Light gradient-boosting machine model provides biologically realistic predictions of southern pine wood extractives content using near infrared spectroscopy hyperspectral imaging compared to artificial neural network and partial least squares regression models

Abstract

Near infrared (NIR) spectroscopy combined with partial least squares (PLS) regression has been widely used to predict the physical, mechanical, and chemical properties of wood. Recently, advanced modeling techniques such as deep learning and machine learning models using spectral data have increasingly been used to predict variability in wood physical and mechanical properties, but studies on chemical properties are scarce. Herein, southern pine wood samples were acquired from multiple height levels from 119 trees to yield 1007 pith-to-bark radial strips. The strips were scanned on an NIR hyperspectral imaging system and the spectral data aligned to ring-level measurements. Acetone soluble extractives content was measured on 50-mm radial length samples with 262 data points assigned to training a PLS regression model, an artificial neural network (ANN) model, and a light gradient-boosting machine learning model (LGBM). The models were then evaluated on the test dataset (140 samples). The fit statistics were good for all models, although the LGBM model (RMSE = 2.1%) performed better than the PLS regression (3.3%) or ANN (2.6%) models. The models were subsequently used to predict extractives content for 31,314 rings. The LGBM model provided biologically realistic values for all rings, whereas the PLS regression model predicted 9648 negative values, and the ANN model extrapolated well beyond the measured values (max predicted = 69.4% vs max measured = 53.2%). This study highlights real benefits of using deep learning and machine learning models compared to PLS regression models for NIR hyperspectral imaging datasets.