Cross-cultivar prediction of quality indicators of tea based on VIS-NIR hyperspectral imaging

Abstract

The rapid detection of quality indicators of fresh tea leaves is helpful to guide tea garden management. Therefore, it is very important to develop a fast and reliable detection method for fresh tea leaf quality indicators. In this study, the feasibility of visible-near-infrared hyperspectral image (VNHI) combined with stoichiometry for the determination of tea polyphenols (TP) and crude fiber (CF) in 14 cultivars of tea plants was investigated. Based on the VNHI, different tea cultivars were distinguished with an accuracy of 99.56% by using one-dimensional ResNet18 (1D-ResNet18). For the quantitative determination of CF and TP contents, the prediction determination coefficient (r2) reached 0.80 and 0.77, respectively, by integrating VNHI with PLS. In addition, we proposed a generalization index (GI) that can evaluate the cross-cultivar generalization ability of models. The GI can judge if the model built on a few tea cultivars can predict the quality indicators of other cultivars. Then, the predicted distribution maps of CF and TP contents were analyzed. The results showed that the quantitative models based on spectra (474–1734 nm) had higher cross-cultivar generalization ability with higher mean GI_0.7 in predicting the cross-cultivar content of CF (mean GI_0.7 = 2.563) and TP (mean GI_0.7 = 1.376). The models based on spectra (474–1030 nm) had stronger visualization ability with lower mean predictive variance. This study proved the feasibility of the VNHI technique in predicting the content of TP and CF of multiple cultivars of tea plants and provided methods with higher generalization, perceptual intuition, and speediness for the detection of quality indicators of tea in the field.