ' and dielectric loss factor ϵ″ of fragrant pear samples in the frequency range of 0.1-26.5GHz, and to analyse the linear relationship between the colour of fragrant pear and the dielectric parameter. Uninformative variables elimination (UVE) and the successive projections algorithm (SPA) were used to extract feature variables from the dielectric spectroscopy data; partial least squares regression (PLSR), support vector regression (SVR), and least squares support vector regression (LSSVR) were used to establish the colour prediction models of Korla f.agrant pear, respectively. The prediction results of color prediction model with full frequency band of dielectric spectrum and feature variable extraction were compared, facilitating the identification of the best prediction model. Results: The results showed that the linear correlation between ϵ', ϵ'' and L Discussion: The results can provide a new method for the accurate detection of the quality of Korla fragrant pear.