Research on Emulsified Oil Spill Detection Methods Based on
Mid-Infrared Spectroscopy Technology
LI Xin-yi1, KONG De-ming1*, NING Xiao-dong2, CUI Yao-yao3
1. School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
2. School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
3. School of Mechanical and Electrical Engineering, Shijiazhuang University, Shijiazhuang 050035, China
Abstract：Quick and accurate acquisition of information, such as emulsified oil spills' types and oil content, is of great significance for monitoring offshore oil spill pollution. Mid-infrared spectroscopy is a simple and efficient detection method that can characterize the structure information of substance molecules by the position and intensity of spectral characteristic peaks. However, applying infrared spectroscopy technology to detect emulsified oil spills has not yet yielded mature research results. Based on this, this paper selected three representative gasoline samples, 92#, 95#, and 98#, to establish an emulsified gasoline system and three representative white oil samples, 5#, 7#, and 10#, to establish an emulsified white oil system. The spectral data of emulsified oil spill samples were obtained by mid-infrared spectroscopy, and pretreatment was carried out. Then, a Linear Discriminant Analysis (LDA) algorithm was used to identify oil species from emulsified oil spills. Based on this, the Competitive Adaptive Reweighted Sampling (CARS) and Random Forest (RF) methods were used to select the feature wavelengths with linear and non-linear relationships with oil content, respectively. This reduces data dimensionality and enriches the diversity of feature data. Then, use eXtreme Gradient Boosting (XGBoost), 1D Convolutional Neural Network (1D-CNN), Support Vector Regression (SVR) as the base learners, and Partial Least Squares Regression (PLSR) as the meta-learner to build a two-layer Stacking integrated learning model to predict the oil content in emulsified oil spills. The test set determination coefficients of emulsified gasoline and emulsified white oil obtained in the Stacking integrated learning model were 0.982 4 and 0.987 3, respectively, and the root mean square errors were 0.041 0 and 0.034 0, respectively. Compared to XGBoost, 1D-CNN, SVR, and PLSR, the Stacking integrated learning model has better stability and accuracy. The above research results indicate that the detection method based on mid-infrared spectroscopy technology combined with LDA and Stacking integrated learning can effectively achieve qualitative and quantitative analysis of emulsified oil spills, providing new ideas for research in emulsified oil spills.
李心怡，孔德明，宁晓东，崔耀耀. 中红外光谱技术的乳化溢油检测方法研究[J]. 光谱学与光谱分析, 2025, 45(03): 631-636.
LI Xin-yi, KONG De-ming, NING Xiao-dong, CUI Yao-yao. Research on Emulsified Oil Spill Detection Methods Based on
Mid-Infrared Spectroscopy Technology. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(03): 631-636.
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