Computer chemometric

M. Otto, Chemometrics. Statistics and Computer Application in Analytical Chemistry. Wiley-VCH, Weinheim, 1998. 

M. Otto, Chemometrie Statistik und Computereinsatz in der Analytik, WHey-VCH, Weinheim, 1997 M. Otto, Chemometrics. Statistics and Computer Application in Analytical Chemistry, Wiley-VCH, Weinheim, 1998. 

Jurs P C1990. Chemometrics and Multivariate Analysis in Analytical Chemistry. In Lipkowitz K B and D B Boyd (Editors) Reviews in Computational Chemistry Volume 1. New York, VCH Publishers, pp. 169-212. 

The quahty of an analytical result also depends on the vaUdity of the sample utilized and the method chosen for data analysis. There are articles describiag Sampling and automated sample preparation (see Automated instrumentation) as well as articles emphasizing data treatment (see Chemometrics Computer technology), data iaterpretation (see Databases Imaging technology), and the communication of data within the laboratory or process system (see Expert systems Laboratory information managet nt systems). 

Evidence of the appHcation of computers and expert systems to instmmental data interpretation is found in the new discipline of chemometrics (qv) where the relationship between data and information sought is explored as a problem of mathematics and statistics (7—10). One of the most useful insights provided by chemometrics is the realization that a cluster of measurements of quantities only remotely related to the actual information sought can be used in combination to determine the information desired by inference. Thus, for example, a combination of viscosity, boiling point, and specific gravity data can be used to a characterize the chemical composition of a mixture of solvents (11). The complexity of such a procedure is accommodated by performing a multivariate data analysis. 

Theoretically based correlations (or semitheoretical extensions of them), rooted in thermodynamics or other fundamentals are ordinarily preferred. However, rigorous theoretical understanding of real systems is far from complete, and purely empirical correlations typically have strict limits on apphcabihty. Many correlations result from curve-fitting the desired parameter to an appropriate independent variable. Some fitting exercises are rooted in theory, eg, Antoine s equation for vapor pressure others can be described as being semitheoretical. These distinctions usually do not refer to adherence to the observations of natural systems, but rather to the agreement in form to mathematical models of idealized systems. The advent of readily available computers has revolutionized the development and use of correlation techniques (see Chemometrics Computer technology Dimensional analysis). 

Chemometrics, in the most general sense, is the art of processing data with various numerical techniques in order to extract useful information. It has evolved rapidly over the past 10 years, largely driven by the widespread availability of powerful, inexpensive computers and an increasing selection of software available off-the-shelf, or from the manufacturers of analytical instruments. 

The proliferation of sophisticated instruments which are capable of rapidly producing vast amounts of data, coupled with the virtually universal availability of powerful but inexpensive computers, has caused the field of chemometrics to evolve from an esoteric specialty at the perhiphery of Analytical Chemistry to a required core competency. 

Procedures used vary from trial-and-error methods to more sophisticated approaches including the window diagram, the simplex method, the PRISMA method, chemometric method, or computer-assisted methods. Many of these procedures were originally developed for HPLC and were apphed to TLC with appropriate changes in methodology. In the majority of the procedures, a set of solvents is selected as components of the mobile phase and one of the mentioned procedures is then used to optimize their relative proportions. Chemometric methods make possible to choose the minimum number of chromatographic systems needed to perform the best separation. 

PLS has been introduced in the chemometrics literature as an algorithm with the claim that it finds simultaneously important and related components of X and of Y. Hence the alternative explanation of the acronym PLS Projection to Latent Structure. The PLS factors can loosely be seen as modified principal components. The deviation from the PCA factors is needed to improve the correlation at the cost of some decrease in the variance of the factors. The PLS algorithm effectively mixes two PCA computations, one for X and one for Y, using the NIPALS algorithm. It is assumed that X and Y have been column-centred as usual. The basic NIPALS algorithm can best be demonstrated as an easy way to calculate the singular vectors of a matrix, viz. via the simple iterative sequence (see Section 31.4.1) ... 

Lucasius, CB (1994) Evolutionary computation a distinctive form of natural computation with chemometric potential. Chapter 9 in Buydens LM, Meissen WJ Chemometrics. Exploring and exploiting chemical information. Katholieke Universiteit Nijmegen... 

Otto M (1998) Chemometrics. Statistics and computer application in analytical chemistry. VCH, Weinheim... 

In creating chemometric calibrations, it is common to transform the spectrum, for any of various reasons, from the measured format, which is usually absorbance, into a different format. One common, widely used transformation is to compute a derivative of the spectrum. First (dA/dA) and second (d2A/dA2) derivatives are often used. Hence, in our next few chapters we will be discussing the properties and behavior of derivatives. 

Workman, J. and Mark, H., Chemometrics in Spectroscopy Comparison of Goodness of Fit Statistics for Linear Regression - Part 3, Computing Confidence Limits for the Correlation Coefficient, Spectroscopy 19(7), 31-33 (2004). 

One part of that equation, [AtA] , appears so commonly in chemometric equations that it has been given a special name, it is called the pseudoinverse of the matrix A. The uninverted term ATA is itself fairly commonly found, as well. The pseudoinverse appears as a common component of chemometric equations because it confers the Least Squares property on the results of the computations that is, for whatever is being modeled, the computations defined by equation 69-1 produce a set of coefficients that give the smallest possible sum of the squares of the errors, compared to any other possible linear model. 

Recent advances in instrumentation range from novel (laser) sources and highly compact spectrometers over waveguide technology to sensitive detectors and detector arrays. This, in combination with the progress in electronics, computer technology and chemometrics, makes it possible to realise compact, robust vibrational spectroscopic sensor devices that are capable of reliable real-world operation. A point that also has to be taken into account, at least when aiming at commercialisation, is the price. Vibrational spectroscopic systems are usually more expensive than most other transducers. Hence, it depends very much on the application whether it makes sense to implement IR or Raman sensors or if less powerful but cheaper alternatives could be used. 

Advanced mathematical and statistical techniques used in analytical chemistry are often referred to under the umbrella term of chemometrics. This is a loose definition, and chemometrics are not readily distinguished from the more rudimentary techniques discussed in the earlier parts of this chapter, except in terms of sophistication. The techniques are applied to the development and assessment of analytical methods as well as to the assessment and interpretation of results. Once the province of the mathematician, the computational powers of the personal computer now make such techniques routinely accessible to analysts. Hence, although it would be inappropriate to consider the detail of the methods in a book at this level, it is nevertheless important to introduce some of the salient features to give an indication of their value. Two important applications in analytical chemistry are in method optimization and pattern recognition of results. 

Other more mathematical techniques, which rely on appropriate computer software and are examples of chemometrics (p. 33), include the generation of one-, two- or three-dimensional window diagrams, computer-directed searches and the use of expert systems (p. 529). A discussion of these is beyond the scope of this text. 

Data reduction and interpretation are much aided by computer methods and the high speed of current microcomputers facilitates the real-time processing and display of data. The principle of extracting as much information as possible from analytical measurements through the application of statistical and other mathematical methods, usually with the aid of appropriate computer software, is known as chemometrics (p. 13). 

Experimental Design A Chemometric Approach, by S.N. Deming and S.L. Morgan Advanced Scientific Computing in BASIC with Applications in Chemistry, Biology and Pharmacology, by P. Valko and S. Vajda PCs for Chemists, edited by J. Zupan... 

The validity of the results is a central issue, and it is confirmed by comparing traditional methods with their robust counterparts. Robust statistical methods are less common in chemometrics, although they are easy to access and compute quickly. Thus, several robust methods are included. 

Computation and practical use are further important concerns, and thus the R package chemometrics has been developed, including data sets used in this book as well as implementations of the methods described. Although some programming skills are required, the use of R has advantages because it is freeware and is continuously updated. Thus interested readers can go through the examples in this book and adapt the procedures to their own problems. Feedback is appreciated and it can lead to extension and improvement of the package. 

Kurt Varmuza was bom in 1942 in Vienna, Austria. He studied chemistry at the Vienna University of Technology, Austria, where he wrote his doctoral thesis on mass spectrometry and his habilitation, which was devoted to the field of chemometrics. His research activities include applications of chemometric methods for spectra-structure relationships in mass spectrometry and infrared spectroscopy, for structure-property relationships, and in computer chemistry, archaeometry (especially with the Tyrolean Iceman), chemical engineering, botany, and cosmo chemistry (mission to a comet). Since 1992, he has been working as a professor at the Vienna University of Technology, currently at the Institute of Chemical Engineering. 

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