中文摘要：

以湄潭翠芽为研究对象，利用可见/近红外光谱技术对其等级进行判别。首先利用卷积平滑、多元散射校正、标准正态变量变换、一阶导数法、二阶导数法、去趋势法等预处理方法对样本原始光谱数据进行处理。然后基于不同光谱预处理方法和原始光谱建立偏最小二乘回归模型，研究分析不同光谱预处理方法对模型的影响，结果表明，使用卷积平滑预处理方法的模型效果最好。然后，研究分别采用逐步回归分析、连续投影算法和竞争性自适应重加权算法3种特征波长选择方法，对卷积平滑预处理后的光谱数据进行特征波长的筛选，以基于不同特征波长选择算法筛选的特征波长和原始全波段数据进行偏最小二乘回归模型建模。结果表明，基于竞争性自适应重加权算法方法筛选的特征波长建立的模型预测效果最好，模型的预测集相关系数达到0.9739，均方根误差为0.2250，这可为湄潭翠芽等级的快速判别提供理论依据。

英文摘要：

In order to distinguish tea grade by using visible-near-infrared spectroscopy technique,Meitan Cuiya tea was used as materials in this study.The spectral data of all different grades Cuiya samples were collected.Firstly,Savitzky-Golay smoothing(SG),multiple scattering correction(MSC),standard normal variable transformation(SNV),first derivative,second derivative,detrending and other pretreatment methods were used to process the original spectral data of the samples.Then,the partial least squares regression(PLSR)model was established based on different preprocessing methods and raw data.The influence of different pretreatment methods on the modeling model was also studied.The results showed that the modeling of SG smoothing pretreatment method had the best effect.In order to simplify the model,three characteristic wavelength selection methods,the stepwise regression analysis(SWR),successive projection algorithm(SPA),and competitive adaptive re-weighting(CARS)were used to select the characteristic wavelength,which would be the pretreatment before the SG smoothing.Finally,PLSR modeling was performed based on the characteristic wavelengths selected by different feature wavelength algorithms.The results showed that the model based on the CARS method had the best prediction effect,with the correlation coefficient of0.9739and the calibration standard deviation of0.2250.The model greatly reduced the number of independent variables,simplified the previous model,and achieved a good prediction effect,which provided a new,quick and effective method for the classification of Cuiya grades.