利用近红外光谱（NIR）技术结合BP神经网络定量预测了杉木中的综纤维素、 木质素和微纤丝角。 首先对杉木的原始近红外光谱数据进行卷积（Savitzky-Golay）平滑和二阶导数处理， 然后利用小波变换压缩， 将由171个数据点组成的近红外光谱压缩为86个数据点， 最后用BP神经网络建模， 采用Leave-n-out交叉验证法对模型进行验证， 并讨论了隐含层神经元个数、 学习速率、 动量因子和学习次数对所建BP网络的影响。 用所建的网络模型预测了测试集中杉木样本的综纤维素、 木质素和微纤丝角 ， 预测的相关系数R2值分别为0.91, 0.90, 0.87， 预测均方根误差RMSEP分别为： 0.86%, 0.33%, 4.99%。 结果表明该方法快速， 无损， 基本能满足定量分析的要求。

The amount of holocellulose, lignin, and microfibril angle of Chinese fir was predicted by using back-propagation neural network (BP-ANN) combined with near infrared (NIR) spectrometry. First, the data of original spectra were pretreated by Savitzky-Golay smoothing algorithm and the second derivative, then the data of near infrared spectrometry with 171 points were compressed to 86 points by using wavelet transform, and finally, the models were established by using BP-ANN. The models were validated using leave-n-out cross-validation approach，and the influences of the number of hidden neurons, learning rate, momentum, and epochs were discussed in the present paper. The prediction samples, which were not used in the model generation, were predicted by using the obtained models, the correlation coefficients (R2) of holocellulose, lignin and microfibril angle were 0.91, 0.90 and 0.87, respectively. The root mean square errors of prediction (RMSEP) of the established models were 0.86%, 0.33%, and 4.99%, respectively. The obtained results showed that the method is fast and nondestructive and can basically satisfy the requirement of quantitative analysis.