Untargeted/Targeted 2D Gas Chromatography/Mass Spectrometry Detection of the Total Volatile Tea Metabolome
Identifying all analytes in a natural product is a daunting challenge, even if fractionated by volatility. In this study, comprehensive two-dimensional gas chromatography/mass spectrometry (GC×GC-MS) was used to investigate relative distribution of volatiles in green, pu-erh tea from leaves...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/24/20/3757 |
| _version_ | 1819260575226527744 |
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| author | Joshua Morimoto Marta Cialiè Rosso Nicole Kfoury Carlo Bicchi Chiara Cordero Albert Robbat |
| author_facet | Joshua Morimoto Marta Cialiè Rosso Nicole Kfoury Carlo Bicchi Chiara Cordero Albert Robbat |
| author_sort | Joshua Morimoto |
| collection | DOAJ |
| description | Identifying all analytes in a natural product is a daunting challenge, even if fractionated by volatility. In this study, comprehensive two-dimensional gas chromatography/mass spectrometry (GC×GC-MS) was used to investigate relative distribution of volatiles in green, pu-erh tea from leaves collected at two different elevations (1162 m and 1651 m). A total of 317 high and 280 low elevation compounds were detected, many of them known to have sensory and health beneficial properties. The samples were evaluated by two different software. The first, GC Image, used feature-based detection algorithms to identify spectral patterns and peak-regions, leading to tentative identification of 107 compounds. The software produced a composite map illustrating differences in the samples. The second, Ion Analytics, employed spectral deconvolution algorithms to detect target compounds, then subtracted their spectra from the total ion current chromatogram to reveal untargeted compounds. Compound identities were more easily assigned, since chromatogram complexities were reduced. Of the 317 compounds, for example, 34% were positively identified and 42% were tentatively identified, leaving 24% as unknowns. This study demonstrated the targeted/untargeted approach taken simplifies the analysis time for large data sets, leading to a better understanding of the chemistry behind biological phenomena. |
| first_indexed | 2024-12-23T19:28:05Z |
| format | Article |
| id | doaj.art-1681a135f7b14ad8970ab5af3d54f0b2 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-12-23T19:28:05Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-1681a135f7b14ad8970ab5af3d54f0b22022-12-21T17:33:59ZengMDPI AGMolecules1420-30492019-10-012420375710.3390/molecules24203757molecules24203757Untargeted/Targeted 2D Gas Chromatography/Mass Spectrometry Detection of the Total Volatile Tea MetabolomeJoshua Morimoto0Marta Cialiè Rosso1Nicole Kfoury2Carlo Bicchi3Chiara Cordero4Albert Robbat5Department of Chemistry, Tufts University, Medford, MA 02155, USADipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, 10125 Turin, ItalyDepartment of Chemistry, Tufts University, Medford, MA 02155, USADipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, 10125 Turin, ItalyDipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, 10125 Turin, ItalyDepartment of Chemistry, Tufts University, Medford, MA 02155, USAIdentifying all analytes in a natural product is a daunting challenge, even if fractionated by volatility. In this study, comprehensive two-dimensional gas chromatography/mass spectrometry (GC×GC-MS) was used to investigate relative distribution of volatiles in green, pu-erh tea from leaves collected at two different elevations (1162 m and 1651 m). A total of 317 high and 280 low elevation compounds were detected, many of them known to have sensory and health beneficial properties. The samples were evaluated by two different software. The first, GC Image, used feature-based detection algorithms to identify spectral patterns and peak-regions, leading to tentative identification of 107 compounds. The software produced a composite map illustrating differences in the samples. The second, Ion Analytics, employed spectral deconvolution algorithms to detect target compounds, then subtracted their spectra from the total ion current chromatogram to reveal untargeted compounds. Compound identities were more easily assigned, since chromatogram complexities were reduced. Of the 317 compounds, for example, 34% were positively identified and 42% were tentatively identified, leaving 24% as unknowns. This study demonstrated the targeted/untargeted approach taken simplifies the analysis time for large data sets, leading to a better understanding of the chemistry behind biological phenomena.https://www.mdpi.com/1420-3049/24/20/3757teametabolomicsgc/mssoftwaredatabasems subtractionspectral deconvolution2dgcvolatilomics |
| spellingShingle | Joshua Morimoto Marta Cialiè Rosso Nicole Kfoury Carlo Bicchi Chiara Cordero Albert Robbat Untargeted/Targeted 2D Gas Chromatography/Mass Spectrometry Detection of the Total Volatile Tea Metabolome Molecules tea metabolomics gc/ms software database ms subtraction spectral deconvolution 2dgc volatilomics |
| title | Untargeted/Targeted 2D Gas Chromatography/Mass Spectrometry Detection of the Total Volatile Tea Metabolome |
| title_full | Untargeted/Targeted 2D Gas Chromatography/Mass Spectrometry Detection of the Total Volatile Tea Metabolome |
| title_fullStr | Untargeted/Targeted 2D Gas Chromatography/Mass Spectrometry Detection of the Total Volatile Tea Metabolome |
| title_full_unstemmed | Untargeted/Targeted 2D Gas Chromatography/Mass Spectrometry Detection of the Total Volatile Tea Metabolome |
| title_short | Untargeted/Targeted 2D Gas Chromatography/Mass Spectrometry Detection of the Total Volatile Tea Metabolome |
| title_sort | untargeted targeted 2d gas chromatography mass spectrometry detection of the total volatile tea metabolome |
| topic | tea metabolomics gc/ms software database ms subtraction spectral deconvolution 2dgc volatilomics |
| url | https://www.mdpi.com/1420-3049/24/20/3757 |
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