Rapid Identification of Alcohol-Based Gasoline Types Using Modelling of Effective Chemical Information from Near-Infrared Spectroscopy
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摘要: 甲醇汽油和乙醇汽油是性质特性不同的两种新型清洁能源,准确识别醇基汽油种类是对于鉴定燃油品质、保证车辆安全行驶具有重要意义。近红外光谱(NIR)是识别醇基汽油种类的重要手段,而利用近红外光谱的有效特征波长建模可以摆脱干扰谱段和噪声谱段对模型识别准确度的影响,本研究使用甲醇分子和乙醇分子的近红外光谱特征谱段建立偏最小二乘法判别(partial least squares discriminant analysis, PLS-DA)模型,用于识别甲醇汽油和乙醇汽油。在相同建模条件下,将全波长光谱模型、变量重要性投影(variable importance in projection, VIP)光谱和特征谱段模型的识别成功率进行对比。结果表明,全波长光谱模型和VIP光谱模型对低含量的醇基汽油样本的识别准确率较差,分别为90%和96.7%;而最佳特征谱段模型,即通过甲醇和乙醇分子全部差异光谱信息(4500~5200+5600~7200+7900~8800)cm−1建立的模型,可准确识别体积分数为0.5%~80%的醇基汽油样本,识别成功率为100%。除最佳特征谱段模型外,其余特征谱段PLS-DA模型的识别成功率普遍优于全波长光谱模型,结果证明通过化学结构筛选特征谱段是一种高效的波长选择方法,利用该特征光谱谱段建模能够提高模型的准确度。综上,成功建立了一种基于化学信息筛选近红外光谱特征谱段的高准确率定性识别醇基汽油种类的模型,有望推广应用在其他燃油的种类识别中。Abstract: Methanol gasoline and ethanol gasoline, as two distinct types of clean energy sources, possess unique properties and characteristics. Accurate identification of these alcohol-based gasoline types is crucial for ensuring fuel quality and vehicle safety. Near-Infrared Spectroscopy (NIR) is a pivotal method for identifying alcohol-based gasoline types, and modelling based on NIR's effective characteristic wavelengths can overcome the effects of interfering and noisy spectral segments on model accuracy. This study employed the characteristic spectral bands of methanol and ethanol molecules in NIR spectra to develop a Partial Least Squares Discriminant Analysis (PLS-DA) model for distinguishing between methanol and ethanol gasoline. The success rates of full-wavelength spectral, Variable Importance in Projection (VIP) spectral, and feature spectral band models were compared under identical modelling conditions. Results indicated that the full-wavelength and VIP spectral models showed lower accuracy in identifying low-content alcohol-based gasoline samples, with success rates of 90% and 96.7% respectively. In contrast, the optimal feature spectral band model, constructed using the complete differential spectral information of methanol and ethanol molecules (4500~5200+5600~7200+7900~8800 cm−1), achieved a 100% success rate in identifying alcohol-based gasoline samples with volume fractions of 0.5% to 80%. The study demonstrates that selecting feature spectral bands based on chemical structure is an effective wavelength selection method, enhancing model accuracy significantly. In summary, this research successfully establishes a highly accurate model for the qualitative identification of alcohol-based gasoline types using selected NIR spectral bands based on chemical information, showing potential for application in other fuel type identifications.
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表 1 醇基汽油样本中醇类含量
Table 1. Alcohol content in alcohol-based gasoline samples
编号 体积分数(v/v, %) 编号 体积分数(v/v, %) 编号 体积分数(v/v, %) 编号 体积分数(v/v, %) 1 0 16 7.5 31 15 46* 25 2* 0.5 17 8 32 15.5 47 26 3 1 18* 8.5 33* 16 48 27 4 1.5 19 9 34 16.5 49* 28 5 2 20 9.5 35 17 50 29 6* 2.5 21* 10 36 17.5 51 30 7 3 22 10.5 37* 18 52 35 8 3.5 23* 11 38 18.5 53 40 9 4 24 11.5 39 19 54* 45 10* 4.5 25 12 40 19.5 55 50 11 5 26 12.5 41* 20 56 55 12 5.5 27 13 42 21 57 60 13 6 28* 13.5 43 22 58 70 14* 6.5 29 14 44 23 59* 80 15 7 30 14.5 45 24 60 90 注: 标有*的样本为预测集。 表 2 基于化学结构信息筛选的特征谱段方案
Table 2. Characteristic spectral band selection based on chemical structure information
编号 波数选择/cm−1 选择依据 1 4500~5200 O-H弯曲和伸缩振动的组合频 2 5600~6000 C-H键伸缩振动的一阶倍频 3 6000~7200 O-H键伸缩振动的一阶倍频 4 7900~8800 C-H 键对称伸缩的二阶倍频 5 4500~5200
6000~7200O-H键 6 5600~6000
7900~8800C-H键 7 4500~5200
5600~7200
7900~8800全部特征谱段 表 3 不同光谱预处理的PLS-DA模型效果
Table 3. Performance of PLS-DA models with different spectral preprocessing
预处理方法 RMSEP $ {R}_{P}^{2} $ 识别准确率 未预处理 0.1590 0.8988 87% VN+SG 1stD 0.1003 0.9597 90% SG+SG 1stD 0.1746 0.8778 87% MSC+SG 1stD 0.2054 0.8555 83% SG 1stD 0.1942 0.8645 83% -
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