It is very difficult for humans to distinguish between two kinds of black tea obtained with similar processing technology. In this paper, an electronic tongue was used to discriminate samples of seven different grades of two types of Chinese Congou black tea. The type of black tea was identified by principal component analysis and discriminant analysis. The latter showed better results. The samples of the two types of black tea distributed on the two sides of the region graph were obtained from discriminant analysis, according to tea type. For grade discrimination, we determined grade prediction models for each tea type by partial least-squares analysis; the coefficients of determination of the prediction models were both above 0.95. Discriminant analysis separated each sample in region graph depending on its grade and displayed a classification accuracy of 98.20% by cross-validation. The back-propagation neural network showed that the grade prediction accuracy for all samples was 95.00%. Discriminant analysis could successfully distinguish tea types and grades. As a complement, the models of the biochemical components of tea and electronic tongue by support vector machine showed good prediction results. Therefore, the electronic tongue is a useful tool for Congou black tea classification.

Tea is an important beverage in humans’ daily lives. For a long time, tea grade identification relied on sensory evaluation, which requires professional knowledge, so is difficult and troublesome for laypersons. Tea chemical component detection usually involves a series of procedures and multiple steps to obtain the final results. As such, a simple, rapid, and reliable method to judge the quality of tea is needed. Here, we propose a quick method that combines ultraviolet (UV) spectra and color difference to classify tea. The operations are simple and do not involve complex pretreatment. Each method requires only a few seconds for sample detection. In this study, famous Chinese green tea, Huangshan Maofeng, was selected. The traditional detection results of tea chemical components could not be used to directly determine tea grade. Then, digital instrument methods, UV spectrometry and colorimetry, were applied. The principal component analysis (PCA) plots of the single and combined signals of these two instruments showed that samples could be arranged according to grade. The combined signal PCA plot performed better with the sample grade descending in clockwise order. For grade prediction, the random forest (RF) model produced a better effect than the support vector machine (SVM) and the SVM + RF model. In the RF model, the training and testing accuracies of the combined signal were all 1. The grades of all samples were correctly predicted. From the above, the UV spectrum combined with color difference can be used to quickly and accurately classify the grade of Huangshan Maofeng tea. This method considerably increases the convenience of tea grade identification.

The effects of calcium cyanamide on the soil fungal communities in successive tea-cuttings nursery soils were investigated based on Illumina high-throughput sequencing. The field experiment was carried out with three treatments, including control (CK), flooding (F) and calcium cyanamide (CC). The treatment with calcium cyanamide increased pH (~1 unit) and reduced the accumulation of phenols (~50%), available phosphorus (~28%) and exchangeable Al (~90%) significantly, and improved soil quality. The predominant phylum in all treatments was Ascomycota. FUNGuild revealed that the dominant trophic mode was saprotrophy in tea-cuttings nursery soil. Plant pathogens had a low abundance in the calcium cyanamide treatment. Alpha diversity analysis showed lower richness in the calcium cyanamide than the other treatments. Network analysis showed a poorly connected but highly modularized network in the calcium cyanamide treatment, with the crucial OTUs functions related to anti-pathogenicity. The results showed that calcium cyanamide should be recommended for improving long-term tea nurseries by increasing the survival rate of tea seedlings due to increasing soil pH value, reducing aluminum toxicity, decreasing the accumulation of polyphenols, diminishing pathogenic fungi and making the taxa related to anti-pathogenicity occupy a more important niche.