On the inter-instrument and inter-laboratory transferability of a tandem mass spectral reference library: 1. Results of an Austrian multicenter study

2009 ◽  
Vol 44 (4) ◽  
pp. 485-493 ◽  
Author(s):  
Herbert Oberacher ◽  
Marion Pavlic ◽  
Kathrin Libiseller ◽  
Birthe Schubert ◽  
Michael Sulyok ◽  
...  
Keyword(s):  
Multicenter Study ◽  
Tandem Mass ◽  
Reference Library ◽  
Mass Spectral

On the inter-instrument and the inter-laboratory transferability of a tandem mass spectral reference library. 3. Focus on ion trap and upfront CID

2012 ◽  
Vol 47 (2) ◽  
pp. 263-270 ◽  
Author(s):  
Herbert Oberacher ◽  
Florian Pitterl ◽  
Eleni Siapi ◽  
Barry R. Steele ◽  
Thomas Letzel ◽  
...  
Keyword(s):  
Ion Trap ◽  
Tandem Mass ◽  
Reference Library ◽  
Mass Spectral

On the inter-instrument and the inter-laboratory transferability of a tandem mass spectral reference library: 2. Optimization and characterization of the search algorithm

2009 ◽  
Vol 44 (4) ◽  
pp. 494-502 ◽  
Author(s):  
Herbert Oberacher ◽  
Marion Pavlic ◽  
Kathrin Libiseller ◽  
Birthe Schubert ◽  
Michael Sulyok ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Tandem Mass ◽  
Reference Library ◽  
Mass Spectral

scholarly journals Rapid detection of dimethyl yellow dye in curry by liquid chromatography-electrospray-tandem mass spectrometry

2010 ◽  
Vol 28 (No. 5) ◽  
pp. 427-432 ◽  
Author(s):  
F. Tateo ◽  
M. Bononi ◽  
F. Gallone
Keyword(s):  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
Tandem Mass Spectrometry ◽  
Limit Of Detection ◽  
Tandem Mass ◽  
Mass Spectral ◽  
Limit Of Quantitation ◽  
Liquid Chromatography Tandem Mass ◽  
High Degree ◽  
Positive Ion

An accurate and rapid method, was devised for the identification and quantitation of dimethyl yellow dye in curry, based on liquid chromatography-tandem mass spectrometry interfaced with electrospray. Mass spectral acquisition was done in positive ion mode applying two fragmentation transitions to provide a high degree of selectivity. The extraction system provided a very high recovery (100.0% to 105.8%) and good results were obtained for the limit of detection (5 μg/kg) and limit of quantitation (16 μg/kg). The applicability of the method to identifing and quantifing the unauthorised dimethyl yellow dye in curry was demonstrated.

Comprehensive structural characterization of phenolics in litchi pulp using tandem mass spectral molecular networking

2019 ◽  
Vol 282 ◽  
pp. 9-17 ◽  
Author(s):  
Qiang Lyu ◽  
Ting-Hao Kuo ◽  
Chongde Sun ◽  
Kunsong Chen ◽  
Cheng-Chih Hsu ◽  
...  
Keyword(s):  
Structural Characterization ◽  
Tandem Mass ◽  
Molecular Networking ◽  
Mass Spectral

scholarly journals Multiple Compounds Recognition from The Tandem Mass Spectral Data Using Convolutional Neural Network

Molecules ◽  
2019 ◽  
Vol 24 (24) ◽  
pp. 4590
Author(s):  
Jiali Lv ◽  
Jian Wei ◽  
Zhenyu Wang ◽  
Jin Cao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Spectral Data ◽  
Test Data ◽  
False Positive ◽  
Tandem Mass ◽  
True Positive ◽  
Mass Spectral Data ◽  
True Negative ◽  
Mass Spectral

Mixtures analysis can provide more information than individual components. It is important to detect the different compounds in the real complex samples. However, mixtures are often disturbed by impurities and noise to influence the accuracy. Purification and denoising will cost a lot of algorithm time. In this paper, we propose a model based on convolutional neural network (CNN) which can analyze the chemical peak information in the tandem mass spectrometry (MS/MS) data. Compared with traditional analyzing methods, CNN can reduce steps in data preprocessing. This model can extract features of different compounds and classify multi-label mass spectral data. When dealing with MS data of mixtures based on the Human Metabolome Database (HMDB), the accuracy can reach at 98%. In 600 MS test data, 451 MS data were fully detected (true positive), 142 MS data were partially found (false positive), and 7 MS data were falsely predicted (true negative). In comparison, the number of true positive test data for support vector machine (SVM) with principal component analysis (PCA), deep neural network (DNN), long short-term memory (LSTM), and XGBoost respectively are 282, 293, 270, and 402; the number of false positive test data for four models are 318, 284, 198, and 168; the number of true negative test data for four models are 0, 23, 7, 132, and 30. Compared with the model proposed in other literature, the accuracy and model performance of CNN improved considerably by separating the different compounds independent MS/MS data through three-channel architecture input. By inputting MS data from different instruments, adding more offset MS data will make CNN models have stronger universality in the future.

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