bluuebunny/crossref_metadata_2025 huggingface dataset:
Light from maillard reaction: Photon counting, emission spectrum, photography and visual perception
Authors: Wondrak Georg , Pier Thomas , Tressl Roland
Published: May 2005

Several authors have reported on high‐sensitivity measurement of oxygen‐dependent low‐level chemiluminescence (CL) from Maillard reactions (MR), i.e. nonenzymatic amino‐carbonyl reactions between reducing sugars and amino acids (also referred to as nonenzymatic browning). Here we report for the first time, that light from Maillard reactions can be seen by the human eye and also can be photographed. In parallel with visual perception and photography CL was monitored by means of a CL‐detection programme of a liquid scintillation counter (LSC, single photon rate counting). CL emission spectrum was recorded by a monochromator‐microchannel plate photomultiplier arrangement. CL intensity from reaction of 6‐aminocaproic acid with D‐ribose (200 mg each) in 5 mL H2O at pH 11 at 95°C was high enough for visual perception after adaptation to absolute darkness. Reaction in dimethylsulphoxide (DMSO) exhibited strongly enhanced CL (10 mg each in 5 mL were sufficient for visual detection) and could be photographed (15 minutes' exposure, ASA 6400); all characteristics of Maillard specific CL (O2‐dependence, no CL from nonreducing sugars, inhibition by sulphur compounds) remained. Visual detection of CL and measurement by LSC were in full concordance. The CL emission spectrum showed two broad peaks at around 500 nm and 695 nm. Fluorescence emission of the brown reaction mixture matched the bluegreen part of the CL emission spectrum. Emission of visible light during Maillard reactions may partly originate from oxygen‐dependent generation of excited states and energy transfer to simultaneously formed fluorescent products of the browning reaction.
Enumeration of phagocytosed Staphylococcus aureus inside cells of the mouse macrophage cell line J774 by bioluminescence, fluorescence and viable counting techniques
Authors: Sunderland Julie , Brenwald Nigel , Smith Elena , Andrews Jenny M. , Wise Richard , Honeybourne David
Published: May 2005

In order to quantify intracellular Staphylococcus aureus within a macrophage‐like cell line by a bioluminescence technique, the mouse cell line J774 and opsonized Staphylococcus aureus were incubated together to allow phagocytosis to occur. Experiments using UV microscopy and fluorescent stained S. aureus were performed to determine an estimate of the mean intracellular bacterial numbers. For enumeration of intracellular bacteria by a bioluminescence technique, extracellular bacteria were removed by washing, the macrophages lysed mechanically and osmotically and treated with apyrase to remove somatic ATP. Bacterial cells were washed and the intracellular ATP measured by firefly luciferase bioluminescence in a luminometer. This new method of enumerating intracellular bacteria was compared to the conventional method of viable counts and found to correlate (r = 0.78). The bioluminescence assay developed was found to be a relatively rapid alternative method to the techniques currently used to enumerate intracellular bacteria and could prove advantageous in studies of intracellular killing and effects of antimicrobial agents on intracellular pathogens.
Using discriminant analysis to estimate linear mixing proportions
Authors: Burdick D. S. , Rayens W. S.
Published: May 2005

This paper proposes an elegant, yet straightforward, model for classifying linear mixtures. A linear mixture is defined as a random vector y in which the variable are a (non‐negative) weighted average of corresponding variables, assumed to characterize g component groups. These weights are referred to as ‘mixing proportions’. The model seeks to identify the mixture constituents and estimate the mixing proportions. It is demonstrated within the context of high resolution gas chromatography and the problem of identifying the constituents in polychlorinated biphenyl mixtures.
Chance classifications by non‐parametric linear discriminant functions
Authors: Lavine B. K. , Jurs P. C. , Henry D. R.
Published: May 2005

In applications of pattern recognition techniques to problems in chemical fingerprinting, only limited knowledge about the underlying statistical distribution of the data is generally available. This means that non‐parametric methods must be used. Non‐parametric discriminant functions have been used to provide insight into relationships contained within sets of chemical measurements. However, classification based on random or chance separation can be a serious problem. Monte Carlo simulation studies have been carried out to assess the probability of chance classification for non‐parametric linear discriminants. The level of expected chance classification is a function of the number of observations (the number of samples), the dimensionality of the problem (the number of independent variables per observation), class membership distribution and the covariance structure of the data being examined. An approach for assessing the level of significance of classification scores obtained from real training sets will be presented. These simulation studies establish limits on the approaches that can be taken with real data sets so that chance classification are improbable, and provide information necessary for integrating the data analysis into the overall experimental design.
The application of submatrix analysis to disordered spectroscopic data
Authors: Cartwright Hugh M. , Farley Heather A.
Published: May 2005

Submatrix analysis has been extended to systems in which the values of adjustable parameters are not known. The technique is illustrated by application to phosphoric acid and to the chlorite‐iodide oscillating reaction.
PLS assessment of the performance of short‐term tests for carcinogens
Authors: Tosato Maria Livia , Cesareo Daniela , Passerini Laura , Clementi Sergio
Published: May 2005

Partial least squares modelling in latent variables is applied to the analysis of short‐term test genotoxicity data obtained by testing 42 chemicals, known carcinogens and non‐carcinogens, in 35 different assay systems that are deemed to be potential indicators of carcinogencity. Results of a preliminary analysis of all data, and of a second analysis on a reduced data base, are presented. The latter analysis provided a model explaining 73% of variance, whereby no false negative or positive was predicted among 38 chemicals, in spite of qualitative predictions indicating five false positives and two false negatives. Only nine out of 35 assays appear to be relevant to the description of carcinogenicity/non‐carcinogenicity of chemical substances.
Savitzky‐Golay filtering in particle characterization
Authors: Wolfrum S. M. , Van Veen N. J. A.
Published: May 2005

Microstructure laboratory equipment can most easily be interfaced to computers of the PC generation, which offers the facility to extract more information from the measurements performed. A formulation of the Savitzky‐Golay smoother with a newly developed calculation of the necessary weighting factors provides enhanced flexibility in the choice of the smoothing parameters.
Tensorial calibration: II. Second‐order calibration
Authors: Sanchez Eugenio , Kowalski Bruce R.
Published: May 2005

Tensorial calibration provides a useful approach to calibration in general. For calibration of instruments that produce two‐dimensional (second‐order) arrays of data per sample, tensoial concepts are as natural a way of solving the calibration problem as vectorial concepts are for the multivariate problem. Similarly, for third‐ and higher‐order data, the tensorial description of calibration is also useful. This paper introduces second‐order calibration from a tensorial point of view. Univariate, multivariate and bilinear approaches to calibration are presented. The generalized rank annihilation method (GRAM) is described from the tensorial perspective, and it is shown that GRAM is equivalent to finding a second‐order tensorial base that spans both tensors (calibration and unknown) with respective diagonal component matrices. GRAM uses a single calibration sample for multicomponent analysis even in the presence of interference. Second‐order bilinear calibration is extended to multiple calibration samples where the effect of collinearities is reduced.
A non‐parametric class modelling technique
Authors: Derde M. P. , Kaufman L. , Massart D. L.
Published: May 2005

A non‐parametric method for supervised pattern recognition is presented. The method is of the class modelling type, meaning that a classification rule is developed for each class, using the dissimilarities between the objects of the class. The dissimilarities between the objects within a class are related to the distances between all pairs of training objects. As distance metric, a measure is proposed that takes the correlation between the interval‐scale variables into account, and that moreover can be used for mixed types of variables. The classification rule is based on the construction of a boundary in the measurement space. For the determination of the class boundary, several strategies are proposed and compared.

The performance of the technique is evaluated on the basis of several data sets. Comparison with the class modelling technique UNEQ shows its usefulness for practical application.
Variance‐decomposition of pure‐component spectra as a measure of selectivity
Authors: Kalivas John H.
Published: May 2005

Care is required for multicomponent analysis if misleading results are to be avoided. The problem of ill‐conditioned calibration matrices is of primary concern. This type of numerical instability is represented as spectral overlap of calibration spectra. Depending on the degree of spectral overlap, the sample concentration estimates can be severely affected. A practical statistical procedure is discussed which tests for the presence of spectral overlap among the pure‐component spectra and simultaneously assesses the degree that concentration estimates may be degraded. Guidelines are developed to ascertain how much departure from spectral orthogonality is acceptable.
The wavelength component vectorgram: A tool for resolving two‐component fluorescent mixtures
Authors: Burdick Donald S. , Tu Xin M.
Published: May 2005

We consider the problem of resolving the total luminescence spectra of two‐component mixtures. Non‐negativity of the constituent spectra is commonly used as a criterion to accomplish this resolution, but its application seldom leads to unique answers. Sometimes, the condition must be relaxed because no solution satisfying non‐negativity exists. In these cases judgments must be made concerning which parts of the spectra will be allowed to go negative and by how much.

The wavelength component vectorgramn (WCV) is presented here as a tool for assisting the experimenter in making these judgments. Even when non‐negative resolutions exist, it may happen that resolutions with slight negative elements will more closely match the spectra of the pure constituents. The WCV can also be used as a tool for detecting these cases. The methodology is illustrated with real data from a two‐component mixture.
Classification: Oldtimers and newcomers
Authors: Frank Ildiko E. , Friedman Jerome H.
Published: May 2005

Classification and regression techniques are among the most used tools by chemometricians. With classification, the two classic methods are discriminant analysis and SIMCA. In this paper we discuss the connection between these two methods and introduce two new ones of the same family: DASCO (discriminant analysis with shrunken covariances) and RDA (regularized discriminant analysis). We demonstrate on both simulated and real data sets that their performance is superior to the old favorites. This is especially true in small‐sample/high‐dimension settings typical in chemistry.
An improved algorithm for the generalized rank annihilation method
Authors: Wilson Bruce E. , Sanchez Eugenio , Kowalski Bruce R.
Published: May 2005

An improved algorithm for the generalized rank annihilation method (GRAM) is presented. GRAM is a method for multicomponent calibration using two‐dimensional instruments, such as GC‐MS. In this paper an orthonormal base is first computed and used to project the calibration and unknown sample response matrices into a lower‐dimensional subspace. The resulting generalized eigenproblem is then solved using the QZ algorithm. The result of these improvements is that GRAM is computationally more stable, particularly in the case where the calibration sample contains chemical constituents not present in the unknown sample and the unknown contains constituents not present in the calibration (the most general case).
Tensorial resolution: A direct trilinear decomposition
Authors: Sanchez Eugenio , Kowalski Bruce R.
Published: May 2005

Modern instrumentation in chemistry routinely generates two‐dimensional (second‐order) arrays of data. Considering that most analyses need to compare several samples, the analyst ends up with a three‐dimensional (third‐order) array which is difficult to visualize or interpret with the conventional statistical tools.

Some of these data arrays follow the so‐called trilinear model,

These trilinear arrays of data are known to have unique factor analysis decompositions which correspond to the true physical factors that form the data, i.e. given the array ℝ, a unique solution can be found in many cases for each order X, Y and Z. This is in contrast to the well‐known second‐order bilinear data factor analysis, where the abstract solutions obtained are not unique and at best cannot be easily compared with the underlying physical factors owing to a rotational ambiguity.

Trilinear decompositions have had the disadvantage, however, that a non‐linear optimization with many parameters is necessary to reach a least‐squares solution. This paper will introduce a method for reducing the problem to a rectangular generalized eigenvalue–eigenvector equation where the eigenvectors are the contravariant form (pseudo‐inverse) of the actual factors. It is shown that the method works well when the factors are linearly independent in at least two orders (e.g. Xir and Yjr are full rank matrices).

Finally, it is shown how trilinear decompositions relate to multicomponent calibration, curve resolution and chemical analysis.
The effect of mislabeled samples on the performance of the linear learning machine
Authors: Lavine Barry K. , Ward Anthony J. I. , Han Jian Hwa , Smith Roy‐Keith , Taylor Orley R.
Published: May 2005

Over the past 15 years the linear learning machine has been applied to a large number of chemical problems. The learning machine approach is conceptually simple and does not require knowledge about the statistical distribution of the data. However, there are problems associated with this approach. One problem which has not been investigated is the influence of mislabeled samples on the positioning of the hyperplane in feature space. If a few samples in a data set are incorrectly tagged prior to training (i.e. the samples are labeled as members of class 2 even though they are actually members of class 1), it is still possible using the linear learning machine to achieve a classification success rate of 100% for the training set. However, unfavorable results will be obtained for the prediction set. The magnitude of this effect and its potential implications regarding the proper use of the linear learning machine are discussed.
The construction of simultaneous optimal experimental designs for several polynomials in the calibration of analytical methods
Authors: Wolters R. , Kateman G.
Published: May 2005

In this paper a criterion is described for the construction of experimental designs for the evaluation of calibration models in analytical chemistry. The proposed criterion seeks a compromise between the D‐optimal designs for estimating the parameters of different polynomial models. A computer algorithm is presented for a sequential construction of experimental designs using the optimality criterion. The performance of the optimality criterion and the computer algorithm is elaborated for the problem of discrimination between a first‐ to a third‐degree polynomial for the calibration of analytical methods. An experimental design consisting of replicate measurements at five distinct levels equally spaced over the calibration range proved a good solution.
Data analysis in the shot noise limit. Part III: Adaptive data smoothing filters
Authors: Bialkowski Stephen E.
Published: May 2005

Digital filter smoothing methods for shot‐noise‐limited data are addressed in this study. The preferred method is based on a Gaussian filter in which the width of the Gaussian filter function is varied depending on the estimate of the second derivative of the raw data. This filter is developed from the standpoint of maximum likelihood parameter estimation of the probability density function which describes shot‐noise‐limited data. The smoothing filter is tested and compared with the conventional sequential regression filter. This adaptive Gaussian smoothing filter works better than both the sequential regression and the adaptive Gaussian filter derived for normal noise. For data containing both high‐ and low‐frequency components, the limiting step in the adaptive filter is an estimation of the smoothing interval. Methods for determining an optimum smoothing interval are discussed. With the optimized smoothing interval, the adaptive Gaussian filter works well for data sets with a wide range of varying frequency components. In particular, synthetic data typical of atomic emission spectra are used to test this smoothing filter.
The use of a cross‐correlation technique to enhance Jansson's deconvolution procedure
Authors: Crilly Paul Benjamin
Published: May 2005

The use of a cross‐correlation prefiltering technique to enhance the ability of Jansson's iterative deconvolution procedure to deconvolve extremely noisy chromatographic data is investigated. Test cases include peaks whose resolutions are as low as 0.35 and whose signal‐to‐noise ratios are as low as 1:1. Evaluation criteria include RMS error, relative peak error and peak area repeatability. For comparison purposes, relative peak area errors and peak area variances are also evaluated for noisy but well resolved peaks that have only been prefiltered with the cross‐correlation filter. Jansson's method in conjunction with cross‐correlation prefiltering is shown not only to resolve overlapped peaks but in some cases to improve their signal‐to‐noise ratios. The study also establishes some limits to the capabilities of Jansson's method will regard to adverse data conditions.
The Free–Wilson paradigm redux: Significance of the Free–Wilson coefficients, insignificance of coefficient ‘uncertainities’ and statistical sins
Authors: Duewer David Lee
Published: May 2005

The Free–Wilson paradigm is an established and powerful tool for quantitatively relating activity with chemical structure. Current implementations of the paradigm, however, are flawed both conceptually and in execution. As part of an attempt to more fully realize the promise of the paradigm, it was necessary to examine these limitations in detail.

This report introduces a robust, theory‐founded Free–Wilson implementation: stepwise principal components regression analysis (SPCRA). SPCRA is computationally superior to previous implementations but does not in itself correct their conceptual flaws.

The development of SPCRA did, however, facilitate derivation of a simple and chemically significant interpretation of the Free–Wilson structure–activity model. A number of statistical aspects of this model commonly misused in previous applications are discussed at length. These discussions provide critical background for the development of an alternative implementation of the Free–Wilson paradigm.
Generalized simulated annealing for calibration sample selection from an existing set and orthogonalization of undesigned experiments
Authors: Kalivas John H.
Published: May 2005

Generalized simulated annealing (GSA) is an optimization procedure for locating the global optimum (maximum or minimum) of multidimenisonal continuous functions. GSA has been modified for optimization of discrete functions. Selection of calibration samples from an existing set defines discrete optimization and GSA is used to select optimal sets of calibration samples for specific analysis samples. The procedure is applied to near‐infrared spectra. When compared to using the complete set of 37 calibration samples, concentration prediction errors were reduced 50%–100% by using select sets of two to seven calibration samples. Additionally, GSA was able to improve a poorly designed experiment. GSA devised augmented experimental designs such that the overall experimental design (original plus augmented) was more orthogonal than the original.
Fuzzy multivariate rule‐building expert systems: Minimal neural networks
Authors: Harrington Peter B.
Published: May 2005

A fuzzy multivariate rule‐building expert system (FuRES) has been devised which also functions as a minimal neural network. This system builds rules from training sets of data that use feature transformation in their antecedents. The rules are constructed using the ID3 algorithm with a fuzzy expression of classification entropy. The rules are optimal with respect to fuzziness and can accommodate overlapped and underlapped clusters of data. The FuRES algorithm combines the benefits obtained from simulated annealing and gradient optimization, which provide robustness and efficiency respectively. FuRES classification trees support OR logic in their inference. The system automatically generates meaningful and consistent certainty factors during rule construction. Unlike other neural networks, FuRES uses local processing which furnishes qualitative information in the rule structure of its classification trees and variable loadings of the weight vectors.
Multivariate analysis of 2D surfaces: Folding and interpretation
Authors: Alsberg Bjørn K. , Remseth Bjørn G.
Published: May 2005

The use of latent variable projection for analysis of non‐congruent 2D surfaces is presented. We give a formal description of the folding/unfolding process. A simulated 2D oscillator evolving in time is studied in detail to illustrate interpretation aspects of the method.
Resolution of strongly overlapping two‐way multicomponent data by means of heuristic evolving latent projections
Authors: Liang Yi‐Zeng , Kvalheim Olav M. , Rahmani Ali , Brereton Richard G.
Published: May 2005

High‐performance liquid chromatography with diode array detection (HPLC––DAD) is used to characterize mixtures from chlorophyll a degradation experiments. Overlapping chromatographic peaks are resolved by means of the heuristic evolving latent projections (HELP) method. The HELP method is a self‐modelling curve resolution method. No assumptions are made about spectral and/or chromatographic peak shape. In the first step the method establishes the real noise level in the data by use of the so‐called zero‐component regions. This information is used to reveal selective chromatographic information and the number of chemical species at every retention time in unresolved chromatographic peaks. Utilising the selective chromatographic regions in combination with the zero‐concentration windows, unique resolution into concentration profiles and spectra of the pure chemical species is accomplished. HPLC–DAD data from six chlorophyll a degradation experiments were analysed. Consistent results were obtained even with very similar spectra for six or seven overlapping chemical components.
Eliminating complex eigenvectors and eigenvalues in multiway analyses using the direct trilinear decomposition method
Authors: Li Shousong , Gemperline Paul J.
Published: May 2005

The direct trilinear decomposition method (DTDM) is an algorithm for performing quantitative curve resolution of three‐dimensional data that follow the so‐called trilinear model, e.g. chromatography–spectroscopy or emission‐excitation fluorescence. Under certain coditions complex eigenvalues and eigenvectors emerge when the generalized eigenproblem is solved in DTDM. Previous publications never treated those cases. In this paper we show how similarity transformations can be used to eliminate the imaginary part of the complex eigenvalues and eigenvectors, thereby increasing the usefulness of DTDM in practical applications. The similarity transformation technique was first used by our laboratory to solve the similar problem in the generalized rank annihilation method (GRAM). Because unique elution profiles and spectra can be derived by using data matrices from three or more samples simultaneously, DTDM with similarity transformations is more efficient than GRAM in the case where there are many samples to be investigated.
A global perspective on multivariate calibration methods
Authors: Lang Patrick M. , Kalivas John H.
Published: May 2005

This paper consists of two distinct but related parts. In the first part a geometric theory of generalized inverses is presented and a methodology based on this theory is developed and applied to solve the K‐matrix and P‐matrix forms of Beer's law. It is shown that most currently accepted and practiced methods for solving these forms of Beer's law are just special cases of this geometric theory of generalized inverses. In addition, this geometric theory is used to explain why the current methods work and why they fail.

In the second part a general methodology that includes as special cases least squares, principal component regression, partial least squares 1 and 2, continuum regression plus a variety of other described and undescribed methodologies is presented and then applied to solve the P‐matrix formulation of Beer's law. This general methodology, like the first, is also geometric in nature and relies on an understanding of projections.

The main emphasis of this paper is one of perspective, which, if understood, provides the proper foundation for answering the general but extremely hard and possibly unanswerable question “what is the best method?”.
Application of orthogonal expansion to mapping and modelling
Authors: Cheng Zhaonian , Zhu Eryi , Chen Nianyi
Published: May 2005

In this paper we discuss the orthogonal expansion of data matrices and its application to mapping and modelling. Two new methods, modified optimal discriminant plane (MODP) for mapping and order Gram‐Schmidt orthogonalization (OGSO) for modelling, are proposed and examples are given.
Use of the Kalman filter for multivariate calibration in a real system and its comparison with CLS and pure component calibration methods
Authors: Péarez‐arribas L. V. , Navarro‐Villoslada F. , León‐Gonzalez M. E. , Polo‐Díez L. M.
Published: May 2005

The usefulness of the Kalman filter as an algorithm for calibration in a real system is shown. Results are compared with classical least squares and pure component calibration. The prediction of four priority pollutant chlorophenols in binary, ternary and quaternary mixtures was also carried out by Kalman filtering. The condition number, standard deviation and prediction error have been employed to choose the most suitable wavelength range. Comparison of the standard error of prediction in the validation set shows significant differences between the evaluated chlorophenols, the best results being obtained with Kalman multivariate calibration.
Determination of a multivariate detection limit and local chemical rank by designing a non‐parametric test from the zero‐component regions
Authors: Liang Yi‐Zeng , Kvalheim Olav M. , Höskuldsson Agnar
Published: May 2005

In this paper we redefine the term detection limit to embrace the inherent multivariate nature of samples, instrumental measurements and chemometrics resolution procedures. The so‐called zero‐component regions, i.e. parts with no chemical components eluting, are used as repeated analytical blanks to estimate a statistical multivariate detection limit for determining the number of chemical species in local regions of a single two‐way chromatogram or a collection of synchronized one‐way chromatograms. For two‐way chromatography the detection limit is determined from the distribution of the first eigenvalues obtained from all possible combinations of spectra in the zero‐component regions. The number of spectra in each calculation should correspond to the number included in the later examination of the local retention time regions. For one‐way chromatography on a collection of samples with similar chemical components at varying concentrations the same procedure is used, with the samples taking the role of the spectra in two‐way chromatography. The detection limit can be chosen at various confidence levels depending on whether false positive or negative detection of minor components is most critical. The results obtained from the zero‐eigenvalue distribution are more robust than those obtained by a previously developed F‐test.
Assessment of the effective rank of a (co)variance matrix: A non‐parametric goodness‐of‐fit test
Authors: Tomis̆ić Vladislav , Simeon Vladimir
Published: May 2005

Each eigenvector of the dispersion matrix [X]T [X] was shown to be a partial predictor of the original data matrix [X], the sum of the predictions from the individual principal components being equal to the expectance of [X]. By comparing the distributions of the members of two neighbouring predicted matrices, [X̃]1…i and [X̃]1…i+1 (i.e. the sums of the first i and i + 1 individual predictions respectively), it was shown that they should be indistinguishable provided that i is equal to or greater than the effective rank of [X], and significantly different otherwise. This was confirmed by analysing the visible absorption spectra of methyl orange and methyl red solutions as well as the Raman spectra of Na2SO4 and MgSO4 solutions. On the grounds of these findings, a non‐parametric goodness‐of‐fit test for assessing the effective rank of [X] was proposed which proved to be comparatively conservative and more robust than most currently used tests.
Standard errors in the eigenvalues of a cross‐product matrix: Theory and applications
Authors: Faber N. M. , Buydens L. M. C. , Kateman G.
Published: May 2005

New expressions are derived for the standard errors in the eigenvalues of a cross‐product matrix by the method of error propagation. Cross‐product matrices frequently arise in multivariate data analysis, especially in principal component analysis (PCA). The derived standard errors account for the variability in the data as a result of measurement noise and are therefore essentially different from the standard errors developed in multivariate statistics. Those standard errors were derived in order to account for the finite number of observations on a fixed number of variables, the so‐called sampling error. They can be used for making inferences about the population eigenvalues. Making inferences about the population eigenvalues is often not the purposes of PCA in physical sciences. This is particularly true if the measurements are performed on an analytical instrument that produces two‐dimensional arrays for one chemical sample: the rows and columns of such a data matrix cannot be identified with observations on variables at all. However, PCA can still be used as a general data reduction technique, but now the effect of measurement noise on the standard errors in the eigenvalues has to be considered. The consequences for significance testing of the eigenvalues as well as the usefulness for error estimates for scores and loadings of PCA, multiple linear regression (MLR) and the generalized rank annihilation method (GRAM) are discussed. The adequacy of the derived expressions is tested by Monte Carlo simulations.
Generalized rank annihilation method. III: Practical implementation
Authors: Faber N. M. , Buydens L. M. C. , Kateman G.
Published: May 2005

In this paper we discuss the practical implementation of the generalized rank annihilation method (GRAM). The practical implementation comes down to developing a computer program where two critical steps can be distinguished: the construction of the factor space and the oblique rotation of the factors. The construction of the factor space is a least‐squares (LS) problem solved by singular value decomposition (SVD), whereas the rotation of the factors is brought about by solving an eigenvalue problem. In the past several formulations for GRAM have been published. The differences essentially come down to solving either a standard eigenvalue problem or a generalized eigenvalue problem. The first objective of this paper is to discuss the numerical stability of the algorithms resulting from these formulations. It is found that the generalized eigenvalue problem is only to be preferred if the construction of the factor space is not performed with maximum precision. This is demonstrated for the case where the dominant factors are calculated by the non‐linear iterative partial least‐squares (NIPALS) algorithm. Several performance measures are proposed to investigate the numerical accuracy of the computed solution. The previously derived bias and variance are proposed to estimate the number of physically significant digits in the computed solution. The second objective of this paper is to discuss the relevance of theoretical considerations for application of GRAM in the presence of model errors.
Classification of pyrolysis mass spectra of biological materials using convex cones
Authors: Mavrovouniotis Michael L. , Harper Alice M. , Ifarraguerri Agustin I.
Published: May 2005

This work addresses the classification of high‐dimensional time‐dependent pyrolysis mass spectra of biological samples. The aim was the detection and classification of biological agents, and the developed approach resembles mixture analysis. The data were projected on to a low‐dimensional subspace using singular value decomposition. Then a convex cone was formed on this subspace, showing as its corners physically meaningful components of the sample. This technique enabled separation of a biological material signal largely independent of the absolute amount of sample. The detection of the presence of any biological material could be accomplished based on the convex cone alone, without other reference to the mass spectra. Automated clustering of samples was successfully carried out using a minimal spanning tree.
Use of double windowing, variable selection, variable ranking and resolvability indices in window factor analysis
Authors: Brereton Richard G. , Elbergali Abdallah K.
Published: May 2005

A method for selecting variables in the non‐sequential direction of a two‐way data matrix (e.g. wavelength in diode array HPLC) is described. Composition 1 and 2 resolvability indices are calculated according to the size of eigenvalues of uncentred data matrices as a window is moved along the sequential direction. A double‐window technique is then performed where resolvability indices are calculated as a window is moved along the non‐sequential direction. Some regions have higher resolvability indices and hence are more useful for resolution. Variables are ranked according to resolvability. Two simulations are analysed and it is shown that it is possible to obtain good resolution on a small subset of the original variables.
Mathematical approximation of sigmoidal shape curves tested on sensitometric curves of photographic material
Authors: Majer Jaroslav
Published: May 2005

This paper studies the application possibilities of various mathematical analytical functions to models of approximation of sigmoid sensitometric curves of photographic materials. The newly developed functions have been tested along with approximation functions taken from the literature: the former fulfil the statistical criteria of fitting the regression and experimental curve in the best way. The model functions can be used not only for objective determination of sensitometric quantities but also for determination of the general sensitivity (speed) of photographic materials.
Simple encoding of infrared spectra for pattern recognition. 1: Statistical examination of the effectiveness and information content by factor analysis
Authors: Schulz Henrik , Tetzlaff Klaus , Vogel Lydia
Published: May 2005

The spectral region from 700 to 3600 cm−1 is subdivided into several wave number intervals. The peaks in each interval are summarized by means of three encoding algorithms. Using a factor model of kcommon factors, the total extractable variacnce (com) of a given set of intervals is calculated and correlated with the redundancy of information in all these intervals. The value of com is verified by analysis of the factor loadings aik (factor pattern). Finally, the information content of some chosen sets of intervals coded by the three selected feature algorithms will be correlated to the probability of information flow through a serial‐parallel network. The encoding using only wave numbers was found to be the most effective.
Simulated‐annealing‐based optimization algorithms: Fundamentals and wavelength selection applications
Authors: Hörchner Uwe , Kalivas John H.
Published: May 2005

Simulated annealing (SA) is a stochastic search method that has been applied to combinatorial search problems in chemometrics. Unlike strict iterative improvement methods, SA tolerates temporary moves to detrimental parameter configurations during an optimization. The method used by SA to decide whether or not to accept detrimental steps is a special case of a more general acceptance rule. The present work investigates the performance of various SA‐type algorithms that differ only in the acceptance rule for detrimental steps when optimizing continuous or discrete problems. A method for step width modulation is introduced to overcome the poor ability of SA type algorithms to locate the exact extreme of a function. The studied search strategies are modified for the discrete problem of wavelength selection. In order to evaluate SA‐type algorithms and their abilities to deal with the wavelength selection problem, two global measures of selectivity are used as criteria to determine the most suitable wavelength subset that maximizes selectivity for pure component ultraviolet–visible spectra.
A professional forum for the field of chemical vapor deposition
Authors: Hitchman Michael
Published: May 2005

The first issue of CVD has arrived! The co‐editor of the new section of Advanced Materials, Michael Hitchman of the University of Strathclyde, sets the scene with a description of the structure, organization, and aims of CVD, a historical account of the development of chemical vapor deposition technology, and his aims in providing a useful service for the materials science, chemistry, physics, and engineering communities.
Preparation and thermolysis of a single‐source precursor to gallium arsenide phosphide
Authors: Aubuchon Steven R. , Lube Michael S. , Wells Richard L.
Published: May 2005

Stoichiometrically controlled, reproducible ternary 13–15 semi‐conductor materials such as GaAsP can now be produced from a single source precursor. Although these materials can be formulated using conventional multi‐precursor CVD techniques, control over the stoichiometry, and thus the behavior of the semiconductors, is difficult. The synthesis and performance of the new single‐source precursor are presented.
Chemical approaches to the Metalorganic CVD of Group‐III Nitrides
Authors: Jones Anthony C. , Whitehouse Colin R. , Roberts John S.
Published: May 2005

Thin films of group‐III nitrides, such as AlN, GaN, and InN, have a number of important technological applications, for example passive barrier layers in silicon circuits, high‐temperature windows, and dielectric optical enhancement layers in magneto‐optic multilayer structures. The materials exhibit interesting physical properties, including hardness, high thermal conductivity, and resistance to corrosive media. This article reviews recent progress in the development of precursors for the CVD of these materials, the doping of the materials, and the growth mechanisms.
Self‐organized microstructure growth
Authors: Nötzel Richard , Fukui Takashi , Hasegawa Hideki , Temmyo Jiro , Kozen Atsuo , Tamamura Toshiaki
Published: May 2005

We present evidence of the new phenomenon of the direct growth of microstructures by metal‐organic vapor‐phase epitaxy (MOVPE) on high‐index GaAs substrates. One‐ and zero‐dimensional self‐faceting by step bunching on GaAs (n11)A substrates produces quantum‐wire‐ and dot‐like arrays on GaAs(311)A and GaAs (211)A substrates. The lateral periodicity of self‐faceting is controlled by the layer thickness and growth temperature and is directly correlated with the red shift of the luminescence of GaAs/AlGaAs heterostructures. On GaAs (n11)B substrates, well‐ordered quantum‐dot arrays are formed in a new self‐organizing growth mode found in the MOVPE of a sequence of AlGaAs and strained InGaAs films. The InGaAs film spontaneously interacts with AlGaAs buffer layers to form ordered arrays of disk‐shaped InGaAs quantum dots buried within AlGaAs microcrystals due to lateral mass transport. The size and distance of the disks can be controlled independently by the In composition and the InGaAs layer thickness. Similar structures are also formed on InP (311)B substrates in the GaInAs/AlInAs and GaInAs/InP material systems. Lateral confinement of carriers in the disks is confirmed by the photoluminescence linewidth which, at room temperature, is as narrow as 13 meV due to reduced thermal broadening.
New forms of luminescent silicon: Silicon–silica composite mesostructures
Authors: Chomski Emmanuel , Dag Ömer , Kuperman Alex , Coombs Neil , Ozin Geoffrey A.
Published: May 2005

Can mesoscopic luminescent silicon clusters be synthesized?

The study reported here answers this question in the affirmative. The CVD of Si2H6 in a 35 Å channel structure MCM‐41 silica host is described, with the growth of the clusters in the host channels receiving particular attention (see figure). Spectroscopic results and a model for the luminescence are presented.

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Considerations of the production of C60 thin films by PECVD, combustion, or pyrolysis
Authors: Rocabois Phillippe , Hitchman Michael L. , Shamlian Sarkis H.
Published: May 2005

A short review of C60 production methods is first presented with an emphasis on thin films for electronic applications. These films are currently made by first producing the fullerene, purifying it and then depositing by different evaporation techniques. Results of an unsuccessful attempt to produce these films by PECVD from organic precursors (cyclopentadiene and naphthalene) are reported. An assessment of the available thermodynamic data of gaseous and solid C60 is then presented. Gibbs free energy minimization for the gaseous system C‐H‐O‐Ar + He including large polyaromatic hydrocarbons and C60 helps to determine under which conditions C60 molecules are stable. Finally, in the light of these calculations, the possibility of the direct production of C60 films by a continuous process such as combustion or pyrolysis is discussed.
Chemical Vapor Deposition Topotaxy in Porous Hosts
Authors: Bowes Carol L. , Malek Andrzej , Ozin Geoffrey A.
Published: May 2005

The application of chemical vapor deposition techniques to the “internal surface” of porous hosts, termed “CVD topotaxy”, is reviewed. Various organometallic species are found to anchor on the internal surfaces of zeolites; species such as methyltrioxorhenium, displaying remarkable optical effects as the result of polymerization processes, and tungsten and molybdenum carbonyls forming redox‐tunable tungsten and molybdenum oxides. Topotaxy has contributed to the quest for spatial confinement of semiconductors through the MOCVD of cadmium, zinc, and tin sulfides in the 13 Å cavities of zeolite Y. This has also been applied to Si and Ge semiconductors, with special applications to the field of luminescent silicon. Finally, Si encapsulation in mesoporous hosts is discussed.
In situ halide chemical vapor deposition of Bi2Sr2CaCu2O8 + x on silver substrates
Authors: Hårsta Anders , Lundquist Stefan
Published: May 2005

The superconducting Bi2Sr2CaCu2O8 + x phase is deposited on polycrystalline silver substrates by halide CVD in the temperature interval 760–820° C. Metal iodides and oxygen are used as precursor materials. The films grow with a pronounced [001] orientation, and a superconducting critical temperature of about 80 K was observed without any post‐annealing. As far as the authors know, this is the first report of in situ CVD of the Bi‐2212 phase on silver substrates. Some of the films were postannealed in a two‐step process. The [001] orientation was now considerably improved, but the superconducting critical temperature was reduced to about 75 K.
Epitaxial growth of Bi2O2.33 by halide Cvd
Authors: Schuisky Mikael , Hårsta Anders
Published: May 2005

Bismuth oxide is an interesting dielectric material with potential applications for optical coatings. MIS capacitors, and microwave integrated circuits. CVD growth has a number of advantages over other deposition techniques, e.g., precise control of oxygen activity, microstructure, and texture. The study presented here shows how this can be achieved for epitaxial Bi2O2.33 films grown on SrTiO3 and MgO (see Figure) using halide CVD with Bil3 as the bismuth source.

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Atomic Layer Epitaxy of Copper on Tantalum
Authors: Mårtensson Per , Carlsson Jan‐Otto
Published: May 2005

The decreasing size and increasing complexity of the components in future microelectronics devices present a great challenge in the development of new deposition techniques. One method for the deposition of semiconductor materials which has generated considerable interest is atomic layer epitaxy (ALE). This method has been shown to yield films with excellent step coverage and high conformity, even on very complex shaped substrates. In this paper we report, for the first time, the deposition of copper using the ALE technique. The films were deposited on tantalum substrates using copper(I) chloride as the precursor and hydrogen as the reducing agent. The films were found to be polycrystalline and practically free from texture. They were also very low in chlorine contamination and possessed a rather rough surface morphology. An initial fast substrate reduction, yielding a film thickness of approximately 300 À, was observed, the rate determining step being the reaction of hydrogen with the adsorbed copper chloride. The activation energy for this step was estimated to be 80 kJ mol−1.
Silica Overlayers Prepared Using Organic Template Molecules on Tin Oxide and Its Molecular Sieving Property
Authors: Kodakari Nobuaki , Katada Naonobu , Niwa Miki
Published: May 2005

In order to create a molecular recognition property on an oxide surface, a silica overlayer was prepared on tin oxide by the chemical vapor deposition (CVD) of silicon alkoxide using pre‐adsorbed benzoate anions as template molecules, which were then removed. Silica overlayers on tin oxide prepared in this way showed the adsorption property to discriminate between molecules based on their molecular size, i.e., to possess a molecular sieving property. The size of the vacancies in the silica overlayers was controlled by the template molecule.
New materials for superconducting electronics: Epitaxial growth of LaSrGaO4 and PrSrGaO4 dielectric thin films by MOCVD
Authors: Belot John A. , Hinds Bruce J. , Chen Jun , Wang Yun‐Yu , Dravid Vinayak , Marks Tobin J.
Published: May 2005

Communication: There is a critical need for improved dielectric materials as substrates, and as buffer and insulator layers for HTS devices. Here the first in situ MOCVD growth of phase‐pure, epitaxially oriented YBCO lattice matched, low dielectric constant perovskites LnSrGaO4 (Ln = La and Pr) is described. The exclusively c‐axis oriented films have an ultimate surface roughness of 3.0 and 1.0 nm for the Pr and La materials, respectively. These films exemplify the requirements which are essential for fabrication of HTS thin‐film devices.