基于GASF-CNN的汽油中煤油含量的中红外光谱检测

Mid-Infrared Spectrum Detection of Kerosene Content in Gasoline Based on GASF-CNN

利用卷积神经网络在图像处理方面的优势,提出基于GASF-CNN的汽油掺假煤油的定量检测方法。利用竞争自适应重加权采样和连续投影算法对采集的中红外光谱进行变量选择,再通过格拉姆角和场对选择的变量进行编码,将其输入CNN进行建模。结果表明,通过CARS-SPA方法提取光谱变量,有利于提高建模质量。GASF-CNN在训练集和测试集上的均方根误差E_(RMS)分别是0.620和0.739,在训练集和测试集上的决定系数(R^(2))分别是0.988和0.983。而1D-CNN、支持向量回归和偏最小二乘法回归在训练集和测试集上的E_(RMS)分别是0.702、0.898、1.500、1.290、1.490、1.320,在训练集和测试集上的R^(2)分别是0.985、0.975、0.932、0.952、0.932、0.949。GASF-CNN结合CARS-SPA可较好地实现汽油中掺杂煤油的定量检测,为汽油掺假光谱检测提供一个新的途径。

Leveraging the potential of convolutional neural network(CNN)in image processing,a novel method based on GASF-CNN was introduced for detection kerosene content in gasoline.Competitive adaptive reweighted sampling(CARS)and successive projections algorithm(SPA)were adopted to select the key variables,and Gram-angle and field(GASF)was used to encode the selected variables,which were then input into CNN.Experimental results revealed that using variables selected by CARS-SPA enhanced the model's performance.The root mean square error(E_(RMS))of GASF-CNN on the training set and the test set was 0.620 and 0.739,respectively.The coefficient of determination(R^(2))on the training set and the test set was 0.988 and 0.983,respectively.However,the E_(RMS)on the training set and the test set of 1D-CNN,support vector regression(SVM)and partial least squares regression(PLSR)are 0.702,0.898,1.500,1.290,1.490 and 1.320,respectively;the R^(2)on the training set and test set are 0.985,0.975,0.932,0.952,0.932 and 0.949,respectively.The amalgamation of GASF-CNN and CARS-SPA allows for more precise quantitative detection of kerosene adulteration in gasoline,thereby offering a promising methodology for spectral detection of gasoline adulteration.