原始近红外光谱数据含有大量的噪声信号和较大的数据量，所以在进行光谱数据分析之前对光谱数据进行预处理是非常必要的。近红外光谱数据的预处理主要有两个任务，一是降噪，提高模型的稳健性和预测结果的准确性；二是数据压缩，以便于数据的存储，提高建模速度。传统的近红外光谱数据预处理方法各有局限，很难在这两方面都得到令人满意的效果。将小波分析用于苹果近红外光谱数据的预处理，并选取峰值信噪比(Peak Signal to Noise Ratio, PSNR)和归一化相关系数(Normalized Correlation, NC)作为评价指标。与常用的Savitzky-Golay平滑滤波和多元散射校正相比，小波方法不仅能有效地实现数据压缩，而且在噪声去除和光谱细节保持等方面都具有优势。

The original Near Infrared (NIR) spectral data inevitably contain a large amount of noise signals and data. So, before spectral analysis, it is necessary to preprocess the spectral data. The preprocessing of NIR spectral data mainly includes two tasks. One task is to de-noise so as to improve the robustness of the model and the accuracy of the prediction result. Another task is to compress the data for storage and modeling speed improvement. Traditional spectral preprocessing methodshave their own limitations in these two aspects. The wavelet analysis method is used to preprocess the NIR data of apples. The Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC) coefficient are selected as the evaluation index. Compared with the common Savitzky-Golay smoothing and multiple scattering correction, the wavelet method not only can compress spectral data effectively, but also has its superiority in noise removal and spectral detail preserving.