## 4 Conclusions  
In this study, a novel variable selecting algorithm based on DWT and BOSS was employed to select the optimal variable set of the moisture content of tea leaves for the Yinghong No. 9 variety. After selecting the optimal variables, a PLS prediction model was built. The prediction effect of this algorithm on the moisture content of tea leaves was explored. Some conclusions and contributions of this research are summarized as follows:  
(1) In the DWT process, the noise was considerably removed. The band was calculated by the correlation coefficient method to select the maximum levels and the maximum levels of decomposition was found to be six. In general, the moisture-related spectrum of L6 was denoised but retained effective information.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variable Set** & \(R_{c}^{2}\) & **RMSEC** & \(R_{cv}^{2}\) & **RMSECV** & \(R_{p}^{2}\) & **RMSEP** & \(n\_\)**VAR** \\ \hline GA & 0.9318 & 0.2589 & 0.8908 & 0.3287 & 0.9420 & 0.2421 & 870 \\ iPLS & 0.9294 & 0.2617 & 0.9021 & 0.3088 & 0.9232 & 0.2838 & 280 \\ V & 0.9410 & 0.2404 & 0.9171 & 0.2851 & 0.9513 & 0.2236 & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 2: PLS model was established by different wavelength selection methods.  
Figure 8: Several interval models (bars) and full spectrum models (solid lines) for cross-validation prediction errors (RMSECV). Note: The preferred interval is given in black.  
(2) BOSS selected the effective information from the spectrum with the noise removed by DWT. By superimposing layers L1-L6 optimized by the BOSS algorithm, 55 optimal variables were obtained as the optimal variable set V. A considerable number of variables fell within the range of 800-1100, 1200-1400, and 1700-2000 nm. Those wavelength ranges were mainly represented by -OH groups and the vibration absorption of combinations and octaves. The number of variables dropped from 3112 to 55 using BOSS.  
(3) Compared with full spectral modeling, DWT-BOSS-PLS had higher accuracy and prediction accuracy, with \(R_{c}^{2}\) of 0.9410, RMSEC of 0.2404, \(R_{cv}^{2}\) of 0.9171, RMSECV of 0.2851, \(R_{p}^{2}\) of 0.9513, and RMSEP of 0.2236. GA and iPLS algorithms were used for comparison with the proposed DWT-BOSS method; the DWT-BOSS results had higher stability and accuracy, with fewer bands used.  
(4) We proposed a novel prediction model that is robust and effective for forecasting the moisture content of Yinghong No. 9 tea leaves.  
However, tea making still has difficulties of extensive application of NIRS technology, such as expensive machinery and equipment, learning to use NIRS technology, and the production line design and so on. Thus, NIRS technology needs to be popularized in tea processing factories. Furthermore, spectral equipment needs some suitable designs for production, while the entrepreneur is willing to pay for technological transformation.  
All authors contributed to the conceptualization. [PERSON] performed the formal analysis and original draft preparation; [PERSON] and [PERSON] finished sample preparation and data visualization; [PERSON] and [PERSON] reviewed and made relative edits; [PERSON] and [PERSON] contributed to the supervision and provided resources. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the special project from the Provincial governor of Guangdong Province [yucenion2019]170], the special project from the Dean of Guangdong Academy of Agricultural Sciences [201939], Enhance the tea technological capability of cities and counties to promote industrial development projects [890-2020-XMZC-2593-01-0001].  
The authors are grateful for the support of South China Agricultural University and Tea Research Institute of Guangdong Academy of Agricultural Sciences. The authors also thank the anonymous reviewers for their critical comments and suggestions to improve the manuscript.  
The authors declare no conflict of interest.