Determination of weight percent gain in solid wood modified with in situ cured furfuryl alcohol by near-infrared reflectance spectroscopy
Introduction
Impregnation of solid wood with a furfuryl alcohol monomer that is subsequently cured using heat and a catalyst to yield an inert polymer inside the cell wall structure, has for a long time been known to provide positive changes in wood properties. Durability towards acids and alkali was one of the first fields of interest [1], but improved mechanical properties [2], dimensional stability [3] and thereby improved biological durability was also discovered some 40 to 50 years ago in several studies. The process was then more or less dormant until Ryu et al. [4] presented work on the biological resistance of furfuryl alcohol-treated wood in 1992. In the following years two research groups simultaneously developed new catalyst-systems for implementation in the furfurylation process which later on made it possible to commercialize furfurylated wood [5].
When introducing new wood products to the market, quality control is essential. Besides the parameters normally documented for wood like modulus of elasticity, bending strength and the like, polymer mass is important for furfurylated wood, since this has been shown to associate with the enhanced properties [1], [4], [5], [6], [7]. In general, these properties are positively correlated with increasing polymer mass. The polymer mass is evaluated by the weight percent gain (WPG):where mof is the final oven-dry mass of the modified sample and moi is the initial oven-dry mass of the untreated sample.
Several spectroscopic methods can be used to monitor the quality of solid wood products directly in a production flow at- or in-line. Near-infrared (NIR) spectroscopy is often preferred routinely, since this has shown to have excellent prediction abilities in biological samples [8], [9], [10], [11] and require little or no sample preparation. Raman and UV–VIS spectroscopy have also been utilised; but to a smaller extent and for more specialized studies [12], [13]. The Mid-IR wavelength spectrum is mainly applied for studies of degradation in wood and generally for investigation of chemical compounds in wood [14], [15], [16]. In the present work focus is put on NIR spectroscopy and chemometrics hereon, since both VIS and Attenuated Total Reflectance IR (ATR-IR) measurements obtained on the same samples gave less good prediction models. ATR-IR results will be presented in another context, since the chemical information in these spectra provides additional and useful knowledge on the furfurylation treatment.
All measurements were performed in laboratory environments on small samples of furfurylated Scots pine (Pinus sylvestris, L.). The challenges in a possible up-scaling of such a method are not part of the present work, but several parameters related to the inherent problems with obtaining good spectra on wood will be touched upon. Eikenes et al. [17] initiated work on a system for quality control of furfurylated wood. In that study small samples of the diffuse-porous hardwood species Birch (Betula pendula, Roth.) were analysed using NIR spectroscopy on inner cross-sectional cuts. A root-mean-square error of prediction (RMSEP) referring to the WPG of 1.23 was reached on samples in the treatment range of 16.7 to 35.1 WPG using a PLS-model with six components.
To test the application range further, we chose to work on a commercially important softwood species and in a WPG range from approximately 0 to 30%. Furthermore, NIR was performed directly on external surfaces from clear tangential to clear radial cuts. Internal cracks – in some cases extending towards the surface – were present in a number of samples (mainly untreated). To summarise, several parameters complicated the recording of good spectra:
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Softwoods have a heterogeneous layer-structure with alternating early and latewood cells with marked differences in both physical and chemical properties.
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Wood surfaces are quite rough and sometimes even cracked which gives rise to scatter effects.
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A coat of furfuryl alcohol polymer can be present on the external surface of samples. It was not known beforehand whether the amount of this coat was correlated to the average WPG of the sample.
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Given different treatment degrees the samples will contain a varying amount of moisture.
Section snippets
Materials and methods
Two batches of modified wood were prepared. The first set (also termed ‘old’) was a large set meant for constructing the basic model. A second set (also termed ‘new’) was a smaller external test set made to test the prediction ability of the model constructed on the basis of the old data set.
Finding optimal wavelength range and best pre-processing
For this part, only the old samples were used in the analysis. There were two clear outliers in the data set, one reference measurement error and one sample with a spectrum behaving differently from all the remaining samples. The data set was then divided into a calibration and a validation set, as described above.
The first step in the analysis was to find the best pre-processing technique and the best NIR range. The best measurement range was selected based on the mean RMSECV per factor of all
Conclusions
This work shows that the degree of furfurylation (WPG) in Scots pine solid wood can be predicted by the use of NIR. Further, the conditioning of wood prior to NIR-analysis can be modelled away by the addition of as little as 30% of samples without the conditioning to the data with conditioned wood samples. The effect of the inclusion of the new samples into the calibration model suggests that the conditioning of the wood samples is only seen as an interfering signal in the NIR spectra. The
Acknowledgement
Åsmund Rinnan wishes to thank Frans van den Berg for good ideas during the work.
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