Application of the area correlation constraint in the MCR-ALS quantitative analysis of complex mixture samples

Abstract

The application of the recently developed area correlation constraint in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) for the quantitative determination of analyte mixtures is shown. The feasibility of the proposed constraint is tested firstly for the calibration and quantitation of PAHs mixtures in their synthetic mixtures (validation samples) and in river water samples dissolved organic matter (DOM) using EEM fluorescent three-way data. In this case, MCR-ALS results obtained with the proposed area correlation constraint are comparable with the results obtained with methods based on the fulfillment of the trilinear model, like PARAFAC and MCR-ALS with the trilinearity constraint. Secondly, the possibility of applying this new area correlation constraint is extended to the analytical determination of lipid mixtures in synthetic and cell culture samples by LC-MS, where the trilinear model does not hold.The applicability of the proposed area correlation constraint is assessed, and it is proposed as a general tool for the quantitative determination of unknown mixtures of analytes in complex natural samples with severe profile overlapping and unknown composition, whatever the data structure is.

Ultraviolet/Visible and Near-Infrared Dual Spectroscopic Method for Detection and Quantification of Low-Level Malaria Parasitemia in Whole Blood

“…One important question remains unanswered, that is, whether or not it is possible to quantify the different life cycle stages in a mixed-stage blood sample (i.e., blood samples infected with R, T, and S at various proportions). MCR-ALS is a well-established method for the quantitative determination of analyte mixtures . It is based on a bilinear data decomposition model, which when iterated can estimate the concentrations of various mixtures (C opt ) and their contributing spectra (S opt ). , A handful of studies have successfully applied this technique to quantify specific biomarkers in biofluids, including, for example, the quantification of proteins in urine at low parts per million levels in diabetic patients and separation of different lipid mixtures …”

“…25,32 A handful of studies have successfully applied this technique to quantify specific biomarkers in biofluids, including, for example, the quantifi- cation of proteins in urine at low parts per million levels in diabetic patients 33 and separation of different lipid mixtures. 32 MCR-ALS was applied to the spectra collected from parasites at different stages of their life cycle (detailed in Table S1). The three replicate samples contained different percentages of S, T, and R, leaving the remaining cells uninfected (U).…”

Restoring trilinearity with the purpose of advanced modeling: towards a more effective analysis of Pericarpium Citri Reticulatae during storage periods

“…The raw data can be transformed into mat file in which each data point is recorded in a matrix (retention time versus response values). Then m MSPA [ 22 ], L COW [ 23 ], L icoshift [ 24 ], MCR-ALS GUI 2.0 [ 11 , 14 ] and ATLD algorithms [ 12 ] worked under MATLAB environment, and their theories was written in detail (Supporting Material). At last, libPLS software was used for the multivariate statistical analysis of the data sets.…”

“…Presently, there are two main solutions for qualitative and quantitative analysis of complex second-order data. Bilinear modeling of an augmented data matrix, such as multivariate curve resolution - alternating least-squares (MCR-ALS) [ 11 ], was extensively used to tackle problems about the mixture analysis. Compared with trilinear modeling, it has the advantage of allowing for varying elution-time profiles across samples, especially chromatographic data.…”

Tautomeric and Aggregational Dynamics of Curcumin-Supersaturated Pluronic Nanocarriers

“…Then, the objective of MCR is to resolve the mixed nonselective information from the instrument ( D ) and provide the real contributions of the pure signals in the system, in which the pure spectral signal S T varies according to its relative concentrations of C . The improvements of MCR are based on the usage of different constraints, such as non-negativity, unimodality, quadrilinearity cases, and area correlation constraint for quantitative analysis of complex mixture samples, among others. Besides this, MCR-ALS has been applied to resolve signals in studies in which the complexity varies in different levels, from voltametric , and spectroscopic , levels, chromatographic, to a wide range of complexity …”