Chemometric methods overview

As we have stated in the introduction to this chapter and as appears from this overview, a wide variety of chemometric methods converges in QSAR, which plays a key role in the design of novel and improved drugs. 

In this review, an in-depth overview is presented, tracing an outline of the chemometric techniques most widely applied in the relatively brief history of artificial tongues, highlighting benefits and drawbacks of each one. Furthermore, some chemometric methods recently introduced and particularly suitable for artificial tongue data processing are discussed. 

Section 1.8 gives an overview of monographs which deal with basic and advanced statistics, and some hints are given about recommended books and journals. Important books on general aspects of chemometrics include SHARAF et al. [1986], MASSART et al. [1988], and BRERETON [1990]. Books which deal with the application of chemometric methods in environmental research are BREEN and ROBINSON [1985], DEVILLERS and KARCHER [1991], and EINAX [1995a 1995b],... 

A brief overview shall be given of the purpose of sampling on the one hand and the chemometric methods applicable on the other hand. In the field of environmental investigation, samples are taken mostly for purposes of ... 

The following case studies cannot and shall not give an overview of all possible applications of chemometric methods, but they may stimulate the reader into using chemo-metrics to solve environmental tasks and problems in his own working field. 

Table 1.3 provides an overview of chemometric methods. The main emphasis is on statistical-mathematical methods. Random data are characterized and tested by the descriptive and inference methods of statistics, respectively. Their importance increases in connection with the aims of quality control and quality assurance. Signal processing is carried out by means of algorithms for smoothing, filtering, derivation, and integration. Transformation methods such as the Fourier or Hadamard transformations also belong in this area. 

This chapter consists of two distinct parts. In the first part, a cursory overview of chemometric methods, as applicable to analysis of and quantitation with NIR data is presented. In Section 10.2 common methods for preprocessing NIR spectra are described. Section 10.3 discusses multivariate methods for developing predictive calibration models with NIR spectra. In Section 10.4, strategies for sample and model validation are presented that exploit the multivariate nature of NIR spectra. The performance of the multivariate methods discussed in Section 10.2 through Section 10.4 are applied to a set of 80 NIR reflectance data of corn flour for determination of four physical properties moisture, oil, protein, and starch. 

This contribution presents a brief and user-oriented overview of the most widely used chemometric methods. The available space does not allow a thorough mathematically treatment. A selection of applications to chemical problems demonstrates capabilities and limits of the chemometric approach. 

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... 

In this chapter, we provide a general overview of the field of chemometrics. Some historical remarks and relevant literature to this subject make the strong connection to statistics visible. First practical examples (Section 1.5) show typical problems related to chemometrics, and the methods applied will be discussed in detail in subsequent chapters. Basic information on univariate statistics (Section 1.6) might be helpful to understand the concept of randomness that is fundamental in statistics. This section is also useful for making first steps in R. 

Basically, the book can be subdivided into three parts. In the first part the fundamentals of the instrumentation for infrared and Raman imaging and mapping and an overview on the chemometric tools for image analysis are covered in two introductory chapters. The second part comprises the chapters 3-9 and describes a wide variety of applications ranging from biomedical via food and agriculture to polymers and pharmaceuticals. Some historical insights are given as well. In the third part the chapters 10-15 cover special methodical developments and their utiHty in specific fields. 

Spectroscopic methods can provide fast, non-destructive analytical measurements that can replace conventional analytical methods in many cases. The non-destructive nature of optical measurements makes them very attractive for stability testing. In the future, spectroscopic methods will be increasingly used for pharmaceutical stability analysis. This chapter will focus on quantitative analysis of pharmaceutical products. The second section of the chapter will provide an overview of basic vibrational spectroscopy and modern spectroscopic technology. The third section of this chapter is an introduction to multivariate analysis (MVA) and chemometrics. MVA is essential for the quantitative analysis of NIR and in many cases Raman spectral data. Growth in MVA has been aided by the availability of high quality software and powerful personal computers. Section 11.4 is a review of the qualification of NIR and Raman spectrometers. The criteria for NIR and Raman equipment qualification are described in USP chapters <1119> and < 1120>. The relevant highlights of the new USP chapter on analytical instrument qualification <1058> are also covered. Section 11.5 is a discussion of method validation for quantitative analytical methods based on multivariate statistics. Based on the USP chapter for NIR <1119>, the discussion of method validation for chemometric-based methods is also appropriate for Raman spectroscopy. The criteria for these MVA-based methods are the same as traditional analytical methods accuracy, precision, linearity, specificity, and robustness however, the ways they are described and evaluated can be different. 

A FTIR spectrum is complex, containing many variables per sample and making visual analysis very difficult. Hence, to extract extra useful information, i.e., latent variables, from the whole spectra chemometric analysis was performed considering the whole FTIR data set using principal components analysis (PCA) for an exploratory overview of data. This method could reveal similarity/dissimilarity patterns among propolis samples, simplifying... 

Here we present a brief overview of separations approaches, with a focus on the data that are derived from different methods and on phenomena in the seprarations approach that lead to challenges in data interpretation. This is followed by a discussion of approaches that exist for the chemometric interpretation of seprarations data, spreofrc challenges that arise in the chemometric treatment of these data, and solutions that have been implemented to deal with these challenges. 

See alsa Chemometrics and Statistics Multivariate Calibration Techniques. Color Measurement. Extraction Solvent Extraction Principles. Flow Injection Analysis Detection Techniques. Food and Nutritional Analysis Water and Minerals. Kinetic Methods Principles and Instrumentation Catalytic Techniques. Optical Spectroscopy Detection Devices. Spectrophotometry Overview Derivative Techniques Biochemical Applications Pharmaceutical Applications. Spot Tests. Water Analysis Overview. 

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