Determination of biodiesel content when blended with mineral diesel fuel using infrared spectroscopy and multivariate calibration

In this work, multivariable calibration models based on middle- and near-infrared spectroscopy were developed in order to determine the content of biodiesel in diesel fuel blends, considering the presence of raw vegetable oil. Soybean, castor and used frying oils and their corresponding esters were used to prepare the blends with conventional diesel. Results indicated that partial least squares (PLS) models based on MID or NIR infrared spectra were proven suitable as practical analytical methods for predicting biodiesel content in conventional diesel blends in the volume fraction range from 0% to 5%. PLS models were validated by independent prediction set and the RMSEPs were estimated as 0.25 and 0.18 (%, v/v). Linear correlations were observed for predicted vs. observed values plots with correlation coefficient (R) of 0.986 and 0.994 for the MID and NIR models, respectively. Additionally, principal component analysis (PCA) in the MID region 1700 to 1800 cm^− 1 was suitable for identifying raw vegetable oil contaminations and illegal blends of petrodiesel containing the raw vegetable oil instead of ester.     

Simultaneous voltammetric determination of levodopa, carbidopa and benserazide in pharmaceuticals using multivariate calibration

An analytical methodology based on differential pulse voltammetry (DPV) on a glassy carbon electrode and the partial least-squares (PLS-1) algorithm for the simultaneous determination of levodopa, carbidopa and benserazide in pharmaceutical formulations was developed and validated. Some sources of bi-linearity deviation for electrochemical data are discussed and analyzed. The multivariate model was developed as a ternary calibration model and it was built and validated with an independent set of drug mixtures in presence of excipients, according with manufacturer specifications. The proposed method was applied to both the assay and the uniformity content of two commercial formulations containing mixtures of levodopa-carbidopa (10:1) and levodopa-benserazide (4:1). The results were satisfactory and statistically comparable to those obtained by applying the reference Pharmacopoeia method based on high performance liquid chromatography. In conclusion, the methodology proposed based on DPV data processed with the PLS-1 algorithm was able to quantify simultaneously levodopa, carbidopa and benserazide in its pharmaceuticals formulations using a ternary calibration model for these drugs in presence of excipients. Furthermore, the model appears to be successful even in the presence of slight potential shifts in the processed data, which have been taken into account by the flexible chemometric PLS-1 approach.     

Simultaneous spectrophotometric determination of four preservatives in foodstuffs by multivariate calibration and artificial neural networks

Benzoic acid(BA),methylparaben(MP),propylparaben(PP)and sorbic acid(SA)are food preservatives,and they have well defined UV spectra.However,their spectra overlap seriously,and it is difficult to determine them individually from their mixtures without preseparation.In this paper,seven different chemometric approaches were applied to resolve the overlapping spectra and to determine these compounds simultaneously.With respect to the criteria of%relative prediction error(RPE)and%recovery, principal component...     

Durum wheat adulteration detection by NIR spectroscopy multivariate calibration

In the present work, we explored the possibility of using near-infrared spectroscopy in order to quantify the degree of adulteration of durum wheat flour with common bread wheat flour. The multivariate calibration techniques adopted to this aim were PLS and a wavelet-based calibration algorithm, recently developed by some of us, called WILMA. Both techniques provided satisfactory results, the percentage of adulterant present in the samples being quantified with an uncertainty lower than that associated to the Italian official method. In particular the WILMA algorithm, by performing feature selection, allowed the signal pretreatment to be avoided and obtaining more parsimonious models.     

Simultaneous determination of halide and thiocyanate ions by potentiometric precipitation titration and multivariate calibration

Halide and thiocyanate ions can be determined by a precipitation titration with silver nitrate as the titrant, and the end-point can be evaluated by a potentiometric method, in which generally a silver indicator electrode is used as the indicator electrode and a double-junction Ag–AgCl electrode as the reference electrode. However, when mixtures of halide and thiocyanate are titrated, it is difficult to determine these components individually for there are overlapping steps in the potentiometric titration curves, especially in the case that there are obvious differences between concentrations of the components. In this paper, the linear equation for the potentiometric precipitation titration of a mixture of halide and thiocyanate ions was developed and it was then used for determining the components in the mixtures simultaneously with the aid of multivariate calibration methods. By application of this model, 27 synthetic mixtures with three- and four-component combinations of chloride, bromide, iodide and thiocyanate with low concentration levels from 1.8×10⁻⁴ to 6.2×10⁻⁴ mol l⁻¹ were analyzed and acceptable results were obtained.     

Near-infrared spectroscopy and multivariate calibration for the quantitative determination of certain properties in the petrochemical industry

Near-infrared (NIR) spectroscopy in conjunction with chemometric techniques allows on-line monitoring in real time, which can be of considerable use in industry. If it is to be correctly used in industrial applications, generally some basic considerations need to be taken into account, although this does not always apply. This study discusses some of the considerations that would help evaluate the possibility of applying multivariate calibration in combination with NIR to properties of industrial interest. Examples of these considerations are whether there is a relation between the NIR spectrum and the property of interest, what the calibration constraints are and how a sample-specific error of prediction can be quantified. Various strategies for maintaining a multivariate model after it has been installed are also presented and discussed.     

Simultaneous determination of five commercial cationic dyes in stream waters using diatomite solid-phase extractant and multivariate calibration

A simple spectrophotometric method was developed for the simultaneous determination of five commercial cationic dyes at 2.0?8.5 μg L^?1 level after using diatomite as solid-phase extractant. The method is based on preconcentration of the five dyes on natural diatomite solid-phase extractant and on multivariate calibration using partial least squares method (PLS-1). Compared with commonly used chromatographic or electrophoretic methods the developed method is simple and sensitive. With enrichment factors between 89 and 96, diatomite outperformed zeolite and activated carbon for dyes preconcentration. Before preconcentration and using PLS-1 method, the cationic dyes were simultaneously analyzed with linear ranges of 0.18–4.5, 0.32–5.0, 0.23–4.5, 0.45–8.0 and 0.82–12.0 mg L^?1 for crystal violet, malachite green, methylene blue, safranine O, and thioflavin T, respectively. The detection limits of dyes were estimated using Lorber’s method and found to be within the range 43–245 μg L^?1. The proposed SPE/PLS-1 method was applied to spiked stream water samples with good accuracy (79–91%) and precision (RSD 1.8–7.3%) but with slightly lower enrichment factors (80–92).     

Multivariate calibration applied to synchronous fluorescence spectrometry. Simultaneous determination of polycyclic aromatic hydrocarbons in water samples

Synchronous fluorescence spectra of mixtures containing ten polycyclic aromatic hydrocarbons (anthracene, benz[a]anthracene, benzo[a]pyrene, chrysene, fluoranthene, fluorene, naphthalene, perylene, phenanthrene and pyrene) have been used for the determination of these compounds by Partial Least Squares Regression (PLSR), using both PLS-1 and PLS-2. Different procedures have been used for the pretreatment of the data in order to obtain better models, and the size of the calibration matrix has also been studied. The best models have been used for the determination of the above mentioned PAHs in spiked natural water samples at concentration levels between 4 and 20 ng ml⁻¹. Recoveries ranged from 80 to 120% in most cases, although fluorene gave significantly lower results.     

Multicomponent determination of pesticides in vegetables by gas chromatography with mass spectrometric detection and multivariate calibration

Three multivariate calibration methods, partial least squares (PLS-1 and PLS-2) and principal component regression (PCR), were applied for the first time to the simultaneous determination of a mixture of six pesticides in vegetables samples by gas chromatography with mass spectrometric detection (GC-MS). PLS-1 method showed better prediction ability than PLS-2 and PCR methods. The GC-MS chromatograms obtained of vegetable samples spiked with the target pesticides were used to build the calibration matrix. The PLS-1 models were evaluated by predicting the concentrations of independent test samples. Also, the proposed models were successfully applied for the determination of these pesticides in vegetable samples after an extraction step with dichloromethane. By using the first derivative signals in PLS-1 models, simultaneous determination of the compounds was not improved.     

Simultaneous spectrophotometric determination of ethinylestradiol and levonorgestrel by partial least squares and principal component regression multivariate calibration

Two spectrophotometric methods for the determination of Ethinylestradiol (ETE) and Levonorgestrel (LEV) by using the multivariate calibration technique of partial least square (PLS) and principal component regression (PCR) are presented. In this study the PLS and PCR are successfully applied to quantify both hormones using the information contained in the absorption spectra of appropriate solutions. In order to do this, a calibration set of standard samples composed of different mixtures of both compounds has been designed. The results found by application of the PLS and PCR methods to the simultaneous determination of mixtures, containing 4–11 μg ml⁻¹ of ETE and 2–23 μg ml⁻¹ of LEV, are reported. Five different oral contraceptives were analyzed and the results were very similar to that obtained by a reference liquid Chromatographic method.     

荧光光谱法研究4-硝基苯胺与牛血清白蛋白的相互作用

在模拟动物生理条件下利用荧光光谱法从分子水平上研究了4-硝基苯胺同牛血清白蛋白(BSA)的相互作用.4-硝基苯胺对BSA的荧光有较强猝灭作用.用Stern-Volmer方程和双对数方程分别处理实验数据发现BSA与4-硝基苯胺发生反应生成了新的复合物,猝灭机理以静态碎灭为主.根据双对数方程求出了不同温度下反应时复合物的形...     

Comparison of multivariate calibration models for glucose,urea, and lactate from near-infrared and Raman spectra

Partial least-squares (PLS) calibration models have been generated from a series of near-infrared (near-IR) and Raman spectra acquired separately from sixty different mixed solutions of glucose, lactate, and urea in aqueous phosphate buffer. Independent PLS models were prepared and compared for glucose, lactate, and urea. Near-IR and Raman spectral features differed substantially for these solutes, with Raman spectra enabling greater distinction with less spectral overlap than features in the near-IR spectra. Despite this, PLS models derived from near-IR spectra outperformed those from Raman spectra. Standard errors of prediction were 0.24, 0.11, and 0.14 mmol L⁻¹ for glucose, lactate, and urea, respectively, from near-IR spectra and 0.40, 0.42, and 0.36 mmol L⁻¹ for glucose, lactate, and urea, respectively, from Raman spectra. Differences between instrumental signal-to-noise ratios were responsible for the better performance of the near-IR models. The chemical basis of model selectivity was examined for each model by using a pure component selectivity analysis combined with analysis of the net analyte signal for each solute. This selectivity analysis showed that models based on either near-IR or Raman spectra had excellent selectivity for the targeted analyte. The net analyte signal analysis also revealed that analytical sensitivity was higher for the models generated from near-IR spectra. This is consistent with the lower standard errors of prediction.     

Simultaneous determination of antimony(III) and antimony(V) by UV-vis spectroscopy and partial least squares method (PLS)

This paper describes a procedure for the speciation of antimony by UV-vis spectroscopy using pyrogallol as complexing agent. A partial least squares (PLS) regression was performed to resolve highly overlapping spectrophotometric signals obtained from mixtures of Sb(III) and Sb(V). The relative error in absolute value was less than 5% when concentrations of several mixtures were calculated. The minimum concentration determined was 3.96 × 10⁻⁵ mol dm⁻³ and 3.98 × 10⁻⁵ mol dm⁻³ for Sb(V) and Sb(III), respectively. The analysis of the possible effect of the presence of foreign ions in the solution was performed and the procedure was successfully applied to the speciation of antimony in pharmaceutical preparations and aqueous samples.     

Infrared spectroscopy and multivariate calibration to monitor stability quality parameters of biodiesel

This paper evaluates analytical methods based on near infrared (NIR) and middle infrared (MIR) spectroscopy and multivariate calibration to monitor the stability of biodiesel. There was a focus on three parameters: oxidative stability index, acid number and water content. Ethylic and methylic biodiesel from different feedstocks were used in experiments of accelerated aging, in order to take into account the wide variety of oilseeds and feedstocks available in Brazil. Partial least squares (PLS) and multiple linear regression (MLR) models were developed. Different pre-processing techniques and spectral variable/regions selection algorithms were evaluated. For MLR models, the successive projection algorithm (SPA) was employed. Interval PLS (iPLS) and selection of variables taking into account the significant regression coefficients were used for PLS models. Results showed that both near and middle infrared regions, and all variable selection methods tested were efficient for predicting these three important quality parameters of B100, the root mean squares error of prediction (RMSEP) values being comparable to the reproducibility of the corresponding standard method for each property investigated.     

Different approaches to multivariate calibration of nonlinear sensor data

In this study, different approaches to the multivariate calibration of the vapors of two refrigerants are reported. As the relationships between the time-resolved sensor signals and the concentrations of the analytes are nonlinear, the widely used partial least-squares regression (PLS) fails. Therefore, different methods are used, which are known to be able to deal with nonlinearities present in data. First, the Box–Cox transformation, which transforms the dependent variables nonlinearly, was applied. The second approach, the implicit nonlinear PLS regression, tries to account for nonlinearities by introducing squared terms of the independent variables to the original independent variables. The third approach, quadratic PLS (QPLS), uses a nonlinear quadratic inner relationship for the model instead of a linear relationship such as PLS. Tree algorithms are also used, which split a nonlinear problem into smaller subproblems, which are modeled using linear methods or discrete values. Finally, neural networks are applied, which are able to model any relationship. Different special implementations, like genetic algorithms with neural networks and growing neural networks, are also used to prevent an overfitting. Among the fast and simpler algorithms, QPLS shows good results. Different implementations of neural networks show excellent results. Among the different implementations, the most sophisticated and computing-intensive algorithms (growing neural networks) show the best results. Thus, the optimal method for the data set presented is a compromise between quality of calibration and complexity of the algorithm.Electronic Supplementary Material Supplementary material is available for this article at     

Second-order multivariate calibration procedures applied to high-performance liquid chromatography coupled to fast-scanning fluorescence detection for the determination of fluoroquinolones

Different second-order multivariate calibration algorithms, namely parallel factor analysis (PARAFAC), N-dimensional partial least-squares (N-PLS) and multivariate curve resolution-alternating least-squares (MCR-ALS) have been compared for the analysis of four fluoroquinolones in aqueous solutions, including some human urine samples (additional four fluoroquinolones were simultaneously determined by univariate calibration). Data were measured in a short time with a chromatographic system operating in the isocratic mode. The detection system consisted of a fast-scanning spectrofluorimeter, which allows one to obtain second-order data matrices containing the fluorescence intensity as a function of retention time and emission wavelength. The developed approach enabled us to determine eight analytes, some of them with overlapped profiles, without the necessity of applying an elution gradient, and thus significantly reducing both the experimental time and complexity. The study was employed for the discussion of the scopes of the applied second-order chemometric tools. The quality of the proposed technique coupled to each of the evaluated algorithms was assessed on the basis of the figures of merit for the determination of fluoroquinolones in the analyzed water and urine samples. Univariate calibration of four analytes led to limits of detection in the range 20–40 ng mL⁻¹ and root mean square errors for the validation samples in the range 30–60 ng mL⁻¹ (corresponding to relative prediction errors of 3–8%). The ranges for second-order multivariate calibration (using PARAFAC and N-PLS) of the remaining four analytes were: limit of detection, 2–8 ng mL⁻¹, root mean square errors, 3–50 ng mL⁻¹ and relative prediction errors, 1–5%.     

Simultaneous determination of fluorophores with overlapped spectra by sequential injection analysis coupled to variable angle scanning fluorescence spectrometry and multivariate linear regression algorithms

An automated and versatile sequential injection spectrofluorimetric procedure for the simultaneous determination of multicomponent mixtures in micellar medium without prior separation processes is reported. The methodology is based upon the segmentation of a sample slug between two different buffer zones in order to attain both an improvement of sensitivity and residual minimization for the whole species. Resolution of overlapping fluorescence profiles is achieved using a variable angle scanning technique coupled to multivariate least-squares regression (MLR) algorithms at both sample edges.The potentialities of the described methodology are illustrated with the spectrofluorimetric determination of four widespread pesticides with different acid-base properties; viz. carbaryl (CBL) (1-naphthyl-N-methylcarbamate), fuberidazole (FBZ) (2-(2′-furyl)benzimidazole), thiabendazole (TBZ) (2-(4′-thiazolyl)benzimidazole) and warfarin (W) (3-α-acetonylbenzyl)-4-hydroxycoumarin). Detection limits at the 3σ level were 3.9, 0.02, 0.03 and 10 μg l⁻¹ for CBL, FBZ, TBZ and W, respectively at the maximum sensitivity pH. Dynamic ranges of 13-720 μg l⁻¹ CBL, 0.10-14 μg l⁻¹ FBZ, 0.19-60 μg l⁻¹ TBZ and 0.05-5 mg l⁻¹ W were achieved. Relative standard deviations (n=10) were 0.2% for 100 μg l⁻¹ CBL and 2.4 μg l⁻¹ FBZ, 0.7% for 8 μg l⁻¹ TBZ and 1.0% for 1 mg l⁻¹ W. The proposed automated methodology, which handles 17 samples/h, was validated and applied to spiked real water samples with very satisfactory results.     

Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration

This study attempted the feasibility to use near infrared (NIR) spectroscopy as a rapid analysis method to qualitative and quantitative assessment of the tea quality. NIR spectroscopy with soft independent modeling of class analogy (SIMCA) method was proposed to identify rapidly tea varieties in this paper. In the experiment, four tea varieties from Longjing, Biluochun, Qihong and Tieguanyin were studied. The better results were achieved following as: the identification rate equals to 90% only for Longjing in training set; 80% only for Biluochun in test set; while, the remaining equal to 100%. A partial least squares (PLS) algorithm is used to predict the content of caffeine and total polyphenols in tea. The models are calibrated by cross-validation and the best number of PLS factors was achieved according to the lowest root mean square error of cross-validation (RMSECV). The correlation coefficients and the root mean square error of prediction (RMSEP) in the test set were used as the evaluation parameters for the models as follows: R = 0.9688, RMSEP = 0.0836% for the caffeine; R = 0.9299, RMSEP = 1.1138% for total polyphenols. The overall results demonstrate that NIR spectroscopy with multivariate calibration could be successfully applied as a rapid method not only to identify the tea varieties but also to determine simultaneously some chemical compositions contents in tea.     

On-line determination of fuel quality parameters using near-infrared spectrometry with fibre optics and multivariate calibration

Near-infrared spectrometry with multivariate calibration has been widely used in the food and chemical industries for the determination of quality parameters. The use of fibre optics opens the possibility of using this technique for on-line analysis. With fuels such as gasoline and diesel, most of the parameters used for their characterization are measured using empirical and sometimes subjective tests. These tests might also require costly and complicated equipment as in the measurement of octane number, cetane number and paraffin, olefin, naphthene and aromatics content (PONA). Near-infrared spectrometry using fibre optics in combination with multivariate calibration was used for the determination of fuel quality parameters. The octane number of gasolines was measured. The sampling problems encountered when this method is implemented on-line were evaluated by monitoring the research and motor octane numbers in a gasoline line. Other applications, such as the determination of PONA in gasolines and the measurement of cetane number in diesel fuels, are discussed. In all instances the results obtained by the proposed technique agree with the values measured with conventional methods.