On-line remote prediction of gasoline properties by combined optical methods

Nondestructive quantitative analysis of erythromycin ethylsuccinate powder drug via short-wave near-infrared spectroscopy combined with radial basis function neural networks

A new assay method for the nondestructive determination of erythromycin ethylsuccinate powder drug via short-wave near-infrared spectroscopy (NIR) combined with radial basis function (RBF) neural networks is investigated. The modern near-infrared spectroscopy analysis technique is efficient, simple and nondestructive, which has been used in chemical analysis in diverse fields. Short-wave NIR is a more rapid, flexible, and cost-effective method to control product concentration in pharmaceutical industry. The RBF neural networks are local approximation networks that have superiorities in function approximation and learning speed. In addition, the structure of RBF networks is simple. Estimate and calibration of the sample concentration via short-wave NIR are made with the aid of RBF models based on conventional spectra, standard normal variate (SNV), multiplicative scatter correction (MSC) and the first-derivative spectra. Various optimum models of them are established and compared. Experiment results show that the models of SNV spectra can give better performance, and the optimized RBF neural network model after SNV treatment were given, by which the root-mean-square-errors (RMSE) for calibration set and test set were 0.3266% and 0.5244%, respectively and the correlation coefficients (R) for calibration set and test set were 0.9942 and 0.9852, respectively. The proposed RBF method based on short-wave NIR is more valuable and economical for quantitative analysis than traditional methods such as partial least squares (PLS).

Spectral range optimization for the near-infrared quantitative analysis of petrochemical and petroleum products: naphtha and gasoline

The proper selection of the spectral range in partial least squares (PLS) calibration is critical when highly overlapping spectra from compositionally complex samples are used, such as naphtha and gasoline. In particular, the relevant spectral information related to a given property is frequently localized in a narrow range, and the most selective region may be difficult to locate. We have presented the importance of range optimization in near-infrared (NIR) spectroscopy for the analyses of petrochemical and petroleum products that are generally highly complex in composition. For this purpose, the determination of a detailed compositional analysis (so called PIONA) and the distillation temperature of naphtha were evaluated. In the same fashion, the research octane number (RON) and Reid vapor pressure (RVP) were selected for gasoline. By optimizing the range using moving window (MW) PLS, the overall calibration performance was improved by finding the optimal spectral range for each property. In particular, for a detailed compositional analysis of naphtha, it was effective to search for localized spectral information in a relatively narrow range with fewer factors.

Moment combined partial least squares (MC-PLS) as an improved quantitative calibration method: application to the analyses of petroleum and petrochemical products

A new quantitative calibration algorithm, called "Moment Combined Partial Least Squares (MC-PLS)", which combines the moment of spectrum and conventional PLS was proposed. Its calibration performance was evaluated for the analyses of three import petroleum and petrochemical products: gasoline, naphtha and polyol samples. The selected properties for these products included the research octane number (RON) and Reid vapor pressure (RVP) for gasoline, the distillation temperature at 10% (D 10%) for naphtha and the hydroxyl (OH) number for polyol. The major concept presented here used the moment to find the closest spectrum of a sample in a given dataset, and generate the difference spectrum and the corresponding difference in the property. These difference spectra and property differences were then used for PLS calibration. The moment has been employed in spectroscopic fields as a simple and effective "spectral feature characteristic" using just a few scalar values (moments). MC-PLS showed improved prediction performance over PLS for each case. In MC-PLS, the difference spectra generated using the moments were used as explained; therefore, additional detail in spectral variations can be utilized for calibrations. Additionally, the difference in the property was employed as reference data, so that its variation range was smaller when compared with that of the original property. Consequently, the MC-PLS performance could be better since the feature-enhanced spectra were used to model a narrower range of property variations. In the case of the D 10% prediction for naphtha, a non-linear prediction pattern that occurred in conventional PLS was effectively corrected using the MC-PLS method.

Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration

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.