A novel NIRS modelling method with OPLS-SPA and MIX-PLS for timber evaluationOA

The identification of timber properties is important for safe application. Near Infrared Spectroscopy(NIRS)technology is widely-used because of its simplicity, efficiency, and positive environmental attributes. However, in its application, weak signals are extracted from complex,overlapping and changing information. This study focused on the stability of NIR modeling. The Orthogonal Partial Least Squares(OPLS) and Successive Projections Algorithm(SPA) eliminates noise and extracts effective spec...

A novel NIRS modelling method with OPLS-SPA and MIX-PLS for timber evaluation

The identification of timber properties is impor-tant for safe application.Near Infrared Spectroscopy (NIRS)technology is widely-used because of its simplicity,effi-ciency,and positive environmental attributes.However,in its application,weak signals are extracted from complex,overlapping and changing information.This study focused on the stability of NIR modeling.The Orthogonal Partial Least Squares(OPLS) and Successive Projections Algorithm(SPA) eliminates noise and extracts effective spectra,and an ensemble learning method MIX-PLS,is applied to estab-lish the model.The elastic modulus of timber is taken as an example,and 201 wood samples of three species,Xylosma-congesta (Lour.) Merr.,Acerpictum subsp.mono,and Betula pendula,samples were divided into three groups to inves-tigate modelling performance.The results show that OPLS can preprocess the near-infrared spectroscopy information according to the target object in the face of the system error and reduce errors to minimum.SPA finally selects 13 spec-tral bands,simplifies the NIR spectral data and improves model accuracy.The Pearson's correlation coefficient of Calibration (Rc) and the Pearson's correlation coefficient of Prediction (Rp) of Mix Partial Least Squares (MIX-PLS)were 0.95 and 0.90,and Root Mean Square Error of Calibra-tion (RMSEC) and Root Mean Square Error of Prediction(RMSEP) are 2.075 and 6.001,respectively,which shows the model has good generalization abilities.