Abstract
Multivariate analysis has rapidly developed in the past few years. This rise is due to advances in intelligent instruments and laboratory automation as well the possibility of using powerful computers and user-friendly software. In the field of analytical chemistry, the capability of newer, mostly multicomponent or multielement analytical methods produces so many data, that only the use of mathematical and statistical techniques can provide a suitable interpretation. The aim of the present work is to develop multivariate methods for processing experimental data obtained through non-destructive techniques, in which it is possible to investigate samples without altering them. For qualitative investigation, such “direct” analytical procedures like infrared spectroscopy are available; however, the univariate approach is not exaustive in case of very complex matrices. The quantitative approach is still an open issue, due to the strong matrix effect hindering the creation of univariate calibration methods in interpolation mode. Multivariate analysis may be the solution. This thesis is organized as follows: In Section 1 the general problem of high-dimensional data is introduced, reviewing the basic principles of Principal Components and their implementation for descriptive and predictive purposes. In the last part of this section the core of the present work is discussed: advanced algorithms aimed to perform standard addition method in multivariate analysis Section 2 is dedicated to theory of the employed analytical technique: the basis of infrared spectroscopy, focusing particular attention to reflectance technique Section 3 describes the typologies of the analysed samples (marine sediments) and the reason of interest of one of their specific components (biogenic silica) In Section 4 experimental data, their computational treatment and a final discussion of results compared with other reference methods are presented.
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