Comprehensive characterization of volatile compounds in Iranian black teas using chemometric analysis of GC-MS fingerprints
Black tea, a widely popular non-alcoholic beverage, is renowned for its unique aroma and has attracted significant attention due to its complex composition. However, the chemical profile of Iranian tea remains largely unexplored. In this research, black tea samples from key tea cultivation regions i...
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Elsevier
2024-12-01
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| Series: | Food Chemistry: X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590157524007478 |
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| author | Adineh Aminianfar Mohammad Hossein Fatemi Fatemeh Azimi |
| author_facet | Adineh Aminianfar Mohammad Hossein Fatemi Fatemeh Azimi |
| author_sort | Adineh Aminianfar |
| collection | DOAJ |
| description | Black tea, a widely popular non-alcoholic beverage, is renowned for its unique aroma and has attracted significant attention due to its complex composition. However, the chemical profile of Iranian tea remains largely unexplored. In this research, black tea samples from key tea cultivation regions in four geographical areas in northern Iran were firstly analyzed using headspace solid-phase microextraction followed by gas chromatography–mass spectrometry (HS-SPME-GC–MS) to separate, identify, and quantify their volatile organic compounds. Subsequently, employing a robust investigative strategy, we utilized for the first time the well-known multivariate curve resolution-alternating least square (MCR-ALS) method as a deconvolution technique to analyze the complex GC–MS peak clusters of tea samples. This approach effectively addressed challenges such as severe baseline drifts, overlapping peaks, and background noise, enabling the identification of minor components responsible for the distinct flavors and tastes across various samples. The MCR-ALS technique significantly improved the resolution of spectral and elution profiles, enabling both qualitative and semi-quantitative analysis of tea constituents. Qualitative analysis involved comparing resolved peak profiles to theoretical spectra, along with retention indices, while semi-quantification was conducted using the overall volume integration (OVI) approach for volatile compounds, providing a more accurate correlation between peak areas and concentrations. The application of chemometric tools in GC–MS analysis increased the number of recognized components in four tea samples, expanding from 54 to 256 components, all with concentrations exceeding 0.1 %. Among them, 32 volatile compounds were present in every tea sample. Hydrocarbons (including alkenes, alkanes, cycloalkanes, monoterpenes and sesquiterpenes), esters and alcohols were the three major chemical classes, comprising 78 % of the total relative content of volatile compounds. Analyzing black teas from four distinct regions revealed variations not only in their volatile components but also in their relative proportions. This integrated approach provides a comprehensive understanding of the volatile chemical profiles in Iranian black teas, enhances knowledge about their unique characteristics across diverse geographical origin, and lays the groundwork for quality improvement. |
| format | Article |
| id | doaj-art-0429cc77b8eb40fe992145c7e3566be2 |
| institution | OA Journals |
| issn | 2590-1575 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Food Chemistry: X |
| spelling | doaj-art-0429cc77b8eb40fe992145c7e3566be22025-08-20T01:59:34ZengElsevierFood Chemistry: X2590-15752024-12-012410185910.1016/j.fochx.2024.101859Comprehensive characterization of volatile compounds in Iranian black teas using chemometric analysis of GC-MS fingerprintsAdineh Aminianfar0Mohammad Hossein Fatemi1Fatemeh Azimi2Department of Analytical Chemistry, Faculty of Chemistry, University of Mazandaran, Babolsar, Iran; Gilan Province Water and Wastewater Quality Monitoring and Supervision Center, National Water and Wastewater Engineering Company (NWWEC), Ministry of Energy, IranDepartment of Analytical Chemistry, Faculty of Chemistry, University of Mazandaran, Babolsar, Iran; Corresponding author.Department of Analytical Chemistry, Faculty of Chemistry, University of Mazandaran, Babolsar, IranBlack tea, a widely popular non-alcoholic beverage, is renowned for its unique aroma and has attracted significant attention due to its complex composition. However, the chemical profile of Iranian tea remains largely unexplored. In this research, black tea samples from key tea cultivation regions in four geographical areas in northern Iran were firstly analyzed using headspace solid-phase microextraction followed by gas chromatography–mass spectrometry (HS-SPME-GC–MS) to separate, identify, and quantify their volatile organic compounds. Subsequently, employing a robust investigative strategy, we utilized for the first time the well-known multivariate curve resolution-alternating least square (MCR-ALS) method as a deconvolution technique to analyze the complex GC–MS peak clusters of tea samples. This approach effectively addressed challenges such as severe baseline drifts, overlapping peaks, and background noise, enabling the identification of minor components responsible for the distinct flavors and tastes across various samples. The MCR-ALS technique significantly improved the resolution of spectral and elution profiles, enabling both qualitative and semi-quantitative analysis of tea constituents. Qualitative analysis involved comparing resolved peak profiles to theoretical spectra, along with retention indices, while semi-quantification was conducted using the overall volume integration (OVI) approach for volatile compounds, providing a more accurate correlation between peak areas and concentrations. The application of chemometric tools in GC–MS analysis increased the number of recognized components in four tea samples, expanding from 54 to 256 components, all with concentrations exceeding 0.1 %. Among them, 32 volatile compounds were present in every tea sample. Hydrocarbons (including alkenes, alkanes, cycloalkanes, monoterpenes and sesquiterpenes), esters and alcohols were the three major chemical classes, comprising 78 % of the total relative content of volatile compounds. Analyzing black teas from four distinct regions revealed variations not only in their volatile components but also in their relative proportions. This integrated approach provides a comprehensive understanding of the volatile chemical profiles in Iranian black teas, enhances knowledge about their unique characteristics across diverse geographical origin, and lays the groundwork for quality improvement.http://www.sciencedirect.com/science/article/pii/S2590157524007478Iranian black teaVolatile organic compoundsHS-SPME-GC–MSMultivariate curve resolution-alternating least squareIdentification |
| spellingShingle | Adineh Aminianfar Mohammad Hossein Fatemi Fatemeh Azimi Comprehensive characterization of volatile compounds in Iranian black teas using chemometric analysis of GC-MS fingerprints Food Chemistry: X Iranian black tea Volatile organic compounds HS-SPME-GC–MS Multivariate curve resolution-alternating least square Identification |
| title | Comprehensive characterization of volatile compounds in Iranian black teas using chemometric analysis of GC-MS fingerprints |
| title_full | Comprehensive characterization of volatile compounds in Iranian black teas using chemometric analysis of GC-MS fingerprints |
| title_fullStr | Comprehensive characterization of volatile compounds in Iranian black teas using chemometric analysis of GC-MS fingerprints |
| title_full_unstemmed | Comprehensive characterization of volatile compounds in Iranian black teas using chemometric analysis of GC-MS fingerprints |
| title_short | Comprehensive characterization of volatile compounds in Iranian black teas using chemometric analysis of GC-MS fingerprints |
| title_sort | comprehensive characterization of volatile compounds in iranian black teas using chemometric analysis of gc ms fingerprints |
| topic | Iranian black tea Volatile organic compounds HS-SPME-GC–MS Multivariate curve resolution-alternating least square Identification |
| url | http://www.sciencedirect.com/science/article/pii/S2590157524007478 |
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