## 3 Results and Discussion
### Construction and Evaluation of Models  
Before establishing the model, the raw spectra of two single-band and fused dual-band underwent SG smoothing pretreatment. Comparison of the spectral curves (Figure 6a) before and after spectral data fusion (Figure 6b) reveals that the latter better retains the raw curve variation trend and shape. Moreover, the spectral curves after fusion in the overlapping wavebands (1050-1100 nm) are smoothly connected and smoothly vary at the nodes. The reflectance at the same waveband is distinct depending on the variations in the response of different instruments, while the average splicing prevents the sudden and drastic changes in the spectra caused by the joining of spectral data. The LS-SVR model was established using the spectral reflectance after SG smoothing pretreatment and the tea polyphenol content determined by standard chemical methods as the independent and dependent variables, respectively. To verify the model performance before and after data fusion, the spectral data of single-band and dual-band were used for modeling. As listed in Table 2, the modeling performance using single-band (400-1050 nm) spectral data is better than that of the model established from single-band (1000-1650 nm) spectral data, which might be relevant to the high signal-to-noise ratio and resolution of the FieldSpec HandHeld 2 spectrometer. Crucially, the absorption peaks of catechins, the main components of tea polyphenols, are mainly distributed in 450-780 nm, hence, the modeling \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Waveband** & **Data Set** & **Samples** & **Max/\%** & **Min/\%** & **Mean/\%** & **STD/\%** & **CV/\%** & **Kurtosis** & **Skewness** \\ \hline Single-band (400–1050 nm) & Whole set & 78 & 28.26 & 16.61 & 20.95 & 2.82 & 13.46 & \(-\)0.46 & 0.62 \\ Calibration set & 52 & 28.26 & 16.61 & 21.27 & 3.11 & 14.62 & \(-\)0.91 & 0.40 \\ Prediction set & 26 & 24.59 & 16.78 & 20.34 & 2.00 & 9.83 & 0.74 & 0.92 \\ Whole set & 78 & 28.26 & 16.61 & 20.89 & 2.76 & 13.21 & \(-\)0.47 & 0.59 \\ Calibration set & 52 & 28.26 & 16.61 & 21.36 & 2.99 & 13.99 & \(-\)0.91 & 0.32 \\ Prediction set & 26 & 24.81 & 16.78 & 19.96 & 1.92 & 9.61 & 1.09 & 0.88 \\ Whole set & 77 & 28.26 & 16.61 & 20.93 & 2.82 & 13.47 & \(-\)0.42 & 0.62 \\ Calibration set & 51 & 28.26 & 16.61 & 21.39 & 3.07 & 14.35 & \(-\)0.84 & 0.35 \\ Prediction set & 26 & 25.61 & 16.92 & 20.04 & 1.95 & 9.73 & 0.42 & 0.90 \\ \hline \hline \end{tabular} Note: Max and Min represent maximum and minimum of tea polyphenol content of samples, respectively. STD and CV are the standard deviation and coefficient of variation, respectively. Kurtosis measures the flatness of the data distribution. Skewness is a measure of the direction and degree of skewness of the statistical data distribution. Single-band (400–1050 nm) is Vis/short-wave NIR spectra. Single-band (1000–1650 nm) is long-wave NIR spectra. Dual-band (400–1650 nm) represent Vis/short-wave and long-wave NIR spectra.  
\end{table} 
Table 1: Statistical characteristics of tea polyphenol content of the calibration and prediction sets in different waveband ranges. is more effective using single band (400-1050 nm) data [34; 40]. The modeling performance using dual-band (400-1650 nm) spectral data is superior to that of the model established from single-band (1000-1650 nm) spectral data, with a larger R\({}_{\text{p}}\) and smaller RMSE and RPD. The dual-band (400-1650 nm) model is slightly better than the single-band (400-1050 nm) model, which may be due to the more comprehensive coverage of the octave and ensemble absorption properties of hydrogen-containing groups in the dual-band spectra [28; 41; 42].