This research demonstrated model development for important wood properties using near infrared spectroscopy (NIR); it considered the effect of outside sources of error, and the ability of NIR to measure fiber morphology.
Strength, stiffness, and density were successfully modeled from wood samples taken throughout 10 longleaf (Pinus palustris Mill) trees. Principal components and multiple linear regression were compared for performance in prediction of density, strength, and stiffness. I found both modeling techniques to yield similar prediction accuracies. However, I found that density could be estimated through Beer-Lamberts law since the absorbance at all wavelengths increased with density. Also, 5 of 6 wavelengths needed to predict strength were also needed to predict stiffness lending support that similar chemical morphology controls the covariance between strength and stiffness.
Klason lignin, extractives, and microfiber angle (MFA) were also measured throughout the tree. I found extractives, lignin, and MFA to decrease from the pith outward regardless of height. A theoretical model was built attempting to explain how lignin content and MFA co-vary. Theoretical and empirical spectroscopic models both predicted MFA with nearly similar root mean square error and supported that lignin was a probable factor responsible for the covariance in spectra with MFA.
Tracheid length was another secondary trait investigated. I demonstrated that tracheid length could be predicted with an R2 of 0.71 due to NIR spectra response with age. Accurate tracheid length prediction was possible due to systematic variation of chemistry with age except for at ring 1 and 4 where some other unknown factor was responsible.
Finally, blue stain and machine variability were investigated as two sources of extraneous error. It was of interest to know if the common extraneous error would bias a prediction equation. With proper modeling, I found I could avoid the blue stain variation present in the spectra when modeling modulus of elasticity (MOE), modulus of rupture (MOR), density, lignin, and extractives. However, when a calibration was built from one machine and then applied to a population of scans made from a separate machine, blue stain became problematic and prediction of MOE, MOR, and extractives were biased.
| Identifer | oai:union.ndltd.org:LSU/oai:etd.lsu.edu:etd-11082004-155505 |
| Date | 08 November 2004 |
| Creators | Via, Brian Kipling |
| Contributors | Anthony Lewis, Todd Shupe, Mike Stine, Les Groom, Tom Dean |
| Publisher | LSU |
| Source Sets | Louisiana State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lsu.edu/docs/available/etd-11082004-155505/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached herein a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to LSU or its agents the non-exclusive license to archive and make accessible, under the conditions specified below and in appropriate University policies, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
Page generated in 0.0015 seconds