Application of correlation constrained multivariate curve resolution alternating least-squares methods for determination of compounds of interest in biodiesel blends using NIR and UV–visible spectroscopic data

Abstract

This study describes two applications of a variant of the multivariate curve resolution alternating least squares (MCR-ALS) method with a correlation constraint. The first application describes the use of MCR-ALS for the determination of biodiesel concentrations in biodiesel blends using near infrared (NIR) spectroscopic data. In the second application, the proposed method allowed the determination of the synthetic antioxidant N,N'-Di-sec-butyl-p-phenylenediamine (PDA) present in biodiesel mixtures from different vegetable sources using UV-visible spectroscopy. Well established multivariate regression algorithm, partial least squares (PLS), were calculated for comparison of the quantification performance in the models developed in both applications. The correlation constraint has been adapted to handle the presence of batch-to-batch matrix effects due to ageing effects, which might occur when different groups of samples were used to build a calibration model in the first application. Different data set configurations and diverse modes of application of the correlation constraint are explored and guidelines are given to cope with different type of analytical problems, such as the correction of matrix effects among biodiesel samples, where MCR-ALS outperformed PLS reducing the relative error of prediction RE (%) from 9.82% to 4.85% in the first application, or the determination of minor compound with overlapped weak spectroscopic signals, where MCR-ALS gave higher (RE (%)=3.16%) for prediction of PDA compared to PLS (RE (%)=1.99%), but with the advantage of recovering the related pure spectral profile of analytes and interferences. The obtained results show the potential of the MCR-ALS method with correlation constraint to be adapted to diverse data set configurations and analytical problems related to the determination of biodiesel mixtures and added compounds therein.

Evaluation of long-heating kinetic process of edible oils using ATR–FTIR and chemometrics tools

“…The details of MCR-ALS theory could be found elsewhere in literature (Jaumot et al 2005;de Juan and Tauler 2006;De Oliveira et al 2014). Briefly, this curve resolution method decomposes the data matrix (here the infrared spectroscopic data matrix of edible oils during heating process) into two absolute parts i.e.…”

NIR Monitoring and Modelling of Soybean Oil Methanolysis with Multivariate Curve Resolution-Alternating Least Squares with Correlation Constraint

“…Therefore, a real quantitative process monitoring is possible by applying the correlation constraint. [26][27][28] In general, an advantage of MCR with correlation constraint over PLS-based approaches is related to the lower number of reference values needed to develop the regression models. This is due to the fact that, since the information of each component is in separate profiles, pseudo univariate calibration models are built that require less calibration samples than a classical multivariate calibration model.…”

“…Thus, this technique was used for sulfate and acidity determination in biodiesel blends, 29,30 for quantitative analysis of blends of biodiesel with mineral diesel, 31 and for determination of biodiesel concentrations and antioxidant content in biodiesel mixtures. 28 In addition, besides the research reported in the aforementioned articles, only Oliveira et al 28 have employed the new correlation constraint to the application of MCR-ALS to biodiesel analysis.…”

Evaluation of conversion during the synthesis of aluminum (III) methacrylate-based copolymers using Raman spectroscopy and multivariate curve resolution

“…Basically, MCR-ALS is characterized by a bilinear decomposition of an original data matrix (D) into two matrices [31][32][33][34][35][36][37][38][39][40][41][42][43]: i) a matrix corresponding to the concentration profiles (C) and; ii) a second matrix related to the pure spectra profiles (S). MCR model can be expressed as D = CS T + ε, where the matrix D (dimensions m different individual spectra by n intensities measured at each spectral points) is approximated by the product of the matrix C (dimensions k components in the different by m rows of the data matrix) and matrix S T (transpose of matrix pure spectral profiles of dimensions k components by n columns of the original data matrix); and ε is the matrix of residuals (dimensions m different individual spectra by n intensities) containing the information not explained by the model.…”

“…MCR-ALS is extensively employed in several technological issues related with the quantitation antibacterial agents presented water samples [44], determination of furosemide in the presence of interfering agent (anti-inflammatory flufenamic acid) [45], quantitative determination of sulfur [32], sulfonamide drugs in biomedical analysis [46], mixtures of nitrophenols pollutants in environmental and biological samples [34], proteinaceous binders in medieval paints [37], antioxidants (butylatedhydroxytoluene and N,N′-di-secbutyl-p-phenylenediamine) in biodiesel blends [41] and concentration profiles in milk lactic acid fermentation [33]; enzymatic determination of levodopa and carbidopa in pharmaceuticals [47], measurements of the relative amounts of different iron oxide and oxyhydroxides present in the passive films [48], interpretation of the kinetic behavior of the photochemical degradation process of decabromodiphenyl ether [43] and photodynamics of salicylidene aniline in ultrafast competitive photoreactions [38].…”