Abstract
The variation of juvenile wood concentration within a southern pine feedstock can impact strand density variation during composite processing. Higher strand density variation can equate to increased variance in product performance and higher manufacturing costs. In this study, near-infrared spectroscopy coupled with linear and nonlinear methods of calibration was used to predict strand density. The best performing model was developed with a 1st derivative pretreatment and 6 factors including a quadratic term and exhibited a root mean square error of prediction (RMSEP) = 0.0566, R 2 = 0.84, and a ratio of performance to deviation (RPD) = 2.30. When only the radial surface was presented and a linear model was utilized, the RMSEP was lowered to 0.033 and the RPD increased to 3.93 and confirmed that a random surface orientation will decrease model precision. The Box-Behnken design was found useful in providing a competitive nonlinear calibration model but with a smaller sample size.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
