Evidence map›Paper›PMID 39403300›Full record

ArticleFood chemistry: X2024

Comprehensive characterization of volatile compounds in Iranian black teas using chemometric analysis of GC-MS fingerprints.

Adineh Aminianfar, Mohammad Hossein Fatemi, Fatemeh Azimi

Abstract read
In one paragraph

Article in Food chemistry: X, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Process optimization ofFrontiers in nutrition · 2026
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Adineh AminianfarDepartment of Analytical Chemistry, Faculty of Chemistry, University of Mazandaran, Babolsar, Iran.
Mohammad Hossein FatemiDepartment of Analytical Chemistry, Faculty of Chemistry, University of Mazandaran, Babolsar, Iran.
Fatemeh AzimiDepartment of Analytical Chemistry, Faculty of Chemistry, University of Mazandaran, Babolsar, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

HS-SPME-GC–MSIdentificationIranian black teaMultivariate curve resolution-alternating least squareVolatile organic compounds

Identifiers

PMID39403300
PMCPMC11471522

What Socratic holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.