Chemometric classification

Latorre, M. J., Pena, R., Pita, C., Botana, A., Garcia, S., and Herrero, C. (1999). Chemometric classification of honeys according to their t q)e. II. Metal content data. Food Chem. 66, 263-268. 

Myshkin, E. and Wang, B. Chemometrical classification of ephrin ligands and eph kinases using GRID/CPCAapproach./. Chem. Inf. Comput. Sci. 2003, 43, 1004-1010. 

Brescia, M. A., Alviti, G., Liuzzi, V., and Sacco, A. (2003a). Chemometric classification of olive cultivars based on compositional data of oils. JAOCS 80, 945-950. 

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. 

E. Myshkin, B. Wang, Chemometrical classification of ephrin ligands and Eph kinases using GRID/CPCA approach,... 

M. M. Mossoba, F. M. Khambaty, and F. S. Fry, Novel Application of a Disposable Optical Fihn to the Analysis of Bacterial Strains A Chemometric Classification of Mid-Infrared Spectra, Hp/r/. Spectrosc. 56, 32-736 (2002). 

One of the filler samples can be selected as a reference column and the probe Tj values in the sample column can plotted as a function of respective Tj values in the reference column. The surface area of filler in each column must be identical. If the surface chemistry of the samples is exactly equivalent a linear plot will result. The direction and magnitude of deviation from linearity provides a measure of the relative strength of interaction. The multiple probe temperature programmed approach to IGC certainly shows potential for rapid screening of fillers and yields data that can be treated using chemometric classification tools such as principle component analysis. 

ECVA [41] is a recent chemometric classification tool representing a new approach for grouping samples based on the standard Canonical Variates Analysis, but with an underlying PLS engine. It is able to cope with several different classes yielding powerful separations. As with PLS-DA, it is vital with a good validation as to avoid overfitting. 

We will explore the two major families of chemometric quantitative calibration techniques that are most commonly employed the Multiple Linear Regression (MLR) techniques, and the Factor-Based Techniques. Within each family, we will review the various methods commonly employed, learn how to develop and test calibrations, and how to use the calibrations to estimate, or predict, the properties of unknown samples. We will consider the advantages and limitations of each method as well as some of the tricks and pitfalls associated with their use. While our emphasis will be on quantitative analysis, we will also touch on how these techniques are used for qualitative analysis, classification, and discriminative analysis. 

Mocak, J. Jurasek, P. Phillips, G.O, Vargas, S. Casadei, E. Ghikamai, B.N. (1998). The classification of natural gums. X. Chemometric characterization of exudate gums that conform to the revised specification of the gum arabic for food use, and the identification of adulterants. Food Hydrocolloids, Vol. 12, No. 2, (April 1998), pp 141-150, ISSN 0268-005X. 

Rousseeuw [4]. Massart and Kaufman [5] and Bratchell [6] wrote specifically for chemometricians. Massart and Kaufman s book contains many examples, relevant to chemometrics, including the meteorite example [7]. More recent examples concern classification, for instance according to structural descriptions for toxicity testing [8] or in connection with combinatorial chemistry [9], according to chemical... 

There are many types of pattern recognition which essentially differ in the way they define classification rules. In this section, we will describe some of the approaches, which we will then develop further in the following sections. We will not try to develop a classification of pattern recognition methods but merely indicate some characteristics of the methods, that are found most often in the chemometric literature and some differences between those methods. 

Yeh and Spiegelman [24], Very good results were also obtained by using simple neural networks of the type described in Section 33.2.9 to derive a decision rule at each branching of the tree [25]. Classification trees have been used relatively rarely in chemometrics, but it seems that in general [26] their performance is comparable to that of the best pattern recognition methods. 

The similarity in approach to LDA (Section 33.2.2) and PLS (Section 33.2.8) should be pointed out. Neural classification networks are related to neural regression networks in the same way that PLS can be applied both for regression and classification and that LDA can be described as a regression application. This can be generalized all regression methods can be applied in pattern recognition. One must expect, for instance, that methods such as ACE and MARS (see Chapter 11) will be used for this purpose in chemometrics. 

Scott DR (1995) Empirical pattern recognition/expert system approach for classification and identification of toxic organic compounds from low resolution mass spectra. In Chemometrics in environmental chemistry - applications. Vol 2, part H (Vol ed J Einax), Springer, Berlin Heidelberg New York, p 25... 

The NIR spectra contain less structural information than the corresponding IR spectra, since only the overtone absorptions of X-Fl (X = C, N, O) are detected. Using chemometric approaches has, however, enlarged the applications of this method, particularly for quantitative and classification analyses. 

This leads us to the other hand, which, it should be obvious, is that we feel that Chemometrics should be considered a subfield of Statistics, for the reasons given above. Questions currently plaguing us, such as How many MLR/PCA/PLS factors should I use in my model , Can I transfer my calibration model (or more importantly and fundamentally How can I tell if I can transfer my calibration model ), may never be answered in a completely rigorous and satisfactory fashion, but certainly improvements in the current state of knowledge should be attainable, with attendant improvements in the answers to such questions. New questions may arise which only fundamental statistical/probabilistic considerations may answer one that has recently come to our attention is, What is the best way to create a qualitative (i.e., identification) model, if there may be errors in the classifications of the samples used for training the algorithm ... 

Also, we do not cover several typical chemometrics types of analyses, such as cluster analysis, experimental design, pattern recognition, classification, neural networks, wavelet transforms, qualimetrics etc. This explains our decision not to include the word chemometrics in the title. 

A. Cichelli and G.P. Pertesana, High-performance liquid chromatographic analysis of chlorophylls, pheophytins and carotenoids in virgin olive oils chemometric approach to variety classification. J. Chromatogr.A 1046 (2004) 141-146. 

Kowalski and Bender presented chemometrics (at this time called pattern recognition and roughly considered as a branch of artificial intelligence) in a broader scope as a general approach to interpret chemical data, especially by mapping multivariate data with the purposes of cluster analysis and classification (Kowalski and Bender 1972). 

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). 

Xu, Y., Zomer, S., Brereton, R. G. Crit. Rev. Anal. Chem. 34, 2006, 177-188. Support vector machines A recent method for classification in chemometrics. 

In this scenario, chemometrics provide scientists with useful tools to interpret the large amounts of data generated by these complex analytical assays and allows for quality control, classification procedures, modelling studies. 

All data obtained by these novel techniques require a very deep and multifaceted analysis, in order to check the principal and fundamentals variables and to reject the others. In this scenario, chemometrics provide scientists with useful tools to interpret the large amounts of data generated by these complex analytical assays and allows for quality control, classification procedures, modelling studies. Discrimination between different molecules available as novel drugs and molecules having no interesting biological activities is easy by means of multivariate analysis. 

Farag M.A. Wessjohann L A. Metabolome classification of commercial hypericum perforatum (StJohn s Wort) preparations via UPLC-qTOF-MS and chemometrics. PlantaMedica, 2012, 78 (5), 488-496. 

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