Huan Fang

A chemometrics-assisted excitation–emission matrix fluorescence method for simultaneous determination of arbutin and hydroquinone in cosmetic products

A fast analytical method that combines second-order calibration based on alternating trilinear decomposition (ATLD) algorithm with excitation–emission matrix (EEM) fluorescence technique is proposed to mathematically separate the overlapped spectra and simultaneously quantify arbutin (AR) and hydroquinone (HQ) in cosmetic products. This method inherits the merit of high sensitivity of traditional fluorescence and fully realizes the “second-order advantage”. For AR and HQ, the calibration ranges are 40.00–400.00 and 20.00–200.00 ng mL−1, respectively. The limits of detection for both analytes are in the range of 1.51–4.01 ng mL−1. The proposed method could be applied to diluted samples of different cosmetic products with satisfactory results. The actual concentrations of AR in the tested cosmetic products are found to be in the allowable concentration range (7%); while, the prohibited skin whitening agent HQ is detected in lotion. The contents of AR and HQ in the tested cosmetic products obtained by the proposed method are also in accordance with those of the validated high-performance liquid chromatographic method. These satisfactory results indicate that the proposed method has the potential to accurately quantify AR and HQ in complex matrices containing uncalibrated interferents, and shows promise as a reliable tool for quality monitoring of cosmetic products.

Direct and interference-free determination of thirteen phenolic compounds in red wines using a chemometrics-assisted HPLC-DAD strategy for authentication of vintage year

In the present work, a novel chemometrics-assisted analytical strategy that combines three-way high performance liquid chromatography-diode array detection (HPLC-DAD) data with a second-order calibration method based on the alternating trilinear decomposition (ATLD) algorithm was developed for direct, accurate and simultaneous determination of thirteen phenolic compounds in complex red wine samples without an intricate clean-up step. All analytes were rapidly eluted out (7.5 min) under a simple gradient LC-separation and then detected in a multi-channel UV window. With the aid of the prominent “second-order advantage” of the ATLD algorithm, four common HPLC problems, i.e. solvent peaks, peak overlaps, unknown interferents and baseline drifts, could be mathematically calibrated, enabling “pure signals” of analytes to be extracted out from interference-heavy but information-rich HPLC-DAD profiles. The new strategy could avoid the loss of analytes of interest to significantly improve the analytical accuracy. Validation parameters, i.e. recovery (97.7–104%), precision (RSD < 7.1%), matrix effect, limits of detection (LODs, 0.02–0.27 μg mL−1) and limits of quantitation (LOQs, 0.06–0.82 μg mL−1) of thirteen analytes, were surveyed and further confirmed by the LC-MS/MS method. Based on the indexes of phenolic compositions in wines, pattern recognition methods, i.e. principal component analysis and linear discriminant analysis (PCA-LDA), were applied for distinguishing wines of different storage years, and the discriminant accuracies were higher than 90%, which proved that this chemometrics-assisted HPLC-DAD strategy was an excellent method for direct and accurate determination of phenolic compositions in complex wine samples as well as the authentication of vintage year.

Chemometrics-assisted liquid chromatography-full scan mass spectrometry for simultaneous determination of multi-class estrogens in infant milk powder

In the present study, a smart analytical strategy that combines liquid chromatography-full scan mass spectrometry with the second-order calibration method based on the alternating trilinear decomposition (ATLD) algorithm was developed for the simultaneous determination of seven estrogens in infant milk powders. The seven estrogens were rapidly eluted out within 7.0 min under a simple gradient condition and then, they were detected by mass spectrometry operated in the full scan mode. With the aid of the prominent “second-order advantage” of the algorithm, specific qualitative and quantitative information about the target analytes could be extracted from the complex system even in the presence of considerable peak overlaps, baseline drifts and unknown interferences. The proposed strategy avoided time-consuming and laborious sample pretreatment procedures, resulting in minimal loss of analytes, which improved the analytical accuracy. Average recoveries of the seven estrogens in two spiked infant milk powder samples were in the range of 91.2–104.2% with the relative standard deviations (RSDs) lower than 5.0% (with the exception for 17α-estradiol), and the limits of detection (LOD) ranged from 0.07 to 2.49 ng mL−1. Besides, to further confirm the feasibility and reliability of the proposed method, the same batch of samples was analyzed using the LC-MS/MS method, and the statistical tests showed that no significant difference existed between the two methods, which fully indicated that the proposed strategy could provide satisfactory prediction results in real infant milk powder samples as well as other actual chemical systems.

A flexible and novel strategy of alternating trilinear decomposition method coupled with two-dimensional linear discriminant analysis for three-way chemical data analysis: Characterization and classification

This paper proposes a flexible and novel strategy that alternating trilinear decomposition (ATLD) method combines 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 simulated 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 obtained 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.