Co-reporter:Hongchao Ji, Fanjuan Zeng, Yamei Xu, Hongmei Lu, and Zhimin Zhang
Analytical Chemistry July 18, 2017 Volume 89(Issue 14) pp:7631-7631
Publication Date(Web):June 16, 2017
DOI:10.1021/acs.analchem.7b01547
Distilling accurate quantitation information on metabolites from liquid chromatography coupled with mass spectrometry (LC-MS) data sets is crucial for further statistical analysis and biomarker identification. However, it is still challenging due to the complexity of biological systems. The concept of pure ion chromatograms (PICs) is an effective way of extracting meaningful ions, but few toolboxes provide a full processing workflow for LC-MS data sets based on PICs. In this study, an integrated framework, KPIC2, has been developed for metabolomics studies, which can detect pure ions accurately, align PICs across samples, group PICs to identify isotope and potential adducts, fill missing peaks and do multivariate pattern recognition. To evaluate its performance, MM48, metabolomics quantitation, and Soybean seeds data sets have been analyzed using KPIC2, XCMS, and MZmine2. KPIC2 can extract more true ions with fewer detecting features, have good quantification ability on a metabolomics quantitation data set, and achieve satisfactory classification on a soybean seeds data set through kernel-based OPLS-DA and random forest. It is implemented in R programming language, and the software, user guide, as well as example scripts and data sets are available as an open source package at https://github.com/hcji/KPIC2.
Co-reporter:Ming Wen, Zhimin Zhang, Shaoyu Niu, Haozhi Sha, Ruihan Yang, Yonghuan Yun, and Hongmei Lu
Journal of Proteome Research April 7, 2017 Volume 16(Issue 4) pp:1401-1401
Publication Date(Web):March 6, 2017
DOI:10.1021/acs.jproteome.6b00618
Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug–target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug–drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.Keywords: deep learning; deep-delief network; drug−target interaction prediction; feature extraction; semisupervised learning;
Co-reporter:Rong Wang, Hongcao Ji, Pan Ma, Huitao Zeng, Yamei Xu, Zhi-Min Zhang, Hong-Mei Lu
Chemometrics and Intelligent Laboratory Systems 2017 Volume 170(Volume 170) pp:
Publication Date(Web):15 November 2017
DOI:10.1016/j.chemolab.2017.10.001
•FPIC searches ions of PIC from its maximum bi-directionally and adaptively.•Outperform traditional and PIC methods in recall, precision and F-score.•Extract features with excellent linear and reliability of quantification.•125-fold speedup over PITracer and 18-fold speedup over XCMS.•Create interactive analysis workflow for LC-MS in Python easily.Liquid chromatography coupled with mass spectrometry (LC-MS) has shown great potential in analysis complex samples. However, informative feature extraction is still challenge since the electrospray ionization in LC-MS tends to produce ninety percent or more ions not originated from compounds of interest. The concept of pure ion chromatogram (PIC) is effective to extract informative ions, but tradition PIC methods are time-consuming because of their theories and programming languages. In this study, we present a novel method, called Fast Pure Ions Chromatograms (FPIC), for extracting PICs from raw LC-MS dataset effectively and quickly. This method can search ion of PIC from its maximum bi-directionally and adaptively, which can improve the stability and reduce the computation time drastically. A further speedup has been achieved by exploiting modern software engineering techniques. FPIC was validated by analyzing four LC-MS datasets: MM14 and MM48, simulated MM48 and quantification (MTBLS234) datasets. Results show that FPIC outperformed traditional methods in the recall, precision and F-score, and it has good reliability of quantification. Furthermore, the method is very fast with few adjustable parameters, which leads to an approximately 125-fold speedup over PITracer and 18-fold speedup over XCMS. An open source implementation of the FPIC method is available at https://github.com/zmzhang/pymass.
Co-reporter:Hongchao Ji, Hongmei Lu and Zhimin Zhang
RSC Advances 2016 vol. 6(Issue 62) pp:56977-56985
Publication Date(Web):02 Jun 2016
DOI:10.1039/C6RA08409E
Untargeted analysis of complex samples with liquid chromatography coupled with mass spectrometry (LC-MS) has shown a great prospect. However, it is still difficult to extract useful information from complicated LC-MS data. Recently, pure ion chromatograms (PIC) were introduced. They are effective for reducing ions not related to meaningful compounds. In this study, a novel method to extract PIC based on optimal k-means clustering (KPIC) is proposed. KPIC uses the clustering tendency of centroids of pure ions to extract PIC from raw LC-MS datasets adaptively. KPIC was tested with 3 datasets: simulated, MM48 and Arabidopsis thaliana datasets. Compared with PITracer and XCMS methods, the results show that KPIC has better accuracy of feature extraction. It is able to provide higher quality chromatographic peaks, particularly for low concentration compounds. KPIC reduces the number of split signals, due to avoiding estimation of ion mass difference tolerances subjectively. KPIC is implemented in R programming language, which is available as an open source package at https://github.com/hcji/KPIC.
Co-reporter:Longhui Ma, Zhimin Zhang, Xingbing Zhao, Sufeng Zhang and Hongmei Lu
Analytical Methods 2016 vol. 8(Issue 23) pp:4584-4589
Publication Date(Web):11 May 2016
DOI:10.1039/C6AY00542J
Despite their popularity and extensive use, some herbs have not been officially recognized in most countries. The main reason is the lack of comprehensive research data and methods. In this study, a rapid approach based on near-infrared spectroscopy (NIR) was developed for the determination of total polyphenols content (TPC) and antioxidant activity (AA) in Dendrobium officinale (D. officinale), an important Chinese herb. Adopting the Folin–Ciocalteu (FC) assay and 2,2-diphenyl-1-picrylhydrazyl radical (DPPH) free radical scavenging activity as the reference methods, TPC and AA in D. officinale samples (n = 83) collected from different locations in China were analyzed. The spectra generated by NIR were pretreated with different pre-processing methods and analyzed with the partial least-square (PLS) method. To obtain a robust and predictive quantitative model, competitive adaptive reweighted sampling (CARS) was applied to screen the key variables. The correlation coefficient of prediction (Rpre2) and root mean square error of prediction (RMSEP) by competitive adaptive reweighted sampling-partial least-square (CARS-PLS) were 0.8412 and 0.2905 for TPC and 0.9062 and 0.1028 for AA. The results show that the combination of NIR spectroscopy with CARS-PLS provides a rapid and precise alternative to existing chemical analysis for the determination of TPC and AA in D. officinale.
Co-reporter:Mingjing Zhang, Zhimin Zhang, Chen Chen, Hongmei Lu, Yizeng Liang
Chemometrics and Intelligent Laboratory Systems 2016 Volume 153() pp:106-109
Publication Date(Web):15 April 2016
DOI:10.1016/j.chemolab.2016.03.002
•The parallel formula generator (PFG) is proposed to analyze mass spectra.•PFG can generate candidate formulas within the given mass range.•To accelerate the speed, PFG utilized parallel computing based on OpenMP.•PFG is accurate and can reduce calculation time when compared with HR2 method.The identification of unknown molecules by mass spectrometry is one of the most challenging problems despite the development of the instrument. One of the crucial steps is to obtain the possible elemental compositions within the limit of the measurement of mass-to-charge ratio and the mass tolerance. However, as the number of possible elements and the molecular weight increase, the more calculation time is needed. Here, a formula generator based on template metaprogramming and parallel computing is proposed to generate the possible candidate formulas. The template metaprogramming has been applied to replace the inefficient recursion to create the nested loops at compile-time for enumerating the possible elements. To accelerate the computation speed, the branch-and-bound algorithm is used to constrain the number of loop for each element. The parallel computing procedure is based on the Open Multi-Processing (OpenMP). The calculation time for calculating the candidates in the mass ranges especially for the higher ones can be significantly reduced when comparing with the popular HR2 program. PFG is implemented in C ++ and available at https://github.com/zmzhang/PFG. It can be compiled easily and run smoothly in both Windows and Linux.
Co-reporter:Xinyi Zhou, Yang Wang, Yonghuan Yun, Zian Xia, Hongmei Lu, Jiekun Luo, Yizeng Liang
Talanta 2016 Volume 147() pp:82-89
Publication Date(Web):15 January 2016
DOI:10.1016/j.talanta.2015.09.040
•Plasma profiles based on GC–MS was utilized to metabolomic analysis of male infertility.•Erectile dysfunction and semen abnormalities were analyzed, respectively.•Two variable selection methods were both applied to screen potential biomarkers.Male infertility has become an important public health problem worldwide. Nowadays the diagnosis of male infertility frequently depends on the results of semen quality or requires more invasive surgical intervention. Therefore, it is necessary to develop a novel approach for early diagnosis of male infertility. According to the presence or absence of normal sexual function, the male infertility is classified into two phenotypes, erectile dysfunction (ED) and semen abnormalities (SA). The aim of this study was to investigate the GC–MS plasma profiles of infertile male having erectile dysfunction (ED) and having semen abnormalities (SA) and discover the potential biomarkers. The plasma samples from healthy controls (HC) (n=61) and infertility patients with ED (n=26) or with SA (n=44) were analyzed by gas chromatography–mass spectrometry (GC–MS) for discrimination and screening potential biomarkers. The partial least squares-discriminant analysis (PLS-DA) was performed on GC–MS dataset. The results showed that HC could be discriminated from infertile cases having SA (AUC=86.96%, sensitivity=78.69%, specificity=84.09%, accuracy=80.95%) and infertile cases having ED (AUC=94.33%, sensitivity=80.33%, specificity=100%, accuracy=87.36%). Some potential biomarkers were successfully discovered by two commonly used variable selection methods, variable importance on projection (VIP) and original coefficients of PLS-DA (β). 1,5-Anhydro-sorbitol and α-hydroxyisovaleric acid were identified as the potential biomarkers for distinguishing HC from the male infertility patients. Meanwhile, lactate, glutamate and cholesterol were the found to be the important variables to distinguish between patients with erectile dysfunction from those with semen abnormalities. The plasma metabolomics may be developed as a novel approach for fast, noninvasive, and acceptable diagnosis and characterization of male infertility.
Co-reporter:Mingjing Zhang;Ming Wen;Zhi-Min Zhang;Hongmei Lu;Yizeng Liang ;Dejian Zhan
Journal of Separation Science 2015 Volume 38( Issue 6) pp:965-974
Publication Date(Web):
DOI:10.1002/jssc.201401235
Retention time shift is one of the most challenging problems during the preprocessing of massive chromatographic datasets. Here, an improved version of the moving window fast Fourier transform cross-correlation algorithm is presented to perform nonlinear and robust alignment of chromatograms by analyzing the shifts matrix generated by moving window procedure. The shifts matrix in retention time can be estimated by fast Fourier transform cross-correlation with a moving window procedure. The refined shift of each scan point can be obtained by calculating the mode of corresponding column of the shifts matrix. This version is simple, but more effective and robust than the previously published moving window fast Fourier transform cross-correlation method. It can handle nonlinear retention time shift robustly if proper window size has been selected. The window size is the only one parameter needed to adjust and optimize. The properties of the proposed method are investigated by comparison with the previous moving window fast Fourier transform cross-correlation and recursive alignment by fast Fourier transform using chromatographic datasets. The pattern recognition results of a gas chromatography mass spectrometry dataset of metabolic syndrome can be improved significantly after preprocessing by this method. Furthermore, the proposed method is available as an open source package at https://github.com/zmzhang/MWFFT2.
Co-reporter:Qianyi Luo, Yonghuan Yun, Wei Fan, Jianhua Huang, Lixian Zhang, Baichuan Deng and Hongmei Lu
RSC Advances 2015 vol. 5(Issue 7) pp:5046-5052
Publication Date(Web):02 Dec 2014
DOI:10.1039/C4RA11421C
A method for rapid quantitative analysis of epimedin A, B, C and icariin in Epimedium was developed based on Fourier transform near infrared (FT-NIR) spectroscopy, and by adopting high performance liquid chromatography-diode array detection (HPLC-DAD) as the reference method. Multivariate calibrations models were built by partial least squares regression (PLSR) based on the full absorbance spectra (10000–4000 cm−1) or only the most informative key variables selected by the competitive adaptive reweighted sampling (CARS) method. In comparison, the accuracy of the CARS-PLSR method was apparently higher than full spectrum-PLSR for four kinds of investigated flavonoids. For CARS-PLSR, the coefficients of determination (R2) for prediction were 0.8969, 0.8810, 0.9273 and 0.9325 and the root mean square errors of prediction (RMSEP) were 0.1789, 0.2572, 1.2872 and 0.3615 for epimedin A, B, C and icariin, respectively. The good performance indicates that the combination of NIR spectroscopy with CARS-PLSR is an effective method for determination of epimedin A, B, C and icariin in Epimedium with fast, economic and nondestructive advantages compared to traditional chemical methods.
Co-reporter:Yong-Huan Yun, Yang-Chao Wei, Xing-Bing Zhao, Wei-Jia Wu, Yi-Zeng Liang and Hong-Mei Lu
RSC Advances 2015 vol. 5(Issue 127) pp:105057-105065
Publication Date(Web):26 Nov 2015
DOI:10.1039/C5RA21795D
Polysaccharides are one of the active components of Dendrobium officinale (D. officinale) and its content is used as one of the main quality assessment criteria. The existing methods for polysaccharide quantification involve sample destruction, tedious sample processing, high cost, and non-environmentally friendly pretreatment. The aim of this study is to develop a simple, rapid, green and nondestructive analytical method based on near infrared (NIR) spectroscopy and chemometrics methods. A set of 84 D. officinale samples from different origins was analyzed using NIR spectroscopy. Potential outlying samples were initially removed from the collected NIR data in two steps using the Monte Carlo sampling (MCS) method. Spectral data preprocessing was studied in the construction of a partial least squares (PLS) model. To eliminate uninformative variables and improve the performance of the model, the pretreated full spectrum was calculated using different wavelength selection methods, including competitive adaptive reweighted sampling (CARS), Monte Carlo-uninformative variable elimination (MC-UVE) and interval random frog (iRF). The selected wavelengths model met the following three points: (1) improved the prediction performance; (2) reduced the number of variables; (3) provided a better understanding and interpretation, which proves that it was necessary to conduct wavelength selection in the NIR analytical systems. When comparing the three wavelength selection methods, the results show that CARS has the best performance with the lowest root mean square error of prediction (RMSEP) on the independent test set and least number of latent variables (nLVs). This study demonstrates that the NIR spectral technique with the wavelength selection algorithm CARS could be used successfully for the quantification of the polysaccharide content in D. officinale.
Co-reporter:Xi Wang, Jianhua Huang, Wei Fan and Hongmei Lu
Analytical Methods 2015 vol. 7(Issue 2) pp:787-792
Publication Date(Web):20 Nov 2014
DOI:10.1039/C4AY02106A
In this study, an approach based on near-infrared spectroscopy (NIRS), ultraviolet-visible spectroscopy (UV-Vis) and chemometric algorithms was developed for discrimination among five varieties of green tea, and further estimation of the total polyphenol content (TPC) in these tea varieties. Principal component analysis (PCA) and the random forest (RF) pattern recognition technique were used to classify these samples. Based on the joint information from the NIR and UV-Vis spectra, a successful classification model was established with RF. The classification accuracy was 96%. Furthermore, a partial least-squares regression (PLSR) model based on the NIR spectra and TPC values measured by the UV-Vis reference method was constructed for rapid analysis of the TPC in these tea samples. The values of RMSECV, RMSEC, and RMSEP were 0.3578, 0.1775 and 0.2693, respectively. The correction coefficients for the calibration and prediction set were 0.9966 and 0.9864, respectively. These results demonstrated that the proposed method can be efficiently utilized for fast, accurate, economic analysis of green tea.
Co-reporter:Ling Dai, Carlos M. Vicente Gonçalves, Zhang Lin, Jianhua Huang, Hongmei Lu, Lunzhao Yi, Yizeng Liang, Dongsheng Wang, Dong An
Talanta 2015 Volume 135() pp:108-114
Publication Date(Web):1 April 2015
DOI:10.1016/j.talanta.2014.12.039
•Selected ion monitoring mode was applied to the accurate quantitation of fatty acids.•Random forests were used to establish a classification and prediction model for MetS.•CCA was utilized to screen potential biomarkers associated with clinical parameters.Metabolic syndrome (MetS) is a cluster of metabolic abnormalities associated with an increased risk of developing cardiovascular diseases or type II diabetes. Till now, the etiology of MetS is complex and still unknown. Metabolic profiling is a powerful tool for exploring metabolic perturbations and potential biomarkers, thus may shed light on the pathophysiological mechanism of diseases. In this study, fatty acid profiling was employed to exploit the metabolic disturbances and discover potential biomarkers of MetS. Fatty acid profiles of serum samples from metabolic syndrome patients and healthy controls were first analyzed by gas chromatography–selected ion monitoring–mass spectrometry (GC–SIM–MS), a robust method for quantitation of fatty acids. Then, the supervised multivariate statistical method of random forests (RF) was used to establish a classification and prediction model for MetS, which could assist the diagnosis of MetS. Furthermore, canonical correlation analysis (CCA) was employed to investigate the relationships between free fatty acids (FFAs) and clinical parameters. As a result, several FFAs, including C16:1n-9c, C20:1n-9c and C22:4n-6c, were identified as potential biomarkers of MetS. The results also indicated that high density lipoprotein-cholesterol (HDL-C), triglycerides (TG) and fasting blood glucose (FBG) were the most important parameters which were closely correlated with FFAs disturbances of MetS, thus they should be paid more attention in clinical practice for monitoring FFAs disturbances of MetS than waist circumference (WC) and systolic blood pressure/diastolic blood pressure (SBP/DBP). The results have demonstrated that metabolic profiling by GC–SIM–MS combined with RF and CCA may be a useful tool for discovering the perturbations of serum FFAs and possible biomarkers for MetS.
Co-reporter:Zhang Lin, Carlos M. Vicente Gonçalves, Ling Dai, Hong-mei Lu, Jian-hua Huang, Hongchao Ji, Dong-sheng Wang, Lun-zhao Yi, Yi-zeng Liang
Analytica Chimica Acta 2014 Volume 827() pp:22-27
Publication Date(Web):27 May 2014
DOI:10.1016/j.aca.2014.04.008
•An effective approach has been developed for analysis complex metabolomics data.•Random forest provides useful tools for data visualization and interpretation.•Metabolic perturbations in metabolic syndrome patients were discovered.Metabolic syndrome (MetS) is a constellation of the most dangerous heart attack risk factors: diabetes and raised fasting plasma glucose, abdominal obesity, high cholesterol and high blood pressure. Analysis and representation of the variances of metabolic profiles is urgently needed for early diagnosis and treatment of MetS. In current study, we proposed a metabolomics approach for analyzing MetS based on GC–MS profiling and random forest models. The serum samples from healthy controls and MetS patients were characterized by GC–MS. Then, random forest (RF) models were used to visually discriminate the serum changes in MetS based on these GC–MS profiles. Simultaneously, some informative metabolites or potential biomarkers were successfully discovered by means of variable importance ranking in random forest models. The metabolites such as 2-hydroxybutyric acid, inositol and d-glucose, were defined as potential biomarkers to diagnose the MetS. These results obtained by proposed method showed that the combining GC–MS profiling with random forest models was a useful approach to analyze metabolites variances and further screen the potential biomarkers for MetS diagnosis.
Co-reporter:Lixian Zhang, Zhimin Zhang, Qianyi Luo, Hongmei Lu and Yizeng Liang
Analytical Methods 2014 vol. 6(Issue 4) pp:1036-1043
Publication Date(Web):25 Nov 2013
DOI:10.1039/C3AY41702F
Epimedium herbs are commonly used as herbal medicine ingredients in China. In this study, firstly the total phenolic content and total flavonoid content (TFC) of Epimedium extracts were determined, respectively. The majority of the Epimedium extracts showed strong 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity, and TFC of Epimedium exhibited highly correlations with DPPH scavenging activity with high correlation coefficient (R2 = 0.9279). Then, high performance liquid chromatography fingerprints of 25 Epimedium samples (5 species) were developed. A data fusion strategy was used to construct multi-wavelength fingerprints by combining 3 profiles with peak-rich wavelengths (230, 270 and 310 nm) into a single vector for each sample. Whittaker smoother, adaptive iteratively reweighted penalized least square (airPLS) and multi-scale peak alignment methods were adopted to preprocess the constructed fingerprint matrix. Variable selection and partial least square were applied to build a calibration model between fingerprints and their antioxidant activities. The potential peaks, which respond for the antioxidant activity, were screened by the selected variable with large regression coefficient. These results suggest that water extracts of Epimedium are a source of antioxidant compounds, and proposed multi-wavelength fingerprints can be used for predicting the antioxidant activity of Epimedium.
Co-reporter:Liu Deng, Chaogui Chen, Chengzhou Zhu, Shaojun Dong, Hongmei Lu
Biosensors and Bioelectronics 2014 Volume 52() pp:324-329
Publication Date(Web):15 February 2014
DOI:10.1016/j.bios.2013.09.005
•GO@SiO2@CeO2 hybrid nanosheets with intrinsic peroxidase-like activity were successfully synthesized.•GSCs have great potential as an alternative to the commonly employed peroxidase.•An easy colorimetric bioactive paper was fabricated based on GSCs.•Glucose, lactate, uric acid and cholesterol can be well detected by the naked eyes.In this paper, Graphene oxide@SiO2@CeO2 hybrid nanosheets (GSCs) have been successfully synthesized by the wet-chemical strategy. TEM, FITR and XPS were applied to characterize the morphology and composition of the nanosheets. The colorimetric assay of these nanosheets indicated that they possessed high intrinsic peroxidase activity, which should be ascribed to the combination of graphene oxide and CeO2. A fully integrated reagentless bioactive paper based on GSCs was fabricated, which were able to simultaneously detect glucose, lactate, uric acid and cholesterol. The results demonstrated that GSCs have great potential as an alternative to the commonly employed peroxidase in daily nursing and general physical examination.
Co-reporter:Pinpin Cai, Jianhua Huang, Zhimin Zhang and Hongmei Lu
Analytical Methods 2013 vol. 5(Issue 23) pp:6807-6813
Publication Date(Web):18 Sep 2013
DOI:10.1039/C3AY40726H
In GC-MS-based metabolomic analysis of serum samples, differential sample handling and storage can induce systematic bias hiding the original metabolic state. Here, several sample sets were examined to characterize the effects of several potential sources of bias. These factors were the thawing temperature (4 °C and 25 °C) and time (0.5–24 h) of the primary sample, storage temperature (4 °C and 25 °C) and time (0–48 h) of the nitrogen dried sample, storage temperature (4 °C and 25 °C), time (0–48 h) and refrigerating/warming cycle of the derivatized sample. The results showed that the primary sample thawed at both 4 °C and 25 °C for up to 24 h appeared to be stable. The storage of nitrogen dried samples and derivatized samples at 4 °C for up to 48 h still provided useable samples. However, the nitrogen dried samples denatured after 24 h storage at 25 °C. And the derivatized samples apparently changed after 10 h storage at 25 °C, or after undergoing two refrigerating/warming cycles.
Co-reporter:Lixian Zhang, Zhimin Zhang, Jianhua Huang, Yisu Jin and Hongmei Lu
Analytical Methods 2013 vol. 5(Issue 19) pp:5331-5338
Publication Date(Web):07 Aug 2013
DOI:10.1039/C3AY40637G
Epimedium has a long history as a traditional Chinese medicine (TCM) and is widely used. With similar morphology, non-official species, some of which have a low efficacy or even side effects, are adulterated into official Epimedium. Quality control of Epimedium is vital to its medical use. In this paper, the unitary and binary chromatographic fingerprints of 48 Epimedium samples were developed on two complementary platforms of high performance liquid chromatography-diode array detector (HPLC-DAD) and gas chromatography-mass spectrometry (GC-MS), by which complex volatile and non-volatile compounds from Epimedium were analyzed and well validated, respectively. Subsequently, principal component analysis (PCA) was performed with the unitary and binary chromatographic fingerprints of the whole chromatographic profile and common peaks. By comparison, the HPLC-DAD fingerprints alone showed a complete separation of all species, while GC-MS just provided a classification for them. Meanwhile, the fused data matrix possessed consistent classification results with HPLC-DAD analysis. Moreover, as a complementary method for conventional unitary HPLC fingerprinting, we proposed the whole fused fingerprints with several characteristic peaks, including peak 34 (n-hexadecanoic acid), 10 (icariin), 8 (epimedin C), 32 (tetradecanoic acid), 7 (epimedin B), 13, 35 (9-octadecenoic acid), 6 (epimedin A) and 11 for the quality control of Epimedium. The proposed method can provide a more comprehensive and objective quality control for Epimedium and other TCMs, and offer the feasibility for characterization and quality control of complex samples in the same genus designated under a single herbal drug entity.
Co-reporter:Li Li, Hongmei Lu, Liu Deng
Talanta 2013 Volume 113() pp:1-6
Publication Date(Web):15 September 2013
DOI:10.1016/j.talanta.2013.03.074
•A simple strategy for the synthesis of graphene–Au nanorods hybrid nanosheets through electrostatic interaction.•Fabrication of NADH and ethanol biosensor.•Practical samples application.•Good selectivity and anti-interference.In this paper, a simple strategy for the synthesis of graphene–Au nanorods hybrid nanosheets (GN–AuNRs) through electrostatic interaction has been demonstrated. Due to the synergistic effect between AuNRs and GN, the hybrid nanosheets exhibited excellent performance toward dihydronicotinamide adenine dinucleotide (NADH) oxidation, with a low detection limit of 6 µM. The linear GN–AuNRs also served as a biocompatible and electroactive matrix for enzyme assembly to facilitate the electron transfer between the enzyme and the electrode. Using alcohol dehydrogenase (ADH) as a model system, a simple and effective sensing platform was developed for ethanol assay. The response displayed a good linear range from 5 to 377 µM with detection limit 1.5 μM. Furthermore, the interference effects of redox active substances, such as uric acid, ascorbic acid and glucose for the proposed biosensor were negligible.
Co-reporter:Jiangang Fu;Xiaoru Li;Hongmei Lu;Yizeng Liang
Journal of Separation Science 2012 Volume 35( Issue 21) pp:2940-2948
Publication Date(Web):
DOI:10.1002/jssc.201200376
Analysis of volatile components in herbal pair (HP) Semen Persicae-Flos Carthami (SP-FC) was performed by GC-MS coupled with chemometric resolution method (CRM). Furthermore, temperature-programmed retention indices were used together with mass spectra for identification of the volatile components. With the help of CRM, the two-dimensional data obtained from GC-MS instruments were resolved into a pure chromatogram and a mass spectrum of each chemical compound. By use of these methods upon two-dimensional data, 26, 49, and 59 volatile chemical components in essential oils of single herb Semen Persicae, Flos Carthami, and HP SP-FC were determined qualitatively and quantitatively, accounting for 78.42, 81.08, and 82.48% total contents of essential oil of single herb Semen Persicae, Flos Carthami, and HP SP-FC, respectively. It is shown that the accuracy of qualitative and quantitative analysis can be enhanced greatly by means of CRM. It is further demonstrated that the numbers of volatile chemical components of HP SP-FC are almost the addition of those of two single herbs, but the main volatile chemical components of the former are completely different from those of single herb Semen Persicae or Flos Carthami because of chemical reactions and physical changes occurring in the process of decocting two single herbs. This means that chemical components especially pharmacologically active compounds in the recipe might be different from those of single herbs.
Co-reporter:Jian-Hua Huang, Ming Wen, Li-Juan Tang, Hua-Lin Xie, Liang Fu, Yi-Zeng Liang, Hong-Mei Lu
Biochimie (August 2014) Volume 103() pp:1-6
Publication Date(Web):August 2014
DOI:10.1016/j.biochi.2014.03.016
Co-reporter:
Analytical Methods (2009-Present) 2013 - vol. 5(Issue 23) pp:
Publication Date(Web):
DOI:10.1039/C3AY40726H
In GC-MS-based metabolomic analysis of serum samples, differential sample handling and storage can induce systematic bias hiding the original metabolic state. Here, several sample sets were examined to characterize the effects of several potential sources of bias. These factors were the thawing temperature (4 °C and 25 °C) and time (0.5–24 h) of the primary sample, storage temperature (4 °C and 25 °C) and time (0–48 h) of the nitrogen dried sample, storage temperature (4 °C and 25 °C), time (0–48 h) and refrigerating/warming cycle of the derivatized sample. The results showed that the primary sample thawed at both 4 °C and 25 °C for up to 24 h appeared to be stable. The storage of nitrogen dried samples and derivatized samples at 4 °C for up to 48 h still provided useable samples. However, the nitrogen dried samples denatured after 24 h storage at 25 °C. And the derivatized samples apparently changed after 10 h storage at 25 °C, or after undergoing two refrigerating/warming cycles.
Co-reporter:
Analytical Methods (2009-Present) 2013 - vol. 5(Issue 19) pp:
Publication Date(Web):
DOI:10.1039/C3AY40637G
Epimedium has a long history as a traditional Chinese medicine (TCM) and is widely used. With similar morphology, non-official species, some of which have a low efficacy or even side effects, are adulterated into official Epimedium. Quality control of Epimedium is vital to its medical use. In this paper, the unitary and binary chromatographic fingerprints of 48 Epimedium samples were developed on two complementary platforms of high performance liquid chromatography-diode array detector (HPLC-DAD) and gas chromatography-mass spectrometry (GC-MS), by which complex volatile and non-volatile compounds from Epimedium were analyzed and well validated, respectively. Subsequently, principal component analysis (PCA) was performed with the unitary and binary chromatographic fingerprints of the whole chromatographic profile and common peaks. By comparison, the HPLC-DAD fingerprints alone showed a complete separation of all species, while GC-MS just provided a classification for them. Meanwhile, the fused data matrix possessed consistent classification results with HPLC-DAD analysis. Moreover, as a complementary method for conventional unitary HPLC fingerprinting, we proposed the whole fused fingerprints with several characteristic peaks, including peak 34 (n-hexadecanoic acid), 10 (icariin), 8 (epimedin C), 32 (tetradecanoic acid), 7 (epimedin B), 13, 35 (9-octadecenoic acid), 6 (epimedin A) and 11 for the quality control of Epimedium. The proposed method can provide a more comprehensive and objective quality control for Epimedium and other TCMs, and offer the feasibility for characterization and quality control of complex samples in the same genus designated under a single herbal drug entity.
Co-reporter:
Analytical Methods (2009-Present) 2014 - vol. 6(Issue 4) pp:NaN1043-1043
Publication Date(Web):2013/11/25
DOI:10.1039/C3AY41702F
Epimedium herbs are commonly used as herbal medicine ingredients in China. In this study, firstly the total phenolic content and total flavonoid content (TFC) of Epimedium extracts were determined, respectively. The majority of the Epimedium extracts showed strong 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity, and TFC of Epimedium exhibited highly correlations with DPPH scavenging activity with high correlation coefficient (R2 = 0.9279). Then, high performance liquid chromatography fingerprints of 25 Epimedium samples (5 species) were developed. A data fusion strategy was used to construct multi-wavelength fingerprints by combining 3 profiles with peak-rich wavelengths (230, 270 and 310 nm) into a single vector for each sample. Whittaker smoother, adaptive iteratively reweighted penalized least square (airPLS) and multi-scale peak alignment methods were adopted to preprocess the constructed fingerprint matrix. Variable selection and partial least square were applied to build a calibration model between fingerprints and their antioxidant activities. The potential peaks, which respond for the antioxidant activity, were screened by the selected variable with large regression coefficient. These results suggest that water extracts of Epimedium are a source of antioxidant compounds, and proposed multi-wavelength fingerprints can be used for predicting the antioxidant activity of Epimedium.
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