CAJ | 학술논문

利用近红外光谱（NIR）技术结合BP神经网络定量预测了落叶松木屑的含水率。首先对采集的落叶松木屑原始近红外光谱进行9点平滑及多元散射校正预处理，然后利用主成分分析法提取光谱数据主成分作为BP神经网络的输入，最后建立BP神经网络预测模型并采用交叉验证法对模型进行验证。所建模型校正集的相关系数R为0．98，校正集的均方根误差RMSEC为0．0017；预测集的相关系数R为0．99，预测集的均方根误差RMSEP为0．0015。研究表明，此方法可以实现对落叶松木屑含水率的快速预测。
이용근홍외광보（NIR）기술결합BP신경망락정량예측료락협송목설적함수솔。수선대채집적락협송목설원시근홍외광보진행9점평활급다원산사교정예처리，연후이용주성분분석법제취광보수거주성분작위BP신경망락적수입，최후건립BP신경망락예측모형병채용교차험증법대모형진행험증。소건모형교정집적상관계수R위0．98，교정집적균방근오차RMSEC위0．0017；예측집적상관계수R위0．99，예측집적균방근오차RMSEP위0．0015。연구표명，차방법가이실현대락협송목설함수솔적쾌속예측。
An integration of BP neural network and PCA for modeling sawdust water content of Dahurian Larch combined with NIRS was investigated in this paper. The raw spectra were collected and pretreated with 9 point smoothing and multiplieative scatter correction （MSC）. Two typical principal components were extracted by PCA with the application of establishing prediction model. U- sing full cross-validation approach to validate the model, the calibration correlation coefficient （R） was 0. 98 and the root mean square error of calibration （RMSEC） was 0. 001 7, the prediction correlation coefficient （R） was 0. 99 while the root mean square error of prediction （RMSEP） was 0. 001 5. The study results showed that this method can rapidly and accurately predict sawdust water content of Dahurian larch.