Multi-way Calibration For Quantitative Analysis In Complex Systems And Studies Of The Interactions Between Small Molecules And DNA

The research work focuses on mulity-way calibration for the quantitative analysis in complex chemical systems and studies on the mechanism for the interactions of small molecules with DNA.The robust information self-extracting asymmetric trilinear decomposition algorithm (RISEATD) has been developed, based on the total least squares principle and an asymmetric way with partial bilinearization to calculate the three underlying profile matrices in the resolution of the trilinear model. It can obtain useful information quickly. The new proposed algorithm combines the way of decomposition for PARAFAC-alternating least squares (PARAFAC-ALS) and alternating trilinear decomposition (ATLD). The results obtained by simulated data and real excitation-emission spectral data sets have shown that RISETLD method retains the second-order advantage of quantification for analyte(s) of interest even in the presence of potentially unknown interferents even when the noise and colinearity are high Comparing with PARAFAC, PARAFAC-ALS and ATLD algorithms, the developed method can supply acceptable results.The collection of EEM fluorescence spectra of a mixture and combination with second-order calibration methods can quantify the analytes even in the presence of uncalibrated interferences that has been called the "second-order advantage". With the property of "mathematical separation" to displace "chemical separation", three second-order calibration methods based on PARAFAC, the alternating penalty trilinear decomposition (APTLD) algorithms, and RISEATD respectively, have been utilized for the direct determination of terazosin hydrochloride (THD) in human plasma samples, coupled with the excitation-emission matrix fluorescence spectroscopy. Meanwhile, the three algorithms combing with the standard addition procedures have been applied for the determination of terazosin hydrochloride in tablets and the results were validated by the high-performance liquid chromatography with fluorescence detection.Three second-order calibration methods were presented to allow accurate and reliable quantitative analysis of dextromethorphan and quinidine in human plasma and urine samples at biological fluids by excitation-emission matrices fluorescence. PARAFAC, self-weighted alternating trilinear decomposition (SWATLD) and APTLD algorithms were applied and the performances of the three methods were compared. It has been found that all the methodologies could obtain good results.With the development of high-order analytical instruments and chemometric algorithms, it becomes easier to obtain and resolve multi-dimensional data from complex systems. The combination of excitation-emission matrix fluorescence and second-order calibration methods could provide a powerful tool for studies of parallel competitive binding reactions of many chemical components with DNA in the presence of interferents. The relative equilibrium concentrations of the component can be directly obtained. In this paper, UV-vis spectroscopy and fluorescence were combined to study the binding of DNA with the anthacycline antibiotic drug pirarubicin (THP). Ethidium bromide (EB) as the fluorescence probe was used to study the competitive binding interactions of THP with DNA by excitation -emission fluorescence matrices coupled with PARAFAC and the alternating normalization-weighted error algorithm (ANWE) with the second-order advantage. The relative equilibrium concentrations of EB-DNA, EB and THP in the equilibrium system can be directly obtained, which makes it possible to determine the reaction pattern of different interacting pairs in a mixture medium.The pesticide is used indiscriminately. It may residue in fruits, vegetables and ground and surface waters that lead to pose a potential hazard for consumers, so the toxicity has raised the public concern about the ecosystem and human health. Therefore, the investigation of genotoxicity and genetic damage via the interaction of DNA with insecticide is very important. Competitive binding interactions of carbaryl and the fluorescence probe EB with DNA have been studied by excitation-emission fluorescence spectroscopy to obtain a three-dimension excitation-emission fluorescence data array. The second-order calibration methods based on PARAFAC and APTLD algorithms were used to resolve the data array obtained.Least squares-support vector machine (LS-SVM) has been introduced into multivariate calibration by many investigators for its attractive features and promising empirical performance. However, the performance of models is strongly dependent upon the homogeneity of the model errors and the uniformity of the data sampling. The representation of training samples for multivariate calibration has been discussed and the concept of weighted sampling has been introduced to multivariate calibration. Due to the high-dimensionality and complexity of spectral data space and the uncertainty involved in sampling process, the representation of training samples in the whole sample space is difficult to evaluate and select representative training samples for multivariate calibration depends largely on experiential methods. If the training samples fail to represent the sample space, sometimes the predictions of new samples can be degraded. In order to solve this problem, a new algorithm for multivariate calibration is developed by combining optimized sampling and least squares-support vector machine, where the original training samples are non-negatively weighted and the complexity and the ability of prediction of the model are considered simultaneously. Two real data sets are investigated and the results demonstrate that sample-weighted least squares-support vector machine models can improve the ability of prediction for a model when the representation of original calibration sample is poor.For multivariate calibration, all of the wavelength variables might carry more or less molecular information, it seems more advisable to investigate all the possible variables rather than traditional variable selection. Based on particle swarm optimization algorithm, a more flexible variable selection and modeling method, variable-weighted least squares-support vector machine is proposed. The strategy of variable weighting allows non-negative weights of variables rather than removing or reserving any variables. Using particle swarm optimization (PSO) to seek the non-negative weights of variables can be seen as an optimized rescaling of the variables in certain sense. If employing PSO to search for the other parameters in the model of least squares-support vector machine at the same time, the variable-weighted least squares-support vector machine would become a total-automatically modeling approach and therefore be more flexible and intelligent than traditional variable methods.