Xiaobing Zhang

Chemometric strategy for automatic chromatographic peak detection and background drift correction in chromatographic data

Peak detection and background drift correction (BDC) are the key stages in using chemometric methods to analyze chromatographic fingerprints of complex samples. This study developed a novel chemometric strategy for simultaneous automatic chromatographic peak detection and BDC. A robust statistical method was used for intelligent estimation of instrumental noise level coupled with first-order derivative of chromatographic signal to automatically extract chromatographic peaks in the data. A local curve-fitting strategy was then employed for BDC. Simulated and real liquid chromatographic data were designed with various kinds of background drift and degree of overlapped chromatographic peaks to verify the performance of the proposed strategy. The underlying chromatographic peaks can be automatically detected and reasonably integrated by this strategy. Meanwhile, chromatograms with BDC can be precisely obtained. The proposed method was used to analyze a complex gas chromatography dataset that monitored quality changes in plant extracts during storage procedure.

A chemometric-assisted method based on gas chromatography-mass spectrometry for metabolic profiling analysis.

An automatic and efficient data analysis method for comprehensive metabolic profiling analysis is urgently required. In this study, a new chemometric-assisted method for metabolic profiling analysis (CAMMPA) was developed to discover potentially valuable metabolites automatically and efficiently. The proposed method mainly consists of three stages. First, automatic chromatographic peak detection is performed based on the total ion chromatograms of samples to extract chromatographic peaks that can be accurately quantified. Second, a novel peak-shift alignment technique based on peak detection results is implemented to resolve time-shift problems across samples. Consequently, aligned results, including aligned chromatograms, and peak area tables, among others, can be successfully obtained. Third, statistical analysis using results from unsupervised and supervised classification results, together with ANOVA and partial least square-discriminate analysis, is performed to extract potential metabolites. To demonstrate the proposed technique, a complex GC-MS metabolic profiling dataset was measured to identify potential metabolites in tobacco plants of different growth stages as well as different plant tissues after maturation. Results indicated that the efficiency of the routine metabolic profiling analysis procedure can be significantly improved and potential metabolites can be accurately identified with the aid of CAMMPA.

Determination of urinary aromatic amines in smokers and nonsmokers using a MIPs-SPE coupled with LC-MS/MS method.

Urinary aromatic amines (AAs) could be used as biomarkers for human exposure to AAs in cigarette smoke. A liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed for the determination of urinary AAs (i.e. 1-naphthylamine (1-NA), 2-naphthylamine (2-NA), 3-aminobiphenyl (3-ABP) and 4-aminobiphenyl (4-ABP)) in smokers and nonsmokers. A molecularly imprinted polymers (MIPs) solid phase extraction (SPE) cartridge was applied to purify urine samples and no derivatization reaction was involved. Each analytes used respective stable isotope internal standards, which could well compensate matrix effect. Lower limit of detections (LODs) for four AAs were obtained and in the range of 1.5-5ngL(-1). Recovery ranged from 87.7±4.5% to 111.3±6.4% and precision were less than 9.9%. The method was applied to analyze urine samples of 40 smokers and 10 nonsmokers. The 24h urinary excretion amounts of total AAs were higher for smokers compared with nonsmokers. Whats more, 1-NA, 3-ABP and 4-ABP excretion amounts showed significant differences (p<0.05) between smokers and nonsmokers.

Systematic screening and characterization of glycosides in tobacco leaves by liquid chromatography with atmospheric pressure chemical ionization tandem mass spectrometry using neutral loss scan and product ion scan

Glycosides in tobacco leaves are highly important aromatic precursors. It is necessary to reveal glycosides in tobacco leaves to improve tobacco planting and processing. This study describes a method for the systematic screening of glycosides in tobacco leaves by liquid chromatography with tandem mass spectrometry. Although glycosides contain numerous aglycones, the number of glycans is limited. Based on a screening table of glycans designed for neutral loss scan, glycosides with different aglycones were systematically screened out. Then, the MS(2) fragment spectra of scanned glycosides were further obtained using product ion scan. By comparison with the spectra in online tandem mass spectral databases, reported references, and verification by commercial standards, 64 glycosides were detected, including 39 glycosides linked with monosaccharides, 18 glycosides linked with disaccharides and 7 glycosides linked with trisaccharides. It is noteworthy that glycosides linked with trisaccharides have previously been rarely reported in tobacco. This method appears to be a useful tool for the systematic screening and characterization of glycosides in tobacco and can potentially be applied to other plants.

Automatic time-shift alignment method for chromatographic data analysis

Time shift among samples remains a significant challenge in data analysis, such as quality control of natural plant extracts and metabolic profiling analysis, because this phenomenon may lead to invalid conclusions. In this work, we propose a new time shift alignment method, namely, automatic time-shift alignment (ATSA), for complicated chromatographic data analysis. This technique comprised the following alignment stages: (1) automatic baseline correction and peak detection stage for providing useful chromatographic information; (2) preliminary alignment stage through adaptive segment partition to correct alignment for the entire chromatogram; and (3) precise alignment stage based on test chromatographic peak information to accurately align time shift. In ATSA, the chromatographic peak information of both reference and test samples can be completely employed for time-shift alignment to determine segment boundaries and avoid loss of information. ATSA was used to analyze a complicated chromatographic dataset. The obtained correlation coefficients among samples and data analysis efficiency indicated that the influences of time shift can be considerably reduced by ATSA; thus accurate conclusion could be obtained.

Quantification of acid metabolites in complex plant samples by using second-order calibration coupled with GC-mass spectrometry detection to resolve the influence of seriously overlapped chromatographic peaks

Accurate quantification of target metabolites, such as organic acid metabolites, in complex natural tobacco samples is a difficult task in metabolic profiling analysis because of the large amount of interferences present in the matrix. Chromatographic peaks of analytes are always overlapped by interferences, although the separation capability of chromatography is optimally enhanced. In this work, the chemometric strategy of second-order calibration of multivariate curve resolution-alternating least squares was employed in combination with gas chromatography-mass selective detection for metabolic profiling analysis to quantify 11 secondary acid metabolites, regardless of interference. The results indicated that the instrumental separation capability can be further improved using a mathematical separation strategy. Chromatographic profiles of analytes can be satisfactorily retrieved from overlapped chromatographic peaks and accurate quantitative results can be obtained. Finally, tobacco samples collected from Henan and Yunnan provinces were successfully grouped based on the obtained quantitative results.