Ethanol poses a significant threat to driver safety, as its effects vary with blood alcohol concentration (BAC). Common methods for estimating BAC include breath alcohol analysis, which calculates BAC from the alcohol concentration in exhaled breath, and direct blood sampling. However, these methods have notable limitations. This study aims to classify alcohol concentration using non-invasive optical signal data obtained from biomimetic samples with varying alcohol levels. To replicate the high scattering characteristics of biological tissue, scattering effects were induced in the samples, and absorbance was measured using near-infrared (NIR) wavelengths, which penetrate biological tissue more deeply. A Random Forest (RF) model was trained using the measured absorbance values to classify alcohol concentration levels. The Area Under the ROC Curve (AUC) for each concentration level indicated effective model learning, and the classification results on the test set demonstrated statistically significant accuracy. These findings suggest that the RF model can classify alcohol concentrations non-invasively and without the loss of samples. Furthermore, incorporating additional optical properties beyond absorbance may improve the accuracy of future non-invasive alcohol concentration classification models.
