Identification of gasoline adulteration using comprehensive two-dimensional gas chromatography combined to multivariate data processing

A method to detect potential adulteration of commercial gasoline (Type C gasoline, available in Brazil and containing 25% (v/v) ethanol) is presented here. Comprehensive two-dimensional gas chromatography with flame ionization detection (GCxGC-FID) data and multivariate calibration (multi-way partial least squares regression, N-PLS) were combined to obtain regression models correlating the concentration of gasoline on samples from chromatographic data. Blends of gasoline and white spirit, kerosene and paint thinner (adopted as model adulterants) were used for calibration; the regression models were evaluated using samples of Type C gasoline spiked with these solvents, as well as with ethanol. The method was also checked with real samples collected from gas stations and analyzed using the official method. The root mean square error of prediction (RMSEP) for gasoline concentrations on test samples calculated using the regression model ranged from 3.3% (v/v) to 8.2% (v/v), depending on the composition of the blends; in addition, the results for the real samples agree with the official method. These observations suggest that GCxGC-FID and N-PLS can be an alternative for routine monitoring of fuel adulteration, as well as to solve several other similar analytical problems where mixtures should be detected and quantified as single species in complex samples.     

Characterization of Gasoline by 1H Nuclear Magnetic Resonance and Chemometrics

Automotive fuel adulteration is an old and significant problem. One common type of fuel adulteration is the addition of diesel to gasoline. Unsupervised models were developed through hierarchical cluster and principal component analysis models. Supervised models through partial least square discriminant analysis using ¹H nuclear magnetic resonance spectra as the input were used to classify samples as adulterated or unadulterated. Quantitative models were developed using partial least squares to determine the gasoline and diesel concentrations in the samples. This set contained samples composed of pure gasoline and anhydrous ethanol reproducing commercial gasoline and other samples treated with diesel. Hierarchical cluster and principal component analysis did not distinguish between adulterated and unadulterated samples except for the most adulterated materials. However, partial least square discriminant analysis classified 100% of the samples correctly. The partial least square algorithm provided excellent regression models for the gasoline and diesel content. The determination coefficient was 0.9920 for both models, whereas the root mean square error of cross-validation and root mean square error of prediction for the diesel model were 2.32 and 1.42%, respectively, and 2.40 and 1.38% for the gasoline model.     

Determination of Basic Nitrogen in Residues of Crude Oil Distillation Using ATR-FTIR and Chemometric Methods

A method for basic nitrogen determination in residues of crude oil distillation using infrared spectroscopy and chemometrics algorithms was developed. Interval partial least squares, synergy interval partial least squares, and backward interval partial least squares were evaluated for calibration model construction. The samples were divided into a calibration and prediction set containing 40 and 15 samples, respectively. The first derivative with a Savitzky-Golay filter and the mean centered data showed the best results and were used in all calibration models. The backward interval partial least squares algorithm with spectra divided in 60 intervals and combinations of 4 intervals (1407 to 1372; 1117 to 1082; 971 to 936; 914 to 879 cm^?1) showed the best root mean square error of prediction of 0.016 wt%. This calibration model displayed a suitable correlation coefficient between reference and predicted values.     

土壤总氮近红外光谱分析的波段优选

利用移动窗口偏最小二乘( MWPLS)和Savitzky-Golay(SG)平滑方法优选土壤总氮的近红外(NIR)光谱分析模型.从全部97个土壤样品中随机选出35个样品作为检验集;基于偏最小二乘交叉检验预测偏差(PLSPB),将余下62个样品划分为具有相似性的建模定标集(37个样品)、建模预测集(25个样品).最优波段为1692～2138 nm,SG平滑的导数阶数(OD)、多项式次数(DP)、平滑点数(NSP)分别为0,6,69,PLS因子数为11,建模预测均方根偏差(M-RMSEP)、建模预测相关系数(M-Rp)分别为0.015％,0.931,检验预测均方根偏差(V-RM-SEP)、检验预测相关系数(V-RP)分别为0.018％,0.882.其结果可为设计专用NIR仪器提供有价值的参考.     

Simultaneous determination of methanol and ethanol in gasoline using NIR spectroscopy: effect of gasoline composition

Near infrared (NIR) spectroscopy was employed for simultaneous determination of methanol and ethanol contents in gasoline. Spectra were collected in the range from 714 to 2500 nm and were used to construct quantitative models based on partial least squares (PLS) regression. Samples were prepared in the laboratory and the PLS regression models were developed using the spectral range from 1105 to 1682 nm, showing a root mean square error of prediction (RMSEP) of 0.28% (v/v) for ethanol for both PLS-1 and PLS-2 models and of 0.31 and 0.32% (v/v) for methanol for the PLS-1 and PLS-2 models, respectively. A RMSEP of 0.83% (v/v) was obtained for commercial samples. The effect of the gasoline composition was investigated, it being verified that some solvents, such as toluene and o-xylene, interfere in ethanol content prediction, while isooctane, o-xylene, m-xylene and p-xylene interfere in the methanol content prediction. Other spectral ranges were investigated and the range 1449-1611 nm showed the best results.     

应用遗传算法和PLS的近红外光谱预测玉米中淀粉含量的研究

以普通玉米籽粒为试验材料,在应用遗传算法结合偏最小二乘回归法对近红外光谱数据进行特征波长选择的基础上,应用偏最小二乘回归法建立了特征波长测定玉米籽粒中淀粉含量的校正模型．试验结果表明,基于11个特征波长所建立的校正模型,其校正误差（RMSEC）、交叉检验误差（RMSECV）和预测误差（RMSEP）分别为0.30％、0.35％和0.27％,校正数据集和独立的检验数据集的预测值与实际测定值之间的相关系数分别达到0.9279和0.9390,与全光谱数据所建立的预测模型相比,在预测精度上均有所改善,表明应用遗传算法和PLS进行光谱特征选择,能获得更简单和更好的模型,为玉米籽粒中淀粉含量的近红外测定和红外光谱数据的处理提供了新的方法与途径．     

偏最小二乘紫外分光光度法同时测定丁烯二酸的顺反异构体

将偏最小二乘法用于紫外分光光度分析,在pH=1.4的磷酸溶液中,同时测定了丁烯二酸的顺、反异构体。确定了测定的最佳波长范围为190～268nm;测得23个混合标样的吸光度值用于建立模型,顺、反丁烯二酸的浓度范围为3.0～14.0mg/L和1.0～13.0mg/L。所建立的测定二者模型的相关系数分别为0.9951和0.9983;平均回收率分别为100.8%和100.7%;均方根误差(RMSE)分别为0.3667和0.2233;预测相对误差(REP)分别为5.05%和3.49%。对3个批次反丁烯二酸样品的测定结果与高效液相色谱法的测定结果进行比较,经成对t检验表明,两种方法的测定结果无显著性差异。     

Determination of Fipronil in Acetamiprid Formulation by Attenuated Total Reflectance-Mid-Infrared Spectroscopy Combined with Partial Least Squares Regression

There is a high demand for rapid determination of fipronil in pesticide preparations because it has been restricted and even prohibited in many countries. An infrared-based methodology was developed for this analyte in acetamiprid formulations by attenuated total reflectance mid-infrared spectroscopy. The quantitative calibration models of fipronil were established by partial least squares regression. The determination coefficients (R²) of the model were above 0.99 while both the root mean square error of prediction and root mean square error of calibration were below 0.0011, which showed the partial least squares model accurately predicted fipronil concentrations in acetamiprid. The accuracy was further demonstrated by comparison with another two models' results of low (<1.0%, w/w) and high concentration sample sets (1.0%–4.5%, w/w). These results demonstrate the potential of infrared spectroscopy to quickly detect fipronil in acetamiprid.     

Genetic Algorithm Applied to Selection of Wavelength in Partial Least Squares for Simultaneous Spectrophotometric Determination of Nitrophenol Isomers

Abstract

Ternary mixtures of nitrophenol isomers have been simultaneously determined in synthetic and real matrix by application of genetic algorithm and partial least squares model. All factors affecting the sensitivity were optimized and the linear dynamic range for determination of nitrophenol isomers found. The simultaneous determination of nitrophenol mixtures by using spectrophotometric methods is a difficult problem, due to spectral interferences. The partial least squares modeling was used for the multivariate calibration of the spectrophotometric data. A genetic algorithm is a suitable method for selecting wavelength for PLS calibration of mixtures with almost identical spectra without loss prediction capacity. The experimental calibration matrix was designed by measuring the absorbance over the range 300–520 nm for 21 samples of 1–20 µg mL^?1, 1–20 µg mL^?1, and 1–10 µg mL^?1 of m‐nitrophenol, o‐nitrophenol, and p‐nitrophenol, respectively. The root mean square error of prediction for m‐nitrophenol, o‐nitrophenol, and p‐nitrophenol with genetic algorithms and without genetic algorithms were 0.3732, 0.5997, 0.3181 and 0.7309, 0.9961, 1.0055, respectively. The proposed method was successfully applied for the determination of m‐nitrophenol, o‐nitrophenol, and p‐nitrophenol in synthetic and water samples.     

Characterization of a Stable Adaptive Calibration Model Using Near-Infrared Spectroscopy and Partial Least Squares with a Kalman Filter

The calibration model of near-infrared (NIR) spectra established using the Kalman filter-partial least square (partial least squares combined with a Kalman filter) method can be adapted to outdated equipment, environmental changes, external samples, and other applications. However, the variance of the measurement noise estimation for NIR spectrum measurements cannot be easily obtained using Kalman filter-partial least squares; therefore, the variance in the measurement noise is often assumed to be zero for the Kalman filter-partial least square calibration model, which affects the stability of the model. In this study, the measured input and output data were used effectively, and the gamma test method for estimating the measurement noise variance was used to improve the stability of the Kalman filter-partial least square calibration model. First, an accurate estimation of the measurement noise variance was obtained, and accurate modeling was then performed using Kalman filter-partial least squares. Finally, 600 abandoned drilling fluid samples were used to confirm the validity of the proposed method. The Kalman filter-partial least square and gamma test-Kalman filter-partial least square methods are compared. Testing of external samples 401–600 demonstrated that the stability of the Kalman filter-partial least square model decreased. The root mean square error of the prediction of the Kalman filter-partial least square model was 27.135, which was worse than that of the gamma test-Kalman filter-partial least square model (20.307). The validation results show that the proposed method has better stability in tracking the evolution of the NIR spectrometer’s measurement state.     

Model selection for partial least squares calibration and implications for analysis of atmospheric organic aerosol samples with mid‐infrared spectroscopy

In developing partial least squares calibration models, selecting the number of latent variables used for their construction to minimize both model bias and model variance remains a challenge. Several metrics exist for incorporating these trade‐offs, but the cost of model parsimony and the potential for underfitting on achievable prediction errors are difficult to anticipate. We propose a metric that penalizes growing model variance against decreasing bias as additional latent variables are added. The magnitude of the penalty is scaled by a user‐defined parameter that is formulated to provide a constraint on the fractional increase in root mean square error of cross‐validation (RMSECV) when selecting a parsimonious model over the conventional minimum RMSECV solution. We evaluate this approach for quantification of four organic functional groups using 238 laboratory standards and 750 complex atmospheric organic aerosol mixtures with mid‐infrared spectroscopy. Parametric variation of this penalty demonstrates that increase in prediction errors due to underfitting is bounded by the magnitude of the penalty for samples similar to laboratory standards used for model training and validation. Imposing an ensemble of penalties corresponding to a 0–30% allowable increase in RMSECV through sum of ranking differences leads to the selection of a model that increases the actual RMSECV up to 20% for laboratory standards but achieves an 85% reduction in the mean error in predicted concentrations for environmental mixtures. Partial least squares models developed with laboratory mixtures can provide useful predictions in complex environmental samples, but may benefit from protection against overfitting. © 2015 The Authors. Journal of Chemometrics published by John Wiley & Sons Ltd.     

近红外光谱技术定量分析连作滁菊土壤中的阿魏酸含量

应用近红外光谱(NIRS)技术定量分析连作滁菊土壤样品中阿魏酸的含量.通过标准杠杆值、学生残差和马氏距离判断异常光谱,经二阶导数和Norris平滑滤噪预处理后,在6000~4000 cm-1范围,最佳因子数为7,采用偏最小二乘法(PLS)构建数学模型.结果表明,模型校正集和验证集与高效液相色谱仪(HPLC)测定的参考值之间均呈现良好相关关系,校正相关系数Rc为0.9914,交叉验证相关系数Rcv为0.9935,校正集误差均方根(RMSEC)为0.484,预测误差均方根(RMSEP)为0.539,交叉验证误差均方根(RMSECV)为0.615.研究结果表明,NIRS分析技术能够实现连作土壤中阿魏酸的快速检测,结果准确可靠.     

基于组合偏最小二乘的特征波段优选方法在氨、碱化处理玉米秸秆粗蛋白检测中的研究

该文构建了玉米秸秆粗蛋白定量分析模型,并对光谱特征波段选取方法进行探讨及验证。首先对107个样本进行预处理,剔除两个异常样本后采用DB2小波缺省阈值4层分解方式进行光谱重构,预处理后粗蛋白模型交互验证决定系数R²CV从0.788 9提高至0.920 8,采用间隔偏最小二乘(IPLS)及其改进型方法后向区间间隔偏最小二乘(BIPLS)、组合间隔偏最小二乘(SIPLS)进行特征波段选取,并对比主成分分析、竞争性自适应重加权采样法、相关系数法、遗传算法、移动窗口最小二乘等结果,发现基于IPLS及其改进型BIPLS、SIPLS均可有效、准确定位特征波段区间,其中采用SIPLS 30波段间隔在10 128～10 398 cm^-1与11 196～11 462 cm^-1时具有最优模型,验证集相关系数(r_p)为0.978 4,验正集决定系数(R²P)为0.957 2,验正集均方误差根(RMSEP)为0.221 1,相比于其他波段选取方法表现出较好的实时准确性,该方法可为玉米秸秆氨碱化最优条件判...     

Characterization of Paris polyphylla var. yunnanensis by Infrared and Ultraviolet Spectroscopies with Chemometric Data Fusion

Paris polyphylla var. yunnanensis has been used for its anti-tumor, anthelmintic, and hemostatic properties. In this investigation, Fourier transform infrared and ultraviolet spectroscopy combined with chemometrics were used for qualitative analysis of P. polyphylla var. yunnanensis from different geographical origins in Yunnan Province. A total of 82 samples for each region were divided into 57 in the calibration set and 25 in the validation set by Kennard–Stone algorithm. Support vector machine and partial least square discrimination on the basis of Fourier transform infrared, ultraviolet, and low- and mid-level data fusion were investigated. Different pretreatments were compared for the appropriate model. The results indicated that the combination of Savitzky–Golay (11 points), second derivative, and standard normal variation has the best performance for support vector machine and partial least square discrimination with the lowest root mean square error of estimation and root mean square error of cross validation and the highest cross validation accuracy rate. The accuracies of calibration and validation for mid-level data fusion in the model of support vector machine were 84.21 and 96% for the partial least square discrimination values of 96.49 and 84%, which was better performance than a single technique or low-level data fusion for the classification. Moreover, the chemical information of sample collected from Kunming and Xishuangbanna was distinguishable from the others. These results provide a rapid and robust strategy for quality control of P. polyphylla var. yunnanensis for further analysis.     

Evolving factor analysis-based method for correcting monitoring delay in different batch runs for use with PLS: On-line monitoring of a transesterification reaction by ATR-FTIR

In this work, the base-catalyzed transesterification of soybean oil with ethanol was monitored on-line using mid-infrared spectroscopy (MIRS) and the yield of fatty acid ethyl esters (biodiesel) was obtained by (1)H NMR spectroscopy. The MIRS monitoring carried out for 12min, was performed using a cylindrical internal reflectance cell of PbSe in the range of 3707-814cm(-1) with eight co-added scans. Two different data treatment strategies were used: in the first, the models were built using the raw data and in the other, evolving factor analysis (EFA) was used to overcome the sensor time delay due to the on-line analysis, producing significantly better results. In addition, models based on partial least squares (PLS) using three batches for calibration and another for validation were compared with models with just one batch for calibration and three for validation. The models were compared between each other using root mean square error of prediction (RMSEP) and root mean square prediction difference (RMSPD), obtaining relative errors under 3%.     

Determination of Riboflavin in Human Plasma by Excitation‐Emission Matrix Fluorescence and Multi‐Way Analysis

Direct determination of riboflavin (Fig. 1), a vitamin, in human plasma was accomplished based on excitation‐emission matrix (EEM) fluorescence measurements and multi‐way chemometrics method based on parallel factor analysis (PARAFAC). The PARAFAC trilinear model, without restrictions and using one factor was used in the data analysis. The excitation wavelength range was from 380 to 460 nm and the emission was recorded from 480 to 600 nm. The calibration set was constructed with sixteen standard solutions in a concentration range of 0.02–0.38 μg mL^?1 for riboflavin. The capabilities of the method for the analysis were evaluated by determination of riboflavin in synthetic and real samples with satisfactory results. The accuracy of the methods, evaluated through the root mean square error of prediction (RMSEP), was 0.0059 for riboflavin by the PARAFAC model. Also, partial least squares (PLS) model was built at one excitation wavelength and used to determine a set of synthetic and real samples. The best model was obtained with PARAFAC. This result shows that molecular fluorescence spectroscopy can be used for the development of robust analytical methods for the direct determination of riboflavin in complex backgrounds such as human plasma.     

Assessment of dentifrice adulteration with diethylene glycol by means of ATR-FTIR spectroscopy and chemometrics

A direct and fast method for determination of the adulterant diethylene glycol (DEG) in toothpaste and gel dentifrices combining attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy with partial least squares (PLS) regression has been proposed. Considering the high heterogeneity of dentifrices available in the market, the possibility of reducing the number of calibration samples for PLS was evaluated. Similar prediction performance was achieved by both employing a large calibration set of 20 dentifrices spiked with different amounts of DEG and a reduced calibration set of seven ones selected by means of hierarchical cluster analysis (HCA). The feasibility of using the simple calibration model to predict DEG adulteration in a wide variety of unknown dentifrice samples increases the applicability of the proposed method. With this approach, DEG was quantified with a root mean squared error of prediction value of 1.1% for a validation set of 40 different dentifrices containing DEG in the range 0–16% (w:w).     

Determination of polyphenols in oats by near-infrared spectroscopy (NIRS) and two-dimensional correlation spectroscopy

Two-dimensional correlation spectroscopy (2DCOS) and near-infrared spectroscopy (NIRS) were used to determine the polyphenol content in oat grain. A partial least squares (PLS) algorithm was used to perform the calibration. A total of 116 representative oat samples from four locations in China were prepared and the corresponding near-infrared spectra were measured. Two-dimensional correlation spectroscopy was employed to select wavelength bands for the PLS regression model for the polyphenol determination. The number of PLS components and intervals was optimized according to the coefficients of determination (R²) and root mean square error of cross validation (RMSECV) in the calibration set. The performance of the final model was evaluated using the correlation coefficient (R) and the root mean square error of validation (RMSEV) in the prediction set. The results showed the band corresponding to the optimal calibration model was between 1350 and 1848?nm and the optimal spectral preprocessing combination was second derivative with second smoothing. The optimal regression model was obtained with an R² of 0.8954 and an RMSECV of 0.06651 in the calibration set and R of 0.9614 and RMSEV of 0.04573 in the prediction set. These measurements reveal the calibration model had qualified predictive accuracy. The results demonstrated that the 2DCOS with PLS was a simple and rapid method for the quantitative determination of polyphenols in oats.     

Classification and quantification analysis of Radix scutellariae from different origins with near infrared diffuse reflection spectroscopy

A rapid near infrared spectroscopy analysis method was developed for the geographical origin discrimination and content determination of Radix scutellariae, a kind of Traditional Chinese Medicine (TCM). 81 R. scutellariae samples from six different origins were analyzed with HPLC-UV as reference method. The NIR spectra were collected in integrating-sphere diffused reflection mode and processed with different spectra pretreated methods. Discriminant analysis (DA) and discriminant partial least squares (DPLS) were applied to classify the geographical origins of those samples, and the latter had a better predictive ability with 100% accuracy after two exceptional samples eliminated from the calibration set. For the quantitative calibration, the samples were divided into calibration set and validation set by Kennard-Stone algorithm. The models of baicalin, wogonoside, baicalein, wogonin were established with partial least squares (PLS) algorithm and the optimal principal component (PC) numbers were selected with Leave-One-Out (LOO) cross-validation. The established models were evaluated with the root mean square error of prediction (RMSEP) and corresponding correlation coefficients. The correlation coefficients of all the four calibration models are above 0.920, and the RMSEPs of baicalin, wogonoside, baicalein and wogonin are 0.752%, 0.094%, 0.418% and 0.139%, respectively. This research indicated that the NIR diffuse reflection spectroscopy could be used for the rapid analysis of R. scutellariae, which is beneficial to the quality control of this raw material in TCM pharmaceutical factory, and will also help to solve analogous problems.     

Characterization of Gasoline by Raman Spectroscopy with Chemometric Analysis

A rapid Raman spectroscopy protocol is reported to classify gasoline according to its distributor and to identify and quantify common adulterants. Gasoline from three distributors was collected from 19 stations in São Paulo, Brazil. Principal component analysis (PCA) showed specific clusters for each distributor, and partial least squares discriminant analysis (PLS-DA) correctly identified the origin of the samples. To evaluate the technique for the identification and quantification of the adulterants, authentic samples from each distributor were fortified at levels from 2.5 up to 25.0% (v/v) using ethanol, methanol, toluene, and turpentine to obtain 120 altered samples. PCA showed clear separation among the samples with the adulterants and PLS-DA precisely identified the adulterants (478 in 480 predictions by cross-validation), irrespective of the distributor and the concentration. One classification model was used to characterize all distributors. To quantify the adulterants, 36 multivariate calibration models were constructed using partial least squares (PLS), interval PLS, and PLS genetic algorithm for each distributor and for each adulterant. Cross-validation errors of less than 5.0% were obtained for all adulterants regardless of the distributor. Raman spectroscopy and multivariate analysis were shown to be powerful for rapid and inexpensive for the characterization of gasoline origin and the identification and quantification of common adulterants.