Hai-Yan Fu

Quantitative analysis of levodopa, carbidopa and methyldopa in human plasma samples using HPLC-DAD combined with second-order calibration based on alternating trilinear decomposition algorithm.

An HPLC method combined with second-order calibration based on alternating trilinear decomposition (ATLD) algorithm has been developed for the quantitative analysis of levodopa (LVD), carbidopa (CBD) and methyldopa (MTD) in human plasma samples. Prior to the analysis of the analytes by ATLD algorithm, three time regions of chromatograms were selected purposely for each analyte to avoid serious collinearity. Although the spectra of these analytes were similar and interferents coeluted with the analytes studied in biological samples, good recoveries of the analytes could be obtained with HPLC-DAD coupled with second-order calibration based on ATLD algorithm, additional benefits are decreasing times of analysis and less solvent consumption. The average recoveries achieved from ATLD with the factor number of 3 (N=3) were 100.1+/-2.1, 96.8+/-1.7 and 104.2+/-2.6% for LVD, CBD and MTD, respectively. In addition, elliptical joint confidence region (EJCR) tests as well as figures of merit (FOM) were employed to evaluate the accuracy of the method.

Quantitative analysis of triazine herbicides in environmental samples by using high performance liquid chromatography and diode array detection combined with second-order calibration based on an alternating penalty trilinear decomposition algorithm

A novel application of second-order calibration method based on an alternating penalty trilinear decomposition (APTLD) algorithm is presented to treat the data from high performance liquid chromatography-diode array detection (HPLC-DAD). The method makes it possible to accurately and reliably analyze atrazine (ATR), ametryn (AME) and prometryne (PRO) contents in soil, river sediment and wastewater samples. Satisfactory results are obtained although the elution and spectral profiles of the analytes are heavily overlapped with the background in environmental samples. The obtained average recoveries for ATR, AME and PRO are 99.7±1.5, 98.4±4.7 and 97.0±4.4% in soil samples, 100.1±3.2, 100.7±3.4 and 96.4±3.8% in river sediment samples, and 100.1±3.5, 101.8±4.2 and 101.4±3.6% in wastewater samples, respectively. Furthermore, the accuracy and precision of the proposed method are evaluated with the elliptical joint confidence region (EJCR) test. It lights a new avenue to determine quantitatively herbicides in environmental samples with a simple pretreatment procedure and provides the scientific basis for an improved environment management through a better understanding of the wastewater-soil-river sediment system as a whole.

Studying the Interaction of Pirarubicin with DNA and Determining Pirarubicin in Human Urine Samples: Combining Excitation -Emission Fluorescence Matrices with Second-order Calibration Methods

In this paper, UV–vis spectroscopy and fluorescence were combined to study the binding of Calf thymus DNA (ct-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 (EEFMs) coupled with the parallel factor analysis (PARAFAC) and the alternating normalization-weighted error algorithm (ANWE) with the second-order advantage. All the results conformed that THP mainly bound with DNA by intercalation. Meanwhile, the two second-order calibration methods have been successfully applied to quantify THP in urine samples. Figures of merit were applied to compare the performance of the two methods. The results presented in this work showed that both the PARAFAC and ANWE methods were the convincing way to be applied in the complex biological systems even in the presence of uncalibrated interferences.

A new third‐order calibration method with application for analysis of four‐way data arrays

A novel third‐order calibration algorithm, alternating weighted residue constraint quadrilinear decomposition (AWRCQLD) based on pseudo‐fully stretched matrix forms of quadrilinear model, was developed for the quantitative analysis of four‐way data arrays. The AWRCQLD algorithm is based on the new scheme that introduces four unique constraint parts to improve the quality of four‐way PARAFAC algorithm. The tested results demonstrated that the AWRCQLD algorithm has the advantage of faster convergence rate and being insensitive to the excess component number adopted in the model compared with four‐way PARAFAC. Moreover, simulated data and real experimental data were analyzed to explore the third‐order advantage over the second‐order counterpart. The results showed that third‐order calibration methods possess third‐order advantages which allow more inherent information to be obtained from four‐way data, so it can improve the resolving and quantitative capability in contrast with second‐order calibration especially in high collinear systems. Copyright

Simultaneous determination of 6-methylcoumarin and 7-methoxycoumarin in cosmetics using three-dimensional excitation-emission matrix fluorescence coupled with second-order calibration methods.

This paper reports a simple, rapid, and effective method for quantitative analysis of 6-methylcoumarin (6-MC) and 7-methoxycoumarin (7-MOC) in cosmetics using excitation-emission matrix (EEM) fluorescence coupled with second-order calibration. After simple pretreatments, the adopted calibration algorithms exploiting the second-order advantage, i.e., parallel factor analysis (PARAFAC) and self-weighted alternating tri-linear decomposition (SWATLD), could allow the individual concentrations of the analytes of interest to be predicted even in the presence of uncalibrated interferences. In the analysis of facial spray, with the external calibration method, the average recoveries attained from PARAFAC and SWATLD with the factor number of 3 (N=3) were 101.4+/-5.5 and 97.5+/-4.1% for 6-MC, and 103.3+/-1.7 and 101.7+/-1.8% for 7-MOC, respectively. Moreover, in the analysis of oil control nourishing toner, the standard addition method (SAM) was suggested to overcome the partial fluorescence quenching of 6-MC induced by the analyte-background interaction, which also yielded satisfactory prediction results. In addition, the accuracy of the two algorithms was also evaluated through elliptical joint confidence region (EJCR) tests as well as figures of merit (FOM), including sensitivity (SEN), selectivity (SEL) and limit of detection (LOD). It was found that both algorithms could give accurate results, only the performance of SWATLD was slightly better than that of PARAFAC in the cases suffering from matrix effects. The method proposed lights a new avenue to determine quantitatively 6-MC and 7-MOC in cosmetics, and may hold great potential to be extended as a promising alternative for more practical applications in cosmetic quality control, due to its advantages of easy sample pretreatment, non-toxic and non-destructive analysis, and accurate spectral resolution and concentration prediction.

Preliminary study on the application of near infrared spectroscopy and pattern recognition methods to classify different types of apple samples.

In this paper, near infrared (NIR) spectroscopy combined with pattern recognition methods was used in an attempt to classify different types of apple samples. Three pattern recognition methods such as K-nearest neighbour (KNN), partial least-squares discriminant analysis (PLSDA) and moving window partial least-squares discriminant analysis (MWPLSDA) were used to classify apple samples of different geographical origins, grades and varieties. The result indicates that MWPLSDA is superior to these two conventional pattern recognition methods. Because MWPLSDA method can select narrow but informative wavelength intervals to reconstruct an efficacious classification model with high predicting accuracy. In conclusion, MWPLSDA coupled with near-infrared fibre-optic technology is proved to be an effective method for fruit classification.

Variable-weighted least-squares support vector machine for multivariate spectral analysis

Multivariate spectral analysis has been widely applied in chemistry and other fields. Spectral data consisting of measurements at hundreds and even thousands of analytical channels can now be obtained in a few seconds. It is widely accepted that before a multivariate regression model is built, a well-performed variable selection can be helpful to improve the predictive ability of the model. In this paper, the concept of traditional wavelength variable selection has been extended and the idea of variable weighting is incorporated into least-squares support vector machine (LS-SVM). A recently proposed global optimization method, particle swarm optimization (PSO) algorithm is used to search for the weights of variables and the hyper-parameters involved in LS-SVM optimizing the training of a calibration set and the prediction of an independent validation set. All the computation process of this method is automatic. Two real data sets are investigated and the results are compared those of PLS, uninformative variable elimination-PLS (UVE-PLS) and LS-SVM models to demonstrate the advantages of the proposed method.

Fluorescent quantification of terazosin hydrochloride content in human plasma and tablets using second-order calibration based on both parallel factor analysis and alternating penalty trilinear decomposition.

Two second-order calibration methods based on the parallel factor analysis (PARAFAC) and the alternating penalty trilinear decomposition (APTLD) method, 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 two 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. These second-order calibrations all adequately exploited the second-order advantages. For human plasma samples, the average recoveries by the PARAFAC and APTLD algorithms with the factor number of 2 (N=2) were 100.4+/-2.7% and 99.2+/-2.4%, respectively. The accuracy of two algorithms was also evaluated through elliptical joint confidence region (EJCR) tests and t-test. It was found that both algorithms could give accurate results, and only the performance of APTLD was slightly better than that of PARAFAC. Figures of merit, such as sensitivity (SEN), selectivity (SEL) and limit of detection (LOD) were also calculated to compare the performances of the two strategies. For tablets, the average concentrations of THD in tablet were 63.5 and 63.2 ng mL(-1) by using the PARAFAC and APTLD algorithms, respectively. The accuracy was evaluated by t-test and both algorithms could give accurate results, too.

New variable selection method using interval segmentation purity with application to blockwise kernel transform support vector machine classification of high-dimensional microarray data.

One problem with discriminant analysis of microarray data is representation of each sample by a large number of genes that are possibly irrelevant, insignificant, or redundant. Methods of variable selection are, therefore, of great significance in microarray data analysis. A new method for key gene selection has been proposed on the basis of interval segmentation purity that is defined as the purity of samples belonging to a certain class in intervals segmented by a mode search algorithm. This method identifies key variables most discriminative for each class, which offers possibility of unraveling the biological implication of selected genes. A salient advantage of the new strategy over existing methods is the capability of selecting genes that, though possibly exhibit a multimodal distribution, are the most discriminative for the classes of interest, considering that the expression levels of some genes may reflect systematic difference in within-class samples derived from different pathogenic mechanisms. On the basis of the key genes selected for individual classes, a support vector machine with block-wise kernel transform is developed for the classification of different classes. The combination of the proposed gene mining approach with support vector machine is demonstrated in cancer classification using two public data sets. The results reveal that significant genes have been identified for each class, and the classification model shows satisfactory performance in training and prediction for both data sets.

Moving window partial least-squares discriminant analysis for identification of different kinds of bezoar samples by near infrared spectroscopy and comparison of different pattern recognition methods

Moving window partial least-squares (MWPLS) regression was coupled with near infrared (NIR) spectra as an interval selection method to improve the performance of partial least squares discriminant analysis (PLSDA) models. This method was applied to the identification of artificial bezoar, natural bezoar and artificial bezoar in natural bezoar and compared with some traditional pattern recognition methods, such as principal component analysis (PCA), linear discriminant analysis (LDA) and PLSDA. The introduction of MWPLS enhanced the performance of PLSDA model. The results obtained showed that moving window partial least-squares discriminant analysis (MWPLSDA) can extract wavelength intervals with useful information and build simple yet effective classification models that can significantly improve the classification accuracy. Then MWPLSDA was used to identify natural bezoar by geographical origin; a promising result was achieved. The work showed that MWPLSDA could be a promising method for quality analysis and discrimination of chinese medical herbs according to geographical origin.