Model Of Chromatography Research Articles

Comparing in silico flowsheet optimization strategies in biopharmaceutical downstream processes.

The challenging task of designing biopharmaceutical downstream processes is initially to select the type of unit operations, followed by optimizing their operating conditions. For complex flowsheet optimizations, the strategy becomes crucial in terms of duration and outcome. In this study, we compared three optimization strategies, namely, simultaneous, top-to-bottom, and superstructure decomposition. Moreover, all strategies were evaluated by either using chromatographic Mechanistic Models (MMs) or Artificial Neural Networks (ANNs). An overall evaluation of 39 flowsheets was performed, including a buffer-exchange step between the chromatography operations. All strategies identified orthogonal structures to be optimal, and the weighted overall performance values were generally consistent between the MMs and ANNs. In terms of time-efficiency, the decomposition method with MMs stands out when utilizing multiple cores on a multiprocessing system for simulations. This study analyses the influence of different optimization strategies on flowsheet optimization and advices on suitable strategies and modeling techniques for specific scenarios.

From protein structure to an optimized chromatographic capture step using multiscale modeling.

Optimizing a biopharmaceutical chromatographic purification process is currently the greatest challenge during process development. A lack of process understanding calls for extensive experimental efforts in pursuit of an optimal process. In silico techniques, such as mechanistic or data driven modeling, enhance the understanding, allowing more cost-effective and time efficient process optimization. This work presents a modeling strategy integrating quantitative structure property relationship (QSPR) models and chromatographic mechanistic models (MM) to optimize a cation exchange (CEX) capture step, limiting experiments. In QSPR, structural characteristics obtained from the protein structure are used to describe physicochemical behavior. This QSPR information can be applied in MM to predict the chromatogram and optimize the entire process. To validate this approach, retention profiles of six proteins were determined experimentally from mixtures, at different pH (3.5, 4.3, 5.0, and 7.0). Four proteins at different pH's were used to train QSPR models predicting the retention volumes and characteristic charge, subsequently the equilibrium constant was determined. For an unseen protein knowing only the protein structure, the retention peak difference between the modeled and experimental peaks was 0.2% relative to the gradient length (60 column volume). Next, the CEX capture step was optimized, demonstrating a consistent result in both the experimental and QSPR-based methods. The impact of model parameter confidence on the final optimization revealed two viable process conditions, one of which is similar to the optimization achieved using experimentally obtained parameters. The multiscale modeling approach reduces the required experimental effort by identification of initial process conditions, which can be optimized.

Formation of PGE- and sulfide-bearing chromitites and associated anorthositic rocks in layered intrusions by the infiltration of reactive, Cl-rich fluid

The chromatographic model for chromitite formation previously presented is updated to include sulfide as a possible participating phase. In the model, chromite is precipitated at an upward-moving reaction front as a Cr-bearing (gabbro)norite protolith reacts with a Cl-rich fluid that becomes progressively undersaturated in pyroxene as it rises into hotter parts of the crystal pile, leaving an anorthositic residue. The liberation of Fe during the dissolution of orthopyroxene can drive concurrent sulfide saturation. If the reaction front encounters an orthopyroxenite the model produces a first-order approximation for the Merensky Reef section of anorthosite–chromite–orthopyroxenite. In addition, MELTS modelling shows that the isothermal addition of Cr 2 O 3 alone to a nearly solid (gabbro)norite assemblage increases the amount of both chromite and liquid at the expense of other silicate minerals owing to the liberation of Ca, Na and Si, the latter two acting as fluxing agents. A generalized constitutional zone refining model is presented in which a variety of spinel–silicate layering types (e.g. Bushveld magnetite layers and Skaergaard trough layering) can develop over time.

Separation of astragaloside IV from Astragalus membranaceus based on high-speed countercurrent chromatography in continuous injection mode.

Astragaloside IV (AS-IV) is an index for the quality evaluation of the traditional Chinese medicine Astragalus and an important material basis for Astragalus to exert its medicinal effects, and it is difficult to obtain a single AS-IV by ordinary separation methods. To find a new isolation method that can prepare AS-IV quickly and efficiently. AS-IV was isolated from Astragalus membranaceus extract by high-speed countercurrent chromatography using a two-phase solvent system consisting of ethyl acetate/n-butanol/water (4.2:0.8:5, v/v) at a speed of 950 rpm at a flow rate of 2mL/min using one of the high-speed countercurrent chromatographic sequential injection models developed during the previous study. Compared with the common countercurrent chromatographic separation, this separation method increased the injection volume and yield by 4-fold and 4.47-fold, respectively, with only about 1.2-fold increase in solvent consumption and separation time, and the purity was basically not reduced, and 55.9mg of AS-IV, with a purity of 96.95%, was finally prepared from 400 mg of the crude extract in 240 min. The continuous injection mode of high-speed countercurrent chromatography was able to successfully prepare a large amount of AS-IV with high purity at one time.

Enhancing thermodynamic consistency: Clarification on the application of asymmetric activity model in multi-component chromatographic separation

The single-component Mollerup model, with over 40 direct applications and 442 citations, is the most widely used activity model for chromatographic mechanistic modeling. Many researchers have extended this formula to multi-component systems by directly adding subscripts, a modification deemed thermodynamically inconsistent (referred to as the reference model). In this work, we rederived the asymmetric activity model for multi-component systems, using the van der Waals equation of state, and termed it the multi-component Mollerup model. In contrast to the reference model, our proposed model accounts for the contributions of all components to the activity. Three numerical experiments were performed to investigate the impact of the three different activity models on the chromatographic modeling. The results indicate that our proposed model represents a thermodynamically consistent generalization of the single-component Mollerup model to multi-component systems. This communication advocates adopting of the multi-component Mollerup model for activity modeling in multi-component chromatographic separation to enhance thermodynamic consistency.

Resin screening and process optimization for erythritol mother liquor chromatographic separation

In order to improve the utilization value of the erythritol mother liquor, the separation and purification of the erythritol mother liquor was selected in this study. The selected chromatographic separation programme for erythritol crystallizing mother liquor is as follows: Firstly, erythritol is resolved from mannitol and arabitol with DTF-01Ca (Suqing Group) resin and then mannitol is resolved from arabitol with 99Ca/320 (Dowex) resin. At the same time, the chromatographic conditions of the DTF-01Ca (Suqing Group) and 99Ca/320 (Dowex) resins were optimized, resulting in an optimal separation temperature and mobile phase flow rate of 70 °C, 10 ml/min. On this basis, a single-column chromatographic model was used to calculate the TD model parameter ( N ) and the mass transfer coefficient ( k m ) of the separation of erythritol mother liquor by DTF-01Ca (Suqing Group) and 99Ca/320 (Dowex) resins. The adsorption isotherms, TD model parameter ( N ) and the mass transfer coefficient ( k m ) provides data references for the design and operation of the simulated moving beds (SMB) separation system for the industrial-scale separation of erythritol crystallizing mother liquor.

Optimization of resolution as a simultaneous function of solvent composition, pH and temperature in reversed-phase liquid chromatography

BackgroundThe latest chromatographic retention models are capable of accurately describe the dependencies of retention over a wide range of experimental conditions. By using a suitable conversion, these models can be transformed into equations expressing the optimization criteria as function of multiples variables. Even though that theoretical models significantly reduce the experimental requirements for optimizations, these models have been barely used. Instead, most optimizations rely on empirical exploration of the relationships between criterions and variables.There is a need for a strategy to reduce the required number of experiments in multivariated optimization of separations, and Fundamental Models offer a clear opportunity for addressing it. ResultsA Fundamental Model is used to give the simultaneous dependence of chromatographic retention of seven ionizable pesticides on the three variables: solvent composition, temperature and pH (w, T, pH). Based on few experiments, the 10 parameters required to predict the chromatographic retention of those compounds, taken as model analytes, can be obtained. Two mathematical treatments to convert retentions into resolutions between pairs are used: one considering extracolumn dispersions and other neglecting these contributions. Using the Overlapped Resolutions Maps, extended to four dimensions, two optimal conditions can be found for the two different mathematical conversions. Chromatographic conditions were empirically evaluated obtaining the best results for the optimization considering extracolumn dispersions, proving that this condition is a true optimal. It was demonstrated that any small shift in any of the variables from this true optimal leads to a loss in resolution. SignificanceFundamental Models describing chromatographic retention as a simultaneous function of multiple variables are nowadays very accurate. In this work is demonstrated that these models are useful not only to predict retentions, but also to optimize separations, even in the more challenging mode: isocratic, isothermal and iso-pH. However, the success in the optimization procedure depends also on the proper definition of the mathematical conversion of the Fundamental Models into optimization criteria.

Linear solvent strength model on porous graphitic carbon stationary phase using high temperature liquid chromatographic method for allopurinol related substances analysis

A high-performance liquid chromatography (HPLC) method was developed for the analysis of Allopurinol and its Ph.Eur. impurities using a porous graphitic carbon (PGC) stationary phase. Retention behavior of solutes was studied across a wide temperature range (30–90 °C) and various gradient times (5–20 min). Analysis of the data revealed distinct retention mechanisms between reversed-phase and PGC phases. However, it was proved that the retention of Allopurinol and its Ph.Eur. impurities on PGC stationary phase can be effectively modeled using the linear solvent strength (LSS) theory. This allows for the utilization of LSS-based method development software to optimize methods under these conditions. By using commercial chromatographic modeling software, separation of Allopurinol and Ph.Eur. impurities was optimized within a large design space. At the optimized operating conditions (pH = 2.0, tG = 6 min, T = 60 °C), all solutes were separated within 6 min with baseline resolution. Comparison between predicted and experimentally measured chromatograms further confirmed the applicability of LSS theory in developing analytical methods for PGC-based HPLC systems. The presented approach offers a general framework for method development on PGC phases.

Improving the Accuracy of Predictive 2D-LC Optimization Strategies: Incorporation of Simulated Elution Profiles to Account for Injection Band Broadening in Online Comprehensive Two-Dimensional Liquid Chromatography.

Method development in online comprehensive two-dimensional liquid chromatography (LC × LC) requires the selection of a large number of experimental parameters. The complexity of this process has led to several computer-based LC × LC optimization algorithms being developed to facilitate LC × LC method development. One particularly relevant challenge for predictive optimization software is to accurately model the effect of second dimension (2D) injection band broadening under sample solvent mismatch and/or sample volume overload conditions. We report a novel methodology that combines a chromatographic numerical simulation model capable of predicting elution profiles of analytes under conditions where peak distortion occurs with a predictive multiparameter Pareto optimization approach for online LC × LC. Preliminary method optimization is performed using a theoretical model to predict 2D injection profiles, and optimal experimental configurations obtained from the Pareto fronts are then subjected to further optimization using the simulation model. This approach drastically reduces the number of simulations and therefore the computational demand. We show that the optimal experimental conditions obtained in this manner are similar to those obtained using a complete optimization using only the simulation model. Online HILIC × RP-LC separation of phenolic compounds was used to compare experimental data to simulated two- and three-dimensional contour plots. The main advantage of the proposed approach is the ability to predict the formation of split or deformed peaks in the 2D, a significant benefit in online LC × LC method optimization, especially for separation combinations with mismatched mobile phases. A further benefit is that simulated elution profiles can be used for the visualization of predicted two-dimensional chromatograms for method selection.

The Applicability of Chromatographic Retention Modeling on Chiral Stationary Phases in Reverse-Phase Mode: A Case Study for Ezetimibe and Its Impurities.

Mechanistic modeling is useful for predicting and modulating selectivity even in early chromatographic method development. This approach is also in accordance with current analytical quality using design principles and is highly welcomed by the authorities. The aim of this study was to investigate the separation behavior of two different types of chiral stationary phases (CSPs) for the separation of ezetimibe and its related substances using the mechanistic retention modeling approach offered by the Drylab software (version 4.5) package. Based on the obtained results, both CSPs presented with chemoselectivity towards the impurities of ezetimibe. The cyclodextrin-based CSP displayed a higher separation capacity and was able to separate seven related substances from the active pharmaceutical ingredient, while the cellulose-based column enabled the baseline resolution of six impurities from ezetimibe. Generally, the accuracy of predicted retention times was lower for the polysaccharide CSP, which could indicate the presence of additional secondary interactions between the analytes and the CSP. It was also demonstrated that the combination of mechanistic modeling and an experimental design approach can be applied to method development on CSPs in reverse-phase mode. The applicability of the methods was tested on spiked artificial placebo samples, while intraday and long-term (2 years) method repeatability was also challenged through comparing the obtained retention times and resolution values. The results indicated the excellent robustness of the selected setpoints. Overall, our findings indicate that the chiral columns could offer orthogonal selectivity to traditional reverse-phase columns for the separation of structurally similar compounds.

Liquid chromatographic retention time prediction models to secure and improve the feature annotation process in high-resolution mass spectrometry

The development of quantitative structure-retention relationship (QSRR) models has, until recently, required an adequate selection of molecular descriptors necessarily obtained based on a known chemical structure. However, these complex descriptors are not always available nor calculable when the high-resolution mass spectrometry (HRMS) annotation process is underway. Depending on the level of annotation, many structures or even various molecular formulas could be candidates. To secure and improve the annotation process and to save time, a QSRR model (using only 0D molecular descriptors) to predict retention times in reverse-phase liquid chromatography (RPLC) based on the molecular formula was developed, and a general QSRR annotation-based methodology was also proposed.

Affinity character analysis of magnolol and honokiol based on stepwise frontal analysis coupled with cell membrane chromatography

Magnolol and honokiol have been reported to exhibit anti-cancer activity. However, few studies are in relation to the interaction of magnolol/honokiol with vascular endothelial growth factor 2 (VEGFR2). In this study, a membrane chromatography method based on VEGFR2 was established for the interaction characteristic analysis between drug and receptor. The selectivity, repeatability and stability of the chromatographic model were evaluated using drugs acting on different receptors. The affinity between VEGFR2 and magnolol/honokiol was verified by cell membrane chromatography. The binding sites of magnolol/honokiol and VEGFR2 were analyzed by zonal elution. Especially, the dissociation equilibrium constants (Kd) of magnolol/honokiol and VEGFR2 were measured by zonal elution and stepwise frontal analysis respectively. In addition, the actions of magnolol/honokiol on VEGFR2 were analyzed by stepwise frontal analysis at different temperatures. The results showed that the binding sites of magnolol and honokiol on VEGFR2 were different from sorafenib, indicating that magnolol and honokiol could be used as competitive agents for self-competitive displacement experiment. The Kd values (order of magnitude) of magnolol/honokiol with VEGFR2 measured by stepwise frontal analysis were consistent with the zonal elution results. Honokiol binds VEGFR2 with higher affinity than magnolol. The main forces that stabilize the interactions of honokiol with VEGFR2 are hydrogen bonds and van der Waal’s forces, and the main force of magnolol is electrostatic forces. These discoveries could assist in the prediction of drug activity and understanding for the underlying mechanism.

Model-assisted process development, characterization and design of continuous chromatography for antibody separation

Continuous manufacturing in monoclonal antibody production has generated increased interest due to its consistent quality, high productivity, high equipment utilization, and low cost. One of the major challenges in realizing continuous biological manufacturing lies in implementing continuous chromatography. Given the complex operation mode and various operation parameters, it is challenging to develop a continuous process. Due to the process parameters being mainly determined by the breakthrough curves and elution behaviors, chromatographic modeling has gradually been used to assist in process development and characterization. Model-assisted approaches could realize multi-parameter interaction investigation and multi-objective optimization by integrating continuous process models. These approaches could reduce time and resource consumption while achieving a comprehensive and systematic understanding of the process. This paper reviews the application of modeling tools in continuous chromatography process development, characterization and design. Model-assisted process development approaches for continuous capture and polishing steps are introduced and summarized. The challenges and potential of model-assisted process characterization are discussed, emphasizing the need for further research on the design space determination strategy and parameter robustness analysis method. Additionally, some model applications for process design were highlighted to promote the establishment of the process optimization and process simulation platform.

Radiostrontium determination by combination of automated Sr isolation and “on-column” Cherenkov detection using chromatographic model

A novel analytical solution of non-linear chromatography in case of parabolic isotherm for frontal analysis was obtained by combination of Cole-Hopf and Laplace transform. It was used for simulation of strontium capturing on chromatographic column with aim to improve quantitative determination of low-level 90Sr activities. From the experimentally determined breakthrough curves, the retention factor and the number of theoretical plates were calculated using the Glueckauf and Wenzel relations and by fitting the breakthrough curves for the linear isotherm using MatLab. These were used to simulate the breakthrough curves using a parabolic isotherm solution where the non-linear term of the isotherm was taken as a small negative deviation of the retention factor. On the base of theoretical prediction and experimental data, procedure for automated capturing of strontium on chromatographic column with specific dimension and off line „on-column“ Cherenkov detection on commercial ultra low-level liquid scintillation counter was developed. It was shown that analytical solution for parabolic isotherm in comparison with solution for linear isotherm gives better prediction of mass of captured Sr on column filled with small amount of Sr resin and SuperLig®620 in case of elevated Sr concentration, even when non-linear effect is not obvious. The solution also makes it possible to predict the mass of resin required for strontium isolation at 100% yield under given conditions. Considering the limited dimensions of the column, and consequently small mass of the resin in them, it resulted in the low efficiency of the columns, which, however, did not affect the yield in real conditions of isolation. The results have shown that the yields achieved after isolation on SuperLig®620 from real samples are 100%. In addition, it is shown that captured 90Sr can be detected through 90Y ingrowth, on column filled with strontium specific resin, with Cherenkov detection efficiency of at least 50%. The efficiency may be enhanced to 60%, depending on parameters which can affect detection efficiency change (type of column, resin type, surrounding solution, etc.). The developed procedures enable quantitative determination of 90Sr in natural water samples with MDAC below 12 mBq l−1 and solid (soil and vegetation) samples with MDAC below 6 Bq kg−1 within 2–3 days. The proposed solution may easily be implemented in radiochemical laboratories where this type of analysis is routinely done within environmental monitoring or other purposes.

Using artificial neural networks to accelerate flowsheet optimization for downstream process development.

An optimal purification process for biopharmaceutical products is important to meet strict safety regulations, and for economic benefits. To find the global optimum, it is desirable to screen the overall design space. Advanced model-based approaches enable to screen a broad range of the design-space, in contrast to traditional statistical or heuristic-based approaches. Though, chromatographic mechanistic modeling (MM), one of the advanced model-based approaches, can be speed-limiting for flowsheet optimization, which evaluates every purification possibility (e.g., type and order of purification techniques, and their operating conditions). Therefore, we propose to use artificial neural networks (ANNs) during global optimization to select the most optimal flowsheets. So, the number of flowsheets for final local optimization is reduced and consequently the overall optimization time. Employing ANNs during global optimization proved to reduce the number of flowsheets from 15 to only 3. From these three, one flowsheet was optimized locally and similar final results were found when using the global outcome of either the ANN or MM as starting condition. Moreover, the overall flowsheet optimization time was reduced by 50% when using ANNs during global optimization. This approach accelerates the early purification process design; moreover, it is generic, flexible, and regardless of sample material's type.

Next generation multimodal chromatography resins via an iterative mapping approach: Chemical diversity, high-throughput screening, and chromatographic modelling

Multimodal chromatography resins are becoming a key tool in the purification of biomolecules. The main objective of this research was the establishment of an iterative framework for the rapid development of new multimodal resins to provide novel selectivity for the future purification challenges. A large chemically diverse virtual library of 100 multimodal Capto™ MMC ligand analogues was created, and a broad array of chemical descriptors were calculated for each ligand in silico. Principal component analysis (PCA) was used to map the chemical diversity and guide selection of ligands for synthesis and coupling to the Capto ImpRes agarose base matrix. Twelve new ligands were prepared in two groups: ‘group one’ consist of L00-L07 and ‘group two’ consist of L08-L12. These ligands are diverse in the influence of varied secondary interactions such as hydrophobic interactions, H-bonding, etc. Additional resin prototypes were also prepared to look at the chromatographic impact of ligand density variation. High-throughput plate-based studies were performed for parallel resin screening for batch-binding of six model proteins at different chromatographic binding pH and sodium chloride concentration conditions. Principal component analysis of the binding data provided a chromatographic diversity map leading to the identification of ligands with improved binding. Further, the new ligands have improved separation resolution between a monoclonal antibody (mAb1) and product related impurities, a Fab fragment and high molecular weight (HMW) aggregates, using linear salt gradient elutions. To quantify the importance of secondary interactions, analysis of the retention factor of mAb1 on the ligands at various isocratic conditions lead to estimations of (a) the total number of water molecules and counter salt ions released during adsorption, and (b) hydrophobic contact area (HCA). The iterative mapping approach of chemical and chromatography diversity maps described in the paper proves to be a promising method for identifying new chromatography ligands for biopharmaceutical purification challenges.

Recent advances for estimating environmental properties for small molecules from chromatographic measurements and the solvation parameter model

The transfer of neutral compounds between immiscible phases in chromatographic or environmental systems can be described by six solute properties (solute descriptors) using the solvation parameter model. The solute descriptors are size (McGowan's characteristic volume), V, excess molar refraction, E, dipolarity/polarizability, S, hydrogen-bond acidity and basicity, A and B, and the gas-liquid partition constant on n-hexadecane at 298.15 K, L. V and E for liquids are accessible by calculation but the other descriptors and E for solids are determined experimentally by chromatographic, liquid-liquid partition, and solubility measurements. These solute descriptors are available for several thousand compounds in the Abraham solute descriptor databases and for several hundred compounds in the WSU experimental solute descriptor database. In the first part of this review, we highlight features important in defining each descriptor, their experimental determination, compare descriptor quality for the two organized descriptor databases, and methods for estimating Abraham solute descriptors. In the second part we focus on recent applications of the solvation parameter model to characterize environmental systems and its use for the identification of surrogate chromatographic models for estimating environmental properties.

Quality by design-based method development for the determination of related substances of ezetimibe by high performance liquid chromatography

Abstract The pharmaceutical industry is one of the fastest developing industries, and therefore, the chromatographic methods used to ensure the quality of products are constantly evolving, based on current regulatory requirements. Relevant guidelines state that a good understanding of the effects of all parameters on method performance should be predicted and controlled to ensure that test methods are safe and reliable. This quality by design-based approach is increasingly applied in chromatography. One of the pillars of this method development is the design space that results from pre-designed and systematically realized experiments, which contributes to a better understanding of the separation processes and provides flexibility and robustness to the final method. Modeling softwarewares can be used to experimental design and data evaluation, providing possibilities that empirical development does not allow, such as predicting optimal operating conditions or in silico robustness testing, to understand the separation process. This paper describes the steps of chromatographic method development according to the ICH Q14 guideline, keeping in mind the quality by design principles and taking advantage of the possibilities offered by the DryLab chromatographic modeling software. As an example, the development of an analytical method for the determination of the related substances of ezetimibe is presented, including the design of experiments, method optimization, robustness testing, validation, method specification and lifecycle management, and finally testing of the method applicability. We have demonstrated that experimental design-based retention modeling is an effective way of method development, and the in silico robustness testing is a reliable approach to identifying critical method parameters and setting up control strategies. In chromatographic method development, instead of using a rigid system based on strict and fixed rules, the understanding of separation processes offers higher flexibility. This paper aims to present and promote this new approach to those involved in chromatographic method development.

Thebaine induces anaphylactic reactions via the MRGPRX2 receptor pathway on mast cells

Morphine derivatives are clinically important anesthetic and sedative drugs, which often show anaphylactic side effects. Mas-related G-protein coupled receptor member X2 (MRGPRX2) triggers mast cell degranulation, which is important process in anaphylactic reactions. MRGPRX2-HEK293 and LAD2 cell membrane chromatographic (CMC) models were used to screen morphine derivatives binding to MRGPRX2. Furthermore, most morphine derivatives significantly enhanced Ca2+ mobilization. More importantly, thebaine was found to effectively promote histamine release. Thebaine induced the increased release of β-hexosaminidase and high secretion level of cytokines, confirming that thebaine could further trigger anaphylactic reactions and promote subsequent inflammatory reactions. Moreover, the ability of thebaine inducing degranulation and the release of allergenic mediators in mast cells was significantly decreased after MRGPRX2 knockdown, which proved that MRGPRX2 is the key media for thebaine-induced anaphylactic reactions. Significant hind paw swelling and hypothermia in mice after injecting thebaine suggested that thebaine could trigger anaphylactic reactions in vivo.

Non-targeted Gas Chromatography Orbitrap Mass Spectrometry qualitative and quantitative analysis of semi-volatile organic compounds in indoor dust using the Regions of Interest Multivariate Curve Resolution chemometrics procedure

In this study, non-targeted gas chromatography-Orbitrap-mass spectrometry (GC-Orbitrap-MS) analysis of semi-volatile organic compounds (SVOCs) in indoor environmental dust samples is proposed. High-resolution mass spectrometry (HRMS) provides massive amounts of information-rich mass data which presents storage and processing challenges. Thus, a combination of the regions of interest (ROI) data filtering and mass compression method, together with the multivariate curve resolution-alternating least squares (MCR-ALS) data resolution method (which is called the ROIMCR procedure), is applied to solve huge data analysis challenges. The ROI method assures a significant reduction of the computer storage requirements of mass spectrometry data without any significant loss of spectral resolution nor of accuracy on m/z measures. On the other side, the MCR-ALS method allows the total resolution of the elution and spectral profiles of the different constituents present in the analyzed samples, not requiring their chromatographic peak alignment nor their chromatographic peak shape modelling using natural constraints like non-negativity. Since all the possible species are investigated by the ROIMCR method, it is a powerful tool for non-targeted analysis. In order to check that the sample constituents are correctly recovered and identified by the proposed ROIMCR procedure when is applied to non-targeted GC-Orbitrap-MS analysis, a set of lab-emulated dust samples at different concentration levels were qualitatively and quantitatively analyzed in detail. Then, to evaluate the performance of the proposed ROIMCR procedure, this method was applied to the same type of non-targeted GC-Orbitrap-MS analysis data of two real dust samples with unknown compositions. Many chemical compounds present in the lab-emulated dust samples were correctly identified and quantified in these dust samples. An additional number of extra chemical compounds were resolved in these real dust samples, whose identification as putative constituents of these samples is proposed. The ROIMCR procedure proposed in this work facilitates the simultaneous data processing of complex analytical samples and allows the detection and identification of possible extra sample constituents. As a final conclusion of this work, the combination of the GC-Orbitrap-MS and ROIMCR methods, is shown to be a reliable tool for the non-targeted qualitative and quantitative analysis of complex analytical and environmental samples.