AntDAS-GCMS: A New Comprehensive Data Analysis Platform for GC-MS-Based Untargeted Metabolomics with the Advantage of Addressing the Time Shift Problem.

Over the years, a number of state-of-the-art data analysis tools have been developed to provide a comprehensive analysis of data collected from gas chromatography-mass spectrometry (GC-MS). Unfortunately, the time shift problem remains unsolved in these tools. Here, we developed a novel comprehensive data analysis strategy for GC-MS-based untargeted metabolomics (AntDAS-GCMS) to perform total ion chromatogram peak detection, peak resolution, time shift correction, component registration, statistical analysis, and compound identification. Time shift correction was specifically optimized in this work. The information on mass spectra and elution profiles of compounds was used to search for inherent landmarks within analyzed samples to resolve the time shift problem across samples efficiently and accurately. The performance of our AntDAS-GCMS was comprehensively investigated by using four complex GC-MS data sets with various types of time shift problems. Meanwhile, AntDAS-GCMS was compared with advanced GC-MS data analysis tools and classic time shift correction methods. Results indicated that AntDAS-GCMS could achieve the best performance compared to the other methods.

A novel comprehensive strategy for high-thoroughly studying released compounds during the combustion process of herbs. A case study for Artemisia argyi Levl. et Vant.

The comprehensive study of compound variations in released smoke during the combustion process is a great challenge in many scientific fields related to analytical chemistry like traditional Chinese medicine, environment analysis, food analysis, etc. In this work, we propose a new comprehensive strategy for efficiently and high-thoroughly characterizing compounds in the online released complex smokes: (i) A smoke capture device was designed for efficiently collecting chemical constituents to perform gas chromatography-mass spectrometry (GC-MS) based untargeted analysis. (ii) An advanced data analysis tool, AntDAS-GCMS, was used for automatically extracting compounds in the original acquired GC-MS data files. Additionally, a GC-MS data analysis guided instrumental parameter optimizing strategy was proposed for the optimization of parameters in the smoke capture device. The developed strategy was demonstrated by the study of compound variations in the smoke of traditional Chinese medicine, Artemisia argyi Levl. et Vant. The results indicated that more than 590 components showed significant differences among released smokes of various moxa velvet ratios. Finally, about 88 compounds were identified, of which phenolic compounds were the most abundant, followed by aromatics, alkenes, alcohols and furans. In conclusion, we may provide a novel approach to the studies of compounds in online released smoke.

Using UHPLC-HRMS-based comprehensive strategy to efficiently and accurately screen and identify illegal additives in health-care foods.

Accurately and high-thoroughly screening illegal additives in health-care foods continues to be a challenging task in routine analysis for the ultrahigh performance liquid chromatography-high resolution mass spectrometry based techniques. In this work, we proposed a new strategy to identify additives in complex food matrices, which consists of both experimental design and advanced chemometric data analysis. At first, reliable features in the analyzed samples were screened based on a simple but efficient sample weighting design, and those related to illegal additives were screened with robust statistical analysis. After the MS1 in-source fragment ion identification, both MS1 and MS/MS spectra were constructed for each underlying compound, based on which illegal additives can be precisely identified. The performance of the developed strategy was demonstrated by using mixture and synthetic sample datasets, indicating an improvement of data analysis efficiency up to 70.3 %. Finally, the developed strategy was applied for the screening of unknown additives in 21 batches of commercially available health-care foods. Results indicated that at least 80 % of false-positive results can be reduced and 4 additives were screened and confirmed.

Identification of Oak-Barrel and Stainless Steel Tanks with Oak Chips Aged Wines in Ningxia Based on Three-Dimensional Fluorescence Spectroscopy Combined with Chemometrics.

With the increased incidence of wine fraud, a fast and reliable method for wine certification has become a necessary prerequisite for the vigorous development of the global wine industry. In this study, a classification strategy based on three-dimensional fluorescence spectroscopy combined with chemometrics was proposed for oak-barrel and stainless steel tanks with oak chips aged wines. Principal component analysis (PCA), partial least squares analysis (PLS-DA), and Fisher discriminant analysis (FDA) were used to distinguish and evaluate the data matrix of the three-dimensional fluorescence spectra of wines. The results showed that FDA was superior to PCA and PLS-DA in classifying oak-barrel and stainless steel tanks with oak chips aged wines. As a general conclusion, three-dimensional fluorescence spectroscopy can provide valuable fingerprint information for the identification of oak-barrel and stainless steel tanks with oak chips aged wines, while the study will provide some theoretical references and standards for the quality control and quality assessment of oak-barrel aged wines.

A comparison of feature extraction capabilities of advanced UHPLC-HRMS data analysis tools in plant metabolomics.

Data analysis of ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) is an essential and time-consuming step in plant metabolomics and feature extraction is the fundamental step for current tools. Various methods lead to different feature extraction results in practical applications, which may puzzle users for selecting adequate data analysis tools to deal with collected data. In this work, we provide a comprehensive method evaluation for some advanced UHPLC-HRMS data analysis tools in plant metabolomics, including MS-DIAL, XCMS, MZmine, AntDAS, Progenesis QI, and Compound Discoverer. Both mixtures of standards and various complex plant matrices were specifically designed for evaluating the performances of the involved method in analyzing both targeted and untargeted metabolomics. Results indicated that AntDAS provide the most acceptable feature extraction, compound identification, and quantification results in targeted compound analysis. Concerning the complex plant dataset, both MS-DIAL and AntDAS can provide more reliable results than the others. The method comparison is maybe useful for the selection of suitable data analysis tools for users.

A study of flavor variations during the flaxseed roasting procedure by developed real-time SPME GC-MS coupled with chemometrics.

Volatile compound variations during the roasting procedure play an essential role in the flaxseed-related product. In this work, we proposed a new strategy to high-throughput characterize the dynamic variations of flavors in flaxseed. Volatile compounds released at various roasting times were comprehensively investigated by a newly developed real-time solid-phase microextraction coupled with gas chromatography-mass spectrometry (GC-MS). Raw data files were analyzed by our advanced GC-MS data analysis software AntDAS-GCMS. Chemometric methods such as principal component analysis and partial least squares-discrimination analysis have realized the differences of samples with various roasting times. Finally, a total of 51 compounds from 11 aromas were accurately identified and confirmed with standards, and their variations as a function of roasting time were studied. In conclusion, we provided a new solution for the online monitoring of volatile compounds during the industrial roasting process.

Association of Mycoplasma pneumoniae infection with increased risk of asthma in children.

The present study was conducted to investigate the relationship between Mycoplasma pneumoniae (MP) infection and the risk of asthma among children by detecting the rate of MP immunoglobulin M (MP-IgM) and the eosinophil (EOS) count. A total of 139 asthmatic children were enrolled as the case group and assigned into three groups: Group A (aged <3 years, n=42), group B (aged 3-8 years, n=45) and group C (aged >8 years, n=52). Additionally, 115 healthy children were enrolled in the control group. Enzyme-linked immunosorbent assay was used to measure the MP-IgM-positive rate. EOS count was detected in the experimental and control groups by using a hemocytometer analyzer. A meta-analysis was performed by using the Comprehensive Meta-Analysis version 2.0 software. The positive rates of the MP-IgM and EOS count in the experimental group were significantly higher than those in control group (both P<0.001). Furthermore, the asthmatic children in group C had a higher MP-IgM-positive rate and EOS count as compared to those in groups A and B, respectively (all P<0.05). Results from groups A and B were not statistically significant (all P>0.05). The meta-analysis further confirmed that asthmatic children had a higher MP-IgM-positive rate as compared to the healthy controls (P<0.001). Age-stratified analysis revealed that the MP-IgM-positive rate in asthmatic children aged ≥8 and <8 years was significantly higher than that in the healthy controls (P=0.003 and P<0.001). Asthmatic children had a higher MP-IgM-positive rate and EOS count as compared with controls, suggesting that the MP infection may be closely associated with the risk of asthma. Additionally, the positive rate of MP-IgM may indicate an important biological marker in predicting the development of asthma.

Correlation of red cell distribution width with the severity of coronary artery disease: a large Chinese cohort study from a single center.

BACKGROUND: It has been reported that increased red blood cell width (RDW) is a marker associated with the presence and adverse outcomes of various cardiovascular diseases. The aim of the present study was prospectively evaluate the severity of coronary artery disease (CAD) and RDW in a large Chinese cohort. METHODS: A total of 677 consecutive individuals who underwent coronary angiography due to the presence of angina-like chest pain and/or positive treadmill exercise test were enrolled in this study. All patients received coronary angiography and were then divided into two groups based on the results of coronary angiography (CAD group (n = 499) and control group (n = 178)). The clinical information including classical CAD risk factors and RDW were analyzed to identify their relationship to CAD. The severity of CAD was evaluated by Gensini score and its relationship with RDW was also analyzed. RESULTS: Patients with angiographic CAD had significantly elevated RDW levels compared with controls ((12.95 ± 0.77)% vs. (12.73 ± 0.83)%, P = 0.001). There was a significant positive correlation between RDW and the Gensini score (r = 0.37, P < 0.001). In multivariate Logistic regression analysis, RDW was demonstrated to be an independent predictor for both angiographic CAD (OR = 1.34, 95%CI: 1.02 - 1.77, P < 0.05) and for a higher Gensini score (> 13, OR = 2.23, 95%CI: 1.62 - 3.08, P < 0.001). In a receiver operating characteristic (ROC) curve analysis, an RDW value of 12.85% was identified as an effective cut-point in predicting the presence or absence of CAD with a sensitivity of 50.0% and a specificity of 65.2%. CONCLUSION: RDW is associated with both presence of CAD and the severity of coronary stenosis, suggesting that it might be a readily available marker for the prediction of CAD and its severity.

Evaluation of red blood cell distribution width in patients with cardiac syndrome X.

BACKGROUND: Cardiac syndrome X (CSX) is a condition characterized by chest pain with normal coronary arteries. However, its pathogenesis has not fully been understood yet. Red blood cell distribution width (RDW) has recently been suggested as a marker of acute and chronic cardiovascular diseases, while no data is available in patients with CSX. METHODS: One hundred and twenty consecutive patients with CSX and 102 normal controls were prospectively enrolled in this study. Blood samples were drawn from all individuals for measuring RDW and high-sensitivity C-reactive protein (CRP). The baseline data were compared between patients with CSX and normal controls. RESULTS: The RDW levels were significantly higher in patients with CSX than that in those with normal controls (13.1 ± 2.1 versus 12.3 ± 1.8, p=0.011). Moreover, the data showed that the levels of plasma CRP were marked higher in patients with CSX than those that were observed in normal controls (CRP: 2.8 ± 2.2 mg/L versus 2.0 ± 1.7 mg/dl, p=0.014). In addition, the multivariate analysis indicated that peripheral monocyte cell, CRP and RDW were the independent variables most strongly associated with CSX. In a receiver operating characteristic (ROC) curve analysis, we found that an RDW value of 12.8% was used as an effective cut-point in the segregation of the presence or absence of cardiac syndrome X, a sensitivity of 52.0% and a specificity of 65.4% were obtained. Finally, correlation analysis suggested that there was positive correlation between plasma levels of CRP and RDW levels (n=120, γ =0.381, P=0.013). CONCLUSIONS: The present study, for the first time, demonstrated that elevated RDW and CRP levels were independently associated with the presence of CSX.