Nanjing Forest Police College, Nanjing, Jiangsu, China.
Nanjing Forestry University, Nanjing, Jiangsu, China.
Sci Rep. 2022 Jul 7;12(1):11507. doi: 10.1038/s41598-022-15719-0.
Near infrared hyperspectral imaging (NIR-HSI) spectroscopy can be a rapid, precise, low-cost and non-destructive way for wood identification. In this study, samples of five Guiboutia species were analyzed by means of NIR-HSI. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used after different data treatment in order to improve the performance of models. Transverse, radial, and tangential section were analyzed separately to select the best sample section for wood identification. The results obtained demonstrated that NIR-HSI combined with successive projections algorithm (SPA) and SVM can achieve high prediction accuracy and low computing cost. Pre-processing methods of SNV and Normalize can increase the prediction accuracy slightly, however, high modelling accuracy can still be achieved by raw pre-processing. Both models for the classification of G. conjugate, G. ehie and G. demeusei perform nearly 100% accuracy. Prediction for G. coleosperma and G. tessmannii were more difficult when using PLS-DA model. It is evidently clear from the findings that the transverse section of wood is more suitable for wood identification. NIR-HSI spectroscopy technique has great potential for Guiboutia species analysis.
近红外高光谱成像（NIR-HSI）光谱学可以成为一种快速、精确、低成本和非破坏性的木材鉴定方法。在这项研究中，使用 NIR-HSI 对 5 种 Guiboutia 物种的样本进行了分析。为了提高模型的性能，在进行不同的数据处理后，使用了偏最小二乘判别分析（PLS-DA）和支持向量机（SVM）。分别分析了横切面、径切面和弦切面，以选择最适合木材鉴定的样本切面。结果表明，NIR-HSI 结合连续投影算法（SPA）和 SVM 可以实现高预测精度和低计算成本。SNV 和 Normalize 预处理方法可以略微提高预测精度，但通过原始预处理仍然可以获得高精度的建模。G. conjugate、G. ehie 和 G. demeusei 的分类模型的预测精度接近 100%。使用 PLS-DA 模型时，对 G. coleosperma 和 G. tessmannii 的预测则更加困难。研究结果清楚地表明，木材的横切面更适合木材鉴定。NIR-HSI 光谱技术在 Guiboutia 物种分析方面具有巨大潜力。
