Gasoline Adulteration Analysis Based on Raman Spectroscopy and a Multi-Scale Convolutional Neural Network

The quality and safety of gasoline are critical for energy distribution and market regulations. Consequently, rapid and nondestructive detection methods based on spectroscopy combined with intelligent algorithms have garnered significant attention. However, existing approaches often separate adulterant identification and concentration prediction, limiting their application in complex adulteration scenarios. To address these limitations, this study develops an attention-based multi-scale convolutional neural network, termed Multi-Scale Attention Fusion Convolutional Neural Network, based on Raman spectroscopy, to jointly perform adulterant classification and concentration regression within a unified framework. Four adulteration systemsgasoline blended with methanol, ethanol, kerosene, and dieselwere constructed, and 1200 Raman spectra were collected across adulteration ratios ranging from 5% to 30%. The performance of the model was systematically evaluated via ablation studies and comparisons with various backbone networks. Additionally, the proposed model was compared with multi-task deep learning models based on ResNet18 and LSTM architectures. The results indicate that the proposed model achieves more stable performance in both classification and regression tasks. In the ablation study, the removal of the Feature Pyramid Network module led to a 1.25% decrease in classification accuracy and a 29.21% increase in root mean square error, underscoring its critical role in multi-scale feature representation. Traditional single-task models were also evaluated for comparison. A Support Vector Machine, K-Nearest Neighbors, and Random Forest were applied to classification tasks, while Support Vector Regression (SVR) and Partial Least Squares Regression were used for regression. Even with optimal variable importance in projection wavelength selection, the best-performing SVR model achieved an R ² of only 0.9785. In contrast, the proposed model achieved a classification accuracy of 99.92% and an R ² exceeding 0.9886 in regression tasks, demonstrating superior qualitative and quantitative detection capability compared with conventional chemometric and single-task learning methods.