TECHNICAL FDELD

This invention relates generally to a method for determining solid native wood chemical constituents in wood samples such as cores, strips, boards, logs and boreholes made in standing trees. The invention specifically relates to the application of visible-light Raman spectrometry for measuring the Raman-scattered light intensity of solid wood samples containing variable amounts of lignin, cellulose, hemicelluloses and other carbohydrates.

BACKGROUND ART

Forest and wood related products are the most important, sustainable, natural resource. They are 100% renewable. They drive a billion-dollar industry in Canada, employing millions of people worldwide and have impact on social, environmental and economic factors.

Greater knowledge and understanding of the physical and chemical properties of wood plays an important role in the processing and utilization of wood, from their conversion from logs to pulp and paper production, to enhanced value-added products. Rapid knowledge of wood properties information, including that of the chemical nature, such as cellulose, lignin, and other carbohydrate components, would be of great benefit to the lumber industry and other wood composite related industries. Wood chemistry plays an important role in the manufacturing of paper, affecting its tensile and tear strengths, paper formation and surface properties. In addition, lignin, hemicelluloses, and carbohydrates provide an excellent indicator of pulp yield. Knowledge of these data at the source (tree or log), through non-destructive measurements, would allow for segregation at sawmills and at the pulp and paper manufacturer so as to tailor paper

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productions to meet customer specifications and allow for marketing of pulp to consumers of previously unexplored products.

Wood chemists have been attempting to analyze solid native wood samples by spectroscopic means for over 40 years. For example, Marton and Sparks [1] and Berben et al. [2] were able to determine the amount of residual lignin in dried wood and wood meal samples by measuring the lignin-to-cellulose ratio. However, the presence of large amounts of moisture in solid native wood prevented the application of this method to the analysis of never-dried solid wood samples. The development of near-infrared spectrometers in the mid-80s [3] combined with the advent of multivariate methods of analysis [4] seemed to provide a method that would be less sensitive to moisture content and the possibility of removing the strong laser-induced fluorescence in solid wood samples associated with high lignin content of wood. Numerous studies have been done with pulp samples [5]. These showed that the best results are achieved with homogenized, dried samples, a problem that precludes native-wood sample applications. As such, there has been less activity in the use of NIR reflectance spectrometry for the measurement of native wood chemical compositions. For example, Ferraz et al. [6], used multivariate analysis for developing NIR-based applications for predicting the chemical properties of ethanol-extracted dry wood samples, i.e. pulp yield, cellulose, lignin, hemicellulose, glucose, and xylose. Schimleck et al. [7] studied both green and dry wood samples and predicted with some success the physical properties of wood samples. They found that predictive errors were relatively large for green samples (R ² = 0.67), whereas dry samples calibrations demonstrated strong predictive correlations, with R ² varying from 0.87 to 0.95. Similarly poor results were found [8], even when the moisture was kept constant at 12%. Axrup et. al.[9] have shown that the chemical composition of wood chips - glucose, galactose, xylose, mannose, arabinose, and lignin could not be predicted on-line in the spectral region from 800 nm to 1100 nm with an accuracy higher than 15% of the total range. Kelley [10], Kelley et al. [11-12] and Meglen [13] have predicted, with some success, the mechanical properties of solid

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wood. The results were similar to those obtained by Schimleck et al, [7] with green samples. These difficulties are overcome with the use of Raman spectrometry.

Raman spectrometry is a technique that measures the intensity of the inelastic scattering of photons from a sample surface as it is irradiated with a monochromatic light source, such as a visible-light laser (415 nm, 514 nm, 785 nm and 810 nm) or ultraviolet-light source (285 nm) in UV Resonance Raman (UVRR) Spectrometry or near-Infrared (NIR) light source (1064 nm). The scattered photons lose part of their energy, equivalent to the transition energy required to excite the molecule from ground state to its excited state, and this results in a frequency shift. A plot of the intensity of the Raman shift and the scattering intensities yields the Raman spectrum. Unlike IR spectrometry, molecules which undergo polarization with excitation are strongly Raman active while molecules which undergo a dipole change are Raman inactive. As such, water is a weak Raman scatterer while benzene is a strong scatterer. Thus Raman spectrometry is less sensitive to moisture content than is near-infrared spectrometry.

Historically, wood chemists have applied visible-light Raman spectrometry to lignocellulosic material, only to find that the laser-induced fluorescence (LIF) strongly masks any Raman bands, thereby rendering the technique unusable. Atalla et al. [14] has described two sampling procedures that are effective in reducing the LIF of wood, pulp, and paper samples: water immersion and oxygen flushing. In the first technique, the sample is extracted with a toluene/ethanol mixture to remove fluorescence- generating extractives, washed with distilled water and then submerged in a solution of D ₂ O for the analysis. In the second technique, the sample is washed and pressurized with oxygen, up to 50 psi, prior to data acquisition, with oxygen being used as a fluorescence quencher. Samples can also be photobleached prior to analysis to minimize LIF. As such, these techniques do not lend well to the high throughput required for many applications. Atalla et. al. [14], Agarwal and Atalla [15] have discussed the challenges facing spectroscopists using conventional Raman spectrometry. In one investigation, Bond et. al. [16] used 514 nm excitation and a

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Raman microprobe to investigate the distribution of cellulose and lignin in latewood cell walls of loblolly pine. To minimize fluorescence by the extractives present in wood, the author had to prepare the microtome samples by utilizing the extraction technique as stated above.

With the development of the NIR excitation source, NIR-FT-Raman spectrometry has been extensively applied to lignocellulosic materials. According to these reviews, FT- Raman with NIR excitation lowers LIF and the authors have applied the technique for the measurement of Kappa number in pulp. However, the use of NIR excitation provides a weaker Raman signal and a much longer acquisition time, which is not suitable for an on-line environment. Also, the results still show a significant fluorescence background in the FT-Raman spectra. Increasing laser power increases the Raman signal, but increases fluorescence background and sample photobleaching, especially when a longer acquisition time is required.

Nevertheless, many investigators have applied FT-Raman to characterize wood samples.

Agarwal et. al. [17] have used FT-Raman to study softwood and hardwood milled wood lignin (MWL) and to determine the spectral effects that are associated with chemical modifications. The authors' objectives were to identify and assign the Raman peaks for potential analytical applications in the future. A careful examination of the data shows that fluorescence was still observed with the wood samples, especially down in the lower wave number regions. There was no attempt to either quantify lignin or cellulose. Rather, a relative distribution of the lignin and cellulose was discussed.

In another paper, Ibrahim et al. [18] details the use of FT-Raman spectrometry for lignin and kappa number determination in pulp samples. Using partially delignifϊed samples with known kappa numbers of 10 to 38, Ibrahim was able to obtain reasonable correlations between the ratio of integrated band area (1600 cm ^'1 : 1200 - 1010 cm ^"1 ) to the kappa number. The author specifically states that visible Raman spectrometry is plagued with limitations, primarily LIF. Again, spectral data shown in the article

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(Figure 3 of [18]) illustrates that there is still a lot of fluorescence present when one applies FT-Raman on partially delignifϊed pulp samples. Although a good correlation is obtained (Figure 4 of [18]), a sigmoidal-like systematic error is observed.

Because of the limitations found with visible Raman and FT-Raman NIR excitation, a few investigators have applied UVRR spectrometry to the measurement of lignin and kappa number of wood pulp. Halttunen et. al. [19] have utilized UVRR spectroscopy for the study of trace residue lignin content remaining after bleaching of the fibres. In resonance Raman spectroscopy, the Raman scattering is greatly enhanced due to the local, symmetric vibrational modes which couple to the electronic excited states and could be enhanced as much as 10 ⁶ . As a result, the resonance effect is observed when the exciting frequency is close to the electronic transition in the molecule of interest. Since lignin absorbs strongly in the UV region while cellulose and hemicellulose do not, a strong resonance effect is observed for lignin. Thus, trace amounts of lignin can be detected with UVRR spectrometry. In this case, UVRR spectrometry is applied on partially and fully delignifϊed pulp and not solid native wood lignin, as per instant invention. The authors further compared UVRR with visible Raman spectrometry and concluded that visible Raman, i.e. 400 nm to 900 nm excitation, induced fluorescence that totally overlapped the Raman signal, thus requiring sample pre-treatment necessary so as to reduce the LIF. Furthermore, with visible excitation, the cellulose and hemicellulose components dominated the spectrum, to the extent that the characteristic aromatic lignin band at 1600 cm ^"1 was not detected. The authors then concluded that Raman with visible excitation does not provide any information on residual lignin.

Interestingly, Trung et al. [20] report that most of the fluorescence in visible-Raman spectrometry can be removed when using pulp samples, i.e. partially delignifϊed, by manually generating baseline-corrected spectra. However, a person of ordinary skill would conclude that this method does not lend itself to automation, since the fluorescence background decreases with lignin content, as shown in Example 4 in [19]. Trung et al. state that, in bleached samples and in the absence of strong fluorescence,

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the lignin content is easily determined with background-corrected spectra and multivariate calibration. One of ordinary skill in the art would then conclude that such determination would be very difficult with solid native wood samples because of the high level of fluorescence, due to the high extractives content as well as high lignin content as compared to partially delignified wood pulp, and the difficulty of automating the background correction with very high and varying levels of fluorescence.

Therefore, the prior art clearly teaches away from the use of visible-light Raman spectrometry on solid native wood samples, especially if one wishes to perform any quantitative analysis. Furthermore, the majority of the investigators who addressed the issue of quantitative analysis of solid native wood and other solid lignocellulosic samples were dealing with partially or fully delignified wood pulp samples, with no extractives being present. Unexpectedly, the instant invention provides a method for the quantitative determination of solid native wood chemical compositions. In solid native wood, approximately 50% is cellulose, 20-25% is hemicellulose, and 20-25% lignin with the remaining contribution originating from extractives content. As extractives also fluoresce very strongly, this further teaches those skilled in the art of wood and wood analysis, away from applying visible Raman spectrometry for the measurement of solid wood chemical compositions. None of the methods cited in the prior art is capable of determining native wood constituents with sufficient accuracy and detail to yield a useful measurement for process and/or quality control. In the following, we disclose such a method.

DISCLOSURE OF THE INVENTION

The instant invention seeks to provide a method for determining solid wood chemical compositions in native wood samples, particularly, but not limited to, total lignin content, Klason lignin, cellulose, hemicellulose, and various carbohydrates, in which the shortcomings of the prior art are overcome.

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The invention also seeks to provide such a method which is rapid and portable and which provides measurements of solid native wood chemical compositions without sample preparations.

Thus, in one aspect of the invention, there is provided a method for determination of a chemical parameter of solid native wood comprising: a) exposing a native wood specimen to visible light excitation and allowing the specimen to scatter the light, b) collecting scattered light and generating a Raman spectrum, c) comparing the generated spectrum with a Raman spectrum for a native wood specimen for which the chemical parameter is known, and d) evaluating the chemical parameter from the comparison in

Accordingly, in one embodiment there is provided a method for the determination of solid wood chemical compositions, particularly, but not limited to, total lignin content, Klason lignin, cellulose, hemicellulose, and carbohydrates content with the aid of a visible-light Raman spectroscopic technique, comprising the steps: of subjecting a solid wood chemical composition to visible-light excitation, such as, but not limited to, 700nm to 850nm, and more preferably, 785nm, collecting the scattered light at either 180° or 90°, generating Raman spectra, correlating the spectral data with a known analysis of such chemical composition, to generate a calibration regression, either as univariate or multivariate calibrations such as PCA or PLS. Subsequent analyses can be done by determining the native wood chemical composition by applying predictions to the unknown spectral data.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The instant invention provides a rapid, novel method for the determination of the chemical composition of wood samples, particularly, but not limited to, total lignin content, Klason lignin, cellulose, hemicellulose, and carbohydrates content. This method overcomes the disadvantages previously discussed. The proposed analysis

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method enables one to measure the chemical constituents of wood samples independently of species variations and fluorescence intensity. The analysis uses a laser power setting that is low enough to prevent sample bleaching. Since data acquisition only takes a few seconds, a high sample throughput will allow many solid native-wood furnishes to be multiplexed to a single analyzer through either the use of fiber optics.

The analysis method described below uses Raman-scattered light intensity measurements obtained from the Raman spectra of solid native-wood samples illuminated by the monochromatic light emitted by a visible-light laser. These measurements are generally free from moisture-content interference. The integrated Raman-scattered light intensity of a wood sample is measured along predetermined spectral regions. With the aid of a multivariate calibration technique (PLS), the Raman- scattered intensity for each sample is made to correlate directly with the concentration of chemical constituents obtained from standard-method laboratory analysis. Although not necessary, it is generally preferable that the concentration of all wood constituents be accounted for within a PLS calibration so that the lignin measurements are accurate and without bias, thereby creating a noise-free model that can be characterised with a small number of basis vectors. The model then uses these basis vectors for characterising components in unknown samples. As such, the concentration of wood chemical constituents is then determined with the PLS model.

Visible-light Raman measurements of solid native-wood chemical constituents could then be used for characterising the variability of the chemical constituents of wood chips entering the pulp manufacturing process. Alternately, these measurements could be applied to wood-core characterisation so as to obtain optimal long-term results in either silviculture or tree-breeding programs. The application of this invention to the analysis of wood samples provides a method for determining the concentration of chemical constituents in solid native-wood samples that is faster, more reliable, and requires less maintenance than existing methods. In summary, the instant invention replaces currently

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used laboratory methods, and addresses the previously discussed shortcomings of these devices, such as sample preparation, throughput and ease of use.

In one embodiment, the present invention provides a method for the on-line spectroscopic determination of the chemical composition of solid native-wood samples. Unlike currently available commercial instrumentation, the method enables one to measure total lignin content, Klason lignin, cellulose, hemicellulose, and carbohydrates content in solid native-wood samples independently of species variations and fluorescence intensity. The method includes the steps of: (1) withdrawing solid native wood samples from standing trees or logs and other manufacturing process; (2) subjecting these samples to a monochromatic visible-light source; (3) recording the resulting scattered light and its Raman spectrum over a predetermined range of wave numbers so as to produce Raman-scattered light intensity measurements; (4) determining the Raman-scattered light intensity of the samples over a predetermined range of wavenumbers shown by different combinations of lignin content, cellulose content, and hemicellulose content; (5) correlating by either univariate or multivariate calibration the relationships between the Raman-scattered light intensity measurements of unknown samples and the Raman-scattered light intensity shown by known combinations of lignin content, cellulose content, and hemicellulose so that the chemical composition of wood samples can be accurately determined for any levels of fluorescence intensity emitted by the samples.

BRIEF DESCRIPTION OF THE DRAWINGS

In drawings which illustrate embodiments of the present invention:

FIG. 1 is a diagrammatic view of a sensing apparatus according to one embodiment of the present invention;

FIG. 2 is a graph of the visible Raman-scattered light intensity versus wave numbers comparing the original (bottom trace) and the pre-processed spectra (top trace) of the

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same native wood sample. The strong fluorescence background is not apparent on the top trace. Lignin, cellulose, hemicellulose and and HexA peaks are still clearly visible on the top trace;

Fig. 3 is a validation graph of the Raman-measured total lignin versus the laboratory- determined total lignin obtained by wet chemical analysis;

Fig. 4 is a calibration graph of Raman-measured versus laboratory-determined concentrations of galactan;

Fig. 5 is a graph showing the correlation between the unscreened pulp yield of hybrid poplars and their holocellulose content;

FIG. 6 shows a summary of wood chemical constituents with their corresponding correlation coefficient as developed with instant invention; and

FIG. 7 is a calibration plot of Raman spectral data to the syringyl/guaiacyl lignin ratio obtained with PLS2.

METHOD FORCARRYINGOUTTHEINVENTION

FIG.1 illustrates a diagrammatic view of a portable, rapid sensing apparatus according to one embodiment of the present invention. Referring to FIG.l, the excitation light 10 from a monochromatic light source, such as a visible-light laser having an excitation wavelength of 785nm or a superluminescent light emitting diode (SLED), connected via fibre optic cable 12, enclosed in a portable spectrometer 14, irradiating the cut- wood sample 16 or a standing tree 18, in a bored hole 20, with the aid of a analyser probe head 22, and collecting the scattered photons, logging and storing the data on a portable computer 24, which interprets the spectral data to provide chemical compositions of the said sample. The probe head can be set up to move across the surface of the sample,

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such as a core, wood strip, log ends, or bored holes to provide a profile of the chemical compositions of the sample.

Furthermore, rapid and real-time information provided by the method connected to a central database via wireless link, provides instant updates and can be used as a tool to enhance best management practices in our forests. In addition, the non-destructive measurement of standing green lumber and native-wood chemistry allows the forester and biologist to rapidly acquire phenotypical data to determine the best clones for the next generations of trees so as to provide desirable traits for specific end-use requirements.

EXPERIMENTAL

Experiments were conducted with a Bruker Optics Sentinel visible Raman spectrometer, which features an excitation wavelength of 785 nm. Integration time was set to 60 seconds and averaged over 5-scans, for a total analysis time of 5 minutes. Since the fluorescence background is a broad electronic transition background, pre-processing the data to yield a first- or second-derivative spectrum as an example, minimizes the fluorescence contribution to the spectrum by straightening out the baseline. This yields a spectrum that can be used for quantitative analysis. A spectral range of 220 nm to

2250 nm is collected for all samples.

EXAMPLE 1

The visible-light Raman spectrum of twenty-seven different poplar wood core samples were collected, scanned with instant invention, and using standard techniques, their chemical compositions were determined. FIG. 2 illustrates a Raman spectrum of a native wood core, taken from a poplar tree, illustrating the unprocessed spectrum (bottom) containing fluorescence and the processed data (top) which is free of any fluorescence background. One can clearly see the peaks associated with the lignin peak at 1600 cm ^"1 , the 4-deoxy-4-hexenuronic acid (HexA) at 1656 cm ^"1 , and cellulose 1095

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cm ^"1 . For hardwood, cellulose peak is generally split into two with maxima at 1095 cm ^{" 1} and 1120 cm ^"1 . As one can observe, the signal to noise ratio is clearly far better that those obtained with FT-Raman spectrometry and results in better sensitivity and dynamic range. FIG. 3 illustrates the results obtained for total lignin content of native wood. A strong correlation is observed with an R ² of 0.90 and a standard error of prediction (RMSEP) of an independent validation dataset of 0.5%. The use of this technique has been applied to rapidly determine a dataset of over 700 wood core samples for total lignin content in a fraction of the time that would be required over conventional wet chemical technique, such as gas chromatography.

EXAMPLE 2

A one-component calibration was performed using a multivariate technique such as partial least-squares (Grams 32/AI, ThermoGalactics, MA, USA) from samples listed in Table I for the purpose of building a prediction model that is capable of determining the galactan in solid native-wood cores. FIG. 4 illustrates the regression obtained with the method of the invention. A strong correlation is obtained with an R ² of 0.90 and a standard error of calibration of 0.04 mg/g.

Table I: Comparison between lab analysis and spectroscopy determination (RMSEC = 0.04%) Sample ID Total Lignin Spectroscopy Lab Prediction

1 0.61 0.536871

2 0.669 0.603674

3 0.527 0.511705

4 0.587 0.625761

5 0.55 0.576583

6 0.422 0.48332

7 0.346 0.382595

8 0.422 0.457053

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9 0.495 0.462343

10 0.658 0.61081

11 0.559 0.558257

12 0.628 0.583218

13 0.769 0.694715

14 0.529 0.546216

15 0.614 0.659701

16 0.548 0.510058

17 0.317 0.373736

18 0.199 0.263287

19 0.356 0.414694

20 0.353 0.394457

21 0.426 0.51564

22 0.394 0.417898

23 0.447 0.434943

24 0.457 0.484081

25 0.61 0.536871

EXAMPLE 3

The unscreened kraft pulp yield of hybrid poplars can also be correlated to their holocellulose content as determined per the instant invention. This is illustrated in FIG. 5.

EXAMPLE 4

Other native wood chemical-composition calibrations have also been developed. A partial list, which is not limited to that provided in FIG. 6, is given, along with their correlation parameters. These results clearly demonstrate the novelty and non- obviousness of instant invention over prior art for one ordinarily skilled in the art of

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native wood analysis. Such a person would generally associate a strong fluorescence background with the application of visible Raman spectroscopy to solid native-wood samples. As a further example, FIG. 7 shows that the syringyl/guaiacyl lignin ratio can be predicted with reasonable accuracy in solid native-wood samples.

The content of the drawings is further summarized hereinafter:

FIG. 1 illustrates schematically a portable Raman spectrometer with visible-light excitation, is used to excite the sample. Data collection is achieved with the fibre optic probe and stored and process with a portable computer.

FIG. 2 is a graph comparing the original and the processed spectra of the same native wood sample, before (bottom trace) and after (top trace) processing. The strong broadband fluorescence background does not affect the data shown on the top trace. The first-derivative signals of lignin, cellulose, and HexA peaks are clearly visible on the top trace.

FIG. 3 is a plot of validation results for total lignin content of hybrid poplar. An excellent correlation is obtained for this independent dataset with RMSEP of 0.5%.

FIG. 4 is a calibration plot of Raman spectral data to concentrations of galactan, i.e. holocellulose, as obtained with PLS2. Good correlation is observed with low RMSEC.

FIG. 5 shows the relationship between the unscreened kraft yield of hybrid Poplar clones and their holocellulose content.

FIG. 6 is a summary of wood chemical constituents and their corresponding correlation coefficient as developed with instant invention.

In particular, Fig. 6 shows the correlation coefficient (R value) for acid soluble lignin, total lignin, Klason lignin, galactan, cellulose, xylan, arabinoxylan, hemicellulose,

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glucan, arabinian and mannan, the content of all of which can be determined by the present invention.

FIG. 7 is a calibration plot of Raman spectral data to the syringyl/guaiacyl lignin ratio obtained with PLS2. Good correlation is observed with low RMSEC.

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REFERENCES

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3. J. M. Pope, '7. Near-Infrared Spectroscopy of Wood Products', in "Surface Analysis of Paper", T.E. Conners and S. Banerjee (Eds.), CRC Press, Boca Raton, 142-151 (1995).

4. D.M. Haaland, '8. Multivariate Calibration Methods Applied to Quantitative FT- IR Analyses', in "Practical Fourier Transform Infrared Spectroscopy: Industrial and Laboratory Chemical Analysis", J. R. Ferraro, K. Krishnan (Eds.), Academic Press, Inc., San Diego CA, pp. 395-468 (1990).

5. E. Yusak and C. Lohrke, "At-line Kappa Number Measurement by Near-Infrared Spectroscopy", in Proceedings of the 1993 TAPPI Pulping Conf. Book 2, pp. 663- 671, TAPPI Press, Atlanta GA.

6. A. Ferraz, R. Mendonca, A. Guerra, J. Ruiz, J. Rodriguez, J. Baeza and J. Freer,

"Near-Infrared Spectra and Chemichal Characteristics of Pinus Taeda (Loblolly Pine) Wood Chips Biotreated by the White-Rot Fungus Ceriporiopsis subvermispora", J. Wood Chem. Technol., 24(2), 99-113 (2004).

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8. L.R. Schimleck, R. Evans, J. Hie and A.C. Matheson, "Estimation of Wood Stiffness of Increment Cores by Near-Infrared Spectroscopy", Can. J. For. Res., 32, 129-135 (2002).

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9. L. Axrup, K. Markides and T. Nilsson, "Using Miniature Diode Array NIR Spectrometers for Analysing Wood Chips and Bark Samples in Motion", J. Chemometrics, 14, 561-572 (2000).

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11. S. S. Kelley, T. G. Rials, L. R. Groom and C-L. So, "Use of Near-Infrared Spectroscopy to Predict the Mechanical Properties of Six Softwoods",

Holφrschung, 58, 252-260 (2004).

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13. R. R. Meglen and S. S. Kelley, "Method for Predicting Dry Mechanical Properties from Wet Wood and Standing Trees", U. S. Patent No. 6,606,568 (August 12, 2003).

14. R.H. Atalla, U.P. Agarwal, and J.S. Bond, '4.6. Raman Spectroscopy', in

"Methods in Lignin Chemistry", S. Y. Lin and CW. Dence (Eds.), Springer- Verlag, Berlin, 162-176 (1992).

15. U.P. Agarwal and R.H. Atalla, '8. Raman Spectroscopy', in "Surface Analysis of Paper", T. E. Comers and S. Banerjee (Eds.), CRC Press, Boca Raton, pp. 152- 181 (1995).

16. J. S. Bond and R, H. Atalla, "A Raman Microprobe Investigation of the Molecular Architecture of Loblolly Pine Tracheids", 10 ^th International

Symposium on Wood and Pulping Chemistry Yokohama, Japan, TAPPI Press Vol. l. pp.96-101 (1999).

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17. U. P. Agarwal. J. D. McSweeny and S. A. Ralph, "An FT-Raman Study of Softwood, Hardwood and Chemically Modified Black Spruce MWLs", 10 ^th International Symposium on Wood and Pulping Chemistry Yokohama, Japan, TAPPI Press Vol. 2, pp.136-140 (1999).

^■ 5

18. A. Ibrahim, P.B. Oldham, T.E. Conners and T.P. Shultz, "Rapid

Characterization of Wood Pulp Lignin by Fourier Transform Raman Spectroscopy", Microchemicall, 56, 393-402 (1997).

10 19. M. Halttunen, J. Vyorykka, B. Hortling, T. Tamminen, D. Batchelder, A. Zimmermann and T. Vuorinen, "Study of Residual Lignin in Pulp by UV Resonance Raman Spectroscopy", Holzforschung, 55, 631-638 (2001).

20. T. P. Trung and D. F. Leclerc, "Method for Determining Lignin Content in 15 Chemical Pulps Using Raman Spectrometry", U. S. Patent No. 6,551,451 (April

22, 2003). •

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