College of Engineering and Technology, Northeast Forestry University, Harbin 150040, China.
Forest Products Development Center, SFWS, Auburn University, Auburn, AL 36849, USA.
Sensors (Basel). 2018 Dec 6;18(12):4306. doi: 10.3390/s18124306.
The data analysis of visible-near infrared (Vis-NIR) spectroscopy is critical for precise information extraction and prediction of fiber morphology. The objectives of this study were to discuss the de-noising of Vis-NIR spectra, taken from wood, to improve the prediction accuracy of tracheid length in Dahurian larch wood. Methods based on lifting wavelet transform (LWT) and local correlation maximization (LCM) algorithms were developed for optimal de-noising parameters and partial least squares (PLS) was employed as the prediction method. The results showed that: (1) The values of tracheid length in the study were generally high and had a great positive linear correlation with annual rings (R = 0.881), (2) the optimal de-noising parameters for larch wood based Vis-NIR spectra were Daubechies-2 (db2) mother wavelet with 4 decomposition levels while using a global fixed hard threshold based on LWT, and (3) the Vis-NIR model based on the optimal LWT de-noising parameters ( R c 2 = 0.834, RMSEC = 0.262, RPD c = 2.454) outperformed those based on the LWT coupled with LCM algorithm (LWT-LCM) ( R c 2 = 0.816, RMSEC = 0.276, RPD c = 2.331) and raw spectra ( R c 2 = 0.822, RMSEC = 0.271, RPD c = 2.370). Thus, the selection of appropriate LWT de-noising parameters could aid in extracting a useful signal for better prediction accuracy of tracheid length.
可见-近红外（Vis-NIR）光谱数据分析对于准确提取纤维形态信息和预测至关重要。本研究的目的是讨论木材 Vis-NIR 光谱的去噪问题，以提高兴安落叶松木材管胞长度的预测精度。为此，开发了基于提升小波变换（LWT）和局部相关最大化（LCM）算法的去噪参数优化方法，并采用偏最小二乘法（PLS）作为预测方法。结果表明：（1）研究中管胞长度值普遍较高，与年轮呈显著正线性相关（R=0.881）；（2）兴安落叶松木材 Vis-NIR 光谱的最佳去噪参数为 Daubechies-2（db2）母小波，分解层次为 4，采用基于 LWT 的全局固定硬阈值；（3）基于最优 LWT 去噪参数的 Vis-NIR 模型（R c 2 = 0.834，RMSEC = 0.262，RPD c = 2.454）优于基于 LWT 与 LCM 算法（LWT-LCM）（R c 2 = 0.816，RMSEC = 0.276，RPD c = 2.331）和原始光谱（R c 2 = 0.822，RMSEC = 0.271，RPD c = 2.370）的模型。因此，选择合适的 LWT 去噪参数有助于提取有用信号，从而提高管胞长度的预测精度。
