## 5 Discussion  
Meat and meat products are complex food matrices with several challenging difficulties. Their biochemical mechanism and composition are affected by breed, processing and storage. In addition to this, consumer needs for quality food with potential health effects as well as challenges in the food production industry regarding distribution and several health and quality risks lead to the need for improving the regulatory system and analytical methods used. Without a doubt, foodomic technologies are able to resolve quality, safety, processing and authenticity issues. Numerous studies have been conducted using proteomics, metabolomics and lipidomics technologies in meat and meat products. Based on the reviewed data, the implementation of such advanced methodologies together with statistical approaches can predict critical issues relating to quality, adulteration, processing and authenticity. First and foremost, it should be noted that by using omic technologies many problems can be overcome, like false positive results or limitations that take place in DNA-based and immunoassay techniques [4]. We should also consider that proteomics has been classified as untargeted or targeted, detecting biomarkers at the beginning of the research or proteins of interest for validation and implementation, respectively, with high sensitivity [6]. In addition to this, metabolomics has been also classified as targeted or untargeted, interpreting the behavior of specific groups of metabolites in connection with determined conditions and quantitatively analyzing preselected metabolites or detecting groups of metabolites, respectively, without necessarily identifying or quantifying specific metabolites [10; 11; 12; 13; 14]. So, it is clear that foodomic technologies in meat science enable us to deeply explore, understand and predict meat quality. In proteomics, different mass spectrometry technologies like GC-MS, UHPLC-MS, LESA-MS, 2 DE-MS, MALDI-TOF/TOF-MS and OFFGEL-MS have been successfully used in order to determine not only specific proteins or undeclared species in meat products and the relationship between proteomes and meat quality but also to understand proteome modifications as a consequence of meat aging [4; 5; 18; 19]. The great advantage of MALDI-TOF-MS is the rapidity of analysis due to the fast sample preparation step that avoids protein digestion, but the main disadvantage of this type of analysis due to only one stage of mass spectrometry analysis (MS1 or MS) is that only the mass of the analyte is acquired. Consequently, since the peptide or protein is not isolated and no product ion spectra are produced, no structural information is found. The comparison in this approach is made purely by pattern recognition, and since no unique biomarker is reported, an extensive validation of such an approach would be essential. Last, but not least, LC-HRMS and LC-MS/MS MRM methods are used for the detection, identification and confirmation of species-specific heat-stable peptides in processed meat products, giving us an excellent tool for distinguishing accidental contamination from intentional adulteration [20]. In metabolomics, high-resolution analytical technologies like NMR as well as MS have been successfully used in order to identify relevant metabolites by comparing database information, while NMR can provide structural information about metabolites [39; 40]. In particular, LC-MS/MS, UPLC-ESI-MS/MS, UHPLC-QTOF-MS/MS and LC-Orbitrap-MS/MS methods are used because of their high sensitivity and wide detection range [9; 41; 42; 43; 44]. The elucidation of unidentified metabolites is one of the main challenges. While significant advances have been achieved in the last decade, the databases of metabolites are still restricted and incomplete; the current metabolic findings may be only the \"tip of the iceberg\" of the whole picture of omic technologies for meat quality.