Analytical Advantages of Multivariate Data Processing. One, Two, Three, Infinity?

A kind of multi-dot ensemble regression AI detector for lubricating oil additive content based on lambert-beer law

In this work, we propose a Multi-dot Ensemble Regression AI detector (MER) based on the Lambert-Beer law. We pre-trained a model using the infrared spectral data of target additives collected in advance to detect the target additives in unknown oil samples. The algorithm's feasibility was validated by assessing the content of additives in a series of simulated commercial oil samples that were not part of the training set. We established models for three common lubricating oil additives (anti-friction, anti-wear, and antioxidant agents), demonstrating their effectiveness in oil sample detection. Additionally, by comparing with other algorithms, we established the superiority of MER in small-sample learning scenarios.

Third-order calibration applied to process surfactant-modulated excitation-emission matrix four-way fluorescence data for the direct determination of four polycyclic aromatic hydrocarbons in oilfield produced water

Fluorescence spectroscopy is a powerful tool to determine polycyclic aromatic hydrocarbons (PAHs) owing to the strong endogenous fluorescence of these compounds. However, the presence of unknown interferences and overlapped spectra hinders the accurate determination of PAHs in oilfield produced water. Moreover, surfactants frequently coexist in oilfield produced water and will seriously affect the fluorescence signals of PAHs. Herein, a new methodology applying third-order calibration to process four-way (4D) fluorescence data was proposed to solve these problems and achieve accurate determination of pyrene, fluorene, phenanthrene, and fluoranthene as an example in oilfield produced water. The methodology is based on excitation-emission matrix fluorescence modulated by different concentrations of sodium dodecyl benzene sulfonate (SDBS) in the analyzed samples. The 4D fluorescence data were processed by third-order calibration methods including four-way parallel factor analysis (4-PARAFAC) and alternating weighted residue constraint quadrilinear decomposition (AWRCQLD), and the results were compared with those of second-order calibration methods. It was proved that third-order calibration was capable of accurately identifying and quantifying PAHs together with SDBS in oilfield produced water, which has better quantitative results and figures of merit compared to second-order calibration. This study provided a new approach to generating 4D fluorescence data and opened up an avenue for the accurate determination of PAHs in complex oilfield produced water with surfactants.

Resolving fluorescence spectra of Maillard reaction products formed on bovine serum albumin using parallel factor analysis

Formation of Maillard reaction products (MRPs) is increasingly studied by the use of fluorescence spectroscopy, and most often, by measuring single excitation/emission pairs or use of unresolved spectra. However, due to the matrix complexity and potential co-formation of fluorescent oxidation products on tryptophan and tyrosine residues, this practice will often introduce errors in both identification and quantification. The present study investigates the combination of fluorescence excitation emission matrix (EEM) spectroscopy and parallel factor analysis (PARAFAC) to resolve the EEMs into its underlying fluorescent signals, allowing for better identification and quantification of MRPs. EEMs were recorded on a sample system of bovine serum albumin incubated at 40 °C for up to one week with either glucose, methylglyoxal or glyoxal added. Ten unique PARAFAC components were resolved, and assignment was attempted based on similarity with fluorescence of pure standards of MRPs and oxidation products and reported data from literature. Of the ten fluorescent PARAFAC components, tyrosine and buried and exposed tryptophan were resolved and identified, as well as the formation of specific MRPs (argpyrimidine and Nα-acetyl-Nδ-(5-methyl-4-imidazolon-2-yl)ornithine) and tryptophan oxidation products (kynurenine and dioxindolylalanine). The formation of the PARAFAC resolved protein modifications were qualitatively validated by liquid chromatography-mass spectrometry.

A rapid and direct method for dating blue pen ink in documents using multiset modeling of infrared spectroscopy and mass spectrometry data

The dating of documents is crucial in forensic chemistry, particularly for verifying their authenticity. This study aimed to develop a rapid and direct method for the dating of pen ink in documents, using a combination of Fourier transform infrared spectroscopy in attenuated reflectance mode (FTIR-ATR), desorption electrospray ionization mass spectrometry (DESI-MS) and multiple ensemble data modeling. Two sets of paper document samples containing writing in blue pen ink were investigated: (I) artificially aged documents and (II) real documents dating from 1960 to 2022. The FTIR-ATR spectra of both sets of samples showed a decrease in absorbance at ∼1584 cm-1, related to the chemical modification of the CN bond in the molecular structure of Basic Violet 3 (BV3), one of the main dyes used in blue pen ink. DESI-MS confirmed the presence of BV3 and its degradation by-products in all the samples, indicating its widespread utilization in blue pen ink production. Moreover, DESI-MS detected combinations of dyes within the ink composition. The models were built using the DESI-MS and FTIR-ATR data separately, but the error and trend were significantly reduced when both sets of data were used. The combination of DESI-MS and FTIR-ATR spectral information resulted in a final predictive model with low error for pen inks from real documents in writing from the years 1960 to 2022. These analyses proved to be effective for the dating of pen inks and are suitable for use in routine forensic analysis, providing a direct and rapid method that allows for accurate prediction.

Molecular and translational biology of the blood-based VeriStrat® proteomic test used in cancer immunotherapy treatment guidance

INTRODUCTION

The VeriStrat® test (VS) is a blood-based assay that predicts a patient's response to therapy by analyzing eight features in a spectrum obtained from matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) analysis of human serum and plasma. In a recent analysis of the INSIGHT clinical trial (NCT03289780), it was found that the VS labels, VS Good and VS Poor, can effectively predict the responsiveness of non-small cell lung cancer (NSCLC) patients to immune checkpoint inhibitor (ICI) therapy. However, while VS measures the intensities of spectral features using MALDI-TOF analysis, the specific proteoforms underlying these features have not been comprehensively identified.

OBJECTIVES

The objective of this study was to identify the proteoforms that are measured by VS.

METHODS

To resolve the features obtained from the low-resolution MALDI-TOF procedure used to acquire mass spectra for VS DeepMALDI® analysis of serum was employed. This technique allowed for the identification of finer peaks within these features. Additionally, a combination of reversed-phase fractionation and liquid chromatography-tandem mass spectrometry (LC-MS/MS) was then used to identify the proteoforms associated with these peaks.

RESULTS

The analysis revealed that the primary constituents of the spectrum measured by VS are serum amyloid A1, serum amyloid A2, serum amyloid A4, C-reactive protein, and beta-2 microglobulin.

CONCLUSION

Proteoforms involved in host immunity were identified as significant components of these features. This newly acquired information improves our understanding of how VS can accurately predict patient response to therapy. It opens up additional studies that can expand our understanding even further.

Detection and Quantification of Bisphenol A in Surface Water Using Absorbance-Transmittance and Fluorescence Excitation-Emission Matrices (A-TEEM) Coupled with Multiway Techniques

In the present protocol, we determined the presence and concentrations of bisphenol A (BPA) spiked in surface water samples using EEM fluorescence spectroscopy in conjunction with modelling using partial least squares (PLS) and parallel factor (PARAFAC). PARAFAC modelling of the EEM fluorescence data obtained from surface water samples contaminated with BPA unraveled four fluorophores including BPA. The best outcomes were obtained for BPA concentration (R2 = 0.996; standard deviation to prediction error's root mean square ratio (RPD) = 3.41; and a Pearson's r value of 0.998). With these values of R2 and Pearson's r, the PLS model showed a strong correlation between the predicted and measured BPA concentrations. The detection and quantification limits of the method were 3.512 and 11.708 micro molar (µM), respectively. In conclusion, BPA can be precisely detected and its concentration in surface water predicted using the PARAFAC and PLS models developed in this study and fluorescence EEM data collected from BPA-contaminated water. It is necessary to spatially relate surface water contamination data with other datasets in order to connect drinking water quality issues with health, environmental restoration, and environmental justice concerns.

A Comprehensive Review on the Excitation-Emission Matrix Fluorescence Spectroscopic Characterization of Petroleum-Containing Substances: Principles, Methods, and Applications

Petroleum-containing substance (PCS) is a general term used for petroleum and its derivatives. A comprehensive characterization of PCSs is crucial for resource exploitation, economic development and environmental protection. Fluorescence spectroscopy, especially excitation-emission matrix fluorescence (EEMF) spectroscopy, has been proved to be a powerful tool to characterize PCSs since its remarkable sensitivity, selectivity, simplicity and high efficiency. However, there is a lack of systematic review focusing on this field in the literature. This paper reviews the fundamental principles and measurements of EEMF for characterizing PCSs, and makes a systematic introduction to various information mining methods including basic peak information extraction, spectral parameterization and some commonly used chemometric methods. In addition, recent advances in applying EEMF to characterize PCSs during the whole life-cycle process of petroleum are also revisited. Furthermore, the current limitations of EEMF in the measurement and characterization of PCSs are discussed and corresponding solutions are provided. For promoting the future development of this field, the urgent need to build a relatively complete EEMF fingerprint library to trace PCSs, not only pollutants but also crude oil and petroleum products, is proposed. Finally, the extensions of EEMF to high-dimensional chemometrics and deep learning are prospected, with the expectation of solving more complex systems and problems.

Variational Mode Decomposition Weighted Multiscale Support Vector Regression for Spectral Determination of Rapeseed Oil and Rhizoma Alpiniae Offcinarum Adulterants

The accurate prediction of the model is essential for food and herb analysis. In order to exploit the abundance of information embedded in the frequency and time domains, a weighted multiscale support vector regression (SVR) method based on variational mode decomposition (VMD), namely VMD-WMSVR, was proposed for the ultraviolet-visible (UV-Vis) spectral determination of rapeseed oil adulterants and near-infrared (NIR) spectral quantification of rhizoma alpiniae offcinarum adulterants. In this method, each spectrum is decomposed into K discrete mode components by VMD first. The mode matrix Uk is recombined from the decomposed components, and then, the SVR is used to build sub-models between each Uk and target value. The final prediction is obtained by integrating the predictions of the sub-models by weighted average. The performance of the proposed method was tested with two spectral datasets of adulterated vegetable oils and herbs. Compared with the results from partial least squares (PLS) and SVR, VMD-WMSVR shows potential in model accuracy.

Chemometric-assisted kinetic determination of oxytetracycline using AgInS2 quantum dots as PL sensing platforms

In this work a kinetic fluorometric methodology relying on the time-based monitoring of the photoluminescence quenching of AgInS₂ ternary quantum dots induced by oxytetracycline, was developed. The kinetic approach allowed not only to reduce the LOD and improve sensitivity and selectivity but also to collect second-order data that was explored for the quantification of the target analyte in the presence of uncalibrated interfering species. Upon processing the acquired second-order kinetic PL data by unfolded partial least-squares (U-PLS), oxytetracycline was quantified in commercially available pharmaceutical formulations. The obtained results, namely an R²_P higher than 0.99 and RE lower than 8%, proved the suitability and accuracy of the developed approach.

Defining a Path Toward the Use of Fast-Scan Cyclic Voltammetry in Human Studies

Fast Scan Cyclic Voltammetry (FSCV) has been used for decades as a neurochemical tool for in vivo detection of phasic changes in electroactive neurotransmitters in animal models. Recently, multiple research groups have initiated human neurochemical studies using FSCV or demonstrated interest in bringing FSCV into clinical use. However, there remain technical challenges that limit clinical implementation of FSCV by creating barriers to appropriate scientific rigor and patient safety. In order to progress with clinical FSCV, these limitations must be first addressed through (1) appropriate pre-clinical studies to ensure accurate measurement of neurotransmitters and (2) the application of a risk management framework to assess patient safety. The intent of this work is to bring awareness of the current issues associated with FSCV to the scientific, engineering, and clinical communities and encourage them to seek solutions or alternatives that ensure data accuracy, rigor and reproducibility, and patient safety.

Rapid determination of sulfamethoxazole and trimethoprim illegally added to health products using excitation-emission matrix fluorescence coupled with the second-order calibration method

In this work, a simple and fast analytical method based on a self-weighted alternating trilinear decomposition (SWATLD) algorithm coupled with excitation-emission matrix (EEM) fluorescence was developed for the simultaneous determination of sulfamethoxazole (SMZ) and trimethoprim (TMP) illegally added to health products. With the second-order advantage, the proposed method obtained satisfactory results in the presence of peak overlap and unknown interferences. The analysis time for a single sample is only 0.8 minutes. The average spiked recoveries of SMZ and TMP in three health product spiked samples were in the range of 91.0-106.2% and 86.8-107.8%, respectively. The relative standard deviations (RSDs) were lower than 8.6%. In addition, verification parameters including sensitivity (SEN), selectivity (SEL), the limit of detection (LOD), the limit of quantification (LOQ), intra-day precision, and inter-day precision were calculated, and the results show that the proposed method is feasible. The quantitative results of the proposed method were further confirmed by the LC-MS/MS method, which proved that the proposed method was efficient and green for drug-abuse monitoring of SMZ and TMP in health products.

Data analysis strategies for the characterization of chemical contaminant mixtures. Fish as a case study

Thousands of chemicals are potentially contaminating the environment and food resources, covering a wide spectrum of molecular structures, physico-chemical properties, sources, environmental behavior and toxic profiles. Beyond the description of the individual chemicals, characterizing contaminant mixtures in related matrices has become a major challenge in ecological and human health risk assessments. Continuous analytical developments, in the fields of targeted (TA) and non-targeted analysis (NTA), have resulted in ever larger sets of data on associated chemical profiles. More than ever, the implementation of advanced data analysis strategies is essential to elucidate profiles and extract new knowledge from these large data sets. Specifically focusing on the data analysis step, this review summarizes the recent progress in integrating data analysis tools into TA and NTA workflows to address the challenging characterization of chemical mixtures in environmental and food matrices. As fish matrices are relevant in both aquatic pollution and consumer exposure perspectives, fish was chosen as the main theme to illustrate this review, although the present document is equally relevant to other food and environmental matrices. The key features of TA and NTA data sets were reviewed to illustrate the challenges associated with their analysis. Advanced filtering strategies to mine NTA data sets are presented, with a particular focus on chemical filters and discriminant analysis. Further, the applications of supervised and unsupervised multivariate analysis methods to characterize exposure to chemical mixtures, and their associated challenges, is discussed.

Multilinear Mathematical Separation in Chromatography

Chromatography is a powerful and generally applicable method for the analytical separation and quantification of the chemical constituents in complex mixtures because chromatographic separation can provide high selectivity by isolating all analytes from interferences. Multiway analysis based on the multilinear model is an increasingly widely used method for interference-free and fast determination of the chemical constituents also in complex mixtures because multilinear mathematical separation can provide high selectivity by extracting the pure signal of the analyte from the mixed signal of a real sample. By combining chromatographic separation with mathematical separation, multiway calibration method, multiway standard additions method, and multiway internal standard method can be established. Chromatography assisted by multiway analysis can reduce the requirements for complete chromatographic separation, save elution time, and decrease the consumption of the mobile phase, particularly when the peak coelution problem is difficult to solve. This review presents the fundamentals and analytical applications of multilinear mathematical separation in chromatography.

Three-way calibration using PARAFAC and MCR-ALS with previous synchronization of second-order chromatographic data through a new functional alignment of pure vectors for the quantification in the presence of retention time shifts in peak position and shape

In the present contribution is shown the application of the recently developed functional alignment of pure vectors (FAPV) as a proper algorithm to align second-order chromatographic data with severe retention time shifts in peak position and shape. FAPV decomposed a three-way chromatographic data array in their three modes (sample, spectral and elution time vectors), using a basis function to pre-process the non-linear mode (elution time) and then it aligns the functionalized pure vectors and reshapes the transformed vectors into matrices, restoring the trilinearity of second-order chromatographic data. The well-aligned three-way chromatographic data array is then successfully decomposed by advanced chemometric models such as parallel factor analysis (PARAFAC) and multivariate curve resolution - alternating least-squares (MCR-ALS) with the trilinearity constraint. The performance of this innovative analytical strategy based on PARAFAC and MCR-ALS with previous synchronization of data through FAPV algorithm is properly evaluated using real second-order chromatographic data with multiple artifacts, i.e., shifts in peak position and shape for the simultaneous quantification of amoxicillin and potassium clavulanate in commercial medicinal drugs. The present contribution compares some analytical results achieved by: (1) the usual MCR-ALS as a bilinear model applied in augmented data matrix without previous synchronization and with interval correlation optimized shifting (ICOSHIFT) and FAPV and (2) trilinear models using PARAFAC with ICOSHIFT and FAPV and trilinearity constraint in MCR-ALS with FAPV. Available results suggest that these strongly shifted and warped elution time profiles cause for the loss of trilinearity, which can be adequately restored by FAPV algorithm. PARAFAC performed a successful trilinear decomposition of three-way chromatographic data array with law values of relative prediction error (REP) in the order of 1.34-1.42% in both analytes.

Time-domain photoacoustic waveform analysis for glucose measurement

Photoacoustic (PA) effect is the product of light-ultrasound interactions and its time-domain waveform contains rich information. Besides optical absorption, the PA waveform inherently consists of other mechanical and thermal properties of the sample. They also have correlation with the target composition but have not been utilized in conventional PA spectroscopy. In this article, we propose a new concept named time-domain photoacoustic waveform spectroscopy (tPAWS) for chemical component quantification. It employs multiple variables inherently contained in the PA waveform excited by a single wavelength laser to extract informative features. The demonstration of glucose measurement in human blood serum (HBS) shows superior sensitivity and accuracy enhancement, compared to conventional amplitude-based PA measurement and NIR spectroscopy. Thanks to the sensitivity and accuracy of tPAWS, multiple wavelength sources and complex instrumentation used in conventional spectroscopic sensing methods can be avoided. TPAWS, as a novel physics-inspired sensing method, shows great potential for complementing or surpassing the current spectroscopic methods as a new sensing technique for chemical analysis.

Antibodies purification from human plasma using fractionation, chromatography and gel electrophoresis assisted by multivariate analysis of complimentary absorption and fluorescence spectra

Employing simple precipitation (fractionation) using Cohn method and weak anion exchange chromatography with DEAE resin, antibodies such as Immunoglobulin G are purified from human plasma. Fractions are eluted from column in four different regions depending on washing NaCl concentrations. Absorbance and excitation-emission fluorescence spectral data are measured for separated chromatographic fractions and analyzed using Multivariate Curve Resolution- Alternating Least Squares (MCR-ALS) and Parallel Factor Analysis (PARAFAC) techniques. Resolved concentration and spectral profiles provided information about existing components in each fraction. Protein and non-protein components are distinguished considering their resolved pure spectra and information from the two applied spectroscopic techniques is complementary. A number of components displayed both fluorescence and absorbance signals. When concentration of component (protein or non-protein) in sample is low and no significant absorbance signal is observed, sensitive fluorescence is useful to recognize the component and for non-fluorescent components absorbance spectra are utilized. Electrophoresis is utilized for separation of proteins in each fraction and showed that one distinguished protein from fluorescence and/or absorbance data can be a group of proteins with similar pure spectra and retention volume. Results showed presence of two protein in the first region (IgM and IgA), a group of proteins in second region (IgM, α-globulin, and IgG), a pure protein in third region (IgG), and a group of β-globulin proteins in fifth region. It is well and clearly shown that multivariate analysis of different data sets with complementary information is necessary for better interpretation of such technically simple and biochemically complicated systems.

Exploring the potential of combining chemometric approaches to model non-linear multi-way data with quantitative purposes - A case study

Second-order based calibration methods have been widely investigated capitalizing on the inherent benefits of the data structure and the decomposition models, demonstrating that second-order advantage is a property that conspires to a high likelihood success in the resolution of systems of varying complexity. This work aims to demonstrate the applicability of a combined chemometric strategy to solve non-linear multivariate calibration systems in the presence of non-multilinear multi-way data. The determination of histamine by differential pulse voltammetry at different pH is presented as case study. The experimental system has the outstanding difficulty arisen from the large displacement along the potential axis by the pH, which was successfully overcome by implementation of the presented combined strategy. For data modeling, MCR-ALS, U-PLS/RBL and U-PCA/RBL-RBF were used. MCR-ALS allowed unraveling the non-linear behavior between the signal and the concentration, and extracting the underlying profiles of the constituent. Quantitative analysis was performed through the three models, and a comparative evaluation of the predictive performance was done. The best results were achieved with U-PCA/RBL-RBF (mean recovery = 101%) whereas, MCR-ALS yield the lowest mean recovery for all samples (70%).

Multivariate optimization of a dispersive liquid-liquid microextraction method for the determination of six antiparasite drugs in kennel effluent waters by using second-order chromatographic data

Six veterinary active ingredients (imidacloprid, albendazole, fenbendazole, praziquantel, fipronil and permethrin) were extracted and quantified by liquid chromatography with diode array detection in water samples from a wetland system used for the treatment of waste from a dog breeding plant. Response surface methodology, based on least-squares and artificial neural networks modelling, was applied for the optimization of a dispersive liquid-liquid microextraction (DLLME) procedure. Firstly, two experimental designs were built for screening and optimization, respectively. Then, the desirability function was implemented for the simultaneous optimization of the six recoveries (chromatographic areas of the six compounds). The optimum conditions were: 600 μL of acetone (dispersive solvent), 670 μL of dichloromethane (extractant solvent) and 0.6 min of vortex mixing. The preconcentration factor was 37.5. Then, in order to identify and quantify the six drugs, second-order calibration with MCR-ALS modeling of HPLC-DAD data was implemented attaining successful results. The limits of quantification were 4 ng mL^-1 for imidaclopril, albendazole and fenbendazole; 8 ng mL^-1 for praziquantel and fipronil; and 26 ng mL^-1 for permethrin. The developed method allowed the quantitation of the target analytes, even in the presence of unexpected compounds from dirty water samples. The following maximum levels of veterinary drugs were found (in ng mL^-1): imidaclopril, 7; albendazole, 46; fenbendazole, 21; praziquantel, 29; fipronil, 29 and permethrin, 217.

Determination of ochratoxin A in coffee and tea samples by coupling second-order multivariate calibration and fluorescence spectroscopy

A new method to quantify the mycotoxin ochratoxin A (OTA) in coffee and tea samples is proposed based on second-order multivariate calibration and excitation-emission fluorescence matrix (EEFM) data. Experimental conditions were optimized by studying the effect of pH and various organized media on the fluorescence signal of OTA. For each analysed matrix (coffee grains and tea leaves), several sample pretreatments and calibration methods (external or standard addition) and data processing by chemometric models (e.g., parallel factor analysis/PARAFAC and multivariate curve resolution-alternating least squares/MCR-ALS) were evaluated and discussed. The MCR-ALS algorithm provided an adequate fit to the data for both samples, while PARAFAC was satisfactory only for the tea samples. Regarding the figures of merit, the limits of detection were in the range of 0.2-0.3 ng mL^-1; furthermore, low relative prediction errors, between 2% and 4%, were achieved in both the fortified and real samples. Accordingly, the proposed methodology was applied to analyse fortified roasted and green coffee and real tea leaf samples. Satisfactory recoveries were achieved (ranging from 92 to 110%), and the obtained concentrations were in agreement with the values obtained by the reference method (based on high-performance liquid chromatography with fluorescence detection/HPLC-FLD). In addition, all samples contained OTA levels lower than the maximum permissible levels. Finally, the proposed strategy allows the use of green analytical chemistry principles; for instance, the use of organic solvents and the generation of waste products were significantly lower than for similar analytical methods reported in the literature.

Simultaneous determination of phenazopyridine HCl and trimethoprim in presence of phenazopyridine HCl impurity by univariate and multivariate spectrophotometric methods - Quantification of phenazopyridine HCl impurity by univariate methods

Three univariate and two multivariate spectrophotometric methods were developed and subsequently validated to determine phenazopyridine HCl (PHZ) and trimethoprim (TMP) in the presence of 2,6-Diaminopyridine (2,6-DAP). The first univariate method depends on direct determination of phenazopyridine by measuring its absorbance at 412 nm and performed in concentration range of 1.00-10.00 μg/mL. Then the contribution of phenazopyridine is removed by dividing the mixture spectrum with PHZ divisor (5 μg/mL) after that the constant is mathematically subtracted and finally the generated spectrum is multiplied with the PHZ divisor. These steps eliminate PHZ contribution and the recovered spectrum is that of TMP and 2,6-DAP only where different methods can be applied to determine TMP and 2,6-DAP through this binary mixture spectrum. The first method to determine both components depends on measuring both TMP and 2,6-DAP through their first derivative (¹DD) spectra at 244.70 and 259.60 nm for TMP and 2,6-DAP, respectively with concentration ranges of 4.00-24.00 μg/mL TMP and 4.00-26.00 μg/mL 2,6-DAP. The second method depends on application of the isoabsorptive method which was used for TMP determination at its isoabsorptive point with 2,6-DAP at 242.64 nm with concentration range 1.00-20.00 μg/mL for TMP. The developed univariate methods were successfully applied to determine PHZ, TMP and PHZ impurity (2,6-DAP). Two multivariate methods were applied for determination of PHZ and TMP in presence of 2,6-DAP namely, Principle Component Regression (PCR) and Partial Least Squares (PLS). The results of the two models show that simultaneous determination of PHZ and TMP in presence of PHZ impurity can be performed in the concentration ranges of 6.00-14.00 μg/mL PHZ and 24.00-56.00 μg/mL TMP. All the proposed methods were successfully applied to analyze PHZ and TMP in pharmaceutical formulations without interference from the dosage form additives and the results were statistically compared with the reported method.

Simultaneous Determination of Medicinal Drugs with Overlapping Profiles Contained in Low Chromatographic Resolution Data using HPLC-DAD and Multivariate Curve Resolution

Background:

The increasing demand of effective pharmaceutical products directed to fight against malaria lead to the combination of at least two antimalarial drugs. This combination aims to minimize the Plasmodium falciparum resistance which is found when the most frequently used drugs are taken individually. Within this context, proguanil hydrochloride and chloroquine phosphate which have independent modes of action are taken together to prevent malaria. This paper aims to develop a fast and powerful analytical method for the simultaneous determination of proguanil hydrochloride and chloroquine phosphate in the commercial Paludrine/Avloclor dosage forms using a multi-way chromatographic calibration based on high-performance liquid chromatography with diode array detection (HPLC-DAD) and multivariate curve resolution – alternating leastsquares (MCR-ALS).

Methods:

A rapid and powerful analytical method based on HPLC-DAD and MCR-ALS was developed for the simultaneous quantification of proguanil hydrochloride and chloroquine phosphate in the commercial Paludrine/Avloclor antimalarial drugs. An isocratic mobile phase composed by 0.2 M ammonium acetate, acetonitrile, and methanol (40:25:35) and a flow rate of 1.2 mL min-1 were employed in the chromatographic runs with an elution time about 5 min.

Results:

This approach demonstrates that chromatographic analysis may become considerably simpler and economical in terms of time, cost, and organic solvent consumption when coupled to multiway calibration models such as MCR-ALS. In fact, this multi-way chromatographic calibration based on second-order HPLC-DAD data matrices (with extremely low chromatographic resolution) and MCR-ALS allows the development of greener analytical methods for complex samples. The proposed analytical method allowed the simultaneous quantification of two antimalarial APIs present in the commercial Paludrine/Avloclor drugs with low REP values below 8% for the simultaneous determination of proguanil hydrochloride and chloroquine phosphate.

Conclusion:

The proposed multi-way chromatographic strategy can be used for routine control of pharmaceutical dosage forms. It should be highlighted that MCR-ALS allowed to: (a) achieve the second- order advantage and the quantification of analytes in the presence of uncalibrated compounds such as coeluted profile measured in different magnitude of the signal in each successive chromatographic run and significant overlapping profiles and (b) separate the contribution of several components from chromatographic runs with extremely low separation of peaks through the deconvolution of the signal obtained, performing the so-called mathematical chromatography.

Multiblock modelling on the study of the kinetic degradation of rosuvastatin calcium in the presence of retention time shifts and rank deficiency

In pharmaceutical development, forced degradation studies are mandatory before the commercialization of any drug product. They aim at identifying the possible degradation routes and the potential products that may be formed during drug product shelf life. The most widely used techniques for monitoring this in the pharmaceutical industry are hyphenated techniques such as Liquid Chromatography coupled to ultraviolet diode array detector (LC-DAD). There are however some drawbacks, such as long analysis times required for the elution of all compounds and coelution, which is not easily detected since degradation products usually have spectra very similar to that of the drug. Chemometrics methods applied to LC-DAD data are capable of solving this issue, but the approaches described in the literature first require peak alignment to solve the rank deficiency problem, which is a delicate preprocessing method for high order data. The present work describes another approach where extra information - the kinetic degradation profiles - is included for the modelling, generating a third-order data set for each sample, resulting in a four-way array (sample x retention times x spectra x degradation profile). This approach has the advantage of using the information in the third mode to solve the peak co-elution problem without the need for peak alignment among samples. With the proposed approach, it was possible to study the degradation of calcium rosuvastatin, a modern cholesterol lowering drug, using a 2 min-run, despite all the challenges in the modelling of this data. The proposed strategy was compared to an approach based on augmenting the matrix in the spectral/kinetic modes (second order modelling strategy).

Recent applications of chemometrics in one- and two-dimensional chromatography

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

Analytical Figures of Merit for Multisensor Arrays

Multisensor arrays employing various sensing principles are a rapidly developing field of research as they allow simple and inexpensive quantification of various parameters in complex samples. Quantitative analysis with such systems is based on multivariate regression techniques, and deriving of traditional analytical figures of merit (e.g., sensitivity, selectivity, limit of detection, and limit of quantitation) for such systems is not obvious and straightforward. Nevertheless, it is absolutely needed for further development of the multisensor research field and for introducing these instruments into the general context of analytical chemistry. Here, we report on the protocol for calculation of sensitivity, selectivity, and detection limits for multisensor arrays. The results are provided and discussed in detail for several real-world data sets.

Rapid and simultaneous determination of three fluoroquinolones in animal-derived foods using excitation-emission matrix fluorescence coupled with second-order calibration method

The matter of fluoroquinolone residues in various foods still arouses wide public concern nowadays. In the present work, the strategy of excitation-emission matrix (EEM) fluorescence data coupled with second-order calibration method based on alternating normalization-weighted error (ANWE) algorithm was used to determine ofloxacin, lomefloxacin and ciprofloxacin in milk powder, milk and beef. Owning the unique "second-order advantage", the ANWE-assisted analytical method was proved to successfully and eco-friendly resolve the overlapped fluorescence spectra of multi-component in complex food matrixes without tedious pretreatment steps and sophisticated high-cost instrumentations. The feasibility of the proposed method was validated by experiments. The average spiked recoveries of three fluoroquinolones range from 82.6% to 110.5% with relative standard deviations lower than 7.4%, and the limits of detection range from 0.18 and 2.41 ng mL^-1. For further evaluation, analytical figures of merit such as sensitivity and selectivity, as well as the RSDs of intra-day (≦10.6%) and inter-day (≦9.4%) were calculated. The satisfactory analytical results demonstrated that the proposed strategy could be a competitive alternative for simple, rapid and simultaneous determination of multiple fluoroquinolones in animal-derived food samples.

Coupling of digital image processing and three-way calibration to assist a paper-based sensor for determination of nitrite in food samples

In this work, a novel and very interesting analytical methodology based on coupling of digital image processing and three-way calibration has been developed for determination of nitrite in food samples.

A Bottom-Up Approach for Data Mining in Bioaromatization of Beers Using Flow-Modulated Comprehensive Two-Dimensional Gas Chromatography/Mass Spectrometry

In this study, we report the combination of comprehensive two-dimensional gas chromatography (GC×GC) with multivariate pattern recognition through template matching for the assignment of the contribution of Brazilian Ale 02 yeast strain to the aroma profile of beer compared with the traditional Nottingham yeast. Volatile organic compounds (VOC) from two beer samples, which were fermented with these yeast strains were sampled using headspace solid-phase microextraction (HS-SPME). The aroma profiles from both beer samples were obtained using GC×GC coupled to a fast scanning quadrupole mass spectrometer. Data processing performed through multiway principal components analysis succeeded in separating both beer samples based on yeast strain. The execution of a simple and reliable procedure succeeded and identified 46 compounds as relevant for sample classification. Furthermore, the bottom-up approach spotted compounds found exclusively in the beer sample fermented with the Brazilian yeast, highlighting the bioaromatization properties introduced to the aroma profile by this yeast strain.

Time-resolved fluorescence and chemometrics-assisted excitation-emission fluorescence for qualitative and quantitative analysis of scopoletin and scopolin in Erycibe obtusifolia Benth

This paper presents a new strategy for qualitative identification of scopoletin and scopolin in Erycibe obtusifolia Benth using time-resolved (lifetimes) fluorescence and quantitative analysis with chemometrics-assisted excitation-emission matrix (EEM) fluorescence. Due to the significant spectral overlapping among analytes and interference, the use of the more selective time-resolved fluorescence is proposed for qualitative identification in quality control of traditional Chinese medicine (TCM) for the first time. Using the strategy of combining EEM fluorescence with second-order calibration method, i.e. parallel factor analysis (PARAFAC), the simultaneous quantification of scopoletin and scopolin in the complex system of Erycibe obtusifolia Benth was achieved successfully. The predicted concentrations were compared with the values obtained using high performance liquid chromatography-coupled to fluorimetric detector (HPLC-FLD), and no significant differences between them were observed. Therefore, the proposed methods using time-resolved fluorescence for qualitative analysis and EEMs coupled with second-order calibration for quantitative analysis in TCM are comparable and provide a suitable alternative to the chromatography-based method.

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 comparison of several second-order algorithms for simultaneous determination of neomangiferin and mangiferin with severe spectral overlapping in Anemarrhenae Rhizoma

This work presents a greener approach for simultaneous determination of neomangiferin and mangiferin, the major bioactive constituents with severe spectral overlapping in Anemarrhenae Rhizoma, combining the sensitivity of molecular fluorescence and the selectivity of chemometric multivariate calibration algorithms. In this study, we compared the analytical performance of two group chemometric algorithms including trilinear algorithms such as parallel factor analysis (PARAFAC), alternating trilinear decomposition (ATLD), self-weighted alternating trilinear decomposition (SWATLD) and alternating penalized trilinear decomposition (APTLD), and PLS-based methods such as unfolded partial least-squares or the multi-dimensional partial least-squares, both combined with residual bilinearization (U-PLS/RBL, N-PLS/RBL). The statistical parameters for the validation set of the second calibration were evaluated through the relative error of prediction (REP%), the average recovery (Rec%), and the root mean square error of prediction (RMSEP). Prediction results for the validation set by trilinear algorithms showed that the values were satisfactory for neomangiferin, and higher and not acceptable values for mangiferin, while U-PLS and N-PLS predictions were very successful for two analytes. Therefore, U- and N-PLS/RBL were chosen to determine neomangiferin and mangiferin in more complex real samples simultaneously, and U-PLS/RBL algorithm showed the best performance. The predicted concentrations by proposed methods were satisfactorily compared with those obtained using high performance liquid chromatography with ultraviolet detection.

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.

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.

Label-Free Quantification of Anticancer Drug Imatinib in Human Plasma with Surface Enhanced Raman Spectroscopy

Therapeutic drug monitoring (TDM) for anticancer drug imatinib has been suggested as the best way to improve the treatment response and minimize the risk of adverse reactions in chronic myelogenous leukemia (CML) and gastrointestinal stromal tumor (GIST) patients. TDM of oncology treatments with standard analytical methods, such as liquid chromatography-tandem mass spectrometry (LC-MS/MS) is, however, complex and demanding. This paper proposes a new method for quantitation of imatinib in human plasma, based on surface enhanced raman spectroscopy (SERS) and multivariate calibration using partial least-squares regression (PLSR). The best PLSR model was obtained with three latent variables in the range from 123 to 5000 ng/mL of imatinib, providing a standard error of prediction (SEP) of 510 ng/mL. The method was validated in accordance with international guidelines, through the estimate of figures of merit, such as precision, accuracy, systematic error, analytical sensitivity, limits of detection, and quantitation. Moreover, the feasibility and clinical utility of this approach have also been verified using real plasma samples taken from deidentified patients. The results were in good agreement with a clinically validated LC-MS/MS method. The new SERS method presented in this preliminary work showed simplicity, short analysis time, good sensitivity, and could be considered a promising platform for TDM of imatinib treatment in a point-of-care setting.