Enhanced efficiency of MS/MS all-ion fragmentation for non-targeted analysis of trace contaminants in surface water using multivariate curve resolution and data fusion.

Data-independent acquisition-all-ion fragmentation (DIA-AIF) mode of mass spectrometry can facilitate wide-scope non-target analysis of contaminants in surface water due to comprehensive spectral identification. However, because of the complexity of the resulting MS2 AIF spectra, identifying unknown pollutants remains a significant challenge, with a significant bottleneck in translating non-targeted chemical signatures into environmental impacts. The present study proposes to process fused MS1 and MS2 data sets obtained from LC-HRMS/MS measurements in non-targeted AIF workflows on surface water samples using multivariate curve resolution-alternating least squares (MCR-ALS). This enables straightforward assignment between precursor ions obtained from resolved MS1 spectra and their corresponding MS2 spectra. The method was evaluated for two sets of tap water and surface water contaminated with 14 target chemicals as a proof of concept. The data set of surface water samples consisting of 3506 MS1 and 2170 MS2 AIF mass spectral features was reduced to 81 components via a fused MS1-MS2 MCR model that describes at least 98.8% of the data. Each component summarizes the distinct chromatographic elution of components together with their corresponding MS1 and MS2 spectra. MS2 spectral similarity of more than 82% was obtained for most target chemicals. This highlights the potential of this method for unraveling the composition of MS/MS complex data in a water environment. Ultimately, the developed approach was applied to the retrospective non-target analysis of an independent set of surface water samples.

Non-target screening in water analysis: recent trends of data evaluation, quality assurance, and their future perspectives.

This trend article provides an overview of recent advancements in Non-Target Screening (NTS) for water quality assessment, focusing on new methods in data evaluation, qualification, quantification, and quality assurance (QA/QC). It highlights the evolution in NTS data processing, where open-source platforms address challenges in result comparability and data complexity. Advanced chemometrics and machine learning (ML) are pivotal for trend identification and correlation analysis, with a growing emphasis on automated workflows and robust classification models. The article also discusses the rigorous QA/QC measures essential in NTS, such as internal standards, batch effect monitoring, and matrix effect assessment. It examines the progress in quantitative NTS (qNTS), noting advancements in ionization efficiency-based quantification and predictive modeling despite challenges in sample variability and analytical standards. Selected studies illustrate NTS's role in water analysis, combining high-resolution mass spectrometry with chromatographic techniques for enhanced chemical exposure assessment. The article addresses chemical identification and prioritization challenges, highlighting the integration of database searches and computational tools for efficiency. Finally, the article outlines the future research needs in NTS, including establishing comprehensive guidelines, improving QA/QC measures, and reporting results. It underscores the potential to integrate multivariate chemometrics, AI/ML tools, and multi-way methods into NTS workflows and combine various data sources to understand ecosystem health and protection comprehensively.

Enhanced photocatalytic treatment using plasmonic Ag@Ag3PO4/Ag@AgCl nanophotocatalyst for simultaneous degradation of multiple parabens and UV-filters in various aquatic environments under visible light irradiation.

In this study, simultaneous photocatalytic degradation of different parabens (methyl-, ethyl-, propyl-, and butyl paraben) and UV filters (benzophenone-3, 4-methylbenzylidene camphor, 2-ethylhexyl 4-(dimethylamino) benzoate, ethylhexyl methoxycinnamate and octocrylene) in water matrices was performed under visible light irradiation using novel double plasmonic Ag@Ag3PO4/Ag@AgCl nanophotocatalyst, synthesized by an easy and fast photochemical conversion and photo-reduction. It was found that the nanophotocatalyst with appropriate mole ratio of Ag@Ag3PO4/Ag@AgCl (1:3) showed superior photocatalytic activity than individual plasmonic nanoparticles. This is because there are two simultaneous surface plasmon resonances (SPR) generated by the metallic Ag nanoparticles, in addition to the hetero-junction structure formed at the interface between Ag@Ag3PO4 and Ag@AgCl. The structures of the synthesized photocatalysts were characterized, and the principal reactive oxygen species in the photocatalytic process were identified via a trapping experiment, confirming superoxide radicals (∙O2-) as the key reactive species of the photocatalytic system. The process of photodegradation of the target pollutants was monitored using an optimized method that incorporated solid-phase extraction in combination with gas chromatography-mass spectrometry. The simultaneous photodegradation process was modeled and optimized using central composite design. The kinetic study revealed that the degradation process over Ag@Ag3PO4 (30%)/Ag@AgCl (70%) under visible light followed a pseudo-first-order kinetic model. The simultaneous degradation of target compounds was further investigated in sewage treatment plant effluent as well as tap water. It was found that the matrix constituents can reduce the photodegradation efficiency, especially in the case of highly contaminated samples.

Non-target Analysis and Chemometric Evaluation of a Passive Sampler Monitoring of Small Streams.

Complex multivariate datasets are generated in environmental non-target screening (NTS) studies covering different sampling locations and times. This study presents a comprehensive chemometrics-based data processing workflow to reveal hidden data patterns and to find a subset of discriminating features between samples. We used ANOVA-simultaneous component analysis (ASCA) to disentangle the influence of spatial and seasonal effects as well as their interaction on a multiclass dataset. The dataset was obtained by a Chemcatcher passive sampler (PS) monitoring campaign of three small streams and one major river over four sampling periods from spring to summer. Monitoring of small streams is important as they are impacted by non-point source introduction of organic micropollutants (OMPs). The use of a PS provides a higher representativeness of sampling, and NTS broadens the range of detectable OMPs. A comparison of ASCA results of target analysis and NTS showed for both datasets a dominant influence of different sampling locations and individual temporal pollution patterns for each river. With the limited set of target analytes, general seasonal pollution patterns were apparent, but NTS data provide a more holistic view on site-specific pollutant loads. The similarity of temporal pollution patterns of two geographically close small streams was revealed, which was not observed in undecomposed data analysis like principal component analysis (PCA). With a complementary partial least squares-discriminant analysis (PLS-DA) and Volcano-based prioritization strategy, 223 site- and 45 season-specific features were selected and tentatively identified.

Evaluation of Nontarget Long-Term LC-HRMS Time Series Data Using Multivariate Statistical Approaches.

The use of liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) has steadily increased in many application fields ranging from metabolomics to environmental science. HRMS data are frequently used for nontarget screening (NTS), i.e., the search for compounds that are not previously known and where no reference substances are available. However, the large quantity of data produced by NTS analytical workflows makes data interpretation and time-dependent monitoring of samples very sophisticated and necessitates exploiting chemometric data processing techniques. Consequently, in this study, a prioritization method to handle time series in nontarget data was established. As proof of concept, industrial wastewater was investigated. As routine industrial wastewater analyses monitor the occurrence of a limited number of targeted water contaminants, NTS provides the opportunity to detect also unknown trace organic compounds (TrOCs) that are not in the focus of routine target analysis. The developed prioritization method enables reducing raw data and including identification of prioritized unknown contaminants. To that end, a five-month time series for industrial wastewaters was utilized, analyzed by liquid chromatography-time-of-flight mass spectrometry (LC-qTOF-MS), and evaluated by NTS. Following peak detection, alignment, grouping, and blank subtraction, 3303 features were obtained of wastewater treatment plant (WWTP) influent samples. Subsequently, two complementary ways for exploratory time trend detection and feature prioritization are proposed. Therefore, following a prefiltering step, featurewise principal component analysis (PCA) and groupwise PCA (GPCA) of the matrix (temporal wise) were used to annotate trends of relevant wastewater contaminants. With sparse factorization of data matrices using GPCA, groups of correlated features/mass fragments or adducts were detected, recovered, and prioritized. Similarities and differences in the chemical composition of wastewater samples were observed over time to reveal hidden factors accounting for the structure of the data. The detected features were reduced to 130 relevant time trends related to TrOCs for identification. Exemplarily, as proof of concept, one nontarget pollutant was identified as N-methylpyrrolidone. The developed chemometric strategies of this study are not only suitable for industrial wastewater but also could be efficiently employed for time trend exploration in other scientific fields.

Comparison of Software Tools for Liquid Chromatography-High-Resolution Mass Spectrometry Data Processing in Nontarget Screening of Environmental Samples.

The field of high-resolution mass spectrometry has undergone a rapid progress in the last years due to instrumental improvements leading to a higher sensitivity and selectivity of instruments. A variety of qualitative screening approaches, summarized as nontarget screening, have been introduced and have successfully extended the environmental monitoring of organic micropollutants. Several automated data processing workflows have been developed to handle the immense amount of data that are recorded in short time frames by these methods. Most data processing workflows include similar steps, but underlying algorithms and implementation of different processing steps vary. In this study the consistency of data processing with different software tools was investigated. For this purpose, the same raw data files were processed with the software packages MZmine2, enviMass, Compound Discoverer, and XCMS online and resulting feature lists were compared. Results show a low coherence between different processing tools, as overlap of features between all four programs was around 10%, and for each software between 40% and 55% of features did not match with any other program. The implementation of replicate and blank filter was identified as one of the sources of observed divergences. However, there is a need for a better understanding and user instructions on the influence of different algorithms and settings on feature extraction and following filtering steps. In future studies it would be of interest to investigate how final data interpretation is influenced by different processing software. With this work we want to encourage more awareness on data processing as a crucial step in the workflow of nontarget screening.

Implementation of Chemometric Tools To Improve Data Mining and Prioritization in LC-HRMS for Nontarget Screening of Organic Micropollutants in Complex Water Matrixes.

One of the most critical steps in nontarget screening of organic micropollutants (OMP) in complex environmental samples is handling of massive data obtained from liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS). Multivariate chemometric methods have brought about great progress in processing big data obtained from high-dimensional chromatographic systems. This work aimed at a comprehensive evaluation of two LC-Q-Orbitrap mass spectrometry full-scan data sets for target and nontarget screening of OMPs in drinking and wastewater samples, respectively. For each data set, following segmentation in the chromatographic dimension, at first multivariate curve resolution alternating least-squares (MCR-ALS) was employed for simultaneous resolution of global matrices. The chromatographic peaks and the corresponding mass spectra of OMP were fully resolved in the presence of highly co-eluting irrelevant and interfering peaks. Then partial least-squares-discriminant analysis was conducted to investigate the behavior of MCR-ALS components in different water classes and selection of most relevant components. Further prioritization of features in wastewater before and after ozonation and their reduction to 24 micropollutants were then obtained by univariate statistics. Two-way information retrieved from MCR-ALS of LC-MS1 data was also used to choose common precursor ions between recovered and measured data through data-dependent acquisition. MS1 and MS2 spectral features were used for tentative identification of prioritized OMPs. This study indicates that the described strategy can be used as a promising tool to facilitate both feature selection through a reliable classification and interference-free identification of micropollutants in nontargeted and class-wise environmental studies.

Determination of ultraviolet filter compounds in environmental water samples using membrane-protected micro-solid-phase extraction.

A method based on membrane-protected micro-solid-phase extraction coupled with gas chromatography and mass spectrometry was developed for the determination of six ultraviolet filter compounds in various aqueous media. Multiwalled carbon nanotubes as the sorbent were encapsulated in a sealed polypropylene membrane packet and immersed in the sample to extract the analytes, and then dichloromethane was used for desorption purpose. The method was sensitive enough for quantitative analysis of the target analytes, with limits of quantification between 0.01 and 0.06 µg/L, and produced a linear response (R2  > 0.991) over the calibration range (0.05-6 µg/L). The obtained reproducibility was practically suitable with relative standard deviation values of less than 14% in pure water (spiked at 0.20/µg L) and less than 15% in real samples. The optimized method was applied for the analysis of real water samples with varying matrix complexity: tap, river, and dam water; geothermal spa; and sewage treatment plant effluent. Various levels and patterns of contamination were observed in the examined samples, while the sample from spa was the most contaminated, regarding the target analytes. Matrix spikes and matrix spike replicates were also analyzed to validate the technique for analysis of real aqueous samples, and satisfactory results were achieved.

Chemometrics-assisted chromatographic fingerprinting: An illicit methamphetamine case study.

The volatile chemical constituents in complex mixtures can be analyzed using gas chromatography with mass spectrometry. This analysis allows the tentative identification of diverse impurities of an illicit methamphetamine sample. The acquired two-dimensional data of liquid-liquid extraction was resolved by multivariate curve resolution alternating curve resolution to elucidate the embedded peaks effectively. This is the first report on the application of a curve resolution approach for chromatogram fingerprinting to identify particularly the embedded impurities of a drug of abuse. Indeed, the strong and broad peak of methamphetamine makes identifying the underlying peaks problematic and even impossible. Mathematical separation instead of conventional chromatographic approaches was performed in a way that trace components embedded in methamphetamine peak were successfully resolved. Comprehensive analysis of the chromatogram, using multivariate curve resolution, resulted in elution profiles and mass spectra for each pure compound. Impurities such as benzaldehyde, benzyl alcohol, benzene, propenyl methyl ketone, benzyl methyl ketone, amphetamine, N-benzyl-2-methylaziridine, phenethylamine, N,N,α-trimethylamine, phenethylamine, N,α,α-trimethylmethamphetamine, N-acetylmethamphetamine, N-formylmethamphetamine, and other chemicals were identified. A route-specific impurity, N-benzyl-2-methylaziridine, indicating a synthesis route based on ephedrine/pseudoephedrine was identified. Moreover, this is the first report on the detection of impurities such as phenethylamine, N,α,α-trimethylamine (a structurally related impurity), and clonitazene (as an adulterant) in an illicit methamphetamine sample.

Exploring the efficacy of metabarcoding and non-target screening for detecting treated wastewater.

Wastewater treatment processes can eliminate many pollutants, yet remainder pollutants contain organic compounds and microorganisms released into ecosystems. These remainder pollutants have the potential to adversely impact downstream ecosystem processes, but their presence is currently not being monitored. This study was set out with the aim of investigating the effectiveness and sensitivity of non-target screening of chemical compounds, 18S V9 rRNA gene, and full-length 16S rRNA gene metabarcoding techniques for detecting treated wastewater in receiving waters. We aimed at assessing the impact of introducing 33 % treated wastewater into a triplicated large-scale mesocosm setup during a 10-day exposure period. Discharge of treated wastewater significantly altered the chemical signature as well as the microeukaryotic and prokaryotic diversity of the mesocosms. Non-target screening, 18S V9 rRNA gene, and full-length 16S rRNA gene metabarcoding detected these changes with significant covariation of the detected pattern between methods. The 18S V9 rRNA gene metabarcoding exhibited superior sensitivity immediately following the introduction of treated wastewater and remained one of the top-performing methods throughout the study. Full-length 16S rRNA gene metabarcoding demonstrated sensitivity only in the initial hour, but became insignificant thereafter. The non-target screening approach was effective throughout the experiment and in contrast to the metabarcoding methods the signal to noise ratio remained similar during the experiment resulting in an increasing relative strength of this method. Based on our findings, we conclude that all methods employed for monitoring environmental disturbances from various sources are suitable. The distinguishing factor of these methods is their ability to detect unknown pollutants and organisms, which sets them apart from previously utilized approaches and allows for a more comprehensive perspective. Given their diverse strengths, particularly in terms of temporal resolution, these methods are best suited as complementary approaches.

Chemometrics assisted dispersive liquid-liquid microextraction for quantification of seven UV filters in urine samples by HPLC-DAD.

In the present study, dispersive liquid-liquid microextraction followed by high performance liquid chromatography-diode array detection has been developed as simple, rapid, accurate, and efficient sample preparation method for simultaneous determination of seven organic UV filters in urine samples. The influence of the main effects as well as their interactions was studied through a 2((6-2)) fractional factorial design. The candidate parameters were: type and volume of dispersant and extraction solvents, sample pH, and salt concentration. Under final optimal conditions, the analytes were extracted from 5 mL of samples by addition of 0.5 mL of acetonitrile (dispersing solvent) containing 70 µL of carbon tetrachloride (extraction solvent), without modifying the pH of the solution and applying the (+1) level of salt concentration (10% w/v NaCl). The assay was linear (R(2) > 0.997), relative recoveries ranged from 86.9 up to 97.3% and the LOQs between 3 and 45 ng mL(-1) were obtained. The intra- and interday RSDs were lower than 5 and 8% at the middle point of the linear range, respectively. The proposed method was successfully applied to different volunteer urine samples and it was shown that the extraction efficiency was not affected by the type of urine samples.

Investigation of simultaneous multiple UV filters degradation efficiency of plasmonic Ag@AgCl photocatalyst in the aquatic environment under sunlight irradiation.

UV filters as an important class of emerging organic pollutants are continuously released into and transported between the aquatic environments. So, the removal of these compounds from aquatic environments is of great importance. This study was conducted to evaluate the simultaneous photodegradation of three widely used UV filter compounds (4-methylbenzylidene camphor, 2-ethylhexyl 4-(dimethylamino) benzoate, ethylhexyl methoxycinnamate), in an aqueous environment under sunlight and Ag@AgCl photocatalyst integrated with plasmonic effect. The plasmonic Ag@AgCl nanocomposite was constructed via photochemical conversion and photoreduction. The enhanced photocatalytic performance can be attributed to the surface plasmon resonance effect of the silver nanoparticles and the hybrid effect caused by AgCl. For the monitoring of the target compounds' degradation before and after photodegradation, an optimized method based on membrane-protected micro-solid-phase extraction coupled with gas chromatography-mass spectrometry (GC-MS) was employed. The simultaneous degradation of selected UV filters was also further investigated in contaminated real samples (river water) and the results showed that the matrix constituents could diminish the photocatalytic degradation efficiency.

Towards smart personalized perspiration analysis: An IoT-integrated cellulose-based microfluidic wearable patch for smartphone fluorimetric multi-sensing of sweat biomarkers.

Practical obstacles, such as intricate designs and expensive equipment/materials, in the fabrication of wearable sweat sensors, have limited their feasibility as a personalized healthcare device. Herein, we have fabricated a cellulose-based wearable patch, which further paired with a smartphone-based fluorescence imaging module and a self-developed smartphone app for non-invasive and in situ multi-sensing of sweat biomarkers including glucose, lactate, pH, chloride, and volume. The developed Smart Wearable Sweat Patch (SWSP) sensor comprises highly fluorescent sensing probes embedded in paper substrates, and microfluidic channels consisted of cotton threads to harvest sweat from the skin surface and to transport it to the paper-based sensing probes. The imaging module was fabricated by a 3D printer, equipped with UV-LED lamps and an optical filter to provide the in situ capability of capturing digital images of the sensors via a smartphone. A smartphone app was also designed to quantify the concentration of the biomarkers via a detection algorithm. Additionally, we have recommended an Internet of Things (IoT)-based model for our developed SWSP sensor to promote its potential application for the future. The field studies on human subjects were also conducted to investigate the feasibility of our developed SWSP sensor for the analysis of sweat biomarkers. Our findings convincingly demonstrated the applicability of our developed SWSP sensor as a smart, user-friendly, ultra-low-cost (~0.03 $ per sweat patch), portable, selective, rapid, and non-invasive healthcare monitoring device for immense applications in health personalization, sports performance monitoring, and medical diagnostics.

A comprehensive study on the enhanced photocatlytic activity of a double-shell mesoporous plasmonic Cu@Cu2O/SiO2 as a visible-light driven nanophotocatalyst.

A novel sunlight-activated double-shell Cu@Cu2O/SiO2 (m-pCu@Cu2O/SiO2) photocatalyst is presented via a combined precipitation and sol-gel methods with a mesoporous silica outer shell. After applying several characterization techniques on the m-pCu@Cu2O/SiO2, it was tested in the photodegradation of ciprofloxacin (CIP). The experimental results demonstrated a higher photocatalytic activity of the double-shell m-pCu@Cu2O/SiO2 nanophotocatalyst than the core-shell pCu@Cu2O nanophotocatalyst under the sunlight irradiation. When the content of pCu@Cu2O was 30 wt.%, it showed the highest activity. The Cu nanoparticles exhibited the surface plasmonic resonance (SPR) effect which increased the light absorption in the visible region of light. It also caused the rapid separation of the photoexcited e-/h+ pairs. Furthermore, the mesoporous structure of outer shell silica favors the transfer of reactants, resulting in the improved photoactivity performance for the supported pCu@Cu2O catalyst. Central composite design (CCD) based on RSM (response surface methodology) approach was used to optimize four of the most important experimental variables. The photodegraded intermediates were identified by HPLC-Mass.

Fiberoptic-Coupled Spectrofluorometer with Array Detection as a Process Analytical Chemistry Tool for Continuous Flow Monitoring of Fluoroquinolone Antibiotics.

Nowadays, there is an increasing need for sensitive real-time measurements of various analytes and monitoring of industrial products and environmental processes. Herein, we describe a fluorescence spectrometer in continuous flow mode in which the sample is fed to the flow cell using a peristaltic pump. The excitation beam is introduced to the sample chamber by an optical fiber. The fluorescence emitted upon excitation is collected at the right angle using another optical fiber and then transmitted to the fluorescence spectrometer which utilizes an array detector. The array detection, as a key factor in process analytical chemistry, made the fluorescence spectrometer suited for multiwavelength detection of the fluorescence spectrum of the analytes. After optimization of the experimental parameters, the system has been successfully employed for sensitive determination of four fluoroquinolone antibiotics such as ciprofloxacin, ofloxacin, levofloxacin, and moxifloxacin. The linear dynamic ranges of four fluoroquinolones were between 0.25 and 20 µg·mL-1, and the detection limit of the method for ciprofloxacin, ofloxacin, levofloxacin, and moxifloxacin were 81, 36, 35, and 93 ng·mL-1, respectively. Finally, the proposed system is carried out for determination of fluoroquinolones in some pharmaceutical formulations.

Untargeted metabolomics changes on Gammarus pulex induced by propranolol, triclosan, and nimesulide pharmaceutical drugs.

The presence of pharmaceuticals and personal care products (PPCPs) in natural water resources due to incomplete removal in Wastewater Treatment Plants (WWTPs) is a serious environmental concern at present. In this work, the effects of three pharmaceuticals (propranolol, triclosan, and nimesulide) on Gammarus pulex metabolic profiles at different doses and times of exposure have been investigated by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). The complex data sets generated in the different exposure experiments were analyzed with the ROIMCR procedure, based on the selection of the MS regions of interest (ROI) data and on their analysis by the Multivariate Curve-Resolution Alternating Least Squares (MCR-ALS) chemometrics method. This approach, allowed the resolution and identification of the metabolites present in the analyzed samples, as well as the estimation of their concentration changes due to the exposure experiments. ANOVA Simultaneous Component Analysis (ASCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were then conducted to assess the changes in the concentration of the metabolites for the three pharmaceuticals at the different conditions of exposure. The three tested pharmaceuticals changed the concentrations of metabolites, which were related to different KEGG functional classes. These changes summarize the biochemical response of Gammarus pulex to the exposure by the three investigated pharmaceuticals. Possible pathway alterations related to protein synthesis and oxidative stress were observed in the concentration of identified metabolites.

Lab-on-nanopaper: An optical sensing bioplatform based on curcumin embedded in bacterial nanocellulose as an albumin assay kit.

Herein, we introduce a nanopaper-based analytical device (NAD) or "lab-on-nanopaper" device for visual sensing of human serum albumin (HSA) in human blood serums, which relies on embedding of curcumin within transparent bacterial cellulose (BC) nanopaper. BC nanopaper is an appropriate candidate to be an excellent platform for the development of optical (bio)sensors due to having exceptional properties such as optical transparency, high flexibility, porosity, biodegradability, and printability. The hydrophilic test zones were created on the fabricated bioplatform through creating the hydrophobic walls via laser printing technology. The color changes of curcumin embedded in BC nanopaper (CEBC) due to the inhibitory effect of HSA on the curcumin degradation in alkaline solutions, which can be monitored visually (naked eye/Smartphone camera) or spectroscopically using a spectrophotometer, were linearly proportional to the HSA concentration in the range of 10-300 µM and 25-400 µM, respectively. The developed NAD/CEBC as a novel albumin assay kit was successfully utilized to the determination of HSA in human blood serum samples with satisfactory results. Building upon the fascinating features of BC nanopaper as a very promising bioplatform in optical (bio)sensing applications we are confident "lab-on-nanopaper" devices/NADs, which take the advantages of the nanopaper and also meet the ASSURED criteria, could be considered as a new generation of optical (bio)sensing platforms that are currently based on paper, glass or plastic substrates.

Development of a fast HPLC-DAD method for simultaneous quantitation of three immunosuppressant drugs in whole blood samples using intelligent chemometrics resolving of coeluting peaks in the presence of blood interferences.

The present study describes a fast high performance liquid chromatography-diode array detection analytical methodology for quantification of tacrolimus, everolimus and cyclosporine A in whole blood samples, with minimum sample preparation steps. A short isocratic chromatographic elution was coupled with second-order calibration using multivariate curve resolution to stablish a smart and green methodology. Due to presence of matrix effect, a sample-added calibration strategy was used for quantification purposes. The serious issues related to background drift, chromatographic shifts and co-elution of non-calibrated blood components, were resolved by a proper background correction and multivariate curve resolution-alternating least squares (MCR/ALS) methods The main features of this study were based on the fact that the acquired data matrices were handled intelligently and all features of the concerned target analytes were taken into account. Satisfactory resolution and quantification results in the presence of matrix interferences were achieved and the second-order advantage was fully exploited. The average recoveries in therapeutic concentration ranges were 102±10%, 99±11% and 104±12% for TAC, EVR and CsA, with average relative prediction errors of less than 7%. Considering the advantages of the present strategy, such as increased selectivity, sensitivity and sufficiency of lower limit of quantification through multivariate advantage, simplicity of sample treatment steps, a fast elution pattern and also a low-cost instrumentation compared with LC-MS/MS, the proposed method has the significant merits as an alternative for simultaneous therapeutic monitoring of immunosuppressants.

Chromatographic fingerprinting through chemometric techniques for herbal slimming pills: A way of adulterant identification.

In the current study, gas chromatography-mass spectrometry (GC-MS) fingerprinting of herbal slimming pills assisted by chemometric methods has been presented. Deconvolution of two-way chromatographic signals of nine herbal slimming pills into pure chromatographic and spectral patterns was performed. The peak clusters were resolved using multivariate curve resolution-alternating least squares (MCR-ALS) by employing appropriate constraints. It was revealed that more useful chemical information about the composition of the slimming pills can be obtained by employing sophisticated GC-MS method coupled with proper chemometric tools yielding the extended number of identified constituents. The thorough fingerprinting of the complex mixtures proved the presence of some toxic or carcinogen components, such as toluene, furfural, furfuryl alcohol, styrene, itaconic anhydride, citraconic anhydride, trimethyl phosphate, phenol, pyrocatechol, p-propenylanisole and pyrogallol. In addition, some samples were shown to be adulterated with undeclared ingredients, including stimulants, anorexiant and laxatives such as phenolphthalein, amfepramone, caffeine and sibutramine.

Handling of highly coeluted chromatographic peaks by multivariate curve resolution for a complex bioanalytical problem: Quantitation of selected corticosteroids and mycophenolic acid in human plasma.

The present study describes the analytical performance of a fast-elution protocol and smart methodology based on multivariate curve resolution-alternating least square (MCR-ALS) modeling of high performance liquid chromatography with photodiode-array detection (HPLC-DAD) data for simultaneous determination of prednisolone (Predl), methylprednisolone (Mpredl) and mycophenolic acid (MPA) in plasma samples. The LC method optimized at two isocratic reverse phase over a symmetric C18 column, a 60:40 (v/v) mixture of acetonitrile and water (0.02 M KH2PO4 (pH = 3.7) buffer solution) and a 10:70:20 (v/v/v) mixture of acetonitrile, methanol and water. The most challenges in the present study were the severe coelution of analytes of interest with each other and the matrix interferences, and high spectral similarity of selected corticosteroids, depending on the mobile phase method. To circumvent these drawbacks, the whole chromatographic runs were divided into two sections. Then, the matrix augmentation in spectra and retention time direction were implemented for the first and second regions, respectively. Highly acceptable resolution and quantification results were obtained. The average recoveries were 97.3% and 102.1% for Predl, 96% and 98.6% for Mpredl and 97.7% and 100.8% for MPA, using two mobile phase conditions, respectively. Accurate and precise results, elimination of expensive and time consuming sample pretreatment steps and a very short chromatographic run time, are among the advantages of the presented method.