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BACKGROUND: Rapid and accurate diagnosis of nitrogen (N) status in field crops is of great significance for site-specific N fertilizer management. This study aimed to evaluate the potential of hyperspectral imaging coupled with chemometrics for the qualitative and quantitative diagnosis of N status in tea plants under field conditions. RESULTS: Hyperspectral data from mature leaves of tea plants with different N application rates were preprocessed by standard normal variate (SNV). Partial least squares discriminative analysis (PLS-DA) and least squares-support vector machines (LS-SVM) were used for the classification of different N status. Furthermore, partial least squares regression (PLSR) was used for the prediction of N content. The results showed that the LS-SVM model yielded better performance with correct classification rates of 82% and 92% in prediction sets for the diagnosis of different N application rates and N status, respectively. The PLSR model for leaf N content (LNC) showed excellent performance, with correlation coefficients of 0.924, root mean square error of 0.209, and residual predictive deviation of 2.686 in the prediction set. In addition, the important wavebands of the PLSR model were interpreted based on regression coefficients. CONCLUSION: Overall, our results suggest that the hyperspectral imaging technique can be an effective and accurate tool for qualitative and quantitative diagnosis of N status in tea plants. RESULTS AND DISCUSSION Statistical analysis of LNCThe N content in the plant leaf is an indicator of soil N status. In general, the richer the N fertilizer in soil, the higher will be the total N content in plants' leaves. Here, the LNC in tea plants treated with different amount of N application was determined by the Kjeldahl method, and the results are shown in Fig. 1. It can be seen that the total N content in the leaves increased significantly with increasing J Sci Food Agric 2020; 100: 161-167

The current method for processing bud yellow tea usually results in astringent taste that is not sufficiently yellowing. Therefore, two factors in the production phase that affect tea quality were studied: number of times the tea leaves undergo the yellowing process and predrying of leaves to reduce their water content before a second yellowing process. We then evaluated the colour and nonvolatile compounds of the produced tea. Results revealed that gallated catechins and the polyphenol–amino acid ratio decreased significantly, and the colour of the tea changed from green to yellow after two yellowing processes. Predrying leaves to a lower water content resulted in the accumulation of free amino acids and soluble sugars, and an increase in the brightness and yellow colour of tea infusion. Bud yellow tea with the highest taste evaluation score and yellowness was obtained when the leave samples underwent two yellowing processes and the water content in the leaves was 40%.

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