光谱学与光谱分析, 2020, 40 (7): 2128, 网络出版: 2020-12-04  

中红外光谱的进口木材树种识别方法

Identification Method of Imported Timber Species by Mid-Infrared Spectrum
作者单位
东北林业大学工程技术学院, 黑龙江 哈尔滨 150040
摘要
基于支持向量机和马氏距离探索了中红外光谱分析识别进口的卢氏黑黄檀、 风车木、 微凹黄檀、 燃料紫檀和东非黑黄檀的能力。 应用中红外光谱仪采集了500组试验样本进行分析, 对试验数据进行了预处理: 首先, 为了保证样本的有效性, 对异常光谱进行了诊断。 基于莱特检验法诊断出卢氏黑黄檀和微凹黄檀各有2组异常, 风车木、 燃料紫檀和东非黑黄檀各有1组异常。 为使样本数量统一, 五种树种分别剔除了包含异常光谱在内的5组数据; 其次, 分析了近红外光谱的树种识别研究, 结果表明: 对光谱数据进行一阶导数处理, 可提高识别的精度。 因此, 对中红外光谱数据进行了平滑处理和一阶导数处理。 采用主成分分析提取了光谱数据的特征值, 测试集的第一和第二主成分得分的散点图显示, 平滑加一阶导数处理的测试集的各自聚类性较平滑处理好。 以主成分的得分为特征, 基于支持向量机和马氏距离进行了识别研究。 考虑到识别方法中主成分个数的选取会直接影响识别的精度, 而通常主成分的选取仅参考累计贡献率, 此处为使主成分的选取更科学, 在支持向量机识别方法中利用粒子群算法进行参数寻优时, 对主成分的个数(范围为[5, 30])与5折检验下的最佳判别准确率的关系进行了试验, 结果表明: 平滑处理和平滑加一阶导数处理的主成分个数在[7, 11]范围内的5折检验下的最佳判别准确率较高, 结合对应的判别准确率, 确定了最佳的主成分个数为8个。 以前8个主成分作为输入变量, 基于支持向量机和马氏距离对测试集进行了测试, 结果得出: 两种识别方法的正确识别率均较高, 支持向量机的识别率略高于马氏距离, 平滑加一阶导数处理的识别率均优于平滑处理, 平滑加一阶导数处理的支持向量机正确识别率达到了98%, 识别效果最好。 因此, 中红外光谱分析可以作为木材树种识别的一种有效手段。
Abstract
Based on support vector machine and Mahalanobis distance, the ability of mid-infrared spectrum analysis to identify imported rosewood, windmill wood, micro ebony, fuel rosewood and east African rosewood was explored. Five hundred group of test samples were collected and analyzed by the mid-infrared spectrometer, and the test data were preprocessed. Firstly, in order to ensure the validity of the samples, the abnormal spectra were diagnosed. Based on Wright’s test, two groups of abnormalities were found in rosewood and micro ebony, one group of abnormalities was found in windmill wood, fuel rosewood and east African rosewood respectively. In order to unify the sample size, five species of trees were excluded from the five sets of data, including the abnormal spectrum. Secondly, the research of tree species recognition in near-infrared spectroscopy was analyzed. The results showed that the first derivative processing of spectral data could improve the recognition accuracy. Therefore, the mid-infrared spectroscopy data were smoothed and first derivative processing. The eigenvalues of the spectral data were extracted by principal component analysis. The scatter plots of the first and second principal component scores of the test set showed that the clustering of the smoothed plus first derivative processed test set was smooth. Based on the scores of principal components, the recognition research was based on support vector machine and Mahalanobis distance. Considering the selection of the number of principal components in the recognition method would directly affect the accuracy of recognition, and usually, the selection of principal components only referred to the cumulative contribution rate. In order to make the selection of principal components more scientific, in the support vector machine identification method, the particle swarm optimization algorithm was used for parameter optimization, the relationship between the number of principal components (range [5, 30]) and the best discrimination accuracy under the 50-fold test was tested. The results showed that the optimal discriminating accuracy of the number of principal components in the range of [7, 11] of smoothing processing and smoothing plus first-order derivative processing was relatively high, and the optimal number of principal components was determined as 8 based on the corresponding discriminating accuracy. The first eight principal components were used as input variables, and the test set was tested based on support vector machine and Mahalanobis distance. The results showed that the correct recognition rates of the two recognition methods were higher, and the recognition rate of support vector machines was slightly higher than that of Mahalanobis distance. The recognition rate of smooth distance plus first-order derivative processing was better than that of smoothing processing. The correct recognition rate of support vector machine with smooth plus first-order derivative processing reached 98%, and the recognition effect was the best. Therefore, the mid-infrared spectrum can be used as an effective means to identify timber species.

冯国红, 朱玉杰, 李耀翔. 中红外光谱的进口木材树种识别方法[J]. 光谱学与光谱分析, 2020, 40(7): 2128. FENG Guo-hong, ZHU Yu-jie, LI Yao-xiang. Identification Method of Imported Timber Species by Mid-Infrared Spectrum[J]. Spectroscopy and Spectral Analysis, 2020, 40(7): 2128.

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