# Untargeted Profiling and Differentiation of Volatiles in Varieties of Meat Using GC Orbitrap MS
## 1 Introduction  
Meat quality includes safety, nutrition, and sensory aspects, which may be largely affected by multiple factors such as heredity, breeding, and the environment [1]. With the improvement in living standards, consumers place more and more emphasis on the nutrition and flavor. Flavor consists of aroma and taste [2]. Aroma, an important part of sensory evaluation, has become one of the important determinants influencing consumers' purchasing choices.  
Chemical composition is the basis of food aroma. With the maturation of analytical techniques and rapid development of instrumentation, more and more aroma compounds and their contributions have been discovered. Constituting the unique style of aroma, aroma compositions are complex, mainly including alcohols, esters, organic acids, ketones, aldehydes, furans, pyrazines, hydrocarbons, sulfur-containing compounds [3, 4], and have very wide ranges of polarity, solubility, volatility, and thermal stability. This would bring a big challenge to the untargeted profiling of the aroma compounds. Basically, the analysis of aroma compounds includes three steps: extraction, separation, and identification.  
Headspace solid phase microextraction (HS-SPME) [3; 4; 5; 6; 7], solvent-assisted flavor evaporation (SAFE) [8], stir bar adsorption extraction (SBSE) [9], and dynamic headspace (DHS) [10] are the mainstream extraction methods. SAFE is considered as the mild solvent-extraction technique without discrimination of volatile compounds. It is usually used to profile and quantitate volatile compounds accurately, showing good potential in aroma reformulation. HS-SPME and SBSE are solvent-free micro-extraction technologies, which integrate sampling, extraction, and concentration [11]. They could directly extract analytes from complex matrices and have been widely used for differential analysis of aroma compounds. The adsorption competition existed in both SPME and SBSE. The extraction efficiencies are largely affected by coating types, extraction time, and extraction temperature [12; 13; 14; 15]. Compared with SPME, the coating in SBSE has a larger surface area and more adsorption sites [16]; thus, the adsorption capacity in SBSE could be enhanced [17; 18]. However, the full automation could not be realized in both SAFE and SBSE, which could largely confine the analysis throughput. Considering the throughput, SPME would be a promising extraction method for profiling.  
Regarding the separation of aroma compounds, gas chromatography-ion mobility spectrometry and GC-MS are mostly utilized [3; 19]. MS could give the structural information with good identification capability, making it the dominant technique in the aroma research area. The introduction of high-resolution mass spectrometry increases the mass accuracy of the fragments and greatly improves the identification accuracy [20]. In order to increase the peak capacity, two-dimensional gas chromatography [21; 22] was also introduced and could reduce the co-elution phenomenon, significantly increasing the volume of volatile compounds identified. Until now, the identification of aroma compounds has been mainly based on the MS pattern and retention index (RI) in NIST and Wiley libraries. However, RI could be slightly varied with different types of different specifications of columns, respectively. Additionally, different MS detectors could largely influence the MS patterns since orbitrap and quadrupole have different detection principles. This would affect the identification accuracy and cause false positive or false negative results when libraries are mismatched.  
Accurate aroma analysis is quite important for meat quality research and the build-up of micro-correlation between the meat sensory attributes and the aroma compounds. In this study, GC combined with high-resolution orbitrap MS and SPME was utilized to profile volatile compounds and differentiate various meat categories such as pork, beef, nutton, chicken, and duck breast with statistical analyses tools.