QPMASS: A parallel peak alignment and quantification software for the analysis of large-scale gas chromatography-mass spectrometry (GC-MS)-based metabolomics datasets

2020 ◽  
Vol 1620 ◽  
pp. 460999
Author(s):  
Lixin Duan ◽  
Aimin Ma ◽  
Xianbin Meng ◽  
Guo-an Shen ◽  
Xiaoquan Qi
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Large Scale ◽  
Gas Chromatography Mass Spectrometry ◽  
Peak Alignment ◽  
Chromatography Mass Spectrometry

scholarly journals Lowering cutoffs for initial and confirmation testing for cocaine and marijuana: large-scale study of effects on the rates of drug-positive results

1997 ◽  
Vol 43 (1) ◽  
pp. 100-103 ◽  
Author(s):  
William E Wingert
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Large Scale ◽  
Gas Chromatography Mass Spectrometry ◽  
Initial Testing ◽  
Chromatography Mass Spectrometry ◽  
Large Scale Study ◽  
Positive Rate ◽  
Positive Results

Abstract A large-scale study was conducted to determine whether lowering the initial testing and confirmation testing cutoffs in urine would significantly affect the positive rates for cocaine (COC) and marijuana (THC). Customary cutoffs for COC are 300 μg/L and 150 μg/L for initial testing (screening) and gas chromatography–mass spectrometry (GC-MS; confirmation), respectively; for THC, the usual respective cutoffs are 50 μg/L and 10 μg/L. By applying a screening cutoff of 100 μg/L for COC and lowering the GC-MS cutoff to 50 μg/L, the COC-positive rate increased from 1.2% to 2.1%. For THC, lowering the screening cutoff to 20 μg/L while leaving the GC-MS cutoff at 10 μg/L increased the THC-positive rate from 2.8% to 4.1%. These increases appear noteworthy.

scholarly journals WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) Data

Metabolites ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 171 ◽  
Author(s):  
Borgsmüller ◽  
Gloaguen ◽  
Opialla ◽  
Blanc ◽  
Sicard ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Large Scale ◽  
Automated Analysis ◽  
Peak Detection ◽  
Performance Comparison ◽  
Gas Chromatography Mass Spectrometry ◽  
Peak Picking ◽  
Chromatography Mass Spectrometry ◽  
Biological Dataset

Lack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GC-MS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult. Here we present a workflow for improved peak picking (WiPP), a parameter optimising, multi-algorithm peak detection for GC-MS metabolomics. WiPP evaluates the quality of detected peaks using a machine learning-based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis. Medium- and low-quality peaks are kept for further inspection. By applying WiPP to standard compound mixes and a complex biological dataset, we demonstrate that peak detection is improved through the novel way to assign peak quality, an automated parameter optimisation, and results in integration across different embedded peak picking algorithms. Furthermore, our approach can provide an impartial performance comparison of different peak picking algorithms. WiPP is freely available on GitHub (https://github.com/bihealth/WiPP) under MIT licence.

scholarly journals Active constituents of Litsea cubeba

2020 ◽  
Vol 24 (3 Part A) ◽  
pp. 1745-1752
Author(s):  
Quan Zhou ◽  
Dongfang Zheng ◽  
Yong Zhao ◽  
Ting Wang ◽  
Yafeng Yang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Large Scale ◽  
Gas Chromatography Mass Spectrometry ◽  
Gravimetric Analysis ◽  
Litsea Cubeba ◽  
Chromatography Mass Spectrometry ◽  
Wide Range ◽  
Large Scale Cultivation ◽  
Active Substances

Litsea cubeba is a plant of Lauraceae and Litsea. It is a valuable plant and has a wide range of uses, including in traditional Chinese medicine. Herein, Litsea cubeba wood was harvested from Henan Province, The active ingredients were extracted from Litsea cubeba wood by modern techniques such as gas chromatography-mass spectrometry (GC-MS), thermal gravimetric analysis, and thermal desorption gas chromatography-mass spectrometry. The analysis results show that the wood of Litsea cubeba contains a large amount of valuable active substances that can be utilized in medicine, bio-energy, and spices and flavorings, and large-scale cultivation of this plant could be beneficial.

scholarly journals An Effective and Efficient Sample Preparation Method for 2-Methyl-Isoborneol and Geosmin in Fish and Their Analysis by Gas Chromatography-Mass Spectrometry

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Liang-liang Tian ◽  
Feng Han ◽  
Essy Kouadio Fodjo ◽  
Wenlei Zhai ◽  
Xuan-Yun Huang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Sample Preparation ◽  
Large Scale ◽  
Solid Phase ◽  
Gas Chromatography Mass Spectrometry ◽  
Chromatography Mass Spectrometry ◽  
Ms Analysis ◽  
Fish Samples

The intensive aquaculture strategy and recirculating aquaculture system often lead to the production of off-flavor compounds such as 2-methyl-isoborneol (2-MIB) and Geosmin (GSM). The regular purge and trap extraction followed by analysis with gas chromatography-mass spectrometry (GC-MS) usually involve a complicated assembly of facilities, more working space, long sample preparation time, and headspace solid-phase microextraction (SPME). In this work, a method with easier sample preparation, fewer and simplified facilities, and without SPME on GC-MS analysis is developed for the determination of 2-MIB and GSM in fish samples. Unlike previous methods, solvent extract from samples, QuEChERS-based cleanup, and solid-phase extraction for concentration are applied. The LOD (S/N > 3) and LOQ (S/N > 10) of this method were validated at 0.6 μg/kg and 1.0 μg/kg for both 2-MIB and GSM, which are under the sensory limit (1 μg/kg). Application of this method for incurred fish samples demonstrated acceptable analytical performance. This method is suitable for large-scale determination of 2-MIB and GSM in fish samples, owing to the use of simple facility and easy-to-operate procedure, rapid sample preparation, and shorter time for GC-MS analysis without SPME.

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