Chemical Multi-way Calibration And High-dimensional Pattern Recognition And Their Applications In Complex Systems

With the emergence of new high-order analytical instruments and automated acquisition of instrumental data,the acquisition of second-,third-and fourth-order or even higher-order chemical data based on tensor consisting of tens of thousands of data points has became easily.At the same time,it also faces the challenge of the "data tsunami".Chemometrics can extract the uttermost useful information by processing and analyzing complex measurement data,thus providing a powerful approach for analytical chemists to cope with this problem.Chemical multiway calibration and pattern recognition are two research fields of great significance in the chemometrics theoretical system.The research work in this thesis mainly focuses on these methodologies and their applications in complex systems.Study works presented in the thesis primarily deal with the following aspects: Section one: Excitation-emission matrix fluorescence coupled with second-order calibration for qualitative and quantitative analysis of complex systemsIn Chapter 2,we proposed a novel and effective strategy for excitation-emission matrix fluorescence(EEM)in combination with second-order calibration method based on Alternating Normalization-Weight Error(ANWE)algorithm to simultaneously detect amiloride(AMI)and triamterene(TRI)in human urine and plasma.Although,with the high collinearity among the fluorescent spectra of the analytes and the intense fluorescence of plasma and urine backgrounds,the proposed approach can correctly decompose the obtained three-way data array and acquire accurate qualitative and quantitative information of the analytes.We ascribe it to the “second-order advantage”,which allows directly qualitative and quantitative analysis of analytes with unknown and uncalibrated interferents coexisting.We further investigate the “second-order advantage” by decomposing trilinear component model including different biological fluid samples,which obtained precise spectral profiles and accurate quantitative information of the analytes.The results indicated that the proposed bioanalytical method has advantages of being simple,fast,low-cost and high-sensitive,which requires no prior purification and separation.These advantages as well as low LODs of the analytes highlight that the proposed bioanalytical method opens the possibility of future on-line doping control and clinical monitoring of native or derivative fluorescent drugs within complex biological matrices in a simple and rapid way.In Chapter 3,a rapid interference-free spectrofluorometric method combined with the excitation-emission matrix fluorescence and the second-order calibration methods based on the alternating penalty trilinear decomposition(APTLD)and the selfweighted alternating trilinear decomposition(SWATLD)algorithms,was proposed for the simultaneous determination of nephrotoxic aristolochic acid I(AA-I)and aristololactam I(AL-I)in five Chinese herbal medicines.The method was based on a chemical derivatization that converts the non-fluorescent AA-I to high-fluorescent AL-I,achieving a high sensitive and simultaneous quantification of the analytes.The variables of the derivatization reaction that conducted by using zinc powder in acetos e methanol aqueous solution,were studied and optimized for best quantification results of AA-I and AL-I.The satisfactory results of AA-I and AL-I for the spiked recovery assay were achieved with average recoveries in the range of 100.4-103.8% and RMSEPs less than 0.78 ng mL-1,which validated the accuracy and reliability of the proposed method.The contents of AA-I and AL-I in five herbal medicines obtained from the proposed method were also in good accordance with those of the validated LC-MS/MS method.In light of high sensitive fluorescence detection,the limits of detection(LODs)of AA-I and AL-I for the proposed method compare favorably with that of the LC-MS/MS method,with the LODs less than 0.35 and 0.29 ng mL-1,respectively.The proposed strategy based on the APTLD and SWATLD algorithms by virtue of the "second-order advantage",can be considered as an attractive and green alternative for the quantification of AA-I and AL-I in complex herbal medicine matrices without any prior separations and clear-up processes.Section two: Novel strategy for qualitative and quantitative analysis based on chemical multiway calibration assisted liquid chromatography-mass spectrometryIn Chapter 4,the aim of the present work was to develop a rapid and interferencefree method based on liquid chromatography-mass spectrometry(LC-MS)for the simultaneous determination of nine B-group vitamins in various energy drinks.A smart and green strategy that modeled the three-way data array of LC-MS with second-order calibration methods based on alternating trilinear decomposition(ATLD)and alternating penalty trilinear decomposition(APTLD)algorithms was developed.By virtue of "mathematical separation" and "second-order advantage",the proposed strategy successfully solved the co-eluted peaks and unknown interferents in LC-MS analysis with the elution time less than 4.5 min and simple sample preparation.Satisfactory quantitative results were obtained by the ATLD-LC-MS and APTLD-LCMS methods for the spiked recovery assays,with the average spiked recoveries ranging from 87.2-113.9 % and 92.0-111.7 %,respectively.These results acquired from the proposed methods were confirmed by the LC-MS/MS method,which shows a quite good consistency with each other.All these results demonstrated that the developed chemometrics-assisted LC-MS strategy had advantages of being rapid,green,accurate and low-cost,and it could be an attractive alternative for the determination of multiple vitamins in complex food matrices,which requi red no laborious sample preparation,tedious condition optimization or more sophisticated instrumentations.In Chapter 5,regarding the co-elution and complex matrix interferences in quantitation of seven targeted anti-kidney cancer drugs by using LC-MS in human plasma,the authors utlized the second-order calibration method based on ATLD algorithm to model the LC-MS data.By virtue of its unique “second-order advantage”,this method can achieve the determination of targeted anti-kidney cancer drugs in human plasma with simple sample pretreatment.Due to the presence of a strong matrix effect in the plasma,the quantitation results of several analytes obtained by the method are biased.Subsequently,we firstly proposed a novel strategy that the secondorder standard addition method(SOSAM)based on the ATLD algorithm(SOSAM/ATLD-LC-MS)modeled LC-MS data to solve the matrix interference and matrix effect in the determination of anti-kidney cancer drugs in human plasma.Satisfactory qualitative and quantitative results indicated that the strategy based on SOSAM/ATLD-LC-MS can effectively solve the matrix interference and matrix effect in plasma,thus achieving directly qualitative and quantitative analysis of seven targeted anti-kidney cancer drugs in plasma samples.This strategy is of great significance for the study of pharmacokinetics and pharmacodynamics of targeted anti-kidney cancer drugs,optimization of clinical dosing regimens,and comparison of drug treatment effects.Section three: Research on high-dimensional pattern recognition strategy for the characterization and classification of chemical multiway dataIn Chapter 6,we proposed a flexible and novel strategy that alternating trilinear decomposition(ATLD)method combined with two-dimensional linear discriminant analysis(2D-LDA).The developed strategy was applied to three-way chemical data for the characterization and classification of samples.In order to confirm the methodology performances of characterization and classification,a series of si mulated three-way data arrays and a real-life EEMs data set involving the characterization and classification of tea samples according to the tea varieties were subjected to ATLD-2DLDA analysis.Further,the obtained results were compared with those obtain ed by using LDA based on relative concentrations of ATLD(ATLD-LDA),discriminant analysis by N-way partial least square(N-PLS-DA)and 2D-LDA method.For the simulated data sets with respect to different levels of noise and class overlap as well as number of groups,the ATLD-2DLDA always obtains superior classification performances than the ATLD-LDA,2D-LDA and N-PLS-DA methods.Regarding the real EEMs data set of tea samples,the proposed methodology not only could provide a chemically meaningful model of the data for characterizing the different tea varieties,but also achieved the best correct classification rate(100%)for the test samples,compared with the results of ATLD-LDA(83.9%),2D-LDA(90.3%)and N-PLS-DA(90.3%).These results demonstrated that the proposed methodology was indeed a feasible and reliable tool for characterization and classification of three-way chemical data arrays in a flexible and accurate manner.