Selected Applications of Chemometrics

Mass spectrometry and chemometric methods cover very diverse fields Different origin of enzymes can be disclosed with LC-MS and multivariate analysis [45], Pyrolysis mass spectrometry and chemometrics have been applied for quality control of paints [46] and food analysis [47], Olive oils can be classified by analyzing volatile organic hydrocarbons (of benzene type) with headspace-mass spectrometry and CA as well as PC A [48], Differentiation and classification of wines can similarly be solved with headspace-mass spectrometry using unsupervised and supervised principal component analyses (SIMCA = soft independent modeling of class analogy) [49], Early prediction of wheat quality is possible using mass spectrometry and multivariate data analysis [50], 

Pyrolysis mass spectrometry and chemometrics have been coupled to analyze the adulteration of orange juice quantitatively [51], to test the authenticity of honey [52], and to discriminate the unffactionated plant extracts [53]. 

GC-MS coupled with chemometric techniques has been used to characterize roasted coffees [54], to detect adulterants in olive oils [55], and to determine fatty acids in fish oils [56], GC-MS data have also been used in toxicology assessments to reveal patterns in complex chemical mixtures with the help of multivariate analyses [57,58], 

Time of flight mass spectrometry has also provided data for chemometric analyses, e.g., for PCA [61,62] and for trilinear (3-way) analysis [63]. 

The chemometric approach has been applied on diverse field of mass spectral data evaluation peak resolution and quantification [64], calibration [65], instrument standardization [66], fast interpretation [67], and evaluation of rate constants [68]. 

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TABLE 10.2. Selected applications of chemometric experimental design for CE analysis in herbal medicines (2004-2008)... 

The second part of the book—Chapters 9-12— presents some selected applications of chemometrics to different topics of interest in the field of food authentication and control. Chapter 9 deals with the application of chemometric methods to the analysis of hyperspectral images, that is, of those images where a complete spectrum is recorded at each of the pixels. After a description of the peculiar characteristics of images as data, a detailed discussion on the use of exploratory data analytical tools, calibration and classification methods is presented. The aim of Chapter 10 is to present an overview of the role of chemometrics in food traceability, starting from the characterisation of soils up to the classification and authentication of the final product. The discussion is accompanied by examples taken from the different ambits where chemometrics can be used for tracing and authenticating foodstuffs. Chapter 11 introduces NMR-based metabolomics as a potentially useful tool for food quality control. After a description of the bases of the metabolomics approach, examples of its application for authentication, identification of adulterations, control of the safety of use, and processing are presented and discussed. Finally, Chapter 12 introduces the concept of interval methods in chemometrics, both for data pretreatment and data analysis. The topics... 

Although application of chemometrics in sample preparation is very uncommon, several optimisation techniques may be used to optimise sample preparation systematically. Those techniques can roughly be divided into simultaneous and sequential methods. The main restrictions of a sequential simplex optimisation [6,7] find their origin in the complexity of the optimisation function needed. This function is a predefined function, often composed of several criteria. Such a composite criterion may lead to ambiguous results [8]. Other important disadvantages of simplex optimisation methods are that not seldom local optima are selected instead of global optima and that the number of experiments needed is not known beforehand. 

Some of the earliest applications of chemometrics in PAC involved the use of an empirical variable selection technique commonly known as stepwise multiple linear regression (SMLR).8,26,27 As the name suggests, this is a technique in which the relevant variables are selected sequentially. This method works as follows ... 

A few of the methods discussed in this chapter, such as 3D PC plots and variable selection, have significant roles in most applications of chemometrics, so the interest in die techniques is by no means restricted to chromatographic applications, but in order to reduce excessive repetition the methods are introduced in one main context. 

The last chapter provides a look at selected applications of multi-way analysis that have appeared in the chemometrics literature. This section of the book is not meant to be a complete literature review of the subject. Rather, applications were selected to aid the reader in understanding what the tools can do and hopefully point the way for the reader to discover new applications in his/her own field of investigation. 

Biomarker Identification using Chemometrics Selected Applications of Metabonomics... 

Kokot S., King G., Teller H. R. and Massart D. L. (1992) Application of chemometrics for the selection of microwave digestion procedures. Anal. Chim. Acta 268, 81-94. 

The application of chemometric procedures coupled with derivative spectroscopy permits achievement of higher selectivity in determination of P-lactam antibiotics. Currently, chemometric procedures based on the estimated ratio of spectra derivative for the selective determination of P-lactam analogs are the most common. It was proved that the application of the ratio of different-order spectra derivatives permitted the separation of binary and tertiary mixtures of P-lactam antibiotics [22]. During the determination of concentrations of three components (e.g., penicillin-G sodium, penicillin-G procain and dihydrostreptomycin sulphate salts) in a mixture the equation describing the ratio spectra derivative spetrophotometry is as follows ... 

In Part 2, specific aspects of optimization in individual techniques are considered. In RP chromatography (Section 2.1), besides the choice of eluents (for this, see also Chapters 1.1 to 1.4), above all the choice of column represents a difficult and time-consuming task. The subject of RP columns is covered by a total of six authors two authors (2.1.1 Stavros Kromidas, 2.1.2 Uwe D. Neue) focus on the more practical aspects of this issue, while Frank Steiner (Chapter 2.1.5) and Uoyd R. Snyder (Chapter 2.1.6) present more fundamental, theoretical considerations, with nevertheless real practical relevance, on the questions of column characterization and colurrm selection. Naturally, the meaningfulness of results increases with the number of experimental data, and so the handling of figures, and above all the identification and interpretation of correlations, is only possible with the aid of mathematical tools. Chemometrics is a suitable tool, for example, for establishing the similarity of columns on the basis of chromatographic data. The application of chemometrics from a practical viewpoint is briefly described in Chapter 2.1.1 Stavros Kromidas) and extensively detailed in Chapters 2.1.3 (Melvin R. Euerby)... 

Chemometrics has been defined as A chemical discipline that uses statistical and mathematical methods, to design or select optimum procedures and experiments, and to provide maximum chemical information by analyzing chemical data. In shorter words it is focused as Chemometrics concerns the extraction of relevant information from chemical data by mathematical and statistical tools. Chemometrics can be considered as a part of the wider field chemoinformatics which has been defined as The application of informatics methods to solve chemical problems (Gasteiger and Engel 2003) including the application of mathematics and statistics. 

Variable selection is an optimization problem. An optimization method that combines randomness with a strategy that is borrowed from biology is a technique using genetic algorithms—a so-called natural computation method (Massart et al. 1997). Actually, the basic structure of GAs is ideal for the purpose of selection (Davis 1991 Hibbert 1993 Leardi 2003), and various applications of GAs for variable selection in chemometrics have been reported (Broadhurst et al. 1997 Jouan-Rimbaud et al. 1995 Leardi 1994, 2001, 2007). Only a brief introduction to GAs is given here, and only from the point of view of variable selection. 

Despite the lack of inherent selectivity, it is still possible to obtain good quantitative data from online UV/vis monitoring by making use of chemometric techniques to resolve the overlapping spectra. The most common application is in dissolution testing [73, 74], where results that are at least as accurate as those of the reference (and much slower and more costly) HPLC method have been demonstrated. 

Another type of classification is outlier selection or contamination identification. As an example, in Fig. 4.23(b), the butter is the desired material and bacteria the contamination. An arbitrary threshold for this image would be 0.02, in which all pixels >0.02 are considered suspect, and hopefully, because this is a food product, decontamination procedures are pursued. In these two examples of classification, only arbitrary thresholds have been defined and, as such, confidence in these classifications is lacking. This confidence can be achieved through statistical methods. Although this chapter is not the appropriate place for an involved discussion of application of statistics toward data analysis, we will give one example often used in chemometric classification. 

Chemometrics is the discipline concerned with the application of statistical and mathematical methods to chemical data [2.18], Multiple linear regression, partial least squares regression and the analysis of the main components are the methods that can be used to design or select optimal measurement procedures and experiments, or to provide maximum relevant chemical information from chemical data analysis. Common areas addressed by chemometrics include multivariate calibration, visualisation of data and pattern recognition. Biometrics is concerned with the application of statistical and mathematical methods to biological or biochemical data. 

Chemometrics has been defined as the chemical discipline that uses mathematical and statistical methods to design or select optimal measurement procedures and experiments and to provide maximum chemical information by analysing chemical data (Kowalski, 1978). It is a relatively new discipline that assists with (i) the planning of experiments, and (ii) the manipulation and interpretation of large data sets. Some aspects of chemometrics can be done using an appropriate speadsheet but the majority of applications require the use of dedicated software. The fundamental principles of most of the processes involved in chemometrics are those of statistics. You are therefore advised to become familiar with the material in Chapters 40 and 41 before proceeding. 

NIRS involves the multidisciplinary approaches of the analytical chemist, statistician, and computer programmer. The word chemometrics refers to the application of mathematical or statistical methods to measurements made on chemical systems of varying complexity. Chemometrics is defined as the chemical discipline that uses mathematical, statistical, and other methods that apply formal logic to design or select optimal measurement procedures and experiments, and to provide maximum relevant chemical information by analyzing chemical data. 

Calibration models are developed to determine the relationship between calibration set spectra and the constituent value of interest for those samples. Calibration involves taking spectra from many samples varying over the measurement range and also measuring the desired parameters. A rugged chemometric model for a complex sample may require hundreds to thousands of samples taken from all possible situations, in and out of specification, that it may encounter. Samples selected for calibration must contain all of the variables affecting the chemical and physical properties of the samples to be analyzed. To characterize each source of variation, it is recommended that 15 to 20 samples be run for each variable. Application of a math treatment, such as second derivative, prepares the raw spectral data for use in a regression and subsequent development of a calibration equation. This type of treatment results in a data file that will yield more information more easily than a raw data file. 

Some basic concepts and definitions of statistics, chemometrics, algebra, graph theory, similarity/diversity, which are fundamental tools in the development and application of molecular descriptors, are also presented in the Handbook in some detail. More attention has been paid to information content, multivariate correlation, model complexity, variable selection, and parameters for model quality estimation, as these are the characteristic components of modern QSAR/QSPR modelling. 

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