Soft classification techniques

Nevertheless, in most of the electronic tongue applications found in the literature, classification techniques like linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) have been used in place of more appropriate class-modeling methods. Moreover, in the few cases in which a class-modeling technique such as soft independent modeling of class analogy (SIMCA) is applied, attention is frequently focused only on its classification performance (e.g., correct classification rate). Use of such a restricted focus considerably underutilizes the significant characteristics of the class-modeling approach. 

Ciarapica, F.E. Giacchetta,G. (2009) Classification and prediction of occupational injury risk using soft computing techniques An Italian study, i t Scien 47 36-49. 

In a related paper Herrador and Gonzalez [144] described the application of PCA and CA and of two supervised techniques, LDA and back-propagated ANN on Al, Ba, Ca, Cu, K, Mg, Mn, and Zn data obtained from commercial Spanish tea samples. A minitorch ICP-AES instrument was used for the determinations. The characterization of three classes of tea was achieved. In a paper that expands previous research described in reference [47], trace metal concentrations measured by ICP-AES and ICP-MS were employed by Moreda-Pineiro et al. [145] for a more elaborated chemometric treatment on 85 samples of tea of Asian, African, commercial, and unknown origin. Seventeen elements (Al, Ba, Ca, Cd, Co, Cr, Cu, Cs, Mg, Mn, Ni, Pb, Rb, Sr, Ti, V, and Zn) were determined. In addition to the techniques employed in the already mentioned papers (PCA, CA, LDA), soft independent modeling (SIM) of class analogy was also applied. The latter method resulted in the totally correct (100 percent) classification of Chinese teas. 

Stefanowski J. The rough set based rule induction technique for classification problems. Proceedings of the 6th European Conference on Intelligent Techniques and Soft Computing EUFIT 98 1998 Sept 7-10 Aachen, p 109-113. 

When we study the structure of materials, we learn about the three basic phases of matter solids, liquids, and gases. While these definitions provide an essential scientific foundation, they fail to describe adequately the majority of familiar materials we see around us and interact with in our everyday lives, including plastics, gels, rubbers, soaps and other detergent products, paints, most foods, and most of the human body (Figure 1.1). The aim of this book is to introduce a new flavor of materials science through descriptions of the different classifications of soft materials and their structures and provide an overview of common experimental techniques and some basic theoretical ideas. 

I have divided the book into several broadly defined classifications of materials these classifications are by no means distinct, however, and there are many soft materials around that can quite happily span pairs of chapters in this book. There are also many crossovers between fields in terms of theoretical descriptions and experimental techniques. In fact, one of the most fascinating aspects of soft matter science lies in the many conceptual connections between different materials. For example, much of the chapter on surfactants could potentially be classified as part of the liquid crystals chapter, and certain topics in the biomaterials chapter could equally find a home as part of the surfactant or polymer chapters. One important concept to take away from this book is the universality of the physics we use to describe soft materials, and although scientists and students may identify with one or more of the main topics presented here, there is much overlap and flexibility in the subject. 

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