Breitkreitz Márcia C, Raimundo Ivo M Júnior, Rohwedder Jarbas J R, Pasquini Celio, Dantas Filho Heronides A, José Gledson E, Araújo Mário C U
Institute of Chemistry, UNICAMP, CP 6154, CEP 13084-971, Campinas, Brazil.
Analyst. 2003 Sep;128(9):1204-7. doi: 10.1039/b305265f.
A method for sulfur determination in diesel fuel employing near infrared spectroscopy, variable selection and multivariate calibration is described. The performances of principal component regression (PCR) and partial least square (PLS) chemometric methods were compared with those shown by multiple linear regression (MLR), performed after variable selection based on the genetic algorithm (GA) or the successive projection algorithm (SPA). Ninety seven diesel samples were divided into three sets (41 for calibration, 30 for internal validation and 26 for external validation), each of them covering the full range of sulfur concentrations (from 0.07 to 0.33% w/w). Transflectance measurements were performed from 850 to 1800 nm. Although principal component analysis identified the presence of three groups, PLS, PCR and MLR provided models whose predicting capabilities were independent of the diesel type. Calibration with PLS and PCR employing all the 454 wavelengths provided root mean square errors of prediction (RMSEP) of 0.036% and 0.043% for the validation set, respectively. The use of GA and SPA for variable selection provided calibration models based on 19 and 9 wavelengths, with a RMSEP of 0.031% (PLS-GA), 0.022% (MLR-SPA) and 0.034% (MLR-GA). As the ASTM 4294 method allows a reproducibility of 0.05%, it can be concluded that a method based on NIR spectroscopy and multivariate calibration can be employed for the determination of sulfur in diesel fuels. Furthermore, the selection of variables can provide more robust calibration models and SPA provided more parsimonious models than GA.
描述了一种采用近红外光谱、变量选择和多元校准测定柴油中硫含量的方法。将主成分回归（PCR）和偏最小二乘法（PLS）化学计量方法的性能与基于遗传算法（GA）或连续投影算法（SPA）进行变量选择后进行的多元线性回归（MLR）的性能进行了比较。97个柴油样品被分为三组（41个用于校准，30个用于内部验证，26个用于外部验证），每组样品涵盖了硫浓度的全范围（从0.07%到0.33% w/w）。在850至1800 nm范围内进行漫反射测量。尽管主成分分析确定存在三组，但PLS、PCR和MLR提供的模型其预测能力与柴油类型无关。使用所有454个波长进行PLS和PCR校准，验证集的预测均方根误差（RMSEP）分别为0.036%和0.043%。使用GA和SPA进行变量选择，分别基于19个和9个波长提供了校准模型，RMSEP分别为0.031%（PLS-GA）、0.022%（MLR-SPA）和0.034%（MLR-GA）。由于ASTM 4294方法的再现性为0.05%，可以得出结论，基于近红外光谱和多元校准的方法可用于测定柴油中的硫含量。此外，变量选择可以提供更稳健的校准模型，并且与GA相比，SPA提供的模型更为简约。
