# Application of Sorbent-Based Extraction Techniques in Food Analysis
## 4 SPME in Food Analysis--Main Fields of Applications  
### Volatile Compounds  
Food matrices contain a great number of VOCs, derived from many metabolic pathways. Such chemical diversity creates difficulties in their analysis. SPME is a commonly used method for the qualitative, but also quantitative analysis of VOCs in food products. A properly optimized SPME method provides a sensitive, robust and solvent-free solution for the analysis of VOCs. SPME can give a linear response over a wide range of analyte concentrations; however, it needs to be optimized for each type of matrix separately [86]. The majority of studies used SPME to extract VOCs from the headspace; however, there is also the possibility of using SPME for direct extraction, which can be more suitable for less volatile compounds [17]. Numerous studies published in the literature showed a possibility of using SPME, mostly coupled with GC-MS for analysis of volatile markers classification of samples according to different criteria, such as geographical or botanical origin, technological processes, quality state and detection of adulteration [87; 88; 89; 90]. A great number of articles concerning SPME as a method of choice in food analysis proved the great potential of this technique. Available articles pertain to diversified foodstuffs including plant products, meat products, alcohols and various processed foods [91; 92; 93; 94]. Many applications of SPME in plants and plant-derived product analysis have been noted for the last 20 years. Volatiles and semi-volatiles play important roles in plant physiological processes, but they can also be different plant protection products, organic pollutants and other toxic substances which can enter the plants from sprays or the atmosphere. Due to growing awareness of health risks, monitoring changes in the VOCs profile is very important. Traditional pre-treatment methods are not always convenient for the analysis of unstable aroma substances, which can be easily decomposed thermally. Therefore, SPME in connection with GC meets all the criteria of a suitable technique for plants VOCs analysis [95]. The great potential of the technique lies in its ability to monitor VOCs in vivo in plants; however, it is another subject related more to the area of plant physiology than food analysis. Analysis of VOCs in the sample can be targeted or non-targeted. Targeted methods are focused on the detection of one or a few defined compounds/groups of compounds, while non-targeted analysis aims to see the maximum number of compounds in the analyzed sample. To date, most of the methods applied for the investigation of VOCs in food were targeted; however, recent advances in HRMS together with user-friendly software enabled the development of non-targeted analysis. Non-targeted analysis of food with subsequent multivariate analysis produces a food fingerprint, which can be described as molecular markers or markers representing characteristic food states or conditions. A complementary approach to metabolic fingerprints is metabolic profiling, which is focused on the analysis of specific groups of compounds, related to certain metabolic pathways [96]. Metabolic fingerprints and metabolic profiling are used in metabolomics. This term describes a study of metabolites/small molecules occurring in a biological system with the aim of analyzing as many components as possible [97]. Recently, the specific branch of metabolomics focused on food has been called foodomics [98]. Food fingerprints help to answer many questions about the authenticity or quality of food [99]. Therefore, it is important to know the VOC pattern of a given food product and the divergences occurring as a result of storage or processing. Minimal sample preparation is especially important in untargeted approaches without a predetermined hypothesis; therefore, SPME is frequently used for this purpose. However, it has to be remembered that the concentration of compounds present in the headspace does not cover their concentration in the matrix.