lyte of interest is the only component giving rise to the recorded signal, is not appropriate for the
study of lignocellulosics Guilbault, 1990. More- over, although the use of fluorescence seems to
hold distinct advantages for process analysis and optimisation, is not widespread in this partic-
ular field Reibe and Eustace, 1990; Beebe et al., 1993.
Furthermore, chemometrics, i.e. the application of mathematical and statistical methods in order
to extract reliable and relevant information from chemical data, can be applied towards arriving at
tangible information from complex analytical data Miller, 1995. Until now, chemometrics-
based methods have not been largely applied in the field of lignocellulosics. We can e.g. mention
the work of Shimleck et al. 1997, who deter- mined the kraft pulp yield and carbohydrate con-
tent of wood using NIR in conjuction with a PLS technique.
Principal Component
Analysis PCA of
13C
C-CPMAS-NMR-spectra has also been employed for the estimation of cellulose I
and II in cellulosic samples Lennholm and Iversen, 1995.
In this paper, multivariate chemometric analy- sis of fluorescence spectra of juvenile and mature
eucalyptus wood is used in order to investigate the existence of any statistically adequate correlation
between fluorescence data, and the physicalchem- ical properties, as well as the pulping data of
mature and juvenile eucalyptus wood samples.
It is known that the stage of the plant develop- ment influences its physicalchemical properties,
which then play a decisive role on their pulping characteristics and the other technical properties
of the pulp and paper produced. Consequently, the age of wood harvesting is an important factor
for defining paper pulp quality. This information could be of great interest, among other things, for
the selection of criteria for the improvement of wood species.
2. Materials and methods
Samples of Eucalyptus globulus wood corre- sponding to mature and juvenile second year
shoots were compared. Their following physical and chemical properties were determined: lignin
content, lignin monomeric structure, ash content, solubility in 1 wv sodium hydroxide. Lignin
content was determined by the Klason method TAPPI, 1983a. The extraction was performed
with hot sodium hydroxide solution 1 wv for 1 h TAPPI, 1983b. The monomeric lignin units
engaged in b-O-4 bonds were determined by thioacidolysis Lapierre, 1993.
The wood samples were submitted to kraft cooking NaOH – Na
2
S, and the total yields as well as the Kappa number of the pulps were
determined Berjings, 1966.
2
.
1
. Fluorescence data Solid wood samples were ground in order to
obtain a homogeneous powder surface. Fluores- cence spectra were recorded twice for each sample
employing a Perkin Elmer LS 50B Luminescence Spectrometer connected to a PC. The Perkin
Elmer LS50B Instrument Program version 5.0 is used for instrument control. Emission spectra
were recorded at excitation wavelengths of 450, 400, 350 and 280 nm, whereas the emission is
measured in the region of 275 – 650 nm with inter- vals of 0.5 nm in total, 751 data points. The
excitation wavelengths were chosen by visual in- spection of emission spectra at excitation wave-
lengths in the range of 230 – 500 nm, recorded with a 10 nm step. For this purpose, an OBEY
program was written in Obey Macro Language Perkin Elmer, 1994. All measurements were per-
formed at 24 9 1°C. Excitation and emission monochromator slit widths were 3 nm, and a
mirror absorbing 99 of the emitting radiation was used. The measurement starts at the highest
and finishes at the lowest excitation wavelength, in order to minimise photodecomposition of the
sample.
Spectral data were converted to ascii files by a computer program provided by Perkin Elmer FL
Data Manager, version 3.50.
2
.
2
. Principal component analysis Principal component analysis PCA is a mathe-
matical manipulation of a data matrix, where the
goal is to represent the variation present in many variables using a small number of ‘factors’. A new
row space is constructed in which to plot the samples, by redefining the axes using factors than
the original measurement variables. These new axes are called principal components PCs, allow
the analyst to probe matrices with many variables and view the true multivariate nature of the data
in a relatively small number of dimensions. With this view, human pattern recognition can be used
to identify structures in the data Beebe et al., 1998.
The first PC explains the maximum amount of variation possible within the data set in one direc-
tion. The coordinates of the sample in a coordi- nate system defined by the Principal Components
are called scores. The loading vectors are the bridge between the variable space and the PC
space. The loadings tell us how much each vari- able contributes to each PC.
In matrix form: X = TP, where X: the analysed data matrix; T is the score matrix, and P is the
loading matrix. Only a significant number of PCs are relavant in describing the information in X.
This leads to the following decomposition: X = T
f
P
f
+ E, where T
f
is the score matrix with dimen-
sions sxf, P
f
is the loading matrix with dimensions
wxf and E is a residual matrix with the same dimensions as X Esbensen et al., 1994. In this
particular case, T contains information about the samples, and P contains information about the
wavelengths.
2
.
3
. Partial least square regression PLS
. PLS is used in order to make a model correlat-
ing X and y, where X contains the fluorescence spectra and y s × 1 is a vector containing the
property of interest. The model performance is validated by cross validation due to our small
data set. Multivariate calibration models were built correlating the fluorescence emission spectra,
and physical as well as chemical properties of the samples.
PCA and PLS regressions were performed with the use of the UNSCRAMBLER program ver-
sion 6.0, Camo AS, Norway. The model perfor- mance is validated by cross validation due to our
small data set Noergaard, 1995. The pre- dictive performances of the PLS models are as-
sessed by RMSEP root mean square error of prediction criterion and Pearson’s correlation co-
efficient R:
RMSEP =
N i = 1
Y
i predicted
− Y
i measured
2
N where N is the number of samples.
Schematically the procedure could be summa- rized as following:
Emission fluorescence spectra recorded at four excitation wavelenghts tranformation to ascii
files introduction to UNSCRAMBLER pro- gram each spectrum is represented by 751
points put the four emission spectra sequen- tially resulting rows of 4 × 751 = 3004 points
creation of a X matrix for the 20 samples with dimension 20 × 3004 removal of the common
peaks the
analysed matrix
becomes 20 ×
1115 application of PCA construction of PLS models with the introduction of Y
i
matrices with dimensions
20 × 1 with
data of
different properties.
3. Results and discussion
