# Development and Optimization of a SPME-GC-FID Method for Ethanol Detection
## 1 Introduction
This ensures that even trace amounts of ethanol in aqueous samples can be accurately quantified. The GC separation coupled with FID detection offers relatively short analysis times, which is particularly advantageous when dealing with routine analyses or situations where a rapid turnaround is essential, especially in applications such as clinical biochemistry and forensic analysis, where accuracy is paramount. The ease of method execution and reproducibility contribute to the method's practicality for routine use in different analytical settings. Furthermore, unlike some other chromatographic techniques, the SPME-GC-FID method allows for the direct analysis of ethanol without the need for derivatization, which simplifies the analytical process and reduces the potential for method-induced errors. In conclusion, the SPME/GC-FID method was specifically chosen for its ability to address the challenges associated with ethanol detection in aqueous samples. The method's selectivity, sensitivity, and quantitative accuracy make it a robust choice for applications where precise and reliable ethanol quantification is paramount. Overall, SPME-GC-FID is recognized as a reliable and robust method for detecting ethanol, with the added advantages of simplicity of sample preparation and lower sample size requirements when compared to the other analytical methods mentioned before. Researchers have made significant advancements in this field, focusing on improving the method's sensitivity, selectivity, and accuracy. The study conducted by [PERSON] et al. [16] aimed to enhance the sensitivity of ethanol detection using SPME-GC-FID by optimizing the SPME parameters, such as fiber type, extraction time, and desorption conditions [16]. They reported improved detection limits and enhanced quantification accuracy, contributing to the advancement of SPME-GC-FID for ethanol analysis [16]. Another study by [PERSON] et al. (2022) addressed the challenge of matrix effects in ethanol analysis using SPME/GC-FID. They developed a sample preparation method to minimize matrix interferences, enabling a more accurate and reliable quantification of ethanol in complex biological samples [18]. In order to advance the state of the art, further research can focus on exploring novel SPME fiber coatings with improved extraction efficiency and selectivity for ethanol. Additionally, optimizing the sample preparation and analysis conditions to minimize matrix interferences and enhance the sensitivity and precision of ethanol detection would be valuable. Furthermore, the investigation of the applicability of SPME-GC-FID for real-time or in situ monitoring of ethanol levels in biological samples could be a potential area of advancement. This could provide valuable insights into the evaluation of dynamic changes in ethanol concentrations over time, enabling a better understanding of ethanol metabolism and its effects [21; 22; 23]. Optimizing SPME-GC-FID methods for ethanol detection is important for achieving the highest level of accuracy, precision, repeatability, and sensitivity in the analysis. By considering the various parameters involved in the analysis, we aimed herein to improve the performance of SPME-GC-FID for ethanol detection, by studying different parameters like fiber type, time and temperature of extraction, and type of matrix, and to obtain reliable results for various applications. In addition to testing with water samples, tests using an artificial sweat matrix will be undertaken to replicate the ethanol content in human fluids for forensic analysis using the SPME-GC-FID technology [24]. Following the optimization of the SPME/GC-FID method, a comprehensive characterization by evaluating various quantitative and qualitative parameters, including precision, linearity, limit of detection, and limit of quantification, was performed. This information can be used to make informed decisions about the suitability of the method for a particular application and to identify areas for improvement and optimization.