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.

Geographical discrimination of Flos Trollii by GC-MS and UHPLC-HRMS-based untargeted metabolomics combined with chemometrics.

For centuries, Flos Trollii has been consumed as functional tea and a folk medicine in China's north and northwest zones. The quality of Flos Trollii highly depends on the producing zones. Unfortunately, few studies have been reported on the geographical discrimination of Flos Trollii. This work comprehensively investigated Flos Trollii compounds with an integration strategy combining gas chromatography-mass spectrometry (GC-MS) and ultrahigh-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) with chemometrics to explore the differences between Flos Trollii obtained from various origins of China. About 71 volatile and 22 involatile markers were identified with GC-MS and UHPLC-HRMS, respectively. Geographical discrimination models were synthetically investigated based on the identified markers. The results indicated that the UHPLC-HRMS coupled with the fisher discrimination model provided the best prediction capability (>97%). This study provides a new solution for Flos Trollii discrimination.

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.

Orientin regulates the proliferation and migration of hepatocellular carcinoma cells.

Orientin is a flavone isolated from medicinal plants used in traditional Chinese medicine (TCM) that suppresses the growth of cancer cells in vitro. The effects of orientin in hepatoma carcinoma cells remain unknown. The aim of this paper is to investigate the effects of orientin on the viability, proliferation, and migration of hepatocellular carcinoma cells in vitro. In this study, we found that orientin could inhibit the proliferation, migration, and the activation of NF-κB signaling pathway in hepatocellular carcinoma cells. An activator of NF-κB signaling pathway, PMA, could abolish the inhibitory effect of orientin on NF-κB signaling pathway and proliferation and migration of Huh7 cells. These findings raise the possibility that orientin can be used in the treatment of hepatocellular carcinoma.

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.

Carnosol inhibits cerebral ischemia-reperfusion injury by promoting AMPK activation.

BACKGROUND: Carnosol is a phytopolyphenol (diterpene) found and extracted from plants of Mediterranean diet, which has anti-tumor, anti-inflammatory and antioxidant effects. However, its role in ischemic stroke has not been elucidated. METHODS: Primary neurons subjected to oxygen-glucose deprivation (OGD) was used to investigate the effect of carnosol in vitro. A mouse MCAO model was used to evaluate the effect of carnosol on ischemic stroke in vivo. The mRNA level of inflammatory and apoptosis-related genes was determined by RT-PCR. The protein level of total and phosphorylated AMPK was determined by WB. H&E and Immunofluorescent assay was used to investigate the necrosis, inflammation and apoptosis in brain tissue. RESULTS: Carnosol protected the activity of primary neurons subjected to oxygen-glucose deprivation (OGD) in vitro, as well as inhibited inflammation and apoptosis. Furthermore, carnosol could significantly reduce the infarct and edema volume and protect against neurological deficit in vivo, and had a significant inhibitory effect on brain neuroinflammation and apoptosis. Mechanically, carnosol could activate AMPK, and the effect of carnosol on cerebral ischemia-reperfusion injury cell model could be abolished by AMPK phosphorylation inhibitor. CONCLUSION: Carnosol has a protective effect on ischemic stroke, and this effect is achieved through AMPK activation. Our study demonstrates the protective effect of carnosol on cerebral ischemia-reperfusion injury and provides a new perspective for the clinical treatment of ischemic stroke.

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.

AntDAS-DDA: A New Platform for Data-Dependent Acquisition Mode-Based Untargeted Metabolomic Profiling Analysis with Advantage of Recognizing Insource Fragment Ions to Improve Compound Identification.

Data-dependent acquisition (DDA) mode in ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) can provide massive amounts of MS1 and MS/MS information of compounds in untargeted metabolomics and can thus facilitate compound identification greatly. In this work, we developed a new platform called AntDAS-DDA for the automatic processing of UHPLC-HRMS data sets acquired under the DDA mode. Several algorithms, including extracted ion chromatogram extraction, feature extraction, MS/MS spectrum construction, fragment ion identification, and MS1 spectrum construction, were developed within the platform. The performance of AntDAS-DDA was investigated comprehensively with a mixture of standard and complex plant data sets. Results suggested that features in complex sample matrices can be extracted effectively, and the constructed MS1 and MS/MS spectra can benefit in compound identification greatly. The efficiency of compound identification can be improved by about 20%. AntDAS-DDA can take full advantage of MS/MS information in multiple sample analyses and provide more MS/MS spectra than single sample analysis. A comparison with advanced data analysis tools indicated that AntDAS-DDA may be used as an alternative for routine UHPLC-HRMS-based untargeted metabolomics. AntDAS-DDA is freely available at http://www.pmdb.org.cn/antdasdda.

A new platform for untargeted UHPLC-HRMS data analysis to address the time-shift problem.

Substantial deviations in retention times among samples pose a great challenge for the accurate screening and identifying of metabolites by ultrahigh-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS). In this study, a coarse-to-refined time-shift correction methodology was proposed to efficiently address this problem. Metabolites producing multiple fragment ions were automatically selected as landmarks to generate pseudo-mass spectra for a coarse time-shift correction. Refined peak alignment for extracted ion chromatograms was then performed by using a moving window-based multiple-peak alignment strategy. Based on this novel coarse-to-refined time-shift correction methodology, a new comprehensive UHPLC-HRMS data analysis platform was developed for UHPLC-HRMS-based metabolomics. Original datasets were employed as inputs to automatically extract and register features in the dataset and to distinguish fragment ions from metabolites for chemometric analysis. Its performance was further evaluated using complex datasets, and the results suggest that the new platform can satisfactorily resolve the time-shift problem and is comparable with commonly used UHPLC-HRMS data analysis tools such as XCMS Online, MS-DIAL, Mzmine2, and Progenesis QI. The new platform can be downloaded from: http://www.pmdb.org.cn/antdas2tsc.

A chemometric strategy to automatically screen selected ion monitoring ions for gas chromatography-mass spectrometry-based pseudotargeted metabolomics.

The pseudotargeted metabolomics based on gas chromatography-mass spectrometry (GC-MS) has the advantage of filtering out artifacts originating from sample treatment and accurately quantifying underlying compounds in the analyzed samples. However, this technique faces the problem of selecting high-quality selective ions for performing selected ion monitoring (SIM) on instruments. In this work, we proposed AntDAS-SIMOpt, an automatic untargeted strategy for SIM ion optimization that was accomplished on the basis of an experimental design combined with advanced chemometric algorithms. First, a group of diluted quality control samples was used to screen underlying compounds in samples automatically. Ions in each of the resolved mass spectrum were then evaluated by using the developed algorithms to identify the SIM ion. A Matlab graphical user interface (GUI) was designed to facilitate routine analysis, which can be obtained from http://www.pmdb.org.cn/antdassimopt. The performance of the developed strategy was comprehensively investigated by using standard and complex plant datasets. Results indicated that AntDAS-SIMOpt may be useful for GC-MS-based metabolomics.

Chemometric discrimination of the geographical origin of licorice in China by untargeted metabolomics.

Licorice is famous as a herbal medicine and food sweetener. This study provided a comprehensive strategy for investigating the quality of licorice based on untargeted metabolomics. A new strategy for identifying metabolite was developed, including fragment ion identification algorithm and ion fusion algorithm. The results showed that it can accurately integrate mass spectra from positive and negative ion modes to benefit metabolite identification. Based on the strategy, a number of significant difference metabolites were identified among licorice samples and 9 metabolites were confirmed by standards. Additionally, the geographical discrimination models of licorice samples were comprehensively investigated by chemometric methods. The results indicated that the supporting vector machine provided the best performance, with a prediction accuracy above 80%. The study results supported the conclusion that the quality of licorice from different regions in China was inconsistent.

A chemometric strategy for accurately identifying illegal additive compounds in health foods by using ultra-high-performance liquid chromatography coupled to high resolution mass spectrometry.

The accurate identification of unknown illegal additive compounds in complex health foods continues to be a challenging task in routine analysis, because massive false positive results can be screened with ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry-based untargeted techniques and must be manually filtered out. To address this problem, we developed a chemometric-based strategy, in which data analysis was first performed by using XCMS, MS-DIAL, Mzmine2, and AntDAS2, to select those that provided acceptable results to extract common features (CFs), which can be detected by all of the selected methods. Then, CFs whose contents were significantly higher in the suspected illegal additive group were screened. Isotopic, adduct, and neutral loss ions were marked based on the CFs by using a new adaptive ion annotation algorithm. Fragment ions originating from the same compound were identified by using a novel fragment ion identification algorithm. Finally, a respective mass spectrum was constructed for each screened compound to benefit compound identification. The developed strategy was confirmed by using a complex Chinese health food, Goujiya tea. The features of all illegal additive compounds were precisely screened by the developed strategy, and massive false positive features from the current data analysis method were greatly reduced. The constructed respective mass spectra can benefit compound identification and avoid the risk of identifying ions from the same illegal compound as different compounds. Moreover, unknown compounds that are contained in an illegal compound library can be screened.

Differentiating Westlake Longjing tea from the first- and second-grade producing regions using ultra high performance liquid chromatography with quadrupole time-of-flight mass spectrometry-based untargeted metabolomics in combination with chemometrics.

There are numerous articles published for geographical discrimination of tea. However, few research works focused on the authentication and traceability of Westlake Longjing green tea from the first- and second-grade producing regions because the tea trees are planted in a limited growing zone with identical cultivate condition. In this work, a comprehensive analytical strategy was proposed by ultrahigh performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based untargeted metabolomics coupled with chemometrics. The automatic untargeted data analysis strategy was introduced to screen metabolites that expressed significantly among different regions. Chromatographic features of metabolites can be automatically and efficiently extracted and registered. Meanwhile, those that were valuable for geographical origin discrimination were screened based on statistical analysis and contents in samples. Metabolite identification was performed based on high-resolution mass values and tandem mass spectra of screened peaks. Twenty metabolites were identified, based on which the two-way encoding partial least squares discrimination analysis was built for geographical origin prediction. Monte Caro simulation results indicated that prediction accuracy was up to 99%. Our strategy can be applicable for practical applications in the quality control of Westlake Longjing green tea.

A comprehensive automatic data analysis strategy for gas chromatography-mass spectrometry based untargeted metabolomics.

Automatic data analysis for gas chromatography-mass spectrometry (GC-MS) is a challenging task in untargeted metabolomics. In this work, we provide a novel comprehensive data analysis strategy for GC-MS-based untargeted metabolomics (autoGCMSDataAnal) by developing a new automatic strategy for performing TIC peak detection and resolution and proposing a novel time-shift correction and component registration algorithm. autoGCMSDataAnal uses original acquired GC-MS datafiles as input to automatically perform TIC peak detection, component resolution, time-shift correction and component registration, statistical analysis, and compound identification. We utilize standards and complex plant samples to comprehensively investigate the performance of autoGCMSDataAnal. The results suggest that the developed strategy is comparable with several state-of-the-art methods that are widely used in GC-MS-based untargeted metabolomics. Based on the proposed strategy, we develop a user-friendly MATLAB GUI for users who are unfamiliar with programming languages to facilitate their routine analysis, which can be freely downloaded at: http://software.tobaccodb.org/software/autogcmsdataanal.

A simple method for direct modeling of second-order liquid chromatographic data with retention time shifts and holding the second-order advantage.

Retention time shifts in chromatographic data severely affect the quantitative analysis of analytes of interest in complex systems. This paper offers a simple method for directly handing second-order liquid chromatographic data with retention time shift, and achieving qualitative and quantitative analysis of target analytes in the presence of overlapping peaks and unknown interference, which is the so-called "second-order advantage". The proposed method is named the alternating trilinear decomposition-assisted multivariate curve resolution (ATLD-MCR) because it absorbs the basic philosophy of alternating trilinear decomposition (ATLD) algorithm and multivariate curve resolution (MCR). ATLD-MCR was implemented by using the pre-decomposition results of ATLD as the initial values, MCR strategy for each sample slice matrix and the least squares optimization strategy. Three simulated data sets, a semi-simulated LC-MS data set and a real HPLC-DAD data set were investigated by the proposed method, respectively. In addition, the resolved qualitative profiles and concentration values were compared with those obtained by the other three classical second-order calibration algorithms. ATLD-MCR performed well and obtained satisfactory qualitative and quantitative results for the analytes of interest in both the simulated and experimental systems, which proved that the newly proposed method could properly model the second-order chromatographic data with retention time shifts and severe signal overlapping.

Automatic peak detection coupled with multivariate curve resolution-alternating least squares for peak resolution in gas chromatography-mass spectrometry.

Gas chromatography-mass spectrometry (GCMS) has been extensively used in complex sample analysis for the high-throughput characterization of volatile and semivolatile compounds. However, the accurate extraction of compound information remains challenging. Here, we present a combined algorithm strategy for GCMS data analysis to accurately screen metabolites across groups. First, chromatographic peaks in a total ion chromatogram (TIC) are extracted by using a Gaussian smoothing strategy and aligned on the basis of their mass spectra by a dynamic programing algorithm. The aligned TIC peaks are then registered into a component list table by applying a nearest-neighbor clustering algorithm. Significantly expressed TIC peaks among groups are screened through statistical analysis, such as ANOVA. Second, a chemometric method of multivariate curve resolution-alternating least squares for the peak resolution of the screened TIC peaks is utilized to retrieve the chromatographic and mass spectral profiles of coeluted components. The developed strategy is employed for the analysis of standard and complex plant sample datasets. Results indicate that our methodology is comparable with several state-of-the-art methods that are widely used in GC-MS-based metabolomics.

Automatic data analysis workflow for ultra-high performance liquid chromatography-high resolution mass spectrometry-based metabolomics.

Data analysis for ultra-performance liquid chromatography high-resolution mass spectrometry-based metabolomics is a challenging task. The present work provides an automatic data analysis workflow (AntDAS2) by developing three novel algorithms, as follows: (i) a density-based ion clustering algorithm is designed for extracted-ion chromatogram extraction from high-resolution mass spectrometry; (ii) a new maximal value-based peak detection method is proposed with the aid of automatic baseline correction and instrumental noise estimation; and (iii) the strategy that clusters high-resolution m/z peaks to simultaneously align multiple components by a modified dynamic programing is designed to efficiently correct time-shift problem across samples. Standard compounds and complex datasets are used to study the performance of AntDAS2. AntDAS2 is better than several state-of-the-art methods, namely, XCMS Online, Mzmine2, and MS-DIAL, to identify underlying components and improve pattern recognition capability. Meanwhile, AntDAS2 is more efficient than XCMS Online and Mzmine2. A MATLAB GUI of AntDAS2 is designed for convenient analysis and is available at the following webpage: http://software.tobaccodb.org/software/antdas2.

Automatic untargeted metabolic profiling analysis coupled with Chemometrics for improving metabolite identification quality to enhance geographical origin discrimination capability.

Untargeted metabolic profiling analysis is employed to screen metabolites for specific purposes, such as geographical origin discrimination. However, the data analysis remains a challenging task. In this work, a new automatic untargeted metabolic profiling analysis coupled with a chemometric strategy was developed to improve the metabolite identification results and to enhance the geographical origin discrimination capability. Automatic untargeted metabolic profiling analysis with chemometrics (AuMPAC) was used to screen the total ion chromatographic (TIC) peaks that showed significant differences among the various geographical regions. Then, a chemometric peak resolution strategy is employed for the screened TIC peaks. The retrieved components were further analyzed using ANOVA, and those that showed significant differences were used to build a geographical origin discrimination model by using two-way encoding partial least squares. To demonstrate its performance, a geographical origin discrimination of flaxseed samples from six geographical regions in China was conducted, and 18 TIC peaks were screened. A total of 19 significant different metabolites were obtained after the peak resolution. The accuracy of the geographical origin discrimination was up to 98%. A comparison of the AuMPAC, AMDIS, and XCMS indicated that AuMPACobtained the best geographical origin discrimination results. In conclusion, AuMPAC provided another method for data analysis.