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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.

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.

The present PhD thesis was focused on the development and application of chemical methodology (Py-GC-MS) and data-processing method by multivariate data analysis (chemometrics). The chromatographic and mass spectrometric data obtained with this technique are particularly suitable to be interpreted by chemometric methods such as PCA (Principal Component Analysis) as regards data exploration and SIMCA (Soft Independent Models of Class Analogy) for the classification. As a first approach, some issues related to the field of cultural heritage were discussed with a particular attention to the differentiation of binders used in pictorial field. A marker of egg tempera the phosphoric acid esterified, a pyrolysis product of lecithin, was determined using HMDS (hexamethyldisilazane) rather than the TMAH (tetramethylammonium hydroxide) as a derivatizing reagent. The validity of analytical pyrolysis as tool to characterize and classify different types of bacteria was verified. The FAMEs chromatographic profiles represent an important tool for the bacterial identification. Because of the complexity of the chromatograms, it was possible to characterize the bacteria only according to their genus, while the differentiation at the species level has been achieved by means of chemometric analysis. To perform this study, normalized areas peaks relevant to fatty acids were taken into account. Chemometric methods were applied to experimental datasets. The obtained results demonstrate the effectiveness of analytical pyrolysis and chemometric analysis for the rapid characterization of bacterial species. Application to a samples of bacterial (Pseudomonas Mendocina), fungal (Pleorotus ostreatus) and mixed- biofilms was also performed. A comparison with the chromatographic profiles established the possibility to: • Differentiate the bacterial and fungal biofilms according to the (FAMEs) profile. • Characterize the fungal biofilm by means the typical pattern of pyrolytic fragments derived fro

The present PhD thesis was focused on the development and application of chemical methodology (Py-GC-MS) and data-processing method by multivariate data analysis (chemometrics). The chromatographic and mass spectrometric data obtained with this technique are particularly suitable to be interpreted by chemometric methods such as PCA (Principal Component Analysis) as regards data exploration and SIMCA (Soft Independent Models of Class Analogy) for the classification. As a first approach, some issues related to the field of cultural heritage were discussed with a particular attention to the differentiation of binders used in pictorial field. A marker of egg tempera the phosphoric acid esterified, a pyrolysis product of lecithin, was determined using HMDS (hexamethyldisilazane) rather than the TMAH (tetramethylammonium hydroxide) as a derivatizing reagent. The validity of analytical pyrolysis as tool to characterize and classify different types of bacteria was verified. The FAMEs chromatographic profiles represent an important tool for the bacterial identification. Because of the complexity of the chromatograms, it was possible to characterize the bacteria only according to their genus, while the differentiation at the species level has been achieved by means of chemometric analysis. To perform this study, normalized areas peaks relevant to fatty acids were taken into account. Chemometric methods were applied to experimental datasets. The obtained results demonstrate the effectiveness of analytical pyrolysis and chemometric analysis for the rapid characterization of bacterial species. Application to a samples of bacterial (Pseudomonas Mendocina), fungal (Pleorotus ostreatus) and mixed- biofilms was also performed. A comparison with the chromatographic profiles established the possibility to: • Differentiate the bacterial and fungal biofilms according to the (FAMEs) profile. • Characterize the fungal biofilm by means the typical pattern of pyrolytic fragments derived fro