# Development and Optimization of a SPME-GC-FID Method for Ethanol Detection
## 1 Introduction  
The detection of ethanol in biological samples is essential in various fields, including medical, forensic, traffic safety, and even in the environment and the food industry. Ethanol is a psychoactive substance consumed worldwide, and its excessive consumption can lead to severe health damage as well as dangerous and illegal behaviors such as driving under the influence of alcohol. Therefore, the accurate and reliable detection of ethanol in biological samples is crucial to assess ethanol exposure and consumption and support criminal investigations. For example, low concentrations of ethanol or its metabolites in animal or plant tissues can be used as biomarkers of exposure or toxicity to ethanol-containing substances in the environment [1; 2]. In the food and beverage industry, ethanol detection is important to monitor the fermentation process in the production of wine, beer, and other fermented products. For example, low levels of ethanol in fruit juices or soft drinks may indicate fermentation or spoilage, while low levels of ethanol in alcoholic beverages could affect their taste, aroma, or alcohol content [3; 4]. In toxicology, the concentration of ethanol in biological samples, such as blood, urine, and even sweat, is required to establish the extent of alcohol impairment in cases of driving under the influence of alcohol. Even small amounts of ethanol found in these biological fluids may be relevant to determine if the person was under the influence of alcohol at the time of an accident, a crime, or a drug test [5; 6]. Finally, in medical diagnostics, theconcentration of ethanol in the blood may reflect alcohol-related diseases, such as liver cirrhosis, and monitor the effectiveness of treatments [5; 7; 8]. Several methods have been developed for the detection of ethanol in biological samples, including liquid chromatography (HPLC), near-infrared spectroscopy (NIR), nuclear magnetic resonance spectroscopy (NMR), gas chromatography (GC), mass spectrometry (MS), and sensor technologies [1; 2; 3; 4; 5]. Each technique has advantages and limitations, and the method used is determined by the sample type, detection limit, and specificity needed. HPLC is a popular technique for detecting ethanol, although it requires sample derivatization before analysis. The derivatization process can be time-consuming and may affect the accuracy of the results. A study by [PERSON] et al. [7] reported that sample derivatization before HPLC analysis for ethanol detection in human blood samples can lead to inaccurate and unreliable results due to the instability of the derivativizing agent [7]. NIR is a non-invasive technique for ethanol detection that has been applied to saliva, blood, and breath samples. However, the accuracy of NIR analysis is often affected by the interference of other compounds present in the sample. In a study by [PERSON]. [8], it was observed that ethanol analysis in saliva samples by NIR presented limitations due to interference from other compounds present in the sample [8; 9]. NIR is a non-destructive technique that does not require sample derivatization, but it has limited sensitivity for ethanol detection [10; 11]. MS is a highly sensitive technique that can provide detailed information about the molecular structure of the analyte, but it requires expensive equipment and expertise [12; 13]. SPME (solid-phase microextraction) GC (gas chromatography) FID (flame ionization detection) is a widely used analytical technique for the detection of volatile organic compounds, including ethanol [3; 14; 15; 16; 17]. This method is highly sensitive and selective, making it reliable for detecting low concentrations of ethanol in a wide range of sample matrices, such as air, water, and biological fluids. The SPME component of the method allows for the extraction and preconcentration of the target analyte, which is then introduced into the GC for separation and detection by the FID. It is a promising technique for ethanol detection in biological samples without the need for derivatization.