# Multi-Omics Approaches to Study Molecular Mechanisms in _Camanabis sativa_
## 2 Studying the Metabolomic Profile of Cannabis
### Cannabis Metabolite Profiling Techniques
Furthermore, these cultivars showed a maximum CBDA yield when the seed completed its maturation. Besides cannabinoids, terpen are another important compound class in cannabis. They are usually analysed by GC, coupled with various detectors, such as Flame Ionization Detection (FID), which is the most commonly applied due to its low cost and ease of use. A study based on this platform allowed the classification of 13 cannabis cultivars based on their terpenoid profile [50]. Specifically, results highlighted how some cultivars fit into one or more specific chemotypes, whereas in other cultivars this association is not so clear-cut [50]. More recently, by taking a combined analysis approach using both a LC-diode array detector (LC-DAD) and GC-FID technologies, further classifications based on cannabinoid and terpenoid contents were proposed [43]. The results confirmed how the chemical composition is specific for each cultivar. Depending on analytical aims, other extraction protocols can be applied to analyse cannabis metabolites, including supercritical fluid extraction (SFE), an extraction method using supercritical CO\({}_{2}\) which is less expensive and more effective compared to chemical solvents, and represents a valid alternative to classic extraction systems [51]. For instance, sequential SFE and solid phase extraction (SPE) processes allowed THC to be extracted at a purity level suitable for quality control, where SPE was used as a purification technique for THC [52]. Furthermore, triple-quadrupole mass spectrometry techniques, known as QQQ and based on tandem MS, in which the first and third quadrupoles act as mass filters and the second fragments the chemical component, have also been applied to cannabis [53]. Due to its improved selectivity and sensitivity, this method was proven to be highly effective in quantitative cannabis metabolite analyses [53]. Therefore, it would also be possible to use this method to evaluate the abundance of cannabinoids in different parts of the crop, which may not be detected with other approaches given their low abundance [54]. Another versatile method increasingly used for the detection of analytes in complex matrices is the nuclear magnetic resonance spectroscopy (NMR) [55]. This technique is characterized by a low sensitivity compared to MS, even if it provides more reliable metabolite structure and does not require destructive sample preparation [56]. Furthermore, NMR allows simultaneous identification of multiple analytes. Despite these potential advantages, NMR has only been rarely applied in the detection of cannabinoids: as far as we know, it was mainly used to the authentication of hemp varieties [57; 58]. In cannabis, due to the complex metabolome, the combination of several analytical methods usually gives the most comprehensive picture [2]. For instance, LC/QQQ/MSand NMR metabolomics analyses revealed the presence of several cannabinoids detected in extracts of cells of capitate-sessile and capitate-stalked trichomes as well [59]. Extracting and analysing the chemical profile of specific trichome lines holds great potential for use in future multi-omics experiments, as transcriptomic analyses could also be performed on these specific cell types [33]. The relationship between compound profile within these cells and their relative gene expression of cannabinoid biosynthetic genes could yield exciting new insights. Another powerful technique used for the analysis of metabolites in cannabis is High-resolution mass spectrometry (HRMS), this technique enables a more precise identification of compounds with the same nominal mass due to the improvement in the calculated mass to charge ratio (\(m/z\)) to several decimal places compared to conventional MS. The use of this technique was found to have a great potential in the definition of cannabis chemovars [60]. Indeed, a recent study employed this method [61]: by using data from 20 varieties of _C. sativa_ and a combined LC-HRMS platform, metabolites were mapped and annotated, and cannabis characteristic markers identified.