Multivariate chemometric techniques

Multivariate chemometric techniques have subsequently broadened the arsenal of tools that can be applied in QSAR. These include, among others. Multivariate ANOVA [9], Simplex optimization (Section 26.2.2), cluster analysis (Chapter 30) and various factor analytic methods such as principal components analysis (Chapter 31), discriminant analysis (Section 33.2.2) and canonical correlation analysis (Section 35.3). An advantage of multivariate methods is that they can be applied in... 

PCA is probably the most widespread multivariate chemometric technique, and because of the importance of multivariate measurements in chemistry, it is regarded by many as the technique that most significantly changed the chemist s view of data analysis. 

The matrix of signal is interpreted by multivariate chemometrics techniques like the PCA, PLS, ANN, and so on. Samples with similar odor fingerprinting generally give rise to similar sensor response patterns, while samples with different odor fingerprinting show differences in their patterns. The sensors of an E-nose can respond to both odorous and odorless volatile compounds. 

Cerrato Oliveros et al. (2002) selected array of 12 metal oxide sensors to detected adulteration in virgin olive oils samples and to quantify the percentage of adulteration by electronic nose. Multivariate chemometric techniques such as PCA were applied to choose a set of optimally discriminant variables. Excellent results were obtained in the differentiation of adulterated and non-adulterated olive oils, by application of LDA, QDA. The models provide very satisfactory results, with prediction percentages >95%, and in some cases almost 100%. The results with ANN are slightly worse, although the classification criterion used here was very strict. To determine the percentage of adulteration in olive oil samples multivariate calibration techniques based on partial least squares and ANN were employed. Not so good results were carried out, even if there are exceptions. Finally, classification techniques can be used to determine the amount of adulterant oil added with excellent results. 

Most uses of flow techniques involve the quantitative determination of some target species. This chapter describes various ways of using flow techniques with quantitative purposes, such as calibration curves, based on peak height or peak area, and titrations, based on distance between equivalence points or single-point method. Stopped-flow technique can be used for both, quantitative approach, for example in kinetic methods, or for qualitative determinations inasmuch as it allows spectral and potential scans to be performed. Multiparameter analysis is presented in two forms to be carried out, by multivariate chemometric techniques or by applying sandwich technique. Finally, smart systems are presented as a step forward in automation, commonly used in multiparameter analysis. 

Given that multivariate chemometric techniques rely on computations, these provide interesting advantages in multiparameter determinations as they afford the simultaneous quantification of several species without the need to modify the experimental setup. 

By using standards to calibrate the system and spectra obtained at the maxima of the SIA peaks, one can simultaneously determine several phenols without the need to separate them simply by using a multivariate chemometric technique, e.g. multiple linear regresion (MLR), as explained in a previous section (see Chapter 2). 

Recently, introductory books about chemometrics have been published by R. G. Brereton, Chemometrics—Data Analysis for the Laboratory and Chemical Plant (Brereton 2006) and Applied Chemometrics for Scientists (Brereton 2007), and by M. Otto, Chemometrics—Statistics and Computer Application in Analytical Chemistry (Otto 2007). Dedicated to quantitative chemical analysis, especially using infrared spectroscopy data, are A User-Friendly Guide to Multivariate Calibration and Classification (Naes et al. 2004), Chemometric Techniques for Quantitative Analysis (Kramer 1998), Chemometrics A Practical Guide (Beebe et al. 1998), and Statistics and Chemometrics for Analytical Chemistry (Miller and Miller 2000). 

Multivariate chemometric methods have claimed considerable attention in the last few decades because of their inherent capacity for resolving multicomponent, complex systems. Applications of multivariate methods in different electrochemical techniques have been recently reported by several authors [192-194],... 

In order to overcome, or at least minimise, such drawbacks we can resort to the use of chemometric techniques (which will be presented in the following chapters of this book), such as multivariate experimental design and optimisation and multivariate regression methods, that offer great possibilities for simplifying the sometimes complex calibrations, enhancing the precision and accuracy of isotope ratio measurements and/or reducing problems due to spectral overlaps. 

In this respect, the first chapter is devoted to a general overview of the most common atomic spectroscopic techniques. The very basics of the analytical techniques are discussed and, most importantly, pros and cons are presented to the reader. Practical difficulties are referred to, their solutions depicted and, when possible, multivariate chemometric solutions pointed out. 

Thousands of chemical compounds have been identified in oils and fats, although only a few hundred are used in authentication. This means that each object (food sample) may have a unique position in an abstract n-dimensional hyperspace. A concept that is difficult to interpret by analysts as a data matrix exceeding three features already poses a problem. The art of extracting chemically relevant information from data produced in chemical experiments by means of statistical and mathematical tools is called chemometrics. It is an indirect approach to the study of the effects of multivariate factors (or variables) and hidden patterns in complex sets of data. Chemometrics is routinely used for (a) exploring patterns of association in data, and (b) preparing and using multivariate classification models. The arrival of chemometrics techniques has allowed the quantitative as well as qualitative analysis of multivariate data and, in consequence, it has allowed the analysis and modelling of many different types of experiments. 

In more recent development, chemometric or multivariate calibration techniques have been applied into spectrophotometric methods. As reported by Palabiyik and Onur [24], principal component regression and partial least square were used to determine ezetimibe in combination with simvastatin. This method offers advanfages such as no chemical prefreafmenf prior to analysis as well as no need to observe graphical spectra and calculations as with the derivative method. In addition, the instrumentation used is also simpler. 

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