Nondestructive Determination of the Compressive Strength of Wood Using Near-Infrared Spectroscopy

Abstract

In this study, Xylosma racemosum was selected as the raw material and its compressive strength was predicted through nondestructive methods. The test data consisted of 160 near-infrared (NIR) absorption spectra of the wood samples obtained using an NIR spectrometer, with the wavelength range of 900 to 1900 nm. The original absorption spectra were pre-processed with multiplicative scatter correction (MSC) and SavitzkyGolay (SG) smoothing and divided into several intervals using the backward interval partial least squares (BiPLS) method. The optimal combination of intervals with the smallest root mean square error of cross validation (RMSECV) value was selected, and a genetic algorithm (GA) was used to select featured wavelengths. Finally, a partial least squares (PLS) regression model was established with the featured wavelengths. The BiPLS-GA-PLS model outperformed the other models, resulting in a high prediction correlation coefficient of 0.927 and a root mean square error rate of 4.06. Based on the results, it is feasible to accurately measure the compressive strength of wood processed by different methods using near-infrared spectroscopy.

An Improved Method for Accurate Radiation Measurement Based on Dark Output Noise Drift Compensation

“…In the processes of scientific production, the spectral information of the product can be obtained through fiber optic spectrometers, and the quality of the product can be detected quickly [12,13]. Some literature has reported temperature measurements by using fluorescence spectroscopy in specific scenarios [14][15][16][17].…”

Compression strength prediction of Xylosma racemosum using a transfer learning system based on near-infrared spectral data

“…Wood compression strength prediction models were successfully developed using NIR spectroscopy. Liang et al (2016) Abstract A transfer learning system was designed to predict Xylosma racemosum compression strength. Near-infrared (NIR) spectral data for Acer mono and its compression strength values were used to resolve the weak generalization problem caused by using a X. racemosum dataset alone.…”

Prediction methodology for wood compression strength based on particle-filtered near-infrared spectroscopy

“…The correlation coefficient method is mainly based on subjective threshold selection (Bin et al 2017). For a small sample size and highdimensional datasets, GA and SAA are subject to poor reliability and long operation times (Tripathi and Mishra 2009;Mahesh et al 2015;Xu et al 2016;Liang et al 2016;Zareef et al 2018). The uniformative variable elimination (UVE) is a wavelength selection algorithm based on partial least-squares regression coefficients, and it is used to eliminate variables that do not provide information (Liu et al 2017).…”