## 1 Introduction  
Processed leaves and leaf buds of tea tree are used to produce tea, which are popular in many parts of the world [1]. Traditional tea making is complicated; the drying of fresh leaves is the primary and indispensable stage of this process [2] and moisture content is a key index in the drying process [3]. However, improper handling may lead to inaccurate measurements when determining moisture content. Therefore, an accurate and rapid detection approach would be indispensable for determining the moisture content of tea leaves during tea making [4]. Many attempts have been made to determine moisture based on near-infrared spectroscopy (NIRS). Moisture measurements are commonly recorded by detecting mass loss after heating to evaporate moisture. However, this procedure damages the samples and is time consuming. In contrast, direct determination of moisture by NIRS is fast, only requiring the acquisition of the sample's reflection spectrum [5]. However, the disadvantages of NIRS include broad overlapping, difficultly interpretingthe attribute absorption bands, and noise [6]. The effective selection of wave bands is used to address these problems. It is difficult to select effective variables that peaks are unresolved and important features cannot be recognized [7]. Therefore, it is crucial to eliminate noise and avoid losing spectral details in the spectral prediction model of determining the moisture content. The current common spectral denoising methods include moving average, Savitzky-Golay filtering, and median computing [8]. [PERSON] et al. proposed a tailoring noise frequency spectrum technique based on the Savitzky-Golay filter and obtained a satisfying result [9]. [PERSON] et al. used the moving weighting algorithm to estimate soil organic carbon content fixing the spectrum bias [10]. Although these methods can remove the noise in spectral data, useful signals may be lost during the process of denoising. To avoid losing effective variables, a discrete wavelet transform (DWT) of spectral signals was developed. The moisture content (MC), soluble solids content (SSC), pH, and hardness of Gala apple samples were tested non-destructively within 350-2500 nm using the wavelet transform pretreatment of raw spectral data [11]. The use of DWT successfully further simplified the genetic algorithm-the partial least squares (GA-PLS) model by reducing variables by 40-44% without reducing the prediction accuracy [12]. Other experimental results [13] showed that the DWT-support vector regression (DWT-SVR) multivariate regression model, having good robustness, can measure protein, starch, and fat contents in corn simultaneously, demonstrating that DWT can effectively remove noise from corn NIRS spectral data. However, an unsolved problem is that DWT cannot reduce the dimensions of huge data, which leads to a redundancy in the data volume during model building. In summary, wavelet decomposition is an effective method for removing noise without reducing data dimension. As NIRS produces a large amount of data, considerable residual redundant noise and irrelevant data remain after spectra denoising. Therefore, variables must be selected before building a prediction model. The benefits of variable selection can be summarized into three aspects: (1) eliminating uninformative or interfering variables, (2) selecting informative variables, and (3) reducing the dimensions of the data [14]. The common selection methods can be divided into three types: (1) single variable selection, where some use different variable ranking criteria such as regression coefficients and variance analysis [9]; (2) random variable selection such as uninformative variable elimination (UVE) [15], genetic algorithm (GA) [16], random forest (RF) [17], etc. ; and (3) interval variable selection such as interval partial least squares (iPLS) [18] and synergy iPLS (SiPLS) [19].