Theory of Analytical Chemistry

Spectrophotometric and Multivariate Calibration Techniques for Simultaneous Determination of Different Drugs in Pharmaceutical Formulations and Human Urine: Evaluation of Greenness Profile

Eco-friendly, rapid, and cost-effective two spectrophotometric methods were developed and validated for the determination of atenolol, paracetamol, hydrochlorothiazide, and levofloxacin. The first method is the newly developed extended derivative ratio (EDR) and the second method is multivariate curve resolution-alternating least squares (MCR-ALS). In the EDR method, the extended derivative ratio amplitudes at 281.6, 237.6, 279.2, and 282.8 nm were used for quantification of atenolol, paracetamol, hydrochlorothiazide, and levofloxacin, respectively. In the MCR-ALS method, calibration model was developed and correlation constraint was employed. External validation data set composed of seven mixtures was used, and different figures of merits such as root mean square error of prediction, standard error of prediction, bias, and relative error of prediction were calculated, and satisfactory results were obtained. Both methods provided comparable results. The methods were validated and applied for the determination of the target analytes in dosage forms, spiked and real human urine. Thereafter, the obtained results were statistically compared to the published methods and revealed no significant difference regarding accuracy and precision. Furthermore, the greenness profile of the methods was evaluated using the National Environmental Methods Index "NEMI" and Analytical Eco-Scale. The developed methods can be used as a valid eco-friendly and simple cost-effective alternative to the commonly used chromatographic methods for the routine analysis of the studied drugs in dosage forms and human urine.

Second-order universal calibration

Quantification and qualification of an analyte of interest in pharmaceutical tablets from different manufacturers/companies are a hard task because of the potential presence of various interfering molecules. Indeed, the composition of the tablets covers a wide range of interferents which can be even unknown. As a consequence, we propose to determine the concentration of an analyte of interest regardless of the interferents using the concept of universal calibration. Universal calibration paves the way to the quantification of a specific chemical entity in samples with various compositions and different interferents. This is possible by the trilinear structure of analyte's signal. In fact, the second-order advantage resulting from the second-order universal calibration models is exploited. However, a new second-order calibration strategy was conducted in this work using Trilinear Factor Extraction (TFE). A simulated data set was exemplified to highlight the ability of the proposed procedure in order to accurate extraction of the analyte's concentration profile. Additionally, two real data sets were also explored in order to test the TFE method. In the first case, Acetaminophen was quantified using fluorescence spectroscopy in tablets with different formulations from 6 companies. In the second experimental data, a peptide (Valine-Tyrosine-Valine) was successfully quantified in different samples using spectrofluorimetric data. Finally, these real data sets were analyzed by Multivariate Curve resolution - Alternating Least-Squares (MCR-ALS) under non-negativity and trilinearity constraints for the sake of comparison. The calculated Root Mean Square Error of Predictions (RMSEP) of Acetaminophen were 0.028 and 0.026 for the MCR-ALS and TFE models, respectively. On the other hand, for the second experimental data set, the RMSEP were 0.216 and 0.165, respectively. Finally, based on a paired t-test, the results of MCR-ALS and TFE were not significantly different.

Non-target analysis of vapor mixtures using silicon nanowire array sampling and thermal desorption

This work presents and evaluates an algorithmic approach to deconvolving the elution profiles of chemical components of vapor mixtures that have been sampled and desorbed from a novel preconcentrator based on highly ordered silicon nanowire arrays. The arrays provide a medium for both preconcentration and partial chromatographic resolution, which is then further leveraged with multichannel detection. Here, mixtures of nitro aromatic vapors are sampled and then thermally desorbed from the device, at which point they are detected by a conventional mass selective detector. The overlapping elution profiles observed from the array are sequentially extracted using a chemometric analysis approach based on evolving factor analysis and multivariate curve resolution by alternating least squares, enabling qualitative and quantitative analysis of individual components without target analyte libraries or complete chromatographic separation. This work examines the analytical capabilities conferred to multichannel detection by silicon nanowire array pre-concentration and partial separation and discusses the technique's limitations, illustrated by both experimental and simulated data.

Impact of diverse background interferences on the alternating trilinear decomposition modeling of excitation-emission matrix fluorescence data acquired from different sample sources

Alternating trilinear decomposition (ATLD) method enables the qualitative and quantitative analysis of excitation-emission matrix fluorescence (EEMF) data acquired from complex samples. However, the impact of diverse background interferences from different sample sources on the performances of ATLD method has never been lucubrated. In this work, simulated and real EEMF data sets from different sample sources with diverse background interferences were collected and subjected to ATLD analysis. The performances of ATLD modeling individual and global EEMF data sets were comprehensively compared in terms of the resolved spectral profiles and quantitative results. It was found that ATLD method can use the same set of calibration samples to resolve and quantify multiple components of interest in multiple complex systems with diverse background interferences, regardless of individual or global modeling. The results revealed that the qualitative and quantitative results provided by ATLD method were affected neither by diversity of background interferences nor by data merging as long as the acquired EEMF data sets conform to the trilinear component model. This property of ATLD method can enrich the "second-order advantage", i.e. the term "unknown interferences" in the concept of "second-order advantage" refers to not only constant background interferences but also diverse background interferences, which will be certain to further expand the practicality of ATLD method in complex sample analysis, especially in the field of fluorescence spectroscopy.

Bioaccumulation and glutathione S-transferase activity on Rhinella arenarum tadpoles after short-term exposure to antiretrovirals

The aim of the present study was to investigate the bioaccumulation and toxicological effects of four antiretrovirals (lamivudine, stavudine, zidovudine and nevirapine) on Rhinella arenarum tadpoles, after short-term (48 h) exposure to these drugs at sublethal concentrations. The analytical procedure involved a simple extraction method followed by ultra-high performance liquid chromatography with diode array detection and chemometric analysis for data processing. Under the conditions studied, the analytes investigated, particularly nevirapine, showed possible bioaccumulation in tadpoles. Besides, an increase in the bioaccumulation was observed when increasing the exposure concentration. In addition, the enzymatic biomarkers measured to evaluate the toxicological effects showed that acethylcholinesterase activity was similar to that of the control group, while glutathione S-transferase activity was increased, indicating potential oxidative stress damage. Our results also allowed demonstrating the usefulness of chemometric algorithms to quantitate analytes in complex matrices, such as those absorbed by tadpoles in aquatic ecosystems. The results also evidenced the short-term antiretroviral bioaccumulation in tadpoles and the alteration of antioxidant systems, highlighting the need of environmental studies to elucidate the ecotoxicological risk of antiretrovirals in humans and wildlife.

Dual-emission CdTe/AgInS2 photoluminescence probe coupled to neural network data processing for the simultaneous determination of folic acid and iron (II)

This work focused on the combination of CdTe and AgInS₂ quantum dots in a dual-emission nanoprobe for the simultaneous determination of folic acid and Fe(II) in pharmaceutical formulations. The surface chemistry of the used QDs was amended with suitable capping ligands to obtain appropriate reactivity in terms of selectivity and sensitivity towards the target analytes. The implementation of PL-based sensing schemes combining multiple QDs of different nature, excited at the same wavelength and emitting at different ones, allowed to obtain a specific analyte-response profile. The first-order fluorescence data obtained from the whole emission spectra of the CdTe/AgInS₂ combined nanoprobe upon interaction with folic acid and Fe(II) were processed by using chemometric tools, namely partial least-squares (PLS) and artificial neural network (ANN). This enabled to circumvent the selectivity issues commonly associated with the use of QDs prone to indiscriminate interaction with multiple species, which impair reliable and accurate quantification in complex matrices samples. ANN demonstrated to be the most efficient chemometric model for the simultaneous determination of both analytes in binary mixtures and pharmaceutical formulations due to the non-linear relationship between analyte concentration and fluorescence data that it could handle. The R²_P and SEP% obtained for both analytes quantification in pharmaceutical formulations through ANN modelling ranged from 0.92 to 0.99 and 5.7-9.1%, respectively. The obtained results revealed that the developed approach is able to quantify, with high reliability and accuracy, more than one analyte in complex mixtures and real samples with pharmaceutical interest.

Enhancing the selectivity of liquid chromatography-mass spectrometry by using trilinear decomposition on LC-MS data: An application to three-way calibration of coeluting analytes in human plasma

The high selectivities of liquid chromatography and mass spectrometry make liquid chromatography-mass spectrometry one of the most popular tools for quantitative analysis in complex chemical, biological, and environmental systems, while the potential mathematical selectivity of liquid chromatography-mass spectrometry is rarely investigated. This work discussed the mathematical selectivity of liquid chromatography-mass spectrometry by three-way calibration based on the trilinear model, with an application to quantitative analysis of coeluting aromatic amino acids in human plasma. By the trilinear decomposition of the constructed liquid chromatography-mass spectrometry-sample trilinear model and individual regression of the decomposed relative intensity versus concentration, the proposed three-way calibration method successfully achieved quantitative analysis of coeluting aromatic amino acids in human plasma, even in the presence of uncalibrated interferent(s) and a varying background. This analytical method can ease the requirements for sample preparation and complete chromatographic separation of components, reduce the use of organic solvents, decrease the time of chromatographic separation, and increase the peak capacity of liquid chromatography-mass spectrometry. As a "green analytical method", the liquid chromatography-mass spectrometry three-way calibration method can provide a promising tool for direct and fast quantitative analysis in complex systems containing uncalibrated spectral interferents, especially for the situation where the coelution problem is difficult to overcome.

Data fusion strategies to combine sensor and multivariate model outputs for multivariate statistical process control

Process analytical technologies (PAT) applied to process monitoring and control generally provide multiple outputs that can come from different sensors or from different model outputs generated from a single multivariate sensor. This paper provides a contribution to current data fusion strategies for the combination of sensor and/or model outputs in the development of multivariate statistical process control (MSPC) models. Data fusion is explored through three real process examples combining output from multivariate models coming from the same sensor uniquely (in the near-infrared (NIR)-based end point detection of a two-stage polyester production process) or the combination of these outputs with other process variable sensors (using NIR-based model outputs and temperature values in the end point detection of a fluidized bed drying process and in the on-line control of a distillation process). The three examples studied show clearly the flexibility in the choice of model outputs (e.g. key properties prediction by multivariate calibration, process profiles issued from a multivariate resolution method) and the benefit of using MSPC models based on fused information including model outputs towards those based on raw single sensor outputs for both process control and diagnostic and interpretation of abnormal process situations. The data fusion strategy proposed is of general applicability for any analytical or bioanalytical process that produces several sensor and/or model outputs. Graphical abstract.

Soft-trilinear constraints for improved quantitation in multivariate curve resolution

Soft trilinearity constraints give a range of feasible solutions (grey) that envelop the true solution (blue). PARAFAC2 (green) and MCR-ALS results (black) are shown for comparison.

A simple high-throughput headspace gas chromatographic method for the determination of dissolved oxygen in aqueous samples

This paper reports on a headspace gas chromatographic method (HS-GC) for the accurate determination of dissolved oxygen (DO) in domestic and industrial wastewater. It was based on GC measurement of oxygen in the headspace gas that is released from the dissolved oxygen in a liquid sample in a closed vial. The results show that the relative standard deviation of the method in quintuplicate testing was < 2.0% and the relative differences of results were less than 4.0% for the real river water samples when compared with the widely used reference method (the electrode method); The linear range of the method is about 0-9.0 mg/L with the limit of detection of 0.30 mg/L. The present method is simple in both sample preparation and method calibration. It is particularly efficient in the batch testing mode. Overall, the method is more reliable than the current reference method when used with samples from a wide range of sources.

Contribution to second-order calibration based on multivariate curve resolution with and without previous chromatographic synchronization

Rotational ambiguity in the bilinear solutions provided by multivariate curve resolution - alternating least-squares (MCR-ALS) leads to an additional source of uncertainty in the estimation of analyte concentrations by second-order multivariate calibration. The phenomenon is particularly important when measuring matrix instrumental data derived from liquid chromatography with spectral detection, where elution time profiles usually vary from sample to sample both in position and shape. This makes the data non-trilinear, precluding the use of unique trilinear decomposition models. The present report compares some analytical results achieved by: (1) the usual MCR-ALS analysis of augmented matrices built from raw matrix data and (2) a previously reported procedure based on synchronizing the MCR-ALS elution time profiles using correlation optimized warping (COW), reconstructing the augmented matrix with the spectra and the aligned chromatograms, and then applying MCR-ALS again with the trilinearity constraint, leading to unique solutions, which is possible because the warping process restores the trilinearity of the data. We show that alternative (2) does not solve the rotational ambiguity issues and artificially modifies the original data, without significant improvements in analytical performance. In the simulated systems, the best average errors for alternative (1) were about 2%, whereas for alternative (2) they were in the range 4-11%. For the experimental system, the corresponding errors were 2-3% and 3-4% respectively, i.e. with no significant improvement in going to alternative (2). All efforts should be directed to reduce the degree of rotational ambiguity by applying a full battery of chemically reasonable constraints.

Simultaneous determination of carbendazim and chlorothalonil pesticide residues in peanut oil using excitation-emission matrix fluorescence coupled with three-way calibration method

A novel fluorescence application for simultaneous determination of two common fungicide pesticides (carbendazim and chlorothalonil) in peanut oil is presented. Using the strategy of combining excitation-emission matrix (EEM) fluorescence with three-way calibration methods, the proposed approach successfully achieved quantitative analysis of carbendazim and chlorothalonil pesticide residues in peanut oil, even with highly overlapped spectra. It needs little preparation, using "mathematical separation" instead of "analytical separation" to achieve concentration prediction of target analytes in complex systems. Each analyte was performed using fluorescence spectroscopy after instrument spectral correction and scatter removal. Then the data were modeled with two three-way calibration algorithms, including alternating trilinear decomposition (ATLD) and alternating penalty trilinear decomposition (APTLD). The results indicated that APTLD performed slightly better than ATLD for this system. The carbendazim and chlorothalonil can be recognized simultaneously with the correlation coefficients all above 0.96 between resolved spectra and actual spectra. Satisfactory results have been achieved with the average recoveries (mean ± standard deviation) of carbendazim and chlorothalonil being 100.2 ± 6.7% and 99.7 ± 6.7%, respectively. Moreover, as for carbendazim and chlorothalonil, the sensitivity (SENs) are 1.50 × 10² and 3.80 × 10² mL ng^-1, the limits of detection (LODs) are 11 ng mL^-1 and 4.3 ng mL^-1, the limit of quantitation (LOQ) are 33.33 ng mL^-1 and 13.03 ng mL^-1, respectively. The above results demonstrated that the proposed method is sensitive, fast and accurate for direct quantitative analysis of multiple pesticide residues in complex matrix such as that of peanut oil.

Determination of uric acid in synthetic urine by using electrochemical surface oxidation enhanced Raman scattering

In this work, a new and easy methodology to determine uric acid in relevant samples using Raman spectroelectrochemistry is presented. The spectroelectrochemistry experiment is based on the in-situ formation of a suitable substrate that enables the enhancement of the Raman signal of an analyte during the oxidation stage of a silver electrode. This phenomenon is known as electrochemical surface oxidation enhanced Raman scattering (EC-SOERS) and has proved to be useful in quantitative analysis using disposable screen printed electrodes. The successful combination of EC-SOERS with PARAFAC analysis allows the determination of uric acid in a relevant complex sample avoiding the use of standard addition method and without using a baseline correction, which simplifies the application of such methodology in routine analysis.

Four- and five-way excitation-emission luminescence-based data acquisition and modeling for analytical applications. A review

The latest advances in both theory and experimental procedures on third-order/four-way and fourth-order/five-way calibration methods are discussed. This report is focused on excitation-emission (fluorescence and phosphorescence) matrices generation, employing different variables as the third data mode (time retention in chromatography, pH gradient, fluorescence/phosphorescence lifetime, kinetics, or other chemical treatments). Fully capitalizing on the second-order advantage, it has been possible to develop appealing analytical applications in spite of the complexity of the data. Extraction of the significant chemical information about the system under study as well as the individual abundance of the contributing constituents after proper higher-order data decomposition has allowed to analytical researchers performing quantitative analysis of complex samples. The experimental works reported up to the present are introduced and discussed in order to illustrate concepts. Throughout this work, the analytical benefits achieved by modeling third- and fourth-order data are exposed, attempting to contribute to the ongoing debate in the chemometric community regarding the existence and the true nature of the third-order advantage.

A novel fluorimetric method for glyphosate and AMPA determination with NBD-Cl and MCR-ALS

We report the development of a new analytical method for the quantification of N-(phosphonomethyl)glycine (glyphosate) and (aminomethyl)phosphonic acid (AMPA) by combining spectrofluorimetry and multivariate calibration. In this study, fluorescence spectroscopy was used to quantify glyphosate and AMPA, which were previously derivatized with the fluorogenic reagent: 4-chloro-7-nitrobenzofurazan (NBD-Cl). Fluorescence excitation-emission matrices (EEM) were recorded by exciting between 400 and 500 nm, and measuring the emission between 500 and 610 nm. The second-order data obtained were processed using the Multivariate Curve Resolution with Alternating Least Square (MCR-ALS) methodology. The developed method was used to predict different concentrations of glyphosate and AMPA in validation samples. In addition, the presence of the herbicide was evaluated in real samples: a commercial formulation and a water sample from a cultivated area. For this purpose, the standard addition method was used to study the matrix effect in each case. The ranges of working concentrations obtained for this new method are in agreement with the amounts found in surface water samples near a direct sowing soybean growing region in Argentina.

High-throughput quantitation of bovine milk proteins and discrimination of commercial milk types by external cavity-quantum cascade laser spectroscopy and chemometrics

Mid-infrared (IR) external cavity-quantum cascade laser (EC-QCL) spectroscopy combined with partial least square modeling (PLS) enables quantitation of bovine milk proteins and discrimination of commercial milk types.

Simultaneously quantifying intracellular FAD and FMN using a novel strategy of intrinsic fluorescence four-way calibration

The simultaneous quantitative analysis of intracellular metabolic coenzymes flavin adenine dinucleotide (FAD) and flavin mononucleotide (FMN) is of interest because they participate in many electron-transfer reactions of metabolism. But, the simultaneous quantitative analysis of FAD and FMN is hard to be achieved by traditional analytical methods. This paper proposes a novel strategy of intrinsic fluorescence coupled with four-way calibration method for simultaneous quantitative analysis of intracellular metabolic coenzymes FAD and FMN. Through mathematical separation, this proposed analytical method efficiently achieved the simultaneous quantitative analysis of metabolic coenzymes FAD and FMN in the cell, despite the fact that uncalibrated spectral interferents coexist in the system. The predicted concentrations of FAD and FMN in the cell are 217.0 ± 6.9 and 155.0 ± 1.7 pmol/10⁶ cells respectively, which were validated by the approved liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. This analytical method with second-order advantage simply requires the cell solution to be diluted by a buffer, it could introduce an interesting analytical strategy for multianalyte direct quantitative analysis in complex biological systems. In addition, we explore the third-order advantage of four-way calibration by a comparative study based on this real fluorescence data. The comparisons indicate that a four-way calibration method can provide higher sensitivity and more resolving power than a three-way calibration method.

Comparison of several third-order correction algorithms applied to fluorescence excitation-emission-sample data array: Interference-free determination of polycyclic aromatic hydrocarbons in water pollution

Interference-free determination of polycyclic aromatic hydrocarbons (PAHs) in water pollution is proposed based on third-order correction algorithms with quadrilinear component modeling applied to the constructed four way fluorescence excitation-emission-sample data array with higher accuracy and better predictive ability than second-order (three-dimension) correction. Alternating weighted residue constraint quadrilinear decomposition (AWRCQLD), quadrilinear parallel factor analysis (4-PARAFAC), alternate penalty quadrilinear decomposition (APQLD) and alternate penalty trilinear decomposition (APTLD) are applied to acenaphthene (ANA), naphthalene (NAP) and fluorene (FLU) respectively. Fulvic acid affects PAHs determination seriously in real-world situation, so it is simulated as an interfering agent. Excitation-emission fluorescence matrixes (EEMs) of PAHs are measured at different volumes of fulvic acid simulated different interference conditions, to construct a four-way data array. After the four-way spectra data is analyzed by AWRCQLD, 4-PARAFAC, and APQLD, three-way EEMs analyzed by APTLD, results show that, on the one hand, PAHs can be measured more accurately with four-way data combined with third-order calibration than lower-order. On the other hand, AWRCQLD algorithm can reflect the superiority of third-order advantage better with higher recovery rate and smaller root mean square error, than other third-order or second-order correction algorithms.

Online Third-Order Liquid Chromatographic Data with Native and Photoinduced Fluorescence Detection for the Quantitation of Organic Pollutants in Environmental Water

Third-order liquid chromatographic data were generated online for the simultaneous quantitation of six organic environmental pollutants. The employed strategy consists in reducing the linear flow rate at the column outlet. A postcolumn UV reactor and a fluorimetric detector allowed to properly record both photoinduced and native excitation-emission fluorescence matrices (EEPIFMs and EEFMs, respectively). The obtained third-order liquid chromatography data were chemometrically processed with the multivariate curve resolution-alternating least-squares model. The sensitivity of the overall analytical method was enhanced by a very simple solid-phase extraction with C18 membranes, to be able to successfully apply it to natural water samples tested as real matrices. Favorable detection limits for the investigated pollutants, ranging from 0.02 to 0.27 ng mL^-1, were attained, with relative prediction errors between 2 and 7%. Since the studied samples contain uncalibrated interferents, the applied strategy achieves the second-order advantage. Implications regarding the potential achievement of the third-order advantage are discussed.

Rapid, simultaneous and interference-free determination of three rhodamine dyes illegally added into chilli samples using excitation-emission matrix fluorescence coupled with second-order calibration method

In this study, a smart and green analytical method based on the second-order calibration algorithm coupled with excitation-emission matrix (EEM) fluorescence was developed for the determination of rhodamine dyes illegally added into chilli samples. The proposed method not only has the advantage of high sensitivity over the traditional fluorescence method but also fully displays the "second-order advantage". Pure signals of analytes were successfully extracted from severely interferential EEMs profiles via using alternating trilinear decomposition (ATLD) algorithm even in the presence of common fluorescence problems such as scattering, peak overlaps and unknown interferences. It is worth noting that the unknown interferents can denote different kinds of backgrounds, not only refer to a constant background. In addition, the method using interpolation method could avoid the information loss of analytes of interest. The use of "mathematical separation" instead of complicated "chemical or physical separation" strategy can be more effective and environmentally friendly. A series of statistical parameters including figures of merit and RSDs of intra- (≤1.9%) and inter-day (≤6.6%) were calculated to validate the accuracy of the proposed method. Furthermore, the authoritative method of HPLC-FLD was adopted to verify the qualitative and quantitative results of the proposed method. Compared with the two methods, it also showed that the ATLD-EEMs method has the advantages of accuracy, rapidness, simplicity and green, which is expected to be developed as an attractive alternative method for simultaneous and interference-free determination of rhodamine dyes illegally added into complex matrices.

New insights into the structure-performance relationships of mesoporous materials in analytical science

Mesoporous materials are ideal carriers for guest molecules and they have been widely used in analytical science. The unique mesoporous structure provides special properties including large specific surface area, tunable pore size, and excellent pore connectivity. The structural properties of mesoporous materials have been largely made use of to improve the performance of analytical methods. For instance, the large specific surface area of mesoporous materials can provide abundant active sites and increase the probability of contact between analytes and active sites to produce stronger signals, thus leading to the improvement of detection sensitivity. The connections between analytical performances and the structural properties of mesoporous materials have not been discussed previously. Understanding the "structure-performance relationship" is highly important for the development of analytical methods with excellent performance based on mesoporous materials. In this review, we discuss the structural properties of mesoporous materials that can be optimized to improve the analytical performance. The discussion is divided into five sections according to the analytical performances: (i) selectivity-related structural properties, (ii) sensitivity-related structural properties, (iii) response time-related structural properties, (iv) stability-related structural properties, and (v) recovery time-related structural properties.

Advanced spectrophotometric chemometric methods for resolving the binary mixture of doxylamine succinate and pyridoxine hydrochloride

The prediction power of partial least squares (PLS) and multivariate curve resolution-alternating least squares (MCR-ALS) methods have been studied for simultaneous quantitative analysis of the binary drug combination - doxylamine succinate and pyridoxine hydrochloride. Analysis of first-order UV overlapped spectra was performed using different PLS models - classical PLS1 and PLS2 as well as partial robust M-regression (PRM). These linear models were compared to MCR-ALS with equality and correlation constraints (MCR-ALS-CC). All techniques operated within the full spectral region and extracted maximum information for the drugs analysed. The developed chemometric methods were validated on external sample sets and were applied to the analyses of pharmaceutical formulations. The obtained statistical parameters were satisfactory for calibration and validation sets. All developed methods can be successfully applied for simultaneous spectrophotometric determination of doxylamine and pyridoxine both in laboratory-prepared mixtures and commercial dosage forms.

Multivariate Curve Resolution for Signal Isolation from Fast-Scan Cyclic Voltammetric Data

The use of multivariate analysis techniques, such as principal component analysis–inverse least-squares (PCA–ILS), has become standard for signal isolation from in vivo fast-scan cyclic voltammetric (FSCV) data due to its superior noise removal and interferent-detection capabilities. However, the requirement of collecting separate training data for PCA–ILS model construction increases experimental complexity and, as such, has been the source of recent controversy. Here, we explore an alternative method, multivariate curve resolution–alternating least-squares (MCR–ALS), to circumvent this issue while retaining the advantages of multivariate analysis. As compared to PCA–ILS, which relies on explicit user definition of component number and profiles, MCR–ALS relies on the unique temporal signatures of individual chemical components for analyte-profile determination. However, due to increased model freedom, proper deployment of MCR–ALS requires careful consideration of the model parameters and the imposition of constraints on possible model solutions. As such, approaches to achieve meaningful MCR–ALS models are characterized. It is shown, through use of previously reported techniques, that MCR–ALS can produce similar results to PCA–ILS and may serve as a useful supplement or replacement to PCA–ILS for signal isolation from FSCV data.

Chemometrics in analytical chemistry-part I: history, experimental design and data analysis tools

Chemometrics has achieved major recognition and progress in the analytical chemistry field. In the first part of this tutorial, major achievements and contributions of chemometrics to some of the more important stages of the analytical process, like experimental design, sampling, and data analysis (including data pretreatment and fusion), are summarised. The tutorial is intended to give a general updated overview of the chemometrics field to further contribute to its dissemination and promotion in analytical chemistry.

Graphitic nanocapsules: design, synthesis and bioanalytical applications

Graphitic nanocapsules are emerging nanomaterials which are gaining popularity along with the development of carbon nanomaterials. Their unique physical and chemical properties, as well as good biocompatibility, make them desirable agents for biomedical and bioanalytical applications. Through rational design, integrating graphitic nanocapsules with other materials provides them with additional properties which make them versatile nanoplatforms for bioanalysis. In this feature article, we present the use and performance of graphitic nanocapsules in a variety of bioanalytical applications. Based on their chemical properties, the specific merits and limitations of magnetic, hollow, and noble metal encapsulated graphitic nanocapsules are discussed. Detection, multi-modal imaging, and therapeutic applications are included. Future directions and potential solutions for further biomedical applications are also suggested.

Prediction of Lignin Content in Different Parts of Sugarcane Using Near-Infrared Spectroscopy (NIR), Ordered Predictors Selection (OPS), and Partial Least Squares (PLS)

The building of multivariate calibration models using near-infrared spectroscopy (NIR) and partial least squares (PLS) to estimate the lignin content in different parts of sugarcane genotypes is presented. Laboratory analyses were performed to determine the lignin content using the Klason method. The independent variables were obtained from different materials: dry bagasse, bagasse-with-juice, leaf, and stalk. The NIR spectra in the range of 10 000-4000 cm^-1 were obtained directly for each material. The models were built using PLS regression, and different algorithms for variable selection were tested and compared: iPLS, biPLS, genetic algorithm (GA), and the ordered predictors selection method (OPS). The best models were obtained by feature selection with the OPS algorithm. The values of the root mean square error prediction (RMSEP), correlation of prediction ( R_P), and ratio of performance to deviation (RPD) were, respectively, for dry bagasse equal to 0.85, 0.97, and 2.87; for bagasse-with-juice equal to 0.65, 0.94, and 2.77; for leaf equal to 0.58, 0.96, and 2.56; for the middle stalk equal to 0.61, 0.95, and 3.24; and for the top stalk equal to 0.58, 0.96, and 2.34. The OPS algorithm selected fewer variables, with greater predictive capacity. All the models are reliable, with high accuracy for predicting lignin in sugarcane, and significantly reduce the time to perform the analysis, the cost and the chemical reagent consumption, thus optimizing the entire process. In general, the future application of these models will have a positive impact on the biofuels industry, where there is a need for rapid decision-making regarding clone production and genetic breeding program.