**1.2 Issues related to Py-GC/MS**

Although the many advantages and applications of Py-GC/MS are evident, different authors consider some problematic aspects. The main ones are: 1) pyrolysis produces a large amount of compounds, therefore, is necessary to deal with the vast amount of information registered by the Mass Spectrometer. 2) Only one part of the compounds produced can be unambiguously identified. 3) Low availability of mass spectra in databases and reference libraries. 4) Altogether, this makes the interpretation of the results from analytical pyrolysis difficult. However, most of these problems can be solved if cheminformatics is applied to the data resulting from Py-GC/MS.

The following sections will describe the use of omics tools for the deconvolution of mass spectra, as well as the alignment and annotation of the compounds identified in the chromatograms (**Figure 1**). This process is useful to compare different samples obtained by Py-GC/MS, under the same operating conditions, even using different equipment. In addition, different multivariate methods will be described to minimize the statistical noise generated by numerous uninformative compounds (i.e., those derived from carbohydrates). Together, the use of omics tools and multivariate methods facilitate the interpretation of the results of analytical pyrolysis. The processes detailed here may also be applicable to Py-GC/MS analysis of materials other than lignocellulosics (i.e., polymers, copolymers, soil samples and organic matter). In addition, they can be applied to raw data generated by other chromatography systems coupled to mass spectrometry (i.e., GC/MS/MS, LC/MS, and LC/MS/MS), including different equipment and output formats.
