采谱方式对竹材气干密度近红外预测模型精度的影响

该文对近红外光谱技术结合化学计量学分析方法快速预测毛竹材气干密度的可行性进行了研究，重点探讨不同采谱方式对所建模型预测精度的影响.为了便于对比，建模过程中光谱数据未经过任何预处理.结果表明，采谱方式影响着模型的预测精度.从竹材横切面采谱所建模型的预测精度最高，内表面（靠近竹黄面）居中，外表面（靠近竹青面）最差.但如果对竹材内外表面的光谱求平均后建模，则可以显著提高模型的预测精度.随后选择最优模型对随机抽取、未参与建模的30个未知样品的密度进行了预测，预测值与测量值的 相关性高，表明近红外光谱技术可以快速、准确地预测竹材的气干密度. 

Effects of sample surface on the accuracy of near infrared predictive model of air dried density of bamboo

The objective of this study is to investigate the feasibility of using near infrared spectroscopy(NIR) combined with chemometrics analysis to rapidly predict the air-dried density of Phyllostachys pubescens Mazei ex H.de Lebaie.Especially,this study focused on the effects of spectrum collection methods on the accuracy of near infrared predictive model.For comparison,no spectrum treatment procedure was adopted when establishing all the NIR models.The results indicated that the accuracy of models was influenced by the sample surface from which NIR spectra were obtained.The model based on transverse section of bamboo owned the highest accuracy,followed sequentially by the models respectively based on the inner and outer surfaces of bamboo.It was also found that the model accuracy can be substantially improved if the model was based on the average spectra obtained from the outer and inner surfaces of bamboo.This model was then used to predict the air-dried density of 30 unknown bamboo samples,and a good correlation between the measured and predicted values was obtained.