Determination of the oxidation stability of biodiesel and oils by spectrofluorimetry and multivariate calibration

Oxidation stability is an important quality parameter for biodiesel. In general, the methods used to evaluate the oxidation stability of oils and biodiesels are time-consuming. This work reports the use of spectrofluorimetry, a fast analytical technique, associated with multivariate data analysis as a powerful analytical tool to prediction of the oxidation stability. The prediction of the oxidation stability showed a good agreement with the results obtained by the EN14112 reference method Rancimat. The models presented high correlation (0.99276 and 0.97951) between real and predicted values. The R² values of 0.98557 and 0.95943 indicated the accuracy of the models to predict the oxidation stability of soy oil and soy biodiesel, respectively. The residual distribution does not follow a trend with respect to the predicted variables indicating the good quality of the fits.     

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.     

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     

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.     

Simultaneous determination of albumin and immunoglobulin G with fluorescence spectroscopy and multivariate calibration

A method is proposed for the simultaneous determination of albumin and immunoglobulin G (IgG1) with fluorescence spectroscopy and multivariate calibration with partial least squares regression (PLS). The influence of some instrumental parameters were investigated with two experimental designs comprising 19 and 11 experiments, respectively. The investigated parameters were excitation and emission slit, detection voltage and scan rate. When a suitable instrumental setting had been found, a minor calibration and test set were analysed and evaluated. Thereafter, a larger calibration of albumin and IgG1 was made out of 26 samples (0-42 μg ml⁻¹ albumin and 0-12.7 μg ml⁻¹ IgG1). This calibration was validated with a test set consisting of 14 samples in the same concentration range. The precision of the method was estimated by analysing two test set samples for six times each. The scan modes tested were emission scan and synchronous scan Δ60 nm. The results showed that the method could be used for determination of albumin and IgG1 (albumin, root mean square error of prediction (RMSEP) <2, relative standard error of prediction (RSEP) <6% and IgG1, RMSEP <1, RSEP <8%) in spite of the overlapping fluorescence of the two compounds. The estimated precision was relative standard deviation (R.S.D.) <1.7%. The method was finally applied for the analysis of some sample fractions from an albumin standard used in affinity chromatography.     

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.     

A review of multivariate calibration methods applied to biomedical analysis

The determination of the contents of therapeutic drugs, metabolites and other important biomedical analytes in biological samples is usually performed by using high-performance liquid chromatography (HPLC). Modern multivariate calibration methods constitute an attractive alternative, even when they are applied to intrinsically unselective spectroscopic or electrochemical signals. First-order (i.e., vectorized) data are conveniently analyzed with classical chemometric tools such as partial least-squares (PLS). Certain analytical problems require more sophisticated models, such as artificial neural networks (ANNs), which are especially able to cope with non-linearities in the data structure. Finally, models based on the acquisition and processing of second- or higher-order data (i.e., matrices or higher dimensional data arrays) present the phenomenon known as “second-order advantage”, which permits quantitation of calibrated analytes in the presence of interferents. The latter models show immense potentialities in the field of biomedical analysis. Pertinent literature examples are reviewed.     

Assessment of infrared spectroscopy and multivariate techniques for monitoring the service condition of diesel-engine lubricating oils

This paper presents two methodologies for monitoring the service condition of diesel-engine lubricating oils on the basis of infrared spectra. In the first approach, oils samples are discriminated into three groups, each one associated to a given wear stage. An algorithm is proposed to select spectral variables with good discriminant power and small collinearity for the purpose of discriminant analysis classification. As a result, a classification accuracy of 93% was obtained both in the middle (MIR) and near-infrared (NIR) ranges. The second approach employs multivariate calibration methods to predict the viscosity of the lubricant. In this case, the use of absorbance measurements in the NIR spectral range was not successful, because of experimental difficulties associated to the presence of particulate matter. Such a problem was circumvented by the use of attenuated total reflectance (ATR) measurements in the MIR spectral range, in which an RMSEP of 3.8 cSt and a relative average error of 3.2% were attained.     

Quantification of two chromatographic unresolved dichlorophenols using gas chromatography-direct deposition-fourier transform infrared spectrometry and multivariate calibration

A method for the quantification of two chromatographically unresolved dichlorophenol isomers in water is described. Acetylation and concentration on graphitized carbon cartridges are carried out as a preliminary step. Detection is made by gas chromatography (GC) coupled to Fourier transform infrared spectroscopy (FTIR), using a direct deposition interface (DD). Infrared spectra in the maximum of the unresolved peak of 2,5- and 2,4-dichlorophenol for a series of standards with different amounts of these two compounds are used, to elaborate a multivariate calibration model (PLS-1 algorithm). By the method described, concentrations of dichlorophenol isomers in water at ng/ml level can be determined.     

Determination of intrinsic viscosity of poly(ethylene terephthalate) using infrared spectroscopy and multivariate calibration method

A methodology was developed to determine the intrinsic viscosity of poly(ethylene terephthalate) (PET) using diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and multivariate calibration (MVC) methods. Multivariate partial least squares calibration was applied to the spectra using mean centering and cross validation. The results were correlated to the intrinsic viscosities determined by the standard chemical method (ASTM D 4603-01) and a very good correlation for values in the range from 0.346 to 0.780 dL g⁻¹ (relative viscosity values ca. 1.185-1.449) was observed. The spectrophotometer detector sensitivity and the humidity of the samples did not influence the results. The methodology developed is interesting because it does not produce hazardous wastes, avoids the use of time-consuming chemical methods and can rapidly predict the intrinsic viscosity of PET samples over a large range of values, which includes those of recycled materials.     

Study of the similarity between distribution maps of concentration in near-infrared spectroscopy chemical imaging obtained by different multivariate calibration approaches

Nowadays, near-infrared spectroscopy chemical imaging (NIR-CI) has been widely used in pharmaceutical analysis since it provides important surface information about the samples. In this work the information of NIR-CI at the pixel level was compared through calculation of the similarity between distribution maps of concentration obtained by different multivariate calibration approaches. The comparison was performed by using four different multivariate methods (MCR, MLR, CLS and PLS) in analysis of carbamazepine pharmaceutical formulations. For global determination, all models developed showed RMSEP below 1.9% (w/w) for active principal ingredient (API) and better than 4.6% (w/w) for excipients. Also, the distribution maps obtained by PLS, CLS and MCR showed great similarity for all compounds of the formulation as well with concentrations in the tablets. However, comparing the distribution maps obtained by MLR with those from the other chemometric tools, a lower similarity was observed. Thus, this fitted model does not ensure, by itself, that the images obtained are reliable or accurate. The paper also compares the distribution maps of concentrations obtained from all constituents present in the pharmaceutical formulation with their respective micrographs.     

Use of indirect multivariate calibration for quality control of agricultural products by near infrared spectroscopy

In the near infrared (NIR) region, (=800–2500 nm) of the spectrum, most organic molecules have weak but reproducibly measurable absorption bands. This phenomenon has been increasingly exploited for the rapid, quantitative analysis of major constituents of agricultural products. As there is strong spectral overlap and an interaction of constituents in NIR, the calibration of instrumentation has to be performed indirectly with a training set of samples applying multivariate methods. A software package for IBM compatible personal computers includes programs to select samples to be used for calibration, to compute multiple linear regression equations, to cross-validate regression equations and to detect unusual samples. These methods are applied to rapidly determine the oil content of over a thousand rapeseed samples of the harvest of 1986. A considerable saving of time, labour, and costs can be achieved, the agreement with the standard laboratory method is very satisfactory.     

Determination of total antioxidant capacity in green tea by near-infrared spectroscopy and multivariate calibration

A principal component regression (PCR) model is built for prediction of total antioxidant capacity in green tea using near-infrared (NIR) spectroscopy. The modelling procedures are systematically studied with the focus on outlier detection. Different outlier detection methods are used and compared. The root mean square error of prediction (RMSEP) of the final model is comparable to the precision of the reference method.     

A new formulation to estimate the variance of model prediction. Application to near infrared spectroscopy calibration

Evaluation of uncertainty affecting predictions is a major trend in analytical chemistry and chemometrics. Several approximate expressions and resampling methods have been proposed for the estimation of prediction uncertainty when using multivariate calibration. This article proposes a new expression for the variance of prediction, adapted to near infrared spectroscopy specificities and particularly to the spectral error structure, induced by the high colinearity of the variables. The proposed analytical expression enables a detailed evaluation of the different contributions and components of uncertainty affecting the model. An application to real data of feedstuff near infrared spectra related to protein content has shown its advantages.     

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.     

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 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.     

Ensemble preprocessing of near-infrared (NIR) spectra for multivariate calibration

Preprocessing of raw near-infrared (NIR) spectral data is indispensable in multivariate calibration when the measured spectra are subject to significant noises, baselines and other undesirable factors. However, due to the lack of sufficient prior information and an incomplete knowledge of the raw data, NIR spectra preprocessing in multivariate calibration is still trial and error. How to select a proper method depends largely on both the nature of the data and the expertise and experience of the practitioners. This might limit the applications of multivariate calibration in many fields, where researchers are not very familiar with the characteristics of many preprocessing methods unique in chemometrics and have difficulties to select the most suitable methods. Another problem is many preprocessing methods, when used alone, might degrade the data in certain aspects or lose some useful information while improving certain qualities of the data. In order to tackle these problems, this paper proposes a new concept of data preprocessing, ensemble preprocessing method, where partial least squares (PLSs) models built on differently preprocessed data are combined by Monte Carlo cross validation (MCCV) stacked regression. Little or no prior information of the data and expertise are required. Moreover, fusion of complementary information obtained by different preprocessing methods often leads to a more stable and accurate calibration model. The investigation of two real data sets has demonstrated the advantages of the proposed method.     

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...