# Development and Optimization of a SPME-GC-FID Method for Ethanol Detection
## 3 Results
### Results Validation  
Linearity, Precision (CV), Limit of Detection (LOD), and Limit of Quantification (LOQ)  
Calibration curves were generated during the optimization of the GC method to select the most appropriate approach for ethanol detection using SPME in aqueous samples. A calibration curve was generated using the optimized fiber, NaCl concentration, extraction time, and temperature (30 \({}^{\circ}\)C, 20 min, 30% NaCl; Section 3.4), from now on called Matrix A. With the calibration curves obtained for each method, as demonstrated in Figure 7, one can evaluate the accuracy, precision, and sensitivity, and select the most appropriate approach  
Figure 6: Effect of sample matrix on the total peak area of analyzed ethanol. Ethanol concentration 10 mg/L; NaCl addition: 30%; extraction time: 20 min; extraction temperature: 30 \({}^{\circ}\)C. Matrix A—ultrapure water; Matrix B—PBS; Matrix C—sweat. Results are shown as the mean \(\pm\) SD (\(n=3\)). * \(p<0.05\) for matrix A when compared with matrix B, using the ANOVA test, followed by [PERSON]’s multiple comparisons tests. for the specific application, as shown in Table 2. Quantitative parameters, such as limit of quantification (LOD), limit of detection (LOD), and precision (CV) were calculated through calibration curve data. Sensitivity (S) is the measure of how sharply the signal changes with variations in the concentration of the target analyte. Calibration curves can be generated with the gradual increase in the concentration of the analyte to determine the sensitivity of the sensor through the slope of the curve obtained in cases where hysteresis is insignificant, as it shown in Equation (1) [28]:  
\[S=\frac{dY}{dX} \tag{1}\]  
Precision (CV) is the ability of the sensor to provide alike results every time a sample is measured, and accuracy indicates the sensor's capacity to provide a mean value close to the true value when a sample is measured more than once and can be calculated through Equation (2), where \"s\" corresponds to standard deviation and \"M\" corresponds to data \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**GC-Conditions** & **Equation** & **R2** & **LOD (mg/L)** &
\begin{tabular}{c} **LOQ** \\ **(mg/mL)** \\ \end{tabular} \\ \hline
30 \({}^{\circ}\)C\_20 min\_30\%NaCl & y = 12520.6x + & & & \\ (Matrix A) & 6790.3 & 0.99875 & 0.22 & 0.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative Parameters Analysis from the Calibration Curve presented in Figure 7.  
Figure 7: Calibration curves for different extraction conditions. Ethanol concentration: 10 mg/L; NaCl addition: 30%; extraction time: 20 min; extraction temperature: 30 \({}^{\circ}\)C.  
average. Reproducible signals provide high reliability and robustness to the inference made on the response of a biosensor [29]. \[CV\%=\frac{s}{M}\times 100 \tag{2}\]  
The detection limit (LOD) indicates the minimum concentration of analyte that a sensor can distinguish from the assay background. In several medical and environmental monitoring applications, a biosensor is required to detect analyte concentration as low as ng/mL or even fg/mL to confirm the presence of traces of analytes in a sample. The signal-noise ratio with a value of 3.3 is generally acceptable [30]. Thus, the limit of detection (LOD) may be expressed by Equation (3):  
\[LOD=\frac{3.3\times s}{S} \tag{3}\]  
Quantification of limit (LOQ) is the lowest concentration of the analyte that is possible to be determined and can be expressed by Equation (4) [30]:  
\[LQQ=\frac{10\times s}{S} \tag{4}\]  
Linearity is the attribute that shows the accuracy of the measured response to a straight line, mathematically represented as shown in Equation (4) whereas the concentration of the analyte, is the output signal, and m is the sensitivity of the biosensor. It is necessary to use enough standard solutions (minimum of five) to adequately define the relationship between concentration and response. This parameter can be obtained through the Pearson correlation index acquired through the calibration curve performed.