## 2 Review Methodology
### Analytical Technologies and Approaches of Omic Technologies  
Based on the analytical objectives, omic technologies can be classified into targeted and untargeted. Regarding proteomics, targeted proteomic approaches are suitable for the validation or detection of proteins of interest with high sensitivity, while untargeted approaches are well-suited from the very beginning of biomarker discovery [60]. While integrating proteomic data with other omic technologies, quality and safety of sea food will be ensured [14]. Concerning metabolomics, targeted metabolomic approaches can be used in order to decode the behavior of specific groups of metabolites in relation to defined conditions and emphasize the identification and quantification of preselected metabolites. In untargeted metabolomics, the global metabolite fingerprint is studied [61], and groups of metabolites can be detected without necessarily identifying or quantifying a specific metabolite [44]. According to [PERSON] et al. [61], untargeted metabolomics have many advantages compared to targeted metabolomics, including the combination of targeted and untargeted screening, the finding of a set of discriminatory metabolites, and the retrospective data analysis. A key technology that should be mentioned and distinguished from the aforementioned metabolomic technology is valotolomics. Volatolomics, a subfield of metabolomics, aims to identify and quantify all volatile metabolites present in a biological system [62]. During fish storage and processing, numerous metabolites like volatile organic compounds (VOCs) are produced [62,63]. Therefore, valotolomics technology brings into being the aroma fingerprint along with flavor profile [62]. Thus, the use of valotolomics provides the identification of the key aroma compounds [64], while the use of metabolomics helps to identify the flavor precursors in seafood [65]. With regard to the lipidomic technologies, targeted approaches emphasize analyzing specific lipid targets, while untargeted approaches intend to analyze the full lipid profile in samples [66]. All the above omic technologies produce tremendous amounts of data, and with the combination of chemometrics, predictive models can be established for food traceability, identification of food adulteration, authentication, safety assessment, and improvement of hazardous substance formation in food processing or food production, and in that manner, improving the food quality [61,66]. Figure 3: Most frequently used omic tools for the examination of quality and control of aquatic food products. Below you can find Tables 1 and 2 describing omic technologies for quality control of aquatic and seafood products. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Omic Technology** & **S seafood** & **Extraction Method** & **Identification Technique** & **Data Analysis** & **Results** & **Reference** \\ \hline Metabolomics & Sea cucumber (_Apostichopus_) & LC-MS & ANOVA, PCA, PLS-DA, HMDB and the LIPIDMaps database & SMZ stress treatment significantly impacted sea cucumber’s metabolism as well as disrupted balance of metabolites like L-threonine, L-tyrosine, aneuronic acid, piperine, and docosquentaenoic acid. & SMZ stress treatment significantly impacted sea cucumber’s metabolism as well as disrupted balance of metabolites like L-threonine, Leukemia, and piperine, and docosquentaenoic acid. & SMZ stress treatment significantly impacted sea cucumber’s metabolism as well as disrupted balance of metabolites like L-threonine, Leukemia, and piperine, and docosquentaenoic acid. & SMZ stress treatment significantly impacted sea cucumber’s metabolism as well as disrupted balance of metabolites like L-threonine, Leukemia, and piperine, and docosquentaenoic acid. & \\ Metabolomics & Chinook salmon (_Oncorhynchus_ _tslshuzytscha_) & LC-MS & several metabolites, including those that were important for energy generation and utilization after exposure to effluent.