EGCG distribution visualization in tea leaves based on hyperspectral imaging technology

Abstract: EGCG (epigallocatechin gallate) is an important functional material in tea, and it is regarded as an indispensable index for evaluating the quality of tea as it's of great benefit to health. With the difference of tea varieties and physiological parts of tea plant, the distribution of EGCG is different. Visualization of EGCG distribution contributes to analyze the distribution and metabolism of EGCG directly. However, no research on the visualization of EGCG distribution in tea leaves has been reported till now. This study took advantage of hyperspectral imaging technology and chemometrics method to realize visualization of EGCG distribution in fresh tea leaves. On the basis of visualization, distribution characteristics of EGCG between different tea varieties and different leaf positions were studied. The operation procedure of visualization was mainly divided into 5 steps: 1) Acquisition of physical and chemical information. To obtain the physical information, 486 fresh leaves from the 1st to the 6th leaf positions at the tender shoots of tea plants with 3 varieties were gathered first, hyperspectral images of these fresh leaves were collected by a hyperspectral imager, and then average spectral information used to build models was extracted from the hyperspectral images. To acquire the chemical information, the fresh leaves were freeze-dried, ground into powder, sieved and heated by water-bath to obtain the EGCG solution, and the EGCG concentration was determined through HPLC (high performance liquid chromatography) at last. 2) Samples division and spectral preprocessing. In order to divide the samples reasonably, an interval-extraction method was adopted to ensure the distribution uniformity of chemical values. All the samples were divided into calibration set and prediction set in a ratio of 2:1. Due to the limited performance of hyperspectral imager, obvious noise region of the spectra was eliminated first in order to avoid the interference to subsequent analysis. For 2 common issues during spectral acquisition, i.e. random noise and baseline drift, the SG (Savitzky-Golay) smoothing and baseline correction were performed. Through comparing different preprocessing methods, it was found that the unprocessed spectra showed the best performance. 3) Model establishment and analysis based on full efficient spectra. To determine the best modeling method, PCR (principal component regression), PLSR (partial least squares regression), RBFNN (radial basis function neural network) and LS-SVR (least squares support vector regression) models between full efficient spectra and EGCG concentration values were established respectively. The results showed that the nonlinear models had better performance, and by comparing the evaluation parameters of different models, LS-SVR was chosen as the best modeling method. 4) Model establishment and analysis based on feature bands. The full efficient spectra contain 478 variables, which carry rich information, and cause a collinear problem between variables at the same time. To reduce the data redundancy and the complexity of the model based on full efficient spectra, SPA (successive projection algorithm) was employed to select feature bands, and the LS-SVR model based on feature bands showed better performance compared with the LS-SVR model based on full efficient spectra, with the Rp2 (determination coefficient of prediction set) and RPD (residual prediction deviation) that is the ratio of standard deviation of measured values to root mean square error of prediction set reaching 0.905 and 3.248 respectively. 5) Generation of EGCG distribution map. Inputting the feature bands of each pixel selected by SPA in the testing hyperspectral images into the SPA-LS-SVR model, the EGCG concentration of each pixel could be calculated, so the distribution maps of EGCG in fresh tea leaves were generated finally. This study proved that EGCG distribution visualization in fresh tea leaves can be realized by hyperspectral imaging technology and chemometrics method. Through the analysis of EGCG distribution between different tea varieties and different leaf positions, the distribution showed significant differences. This study provides an effective method for cultivation of tea plant variety with high EGCG concentration, analysis on the metabolism rule of EGCG and recognition of tea shoots.