基于太赫兹时域光谱技术的红木检测方法

Method of Identifying Red Wood Based on Terahertz Time-Domain Spectroscopy

提出了一种基于随机森林预测模型的太赫兹时域光谱的木材鉴别方法。对4种木材(2种红木、2种非红木)在0.2~1.2THz频率范围的吸收光谱的差异进行分析;对得到的光谱吸光度数据进行主成分分析的数据降维处理,并提取方差贡献率最高的五种主成分(总贡献率高达99.65%);将其代入随机森林预测模型预测鉴别红木的真伪,得出相应训练集和测试集的识别率。实验结果表明,与传统的支持向量机预测模型和单一决策树模型比较,使用时域光谱技术结合随机森林预测模型能够得到更高的识别率,识别率可达91.25%,能够准确对红木和非红木进行检测。

A method to identify wood based on random forests prediction model of terahertz time-domain spectroscopy is proposed. We analyzed the differences of the absorption spectrum of four kinds of the woods （two kinds of real red wood and two kinds of false red wood） in the frequency range of 0.2-1.2 THz. Then the principal component analysis was applied to decrease the dimension of the spectral absorbance data, and the five principal components with top cumulative variance contribution rates were extracted （the total contribution rate is up to 99.65 % ）. The processed spectral data were substituted into the random forest prediction model to identify real red wood and false red wood, and then the recognition rate of the training set and test set were obtained. The experimental results show that the terahertz time-domain spectroscopy combined with random forest prediction model can obtain a higher recognition rate, the recognition rate can reach 91.25 %, when comparing with that using the traditional support vector machine prediction model and single decision tree model. The research results show that it is feasible to apply the terahertz time-domain spectroscopy combined with random forest prediction model into the identification of red wood.