Study on the Detection Method of Soil-Motor Oil Contamination Combined with Genetic Algorithm Spectral Wavelength Selection

IF 0.8 4区 化学 Q4 SPECTROSCOPY Journal of Applied Spectroscopy Pub Date : 2024-09-12 DOI:10.1007/s10812-024-01803-y
Ningchao Jiang, Min Jing, Bingqi Si, Zhaonan He, Hengtong Han, Manlong Chen
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Abstract

To classify and detect the type and content of petroleum hydrocarbon contaminants in the soil surface layer, fluorescence spectrometry is commonly used. The experimental oils were selected from three common engine oils available in the market: Loxson L-CKC220 gear oil, APSIN 10W-40 engine oil and Jaguar 200 SF MA 15W-40 motorcycle oil. The fluorescence spectra of the oils were obtained using the fluorescence-induced technique, the spectral wavelengths were selected using a genetic algorithm (GA), and the detection models were constructed by combining RF (Random Forest), AdaBoost, and Gradient Enhanced Decision Tree (GBDT) regression algorithms for classification, identification, and concentration prediction analysis. The experimental results show that the average accuracy of classification and identification of gear oil, engine oil and motorcycle oil reach 83.9, 97.8, and 92.2%, respectively. Comparative analysis of the prediction results of the three concentration regression models shows that while all algorithms have high model prediction accuracy, GA combined with GBDT regression model has the best prediction performance for gear oils, engine oils and motorcycle oils, and improves the prediction accuracies by 62.7, 42.3, and 48.3% compared to the prediction accuracies of the wavelength selection without the use of GA, respectively. In summary, GA-based spectral wavelength selection combined with machine learning algorithms has high prediction accuracy and precision for the classification and prediction of motor oil contaminants in selected specific soils, and can be used as an effective detection method.

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结合遗传算法选择光谱波长的土壤机油污染检测方法研究
为了对土壤表层中石油烃污染物的类型和含量进行分类和检测,通常采用荧光光谱法。实验用油选自市场上常见的三种发动机油:Loxson L-CKC220 齿轮油、APSIN 10W-40 发动机油和 Jaguar 200 SF MA 15W-40 摩托车油。利用荧光诱导技术获得了油品的荧光光谱,利用遗传算法(GA)选择了光谱波长,并结合 RF(随机森林)、AdaBoost 和梯度增强决策树(GBDT)回归算法构建了检测模型,用于分类、识别和浓度预测分析。实验结果表明,齿轮油、发动机油和摩托车油的分类和识别平均准确率分别达到 83.9%、97.8% 和 92.2%。对三种浓度回归模型预测结果的对比分析表明,虽然所有算法都具有较高的模型预测精度,但 GA 与 GBDT 回归模型相结合对齿轮油、发动机油和摩托车油的预测性能最好,与不使用 GA 的波长选择预测精度相比,预测精度分别提高了 62.7%、42.3% 和 48.3%。综上所述,基于 GA 的光谱波长选择与机器学习算法相结合,对特定土壤中机油污染物的分类和预测具有较高的预测精度和准确度,可作为一种有效的检测方法。
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来源期刊
CiteScore
1.30
自引率
14.30%
发文量
145
审稿时长
2.5 months
期刊介绍: Journal of Applied Spectroscopy reports on many key applications of spectroscopy in chemistry, physics, metallurgy, and biology. An increasing number of papers focus on the theory of lasers, as well as the tremendous potential for the practical applications of lasers in numerous fields and industries.
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