Adineh Aminianfar , Mohammad Hossein Fatemi , Fatemeh Azimi
{"title":"利用 GC-MS 指纹的化学计量分析全面鉴定伊朗红茶中的挥发性化合物","authors":"Adineh Aminianfar , Mohammad Hossein Fatemi , Fatemeh Azimi","doi":"10.1016/j.fochx.2024.101859","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":12334,"journal":{"name":"Food Chemistry: X","volume":"24 ","pages":"Article 101859"},"PeriodicalIF":6.5000,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comprehensive characterization of volatile compounds in Iranian black teas using chemometric analysis of GC-MS fingerprints\",\"authors\":\"Adineh Aminianfar , Mohammad Hossein Fatemi , Fatemeh Azimi\",\"doi\":\"10.1016/j.fochx.2024.101859\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":12334,\"journal\":{\"name\":\"Food Chemistry: X\",\"volume\":\"24 \",\"pages\":\"Article 101859\"},\"PeriodicalIF\":6.5000,\"publicationDate\":\"2024-09-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Food Chemistry: X\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590157524007478\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Food Chemistry: X","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590157524007478","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
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 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.
期刊介绍:
Food Chemistry: X, one of three Open Access companion journals to Food Chemistry, follows the same aims, scope, and peer-review process. It focuses on papers advancing food and biochemistry or analytical methods, prioritizing research novelty. Manuscript evaluation considers novelty, scientific rigor, field advancement, and reader interest. Excluded are studies on food molecular sciences or disease cure/prevention. Topics include food component chemistry, bioactives, processing effects, additives, contaminants, and analytical methods. The journal welcome Analytical Papers addressing food microbiology, sensory aspects, and more, emphasizing new methods with robust validation and applicability to diverse foods or regions.