Design and Experiment of the Tea Quality Online Detector

To meet the urgent need for online detection of tea quality components during tea processing, a tea quality online detection instrument was developed based on near -infrared spectroscopy technology. This paper systematically introduces the overall structure, working principle, key components, and accompanying analysis software of the detection instrument. 280 samples of fixed leaves with different degrees of fixation were collected, and a detection model was established for caffeine, water extract, tea polyphenols, moisture content, and free amino acid content in fixed leaves using partial least squares (PLS). Internal and external cross validation of the model were conducted. The results showed that the standard error of calibration (SEC) and standard error of cross validation (SECV) for the moisture content model were 0.44 and 0.47, respectively, with a minimal difference of 0.03, and the calibration coefficient (R_C) reached 0.92; For the caffeine model, the SEC and SECV were 0.43 and 0.35, respectively, and the R_C reached 0.95; The linear regression coefficients between predicted values and actual values for both models exceeded 0.90. The external validation of the moisture content and caffeine models showed that the mean absolute deviation between predicted and actual values was within 1.00%, specifically 0.68% and 0.80%, respectively. In summary, the developed tea quality online detection instrument exhibits good stability and precision, meeting the needs of real-time field detection and providing technical support for the intelligentization of tea processing equipment.