Lifting Wavelet Transform De-noising for Model Optimization of Vis-NIR Spectroscopy to Predict Wood Tracheid Length in Trees

• Main Authors: Ying Li, Brian K. Via, Qingzheng Cheng, Yaoxiang Li
• Format: Article
• Language: English
• Published: MDPI AG 2018-12-01
• Series: Sensors
• Subjects: lifting wavelet transform; Vis-NIR spectroscopy; larch; tracheid length
• Online Access: https://www.mdpi.com/1424-8220/18/12/4306

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 (<inline-formula> <math display="inline"> <semantics> <mrow> <msubsup> <mi mathvariant="normal">R</mi> <mi mathvariant="normal">c</mi> <mn>2</mn> </msubsup> </mrow> </semantics> </math> </inline-formula> = 0.834, <inline-formula> <math display="inline"> <semantics> <mrow> <mi>RMSEC</mi> </mrow> </semantics> </math> </inline-formula> = 0.262, <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mrow> <mi>RPD</mi> </mrow> <mi mathvariant="normal">c</mi> </msub> </mrow> </semantics> </math> </inline-formula> = 2.454) outperformed those based on the LWT coupled with LCM algorithm (LWT-LCM) (<inline-formula> <math display="inline"> <semantics> <mrow> <msubsup> <mi mathvariant="normal">R</mi> <mi mathvariant="normal">c</mi> <mn>2</mn> </msubsup> </mrow> </semantics> </math> </inline-formula> = 0.816, <inline-formula> <math display="inline"> <semantics> <mrow> <mi>RMSEC</mi> </mrow> </semantics> </math> </inline-formula> = 0.276, <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mrow> <mi>RPD</mi> </mrow> <mi mathvariant="normal">c</mi> </msub> </mrow> </semantics> </math> </inline-formula> = 2.331) and raw spectra (<inline-formula> <math display="inline"> <semantics> <mrow> <msubsup> <mi mathvariant="normal">R</mi> <mi mathvariant="normal">c</mi> <mn>2</mn> </msubsup> </mrow> </semantics> </math> </inline-formula> = 0.822, <inline-formula> <math display="inline"> <semantics> <mrow> <mi>RMSEC</mi> </mrow> </semantics> </math> </inline-formula> = 0.271, <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mrow> <mi>RPD</mi> </mrow> <mi mathvariant="normal">c</mi> </msub> </mrow> </semantics> </math> </inline-formula> = 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.