Near Infrared Spectroscopy Calibration for Wood Chemistry: Which Chemometric Technique Is Best for Prediction and Interpretation?

Sensors

Brian K. Via¹  Chengfeng Zhou¹  Gifty Acquah¹  Wei Jiang¹ 

[1] Forest Products Development Center, School of Forestry and Wildlife Sciences, Auburn University, 520 Devall Dr., Auburn, AL 36849, USA; E-Mails:

关键词: NIR;    chemometric;    PLS;    PCR;    regression;    loading;    coefficient;    error;    wood chemistry;   

DOI  :  10.3390/s140813532

来源: mdpi

【 摘 要 】

This paper addresses the precision in factor loadings during partial least squares (PLS) and principal components regression (PCR) of wood chemistry content from near infrared reflectance (NIR) spectra. The precision of the loadings is considered important because these estimates are often utilized to interpret chemometric models or selection of meaningful wavenumbers. Standard laboratory chemistry methods were employed on a mixed genus/species hardwood sample set. PLS and PCR, before and after 1st derivative pretreatment, was utilized for model building and loadings investigation. As demonstrated by others, PLS was found to provide better predictive diagnostics. However, PCR exhibited a more precise estimate of loading peaks which makes PCR better for interpretation. Application of the 1st derivative appeared to assist in improving both PCR and PLS loading precision, but due to the small sample size, the two chemometric methods could not be compared statistically. This work is important because to date most research works have committed to PLS because it yields better predictive performance. But this research suggests there is a tradeoff between better prediction and model interpretation. Future work is needed to compare PLS and PCR for a suite of spectral pretreatment techniques.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.