An exhaustive analysis of the use of image moments for second-order calibration. A comparison with multivariate curve resolution-alternating least-squares.

BACKGROUND: the chemometric processing of second-order chromatographic-spectral data is usually carried out with the aid of multivariate curve resolution-alternating least-squares (MCR-ALS). Recently, an alternative procedure was described based on the estimation of image moments for each data matrix and subsequent application of multiple linear regression after suitable variable selection. RESULTS: The analysis of both simulated and experimental data leads to the conclusion that the image moment method, although can cope with chromatographic lack of reproducibility across injections, it only performs well in the absence of uncalibrated interferents. MCR-ALS, on the other hand, provides good analytical results in all studied situations, whether the test samples contain uncalibrated interferents or not. SIGNIFICANCE: The results are useful to assess the real usefulness of newly proposed methodologies for second-order calibration in the case of chromatographic-spectral data sets, especially when samples contain unexpected chemical constituents.

Second-order calibration high-resolution continuum source graphite furnace molecular absorption spectrometry-based determination of bromine and fluorine.

In this study, we report the simultaneous determination of bromine and fluorine using Second-Order Calibration High-Resolution Continuum Source Graphite Furnace Molecular Absorption Spectrometry (HR CS MAS). The instrumental data acquired correspond to the time versus wavelength matrix per sample that were analyzed using Parallel Factor Analysis (PARAFAC), along with Unfold and N-way Partial Least Squares combined with a post-calibration step known as Residual Bilinearization (U and N PLS/RBL). Despite the significant difference in sensitivity between bromine and fluorine, all approaches provided reasonably accurate results when predicting both analytes in synthetic mixtures within a controlled environment. The relative prediction error (REP) values for bromine were 29.8 % (PARAFAC), 23.6 % (N-PLS/RBL), and 13.1 % (U-PLS/RBL), while for fluorine, the REP values were 3.4 % (PARAFAC), 3.5 % (N-PLS/RBL), and 3.2 % (U-PLS/RBL). When applying this approach to predict unknown samples, a comparison was made between the estimated nominal concentrations of fluorine and bromine obtained using either a reference method or based on labeled values or spiked mass, and those obtained using the proposed method. It was observed that PARAFAC was unable to predict the samples accurately, whereas the REP values for the prediction of bromine and fluorine using N-PLS/RBL and U-PLS/RBL methods were 19.3 %/19.2 % and 13.6 %/13.1 %, respectively.

Simultaneous determination of methyl, ethyl, propyl, and butyl parabens in sweetener samples without any previous pretreatment using square wave voltammetry and multiway calibration.

Parabens are compounds used as chemical preservatives in cosmetics, drugs, and food. Some can cause adverse effects on human health. In this study, a square wave voltammetric method using a glassy carbon electrode was developed for simultaneous determination of methyl, ethyl, propyl, and butyl parabens in sweeteners. To overcome the strong overlap of voltammetric signals caused by calibrated and uncalibrated constituents, unfolded partial least squares with residual bilinearization (U-PLS/RBL) was used. The U-PLS/RBL calibration model was constructed and evaluated using a validation set obtained using a Taguchi design. Satisfactory and unbiased results were obtained with a linear response in the range of 0.78-4.48 µmol L-1 and recoveries from 82.64% to 121.77%. As far as the authors know, a voltammetric method that simultaneously determines four parabens in complex samples such as sweeteners without any previous pretreatment has not yet been reported in the literature.

Why should the pharmaceutical industry claim for the implementation of second-order chemometric models-A critical review.

Today, pharmaceutical products are submitted to a large number of analytical tests, planned to either ensure or construct their quality. The official methods of analysis used to perform these determinations are very different in nature, but almost all demand the intensive use of reagents and manpower as major drawbacks. Thus, analytical development is continuously evolving to find fast and smart approaches. First-order chemometric models are well-known in the pharmaceutical industry, and are extensively used in many fields. Such is the impact of chemometric models that regulatory agencies include them in guidelines and compendia. However, the mention or practical application of higher-order models in the pharmaceutical industry is rather scarce. Herein, we try to bring a brief introduction to chemometric models and useful literature references, focusing on higher-order chemometric models (HOCM) applied to reduce manpower, reagent consumption, and time of analysis, without sacrificing accuracy or precision, while gaining selectivity and sensitivity. The advantages and drawbacks of HOCM are also discussed, and the comparison to first-order chemometric models is also analyzed. Along the work, HOCM are evidenced as a powerful tool for the pharmaceutical industry; moreover, its implementation is shown during several steps of production, such as identification, purity test and assay, and other applications as homogeneity of API distribution, Process Analytical Technology (PAT), Quality by Design (QbD) or natural product fingerprinting. Among these topics, qualitative and quantitative applications were covered. Experimental approaches of chemometrics coupled to several analytical techniques such as UV-vis, fluorescence and vibrational spectroscopies (NIR, MIR and Raman), and other techniques as hyphenated-chromatography and electrochemical techniques applied to production and analysis are discussed throughout this work.

Modeling nonbilinear total synchronous fluorescence data matrices with a novel adapted partial least squares method.

A new residual modeling algorithm for nonbilinear data is presented, namely unfolded partial least squares with interference modeling of non bilinear data by multivariate curve resolution by alternating least squares (U-PLS/IMNB/MCR-ALS). Nonbilinearity represents a challenging data structure problem to achieve analyte quantitation from second-order data in the presence of uncalibrated components. Total synchronous fluorescence spectroscopy (TSFS) generates matrices which constitute a typical example of this kind of data. Although the nonbilinear profile of the interferent can be achieved by modeling TSFS data with unfolded partial least squares with residual bilinearization (U-PLS/RBL), an extremely large number of RBL factors has to be considered. Simulated data show that the new model can conveniently handle the studied analytical problem with better performance than PARAFAC, U-PLS/RBL and MCR-ALS, the latter modeling the unfolded data. Besides, one example involving TSFS real matrices illustrates the ability of the new method to handle experimental data, which consists in the determination of ciprofloxacin in the presence of norfloxacin as interferent in water samples.

Generation of non-multilinear three-way voltammetric arrays by an electrochemically oxidized glassy carbon electrode as an efficient electronic device to achieving second-order advantage: challenges, and tailored applications.

For the first time, several second-order calibration models based on artificial neural network-residual bilinearization (ANN-RBL), unfolded-partial least squares-RBL (U-PLS/RBL), multidimensional-partial least squares-RBL (N-PLS/RBL), multivariate curve resolution-alternating least squares (MCR-ALS), and parallel factor analysis 2 (PARAFAC2) were used to exploiting second-order advantage to identify which technique offers the best predictions for the simultaneous quantification of norepinephrine (NE), paracetamol (AC), and uric acid (UA) in the presence of pteroylglutamic acid (FA) as an uncalibrated interference at an electrochemically oxidized glassy carbon electrode (OGCE). Three-way differential pulse voltammetric (DPV) arrays were obtained by recording the DPV signals at different pulse heights. The recorded three-way arrays were both non-bilinear and non-trilinear therefore, the observed shifts in the recorded DPV data were corrected using correlation optimised warping (COW) algorithm. All the algorithms achieved the second-order advantage and were in principle able to overcome the problem of the presence of unexpected interference. Comparison of the performance of the applied second-order chemometric algorithms confirmed the more superiority of U-PLS/RBL to resolve complex systems. The results of applying U-PLS/RBL for the simultaneous quantification of the studied analytes in human serum samples were also encouraging.

Chemometric processing of second-order liquid chromatographic data with UV-vis and fluorescence detection. A comparison of multivariate curve resolution and parallel factor analysis 2.

Second-order liquid chromatographic data with multivariate spectral (UV-vis or fluorescence) detection usually show changes in elution time profiles from sample to sample, causing a loss of trilinearity in the data. In order to analyze them with an appropriate model, the latter should permit a given component to have different time profiles in different samples. Two popular models in this regard are multivariate curve resolution-alternating least-squares (MCR-ALS) and parallel factor analysis 2 (PARAFAC2). The conditions to be fulfilled for successful application of the latter model are discussed on the basis of simple chromatographic concepts. An exhaustive analysis of the multivariate calibration models is carried out, employing both simulated and experimental chromatographic data sets. The latter involve the quantitation of benzimidazolic and carbamate pesticides in fruit and juice samples using liquid chromatography with diode array detection, and of polycyclic aromatic hydrocarbons in water samples, in both cases in the presence of potential interferents using liquid chromatography with fluorescence spectral detection, thereby achieving the second-order advantage. The overall results seem to favor MCR-ALS over PARAFAC2, especially in the presence of potential interferents.

The Successive Projections Algorithm for interval selection in trilinear partial least-squares with residual bilinearization.

In this work the Successive Projection Algorithm is presented for intervals selection in N-PLS for three-way data modeling. The proposed algorithm combines noise-reduction properties of PLS with the possibility of discarding uninformative variables in SPA. In addition, second-order advantage can be achieved by the residual bilinearization (RBL) procedure when an unexpected constituent is present in a test sample. For this purpose, SPA was modified in order to select intervals for use in trilinear PLS. The ability of the proposed algorithm, namely iSPA-N-PLS, was evaluated on one simulated and two experimental data sets, comparing the results to those obtained by N-PLS. In the simulated system, two analytes were quantitated in two test sets, with and without unexpected constituent. In the first experimental system, the determination of the four fluorophores (l-phenylalanine; l-3,4-dihydroxyphenylalanine; 1,4-dihydroxybenzene and l-tryptophan) was conducted with excitation-emission data matrices. In the second experimental system, quantitation of ofloxacin was performed in water samples containing two other uncalibrated quinolones (ciprofloxacin and danofloxacin) by high performance liquid chromatography with UV-vis diode array detector. For comparison purpose, a GA algorithm coupled with N-PLS/RBL was also used in this work. In most of the studied cases iSPA-N-PLS proved to be a promising tool for selection of variables in second-order calibration, generating models with smaller RMSEP, when compared to both the global model using all of the sensors in two dimensions and GA-NPLS/RBL.