Dataset Information

GC/MS based metabolomics: development of a data mining system for metabolite identification by using soft independent modeling of class analogy (SIMCA).

ABSTRACT:

Background

The goal of metabolomics analyses is a comprehensive and systematic understanding of all metabolites in biological samples. Many useful platforms have been developed to achieve this goal. Gas chromatography coupled to mass spectrometry (GC/MS) is a well-established analytical method in metabolomics study, and 200 to 500 peaks are routinely observed with one biological sample. However, only ~100 metabolites can be identified, and the remaining peaks are left as "unknowns".

Result

We present an algorithm that acquires more extensive metabolite information. Pearson's product-moment correlation coefficient and the Soft Independent Modeling of Class Analogy (SIMCA) method were combined to automatically identify and annotate unknown peaks, which tend to be missed in routine studies that employ manual processing.

Conclusions

Our data mining system can offer a wealth of metabolite information quickly and easily, and it provides new insights, particularly into food quality evaluation and prediction.

SUBMITTER: Tsugawa H

PROVIDER: S-EPMC3102042 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature

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Publications

GC/MS based metabolomics: development of a data mining system for metabolite identification by using soft independent modeling of class analogy (SIMCA).

Tsugawa Hiroshi H Tsujimoto Yuki Y Arita Masanori M Bamba Takeshi T Fukusaki Eiichiro E

BMC bioinformatics 20110504

<h4>Background</h4>The goal of metabolomics analyses is a comprehensive and systematic understanding of all metabolites in biological samples. Many useful platforms have been developed to achieve this goal. Gas chromatography coupled to mass spectrometry (GC/MS) is a well-established analytical method in metabolomics study, and 200 to 500 peaks are routinely observed with one biological sample. However, only ~100 metabolites can be identified, and the remaining peaks are left as "unknowns".<h4>R ...[more]

PMID: 21542920

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Project description:INTRODUCTION:This study aimed to develop a method for discriminating cigarette brands based on the profiles of volatile components extracted from the tobacco fraction of the finished cigarettes to authenticate branded cigarettes of unknown origin. METHODS:An analytical method comprising direct thermal desorption coupled with gas chromatography-quadrupole time-of-flight mass spectrometry was developed for acquiring volatile profiles of cigarettes. About 290 samples of commercially available cigarettes were analyzed. Within this batch, 123 samples represented four popular cigarette brands. They were selected for in-depth characterization. Multivariate analysis was used to investigate the interrelations among volatile compounds of cigarettes and to identify characteristic markers for the cigarette discrimination. Supervised pattern recognition techniques were used for designing classification models. RESULTS:Principal component analysis covering all detected volatiles allowed the differentiation of cigarettes based on the brand. A number of 56 volatile components were identified as markers with high discrimination power. These compounds were used for establishing classification models. A method of soft independent modeling of class analogy developed for the four studied cigarette brands proved to be efficient in the classification of unknown cigarettes, with accuracy between 95.9% and 100%. CONCLUSIONS:The data evaluation by soft independent modeling of class analogy was highly accurate in classification of unknown cigarettes with a low rate of false positives and false negatives. The developed models can be used for discrimination of genuine from non-genuine products with high level of probability. IMPLICATIONS:Profiling of volatiles, which is commonly used for authentication of different food commodities, was applied for the characterization of cigarette tobacco for the purpose of authentication a cigarette brand. Volatile components with a high discrimination power were identified by means of multivariate statistical methods and used for establishing of a classification model. The classification model was able to discriminate genuine from non-genuine cigarettes with a high level of prediction accuracy. This model could be a powerful tool for tobacco control to judge the authenticity of cigarettes.

Dataset Information

GC/MS based metabolomics: development of a data mining system for metabolite identification by using soft independent modeling of class analogy (SIMCA).

Background

Result

Conclusions

Publications

GC/MS based metabolomics: development of a data mining system for metabolite identification by using soft independent modeling of class analogy (SIMCA).

Similar Datasets

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