**6. Conclusion**

A final word should also be addressed to spectral libraries. Commercial spectral libraries are becoming increasingly more complete and specific, making GC/MS one of the most used techniques for routine identifications. However, several compounds are not yet described in library databases and, in spite of better algorithmic calculations, databases are only reliable for target analysis, or when the compounds under study are known, and already characterized with a known mass spectra. Additionally, the full separation of peaks to ensure clean mass spectra, in order to achieve a reliable peak analyte confirmation, is still a necessary goal.

Until now most of the analytical systems used to analyse olive oil volatile compounds are performed in 1D-GC systems with polar or apolar column phases. Since olive oil volatile fraction is very complex, frequent co-elutions occur. Mass spectra obtained are, consequently, not pure, which should preclude the possibility to compare the spectra obtained with the, claimed pure, spectra in the databases. However, tentative identifications are reported in the literature, and it is not rare that some inconsistencies occur, even when linear retention indices LRIs are presented. Because of their nature, the LRIs obtained in apolar columns are more reliable. Nevertheless, a better separation is obtained in 1D-GC systems when polar stationary phases are used, because of the wide chemical variety

and sometimes is the major fragment of the spectra. Thus, molecular weight determination of an analyte becomes possible. Other soft ionization techniques are field ionization (FI) and field desorption (FD). Both produce abundant molecular ions with minimal fragmentation (Herbert and Johnstone, 2003). FI and FD are appliable to volatile and thermally stable samples (Niessen, 2001; Dass, 2007). If high resolution mass analysers are coupled with these ionization techniques, high capability of identification can be achieved. Together with GC × GC a potentially new tool in olive oil compound identification is reachable and

The application of a multimolecular marker approach to fingerprint allows, in an easy way, the identification of certain sample characteristics. Chromatographic profiles can be processed as continuous and non-specific signals through multivariate analysis techniques. This allow to select and identify the most discriminant volatile marker compounds (Pizarro et al., 2011). The quantity and variety of information, provided by two-dimensional-GC (2D-GC) systems, promoted the increasingly application of chemometrics in order to achieve data interpretation in a usefull and, potentially, easy way. Linear discriminant analysis (LDA) and artificial neural networks (ANN), among other statistical classification methods, can be applied in order to control economic fraud. These applications have been carefully reviewed recently (Cajka et al., 2010). Together with 2D-GC systems the advantage is clear, since, instead of a time consuming trial to determine which variables should be considered for the statistical classification method, the selection may now become as simple as inspecting the 2D contour plots obtained (Cardeal et al 2008, de Koning et al., 2008). Also the use of statistical image treatment, of 2D-GC generated contour plots, can be applied for fingerprint recognitions, precluding the alignment of the contour plots obtained, which already allowed the identification of varieties as well as extraction technologies used to

A final word should also be addressed to spectral libraries. Commercial spectral libraries are becoming increasingly more complete and specific, making GC/MS one of the most used techniques for routine identifications. However, several compounds are not yet described in library databases and, in spite of better algorithmic calculations, databases are only reliable for target analysis, or when the compounds under study are known, and already characterized with a known mass spectra. Additionally, the full separation of peaks to ensure clean mass spectra, in order to achieve a reliable peak analyte confirmation, is still a

Until now most of the analytical systems used to analyse olive oil volatile compounds are performed in 1D-GC systems with polar or apolar column phases. Since olive oil volatile fraction is very complex, frequent co-elutions occur. Mass spectra obtained are, consequently, not pure, which should preclude the possibility to compare the spectra obtained with the, claimed pure, spectra in the databases. However, tentative identifications are reported in the literature, and it is not rare that some inconsistencies occur, even when linear retention indices LRIs are presented. Because of their nature, the LRIs obtained in apolar columns are more reliable. Nevertheless, a better separation is obtained in 1D-GC systems when polar stationary phases are used, because of the wide chemical variety

produce high quality Portuguese olive oils (Vaz Freire et al., 2009).

desirable.

**6. Conclusion** 

necessary goal.

comprised in the volatile fraction of olive oils. Unfortunately, these columns present a high variability, at least, among different purchasers, which do not facilitate LRIs comparison with literature data. Multidimensional techniques, hyphenated with mass-spectrometry, are now fullfiling this gap also in the separation of optical active compounds, when chiral column phases are used. Clean mass spectra together with compound LRIs in polar, apolar and chiral column phases represents an improved tool in compound identification and thus in olive oil matrices characterization. LRIs considering probability regions in the 2D resulting plot of a GC × GC experiment (with different column set combinations, e.g. polar × apolar, polar × chiral, etc.), can enable comparing standard compounds with the sample compounds retention indices and thus a more reliable peak identification can be achieved, if mass spectrometric data are simultaneously recorded. In the future, for 2D systems, more comprehensive mass spectral libraries should include retention index probability regions for different column sets in order to allow correlation of the results obtained in the used systems with spectral matches and literature LRIs.

#### **7. Acknowledgment**

Authors wish to thank Fundação para a Ciência e Tecnologia, Ministério da Ciência, Tecnologia e Ensino Superior and Programa Operacional Ciência e Inovação for financial support (Projects PTDC/AGR-AAM/103377/2008 and PTDC/QUI-QUI/100672/2008).

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**3** 

*Italy* 

**Optical Absorption Spectroscopy for** 

*2CNR – Istituto per la Valorizzazione del Legno e delle Specie Arboree* 

Anna Grazia Mignani1, Leonardo Ciaccheri1\*, Andrea Azelio Mencaglia1 and Antonio Cimato2 *1CNR – Istituto di Fisica Applicata "Nello Carrara"* 

**Quality Assessment of Extra Virgin Olive Oil** 

Light travels through space in the form of electromagnetic waves of different wavelengths. The entire wavelength range represents the electromagnetic spectrum. Spectroscopy studies the interaction between light and matter, in order to draw information about the chemical composition inside (Lee et al., 2011). Figure 1 shows the various bands of the electromagnetic spectrum. This chapter refers to measurements performed in the 200-2500 nm band, which is usually subdivided into three portions: the ultraviolet (UV), the visible (VIS) – perceivable by human eyes – and the near-infrared (NIR). They correspond to the

200-400 nm, 400-780 nm, and 780-2500 nm ranges, respectively.

Fig. 1. The electromagnetic spectrum

Corresponding Author

 \*

**1. Introduction** 

