Three-Mode Abstracts, Part N
With one can go to the index of
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|Na | Nb |
Nc | Nd |
Ne | Nf |
Ng | Nh |
Ni | Nj |
Nk | Nl |
Nm | Nn |
No | Np |
Nq | Nr |
Ns | Nt |
Nu | Nv |
Nw | Nx |
Ny | Nz |
Nahorniak, M. L., & Booksh, K. S. (2003).
Optimizing the implementation of the PARAFAC method for near-real time calibration
of excitation-emission fluorescence analysis.
Journal of Chemometrics, 17, 608-617.
A field-portable, single-exposure excitation-emission matrix (EEM) fluorometer is
used in conjunction with parallel factor analysis (PARAFAC) for sub-ppb polycyclic
aromatic hydrocarbon (PAH) determinations in the presence of spectral interferents.
Several strategies for bringing multiway calibration methods such as PARAFAC into
the field were explored. It was shown that automated methods of PARAFAC model selection
can be as effective as manual selection. In addition, it was found that there is
not always a single best model to employ for prediction. Second, the effect that
reducing data density by systematically decreasing calibration set size and spectral
resolution has on PARAFAC speed and prediction accuracy was investigated. By decreasing
data density, the computational intensity of the PARAFAC algorithm can be reduced to
increase the plausibility of on-the-fly data analysis. It was found that reducing
eight sample PAH calibration sets to two or three calibration standards significantly
decreased computation intensity yet generated adequate predictions. It was also found
that spectral resolution can be decreased to reach an optimal compromise between
calibration accuracy and analysis speed while minimizing instrumental requirements.
Nannerup, L. D., Jakobsen, M, Van Den Berg, F., Jensen, J. S., Moller, J. K. S., & Bertelsen, G. (2004).
Optimizing colour quality of modified atmosphere packed sliced meat products by control of critical packaging parameters.
Meat Science, 68, 577-585.
To study the influence of different packaging and storage parameters on
the colour stability of modified atmosphere packed, cured, cooked ham, a multiplicative
analysis of variance model (GEMANOVA) was developed. The critical parameters investigated
were % residual-O-2, product to headspace volume ratio (P/H volume ratio), temperature,
light intensity and oxygen transmission rate (OTR). The model illustrated that all the
investigated parameters interacted, but especially % residual-O-2 and P/H volume
ratio - i.e., the absolute O-2 content, influenced the degree of discoloration. The
complex interactions of the parameters justified the selected model, as it emphasised
the necessity of evaluating the parameters simultaneously instead of considering them
individually. The importance of absolute O-2 content was further validated through an
industrial experiment including three different kinds of sliced meat products.
Navea, S., De Juan, A., & Tauler, R. (2001).
Three-way data analysis applied to multispectroscopic monitoring of
Analytical Chimica Acta, 446, 187-197.
Multivariate curve resolution-alternating least squares
(MCR-ALS) is proposed as a three-way analysis method to deal with
multispectroscopic monitoring of protein folding. MCR-ALS provides the
concentration profiles associated with the different protein conformations
occurring during the process and their related spectra. The concentration
profiles describe the folding mechanism and the spectra provide the structural
information of the conformations involved. Analysis either of the protein
folding process monitored with different techniques (i.e. a row-wise
augmented data matrix) or of several experiments done in different
conditions using the same technique (i.e. a column-wise augmented matrix)
or both possibilities at the same time (i.e. a row- and column-wise
augmented matrix), can be performed. Thermal unfolding and refolding of
alpha -lactalbumin, monitored using far- and near-UV circular dichroism,
fluorescence and UV spectrometry, is shown as example. Information related
to changes in the tertiary and the secondary structure of the protein,
to the presence of intermediates along the protein folding process and
to the reversibility of the thermal process can be obtained.
Navea, S., De Juan, A., & Tauler, R. (2002).
Detection and resolution of intermediate species in protein folding processes
using fluorescence and circular dichroism spectroscopies and multivariate curve
Analytical Chemistry, 74, 6031-6039.
Thermally induced protein unfolding/folding processes have been studied on
alpha-lactalbumin and alpha-apolactalbumin. Experiments monitored by fluorescence and circular
dichroism spectroscopic techniques on alpha-apolactalbumin showed the formation of an intermediate
species, whereas in the case of a-lactalbumin, this intermediate species was not detected. The
presence and resolution of this intermediate species, its spectrum, and the evolution of all
conformations during protein unfolding/folding processes were estimated using the multivariate
curve resolution-alternating least-squares method. Elucidation of the nature and contribution
of the different secondary structure motifs in each of the resolved protein conformations,
including the intermediate, was also carried out. Multivariate resolution has shown to be an
excellent tool for the complete characterization of all protein conformations involved in
folding processes, including intermediate species that cannot be isolated by physical or
chemical means. Indeed, it is in the determination and modeling of these intermediates that
this chemometric approach outperforms in power and reliability previous methodologies based
on simpler measurements and data treatments and fills the void linked to the elucidation and
interpretation of complex mechanisms in protein folding processes.
Navea, S., De Juan, A., & Tauler, R. (2003).
Modeling temperature-dependent protein structural transitions by combined near-IR
and mid-IR spectroscopies and multivariate curve resolution.
Analytical Chemistry, 75, 5592-5601.
The combination of near- and midinfrared spectroscopies (NIR and MIR) is
proposed to monitor temperature-dependent transitions of proteins. These techniques offer
a high discriminating power to distinguish among protein structural conformations but, in
temperature-dependent processes, present the drawback associated with the intense and
evolving absorption of the deuterium oxide, used as a solvent in the protein solutions.
Multivariate curve resolution-alternating least squares (MCR-ALS) is chosen as the data
analysis technique able to unravel the contributions of the pure protein and deuterium
oxide species from the mixed raw experimental measurements. To do so, MCR-ALS works by
analyzing simultaneously experiments from MIR and NIR on pure deuterium oxide solutions
and protein solutions in D2O. This strategy has proven to be effective for modeling the
protein process in the presence of D2O and, therefore, for avoiding the inclusion of
artifacts in the data stemming from inadequate baseline corrections. The use of MIR and
NIR and MCR-ALS has been tested in the study of the temperatare-dependent evolution of
beta-lactoglobulin. Only the combined use of these two infrared techniques has allowed for
the distinction of the three pure conformations involved in the process in the working
thermal range: native, R-type state, and molten globule.
Neal, R.J., Snyder Jr, C.W., & Kroonenberg, P.M. (1991).
Individual differences and segment interactions in throwing.
Human Movement Journal, 10, 653-676.
Arm segment velocities of 12 athletes throwing three differently weighted balls
were analyzed by three-mode principal component analysis. Individual differences
were characterized in terms of the combined influences of the phases of the
throwing motion and the arm segment velocity relationships established in those
phases. Using three individual differences components, 75% of the variance was
described. The arm segment velocity relationships were described by two main
components identified as directional velocity and proximal versus distal
velocity. The time periods components distinguished between relationships among
the arm segment velocities that occur in the windup versus those of the release
phase. Three individual differences components are identified and appeared to be
related to a general throwing style, the influence of skill level on technique,
and the differential effect of the varying ball weights, respectively. Each
athlete's throws are weighted combinations of these three components. The timing
of segment involvement is investigated and the results indicate sequential
patterns from proximal to distal as the throw unfolds. However, the summation of
speed principle should not be applied universally to explain segment motion and
Neuenschwander, B.E., & Flury, B.D. (1995).
Common canonical variates. Biometrika,
Canonical correlation analysis measures the linear relationship between two
random vectors X1 and X2 as the maximum
correlation between linear combinations of X1 and linear
combinations of X2. Several generalizations of canonical
correlation analysis to k > 2 random vectors
X1,..., Xk have been proposed in
the literature (Kettenring, 1971, 1985), based on the principle of
maximising some generalised measure of correlation. In this paper we propose
an alternative generalisation, called common canonical variates, based on
the assumption that the canonical variates have the same coefficients in all
k sets of variables. This generalisation is applicable in situations
where all Xi have the same dimension. The authors
present normal theory maximum likelihood estimation of common canonical
variates, and illustrate their use on a morphometric data set.
Neuhold, Y. M. & Maeder M. (2002).
Hard-modelled trilinear decomposition (HTD) for an enhanced kinetic multicomponent
analysis. Journal of Chemometrics, 16, 218-227.
We present a novel approach for kinetic, spectral and chromatographic resolution
of trilinear data sets acquired from slow chemical reaction processes via repeated
chromatographic analysis with diode array detection. The method is based on fitting
rate constants of distinct chemical model reactions (hard-modelled, integrated
rate laws) by a Newton-Gauss-Levenberg/Marquardt (NGL/M) optimization in combination
with principal component analysis (PCA) and/or evolving factor analysis (EFA),
both known as powerful methods from bilinear data analysis. We call our method
hard-modelled trilinear decomposition (HTD). Compared with classical bilinear
hard-modelled kinetic data analysis, the additional chromatographic resolution
leads to two major advantages: (1) the differentiation of indistinguishable rate
laws, as they can occur in consecutive first-order reactions; and (2) the circumvention
of many problems due to rank deficiencies in the kinetic concentration profiles.
In this paper we present the theoretical background of the algorithm and discuss
selected chemical rate laws. Copyright (C) 2002 John Wiley Sons, Ltd.
Nguyen, N., Hoole, P., & Marchal, A. (1994).
Regenerating the spectral shapes of s-phone and
sh-phone from a limited set of articulatory
Journal of the Acoustical Society of America, 9, 9.**
This work was aimed at exploring articulatory-acoustic relationships in the
production of French fricatives. More precisely, an attempt was made to
find out whether the spectral shapes of /s/ and /sh/ can be regenerated
from the x and y coordinates of three electromagnetic transducers affixed
to the tongue in the midsagittal plane. The corpus was composed of the two
fricatives /s/ and /sh/ combined with the vowels /a/ and /i/ in sequences
of the type /vs sh v/ and /v sh sv/, and was read by one male native
speaker of French, the spectrum regeneration was based on a statistical
procedure which consisted of estimating the factors explaining the main
part of the acoustic variance from the position of the transducers, by
means of multiple linear regression. The articulatory-acoustic correlations
were high and allowed us to regenerate the fricative spectra with a good
accuracy. The way in which the acoustic parameters varied as a function of
the articulatory ones in the statistical model was in good agreement with
data reported in previous works. The results support the idea that the
tongue has relatively few degrees of freedom in the production of /s/ and
Ni, Y. N., Huang, C. F., & Kokot, S. (2004).
Application of multivariate calibration and artificial neural networks to simultaneous
kinetic-spectrophotometric determination of carbamate pesticides.
Chemometrics and Intelligent Laboratory Systems, 71, 177-193.
A method for the simultaneous determination of the pesticides, carbofuran,
isoprocarb and propoxur in fruit and vegetable samples has been investigated and developed.
It is based on reaction kinetics and spectrophotometry, and results are interpreted with
the aid of chemometrics. The analytical method relies on the differential rates of
coupling reactions between the hydrolysis product of each carbamate and 4-aminophenol in
the presence of potassium periodate in an alkaline solution. The optimized method was
successfully tested by analyzing each of the carbamates independently, and linear
calibration models are described. For the simultaneous determinations of the carbamates
found in ternary mixtures, kinetic and spectral data were processed either by three-way
data unfolding method or decomposed by trilinear modeling. Subsequently, 10 different
RBF-ANN, PARAFAC and NPLS calibration models were constructed with the use of synthetic
ternary mixtures of the three carbamates, and were validated with a separate set of
mixtures. The performance of the calibration models was then ranked on the basis of
several different figures of merit with the aid of the multi-criteria decision making
approach, PROMETHEE and GAIA. RBF-ANN and PC-RBF-ANN were the best performing methods
with %Relative Prediction Errors (R-PE) in the 3-4% range and Recovery of about 97%. When
compared with other recent studies, it was also noted that RBF-ANN has consistently
outperformed the more common prediction methods such as PLS and PCR as well as BP-ANN.
The successful RBF-ANN method was then applied for the determination of the three
carbamate pesticides in purchased vegetable and fruit samples.
Nianyi, C., Wencong, L., Ruiliang, C., Chonghe, L., & Pei, Q. (1999).
Chemometrics methods applied to industrial optimization and materials optimal design.
Chemometrics and Intelligent Laboratory Systems, 45, 329-333.
Based on the methods of multivariate data processing of the data records for complicated chemical
reaction systems, computer software named ‘process analyzer’ and ‘materials research advisor’ has been built. The
methods used include some new methods of pattern recognition, such as back-mapping methods, local-structure view
and data transformation methods. These new computational techniques have been widely used in the chemical,
petrochemical and metallurgical industries for industrial optimization and materials design with good results.
Nielsen, J. P., Bertrand, D., Micklander, E., Courcoux, P., & Munck, L. (2001).
Study of NIR spectra, particle size distributions and chemical parameters of wheat flours: a multi-way approach.
Journal of Near Infrared Spectroscopy, 9, 275-285.
Near infrared (NIR) reflectance spectra contain information about both physical
and chemical characteristics of flour samples and have great potential for on-line/at-line
quality control in a flour mill. The addition of physical characteristics such as particle size
distribution data to the NIR spectra and chemical composition data of wheat flour samples was
anticipated to provide a better understanding and translation of multivariate measurements
into the operational routines and experiences of mill operators. This was studied using a
multi-way model called "Analysis of Common Dimensions and Specific Weights" (COMDIM). By this
method the underlying dimensions across several data tables with different numbers of variables
are defined and the scores and loadings are interpretable in the same way as in a classical
Principal Component Analysis. The method was applied on raw NIR spectra as well as after correcting
the NIR spectra using the Standard Normal Variate (SNV). The model output in terms of weights,
scores and loadings were highly interpretable and in agreement with common characteristics of
wheat flour samples. Four underlying dimensions explained 99.4% of the total variation, both
when analysing raw and SNV-corrected spectra. A comparison of the two analyses clearly shows
that correcting the spectra puts more emphasis on the chemical information in the spectra.
However, even corrected NIR spectra contain considerable information about the particle size
properties of the flour samples. It is suggested that the COMDIM model can be a useful tool
in the process control in a flour mill and it can be used on a wide range of multi-way data
problems to assure a high degree of interpretability.
Nierop, A.F.M. (1993).
The INDRES model: An INDSCAL model with residuals orthogonal to INDSCAL dimensions.
In R. Steyer, K.F. Wender, & K.F. Widaman (Eds.), Psychometric Methodology.
Proceedings of the 7th European Meeting of the Psychometric Society in Trier,
(pp. 366-370). Stuttgart and New York: Gustav Fischer Verlag.
The main purpose of this paper is to develop a model that improves the recovery
of the true INDSCAL dimensions. This purpose is attained by making the residuals
as orthogonal as possible to the recovered dimensions. A simulation study assesses
Nikolajsen, R. P. H., Hansen, A. M. & Bro, R. (2001).
Attempt to separate the fluorescence spectra of adrenaline and noradrenaline using chemometrics.
Luminescence, 16, 91-101.
An investigation was conducted on whether the fluorescence
spectra of the very similar catecholamines adrenaline and noradrenaline
could be separated using chemometric methods. The fluorescence landscapes
(several excitation and emission spectra were measured) of two data sets
with respectively 16 and 6 samples were measured, the smaller data set
with higher resolution and i.e. precision. The samples were artificial
urine (pH similar or equal to 3) spiked with the catecholamines in the
concentration ranges 40-1200 nmol/L and 5.5-18 mu mol/L, respectively.
Unfold partial least squares regression (Unfold-PLSR) on the larger data
set and parallel factor analysis (PARAFAC) of the six samples of the
smaller set showed that there was no difference between the fluorescence
landscapes of adrenaline and noradrenaline. It can be concluded that
chemometric separation of adrenaline and noradrenaline is not obtainable
using this type of fluorescence measurement. Raman scatter. which overlaps
the catecholamine spectra, was shown not to have any influence on the
Nikolajsen, R. P. H., Booksh, K. S., Hansen, A. M. & Bro, R. (2003).
Quantifying catecholamines using multi-way kinetic modelling
Analytica Chimica Acta, 475, 137-150.
A new method for quantifying adrenaline and noradrenaline concentrations from
mixtures of catecholamine standards is described. The method derives selectivity
from the different rates, at which the fluorescing 3,5,6-trihydroxyindole derivatives
(lutines) of the catecholamines are formed and degraded for adrenaline and
noradrenaline. The standards used had the concentration ranges 50-1200 nmol/l for
adrenaline and 30-1400 nmol/l for noradrenaline. Fluorescence landscapes were measured
at consecutive time points for every sample hereby creating a four-way data array.
It is shown that the raw dataset can be dramatically reduced in size without loosing
significant information hereby making calculations much faster and lessening instrumental
performance requirements. The data follow a two-component four-way parallel factor
analysis model (PARAFAC), from which quantitative information is also obtained.
Two-component multilinear partial least squares regression (N-PLSR) was also employed
for the quantification of the catecholamines. The results for PARAFAC and N-PLSR
were very similar with root mean squared errors of cross-validation (RMSECV) being
in the range 24-30 nmol/l. Several improvements of the method are suggested, and
it is expected that the method will be suitable for determination of catecholamines
in urine from healthy subjects. (C) 2002 Elsevier Science B:V. All rights reserved.
Nilsson, J., Homan, E. J., Smilde, A. K., Grol, C. J., & Wikstrom, H. (1998).
A multiway 3D QSAR analysis of a series of(S)-N-[(1-ethyl-2-pyrrolidinyl)methyl]-6-methoxybenzamides.
Journal of Computer-Aided Molecular Design, 12, 81-93.
Recently, the multilinear PLS algorithm was presented by Bro and later implemented as a regression method in
3D QSAR by Nilsson et al. In the present article a well-known set of (S)-N-[(1-ethyl-2-pyrrolidinyl)methyl]-6-
methoxybenzamides, with affinity towards the dopamine D2 receptor subtype, was utilised for the validation of the
multilinear PLS method. After exhaustive conformational analyses on the ligands, the active analogue approach
was employed to align them in their presumed pharmacologically active conformations, using (–)-piquindone as
a template. Descriptors were then generated in the GRID program, and 40 calibration compounds and 18 test
compounds were selected by means of a principal component analysis in the descriptor space. The final model was
validated with different types of cross-validation experiments, e.g. leave-one-out, leave-three-out and leave-fiveout.
The cross-validated Q2 was 62% for all experiments, confirming the stability of the model. The prediction of
the test set with a predicted Q2 of 62% also established the predictive ability. Finally, the conformations and the
alignment of the ligands in combination with multilinear PLS, obviously, played an important role for the success
of our model.
Nilsson, J., De Jong, S., & Smilde, A.K. (1997).
Multiway calibration in 3D QSAR. Journal of Chemometrics, 11, 511-524.
We have introduced multilinear PLS in 3D QSAR and
applied it to GRID descriptors from a set of
benzamides with affinity to the dopamine D3
receptor subtype, synthesized as potential drugs
against schizophrenia. The key issue in 3D QSAR
modelling is to obtain a predictive model that is
easy to interpret. Each component in the
multilinear PLS model explains clearly defined
details, e.g. substituent positions, while the
bilinear PLS solution is general and more
difficult to interpret. The best models were
obtained after four components with multilinear
PLS (Q² = 51%) and after only one component with
bilinear PLS (Q² = 50%). The external test set
was predicted better with multilinear PLS (Q² =
31%) than with bilinear PLS (Q² = 25%). With
multilinear PLS one loses in fit and gains in
stability and simplicity owing to the fewer
parameters that need to be estimated as compared
with bilinear PLS. Finally, multilinear PLS is
also less influenced by insignificant variation in
the descriptor block, which is an advantage in 3D
Nix, D.A., Papcun, G., Hogden, J., & Zlokarnik, I. (1996).
Two cross-linguistic factors underlying tongue
shapes for vowels.
Journal of the Acoustical Society of America, 99, 3707-3717.
Desirable characteristics of a vocal-tract
parametrization include accuracy, low
dimensionality, and generalizability across
speakers and languages. A low-dimensional,
speaker-independent linear parametrization of
vowel tongue shapes can be obtained using the
Parafac three-mode factor analysis procedure
[Harshman et al., J. Acoust. Soc. Am. 62, 693-707
(1977)]. Harshman et al. applied Parafac to
midsagittal x-ray vowel data from five English
speakers, reporting that two speaker-independent
factors are required to accurately represent the
tongue shape measured along anatomically
normalized vocal-tract diameter grid lines.
Subsequently, the cross-linguistic generality of
this parametrization was brought into question by
the application of Parafac to Icelandic vowel
data, where three nonorthogonal factors were
reported [Jackson, J. Acoust. Soc. Am. 84, 124-143
(1988)]. This solution is shown to be degenerate;
a reanalysis of Jackson's Icelandic data produces
two factors that match Harshman et al.'s factors
for English vowels, contradicting Jackson's
distinction between English and Icelandic
language-specific "articulatory primes." To
obtain vowel factors not constrained by artificial
measurement grid lines, x-ray tongue shape traces
of six English speakers were marked with 13
equally spaced points. Parafac analysis of this
unconstrained (x,y) coordinate data results :In
two factors that are clearly interpretable in
terms of the traditional vowel quality dimensions
Nomikos, P., & MacGregor, J.F. (1994).
Monitoring batch processes using multiway
principal component analysis.
AIChE Journal, 40, 1361-1375.
Multivariate statistical procedures for monitoring
the progress of batch processes are developed. The
only information needed to exploit the procedures
is a historical database of past successful
batches. Multiway principal component analysis is
used to extract the information in the
multivariate trajectory data by projecting them
onto low-dimensional spaces defined by the latent
variables or principal components. This leads to
simple monitoring charts, consistent with the
philosophy of statistical process control, which
are capable of tracking the progress of new batch
runs and detecting the occurrence of observable
upsets. The approach is contrasted with other
approaches which use theoretical or
knowledge-based models, and its potential is
illustrated using a detailed simulation study of a
semibatch reactor for the production of
Nomikos, P., & MacGregor, J.F. (1995a).
Multivariate SPC charts for monitoring batch
Technometrics, 37, 41-59.
The problem of using time-varying trajectory data
measured on many process variables over the finite
duration of a batch process is considered.
Multiway principal-component analysis is used to
compress the information contained in the data
trajectories into low-dimensional spaces that
describe the operation of past batches. This
approach facilitates the analysis of operational
and quality-control problems in past batches and
allows for the development of multivariate
statistical process control charts for on-line
monitoring of the progress of new batches. Control
limits for the proposed charts are developed using
information from the historical reference
distribution of past successful batches. The
method is applied to data collected from an
industrial batch polymerization reactor.
Nomikos, P., & MacGregor, J.F. (1995b).
Multi-way partial least squares in monitoring
Chemometrics and Intelligent Laboratory Systems, 30, 97-108.
Multivariate statistical procedures for monitoring
the progress of batch processes are developed.
Multi-way partial least squares (MPLS) is used to
extract the information from the process
measurement variable trajectories that is more
relevant to the final quality variables of the
product. The only information needed is a
historical database of past successful batches.
New batches can be monitored through simple
monitoring charts which are consistent with the
philosophy of statistical process control. These
charts monitor the batch operation and provide
on-line predictions of the final product
qualities. Approximate confidence intervals for
the predictions from PLS models are developed. The
approach is illustrated using a simulation study
of a styrene-butadiene batch reactor.
Nørgaard, L. (1995a).
A multivariate chemometric approach to fluorescence spectroscopy.
Talanta, 42, 1305-1324.
A multivariate approach to the solution of problems often encountered in the
spectrofluorometry of natural samples, utilising information from whole spectra is presented.
(a) Piecewise direct standardisation is implemented and employed to transfer emission spectra
measured with two different xenon lamps of different ages as if the spectra were measured with
the same lamp. (b) It has been shown using a multivariate analysis approach that it is possible
to use the raw data points instead of the smoothed data based on an algorithm included in the
instrument software by the manufacturer. (c) It is documented that Raman scattering does not
hamper the performance of multivariate calibration; on the contrary, in an experiment with
sugar samples the concentration prediction errors become about five times lower by including
the whole emission spectrum in the analysis instead of using a univariate calibration based on
an emission wavelength that only reflects the analyte of interest. (d) An algorithm for
variable selection is implemented and employed in the selection of optimal excitation
wavelengths. Among 13 emission spectra recorded for a sugar sample at different excitation
wavelengths, four of these are chosen that describe 98.51% of the total variance in the original
data. (e) Finally the combination of fluorescence spectroscopy and multivariate calibration with
conventional chemical data according to the near-infrared black box model is presented. The
refined sugar quality parameter, the ash content and the fluorescence emission spectra are
correlated by a partial least-squares regression model. Five experiments employing different
monochromator slit widths and sugar concentrations are performed, and the best correlation.
obtained by full cross-validation of the 15 sugar samples is R = 0.98.
Nørgaard, L. (1995b).
Classification and prediction of quality and process parameters of thick
juice and beet sugar by fluorescence spectroscopy and chemometrics.
Zuckerindustrie, 120, 970-981.
Full spectrum fluorescence spectroscopy in combination with multivariate statistical methods
(chemometrics) is used to withdraw information from white sugar solutions and thick juice samples
from 6 different sugar factories within the same region in Northern Europe with respect to
classification and prediction of quality and process parameters. The weekly samples are
collected during the 1993 campaign. Classification of the sugar samples according to factory was
possible by explorative data analysis (soft independent modelling of class analogy, SIMCA)
with 6% misclassification. The factory classification was not so distinct, probably due
to sampling problems, when based on thick juice samples diluted 976 times, where circa 30% of the
samples were misclassified. These results demonstrate that the fluorescence spectra recorded
reflect the quality of the raw material superimposed by the influence of the process,
resulting in a characteristic spectral pattern of each factory. By multivariate calibration (partial
least squares, PLS) the quality parameters ash, color, alpha-amino-N, and SO2 content of sugar
samples were predicted from the fluorescence emission spectra. The test set correlation
coefficients (R) between laboratory analysed values and the values predicted by the calibration
model based on the fluorescence spectra are 0.91, 0.94, 0.98, and 0.85, respectively. In an analogue
way the parameters ash and color were predicted in thick juice samples with correlation coefficients
of 0.89 and 0.90. When the pH value of the sugar samples was corrected to pH = 7.0, the
misclassification percentage of the factories increased to circa 10%, and the correlation
coefficients to ash, color, alpha-amino-N, and SO2 content became 0.88, 0.93, 0.98, and 0.84,
indicating that some information is lost. Five pairs of excitation-emission wavelengths were
selected by a chemometric algorithm (principal variables, PV), giving reasonable correlations
results compared to full spectrum models..
Nørgaard, L. (1996).
Spectral resolution and prediction of slit widths
in fluorescence spectroscopy by two- and three-way
Journal of Chemometrics, 10, 615-630.
Modem spectrofluorometers have several
instrumental settings to be adjusted and decided
on before sample measurement, e.g. excitation and
emission slit widths, emission scan velocity and
spectral ranges to be recorded. The influences of
these settings on the recorded spectra are
crucial, particularly when applying full
fluorescence spectra in the analysis of a given
problem. The effect on the fluorescence emission
spectra when changing the slit widths is studied
in detail by recording the emission spectra of an
ovalene standard block at all possible excitation
(3-15 nm) and emission (3-20 nm) slit width
combinations. By the two-way curve resolution
method alternating regression (AR) it is possible
to resolve the emission spectra into three hidden
spectra describing the coarse, medium coarse/fine
and fine structure of the recorded spectra. By the
three-way methods Parafac and Tucker it is
possible to separate the effects of both the
excitation and emission slit widths on the
recorded spectra. An analogous analysis of a
natural sugar sample results in a one factor
Parafac solution, probably because of the large
amount of different substances found in a table
sugar sample resulting in rather featureless
emission spectra not so susceptible to influence
by the instrumental settings. Finally, it is
demonstrated that two-way unfold PLS, Parafac and
Tucker regression models are able to predict the
excitation and emission slit widths from the
recorded emission spectra. The root mean square
errors of prediction (RMSEP) are about 0.5 nm (R
approximate to 0.990) for the excitation slit and
0.3 nm (R approximate to 0.999) for the emission
Norup, L. R., Hansen, P. W., Ingvartsen, K. L., & Friggens, N. C. (2001).
An attempt to detect oestrus from changes in Fourier transform infrared spectra of milk
from dairy heifers.
Animal Reproduction Science, 65, 43-50.
This study was carried out to investigate if there were
systematic changes in milk Fourier transform infrared (FT-IR) spectra
relative to stage of the oestrous cycle in cattle. Oestrous cycles of 22
lactating heifers were synchronized with two injections of prostaglandin
F2 alpha (PGF) administered 11 days apart. The heifers were milked twice
daily, and milk samples were collected from each heifer at each milking for
a period of 70 days, starting on the day of the second PGF injection.
Oestrus was diagnosed by visual detection in conjunction with monitoring
rectal temperature. Milk samples were analyzed by FT-IR spectroscopy and
the spectra data were analyzed using partial least squares (PLS) methods
in relation to time of observed oestrus in heifers. In this investigation,
it was not possible to identify reliable changes in milk FT-IR spectra in
relation to oestrus on a single heifer basis, though there was a weak
correlation between FT IR spectra and expected time of oestrus when the
analysis was carried out across all the heifers.
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Department of Educational
The Three-Mode Company |
Faculty of Social and Behavioural Sciences, Leiden University
The Three-Mode Company, Leiden, The Netherlands
E-mail: kroonenb at fsw.leidenuniv.nl
First version: 12/02/1997;