2014•PubMedRequires access

[SPME-GC-MS combined with Kovat's retention index analysis for volatile components in Pistacia chinensis].

Qian Yu, Shuanghong Song, Cuiqin Li

Open publisher page 0 citations

Abstract

OBJECTIVE: To analyze and compare the volatile components in fruits and leaves of Pistacia chinesis. METHODS: The volatile components were extracted from the fruits and leaves of Pistacia chinesis by solid-phrase microextration, and were analyzed and identified by gas chromatography-mass spectrometry(GC-MS) combined with Kovat's retention index. The relative content of each component was calculated by normalization method. RESULTS: 29 and 17 volatile components were identified from the fruits and leaves respectively, representing the relative content of 95. 30% and 96. 91% of the volatile components. 13 common components were identified in both the fruits and leaves. CONCLUSION: The volatile components in the fruits vary from that in the leaves in type and content, terpenoids are major components in the fruits and leaves of Pistacia chinesis in Shaanxi. Monoterpenes(76. 32%) are the major components of the fruits, while sesquiterpenes(65. 42%) are the major components of the leaves.

About this research paper

What this paper is about

OBJECTIVE: To analyze and compare the volatile components in fruits and leaves of Pistacia chinesis. METHODS: The volatile components were extracted from the fruits and leaves of Pistacia chinesis by solid-phrase microextration, and were analyzed and identified by gas chromatography-mass spectrometry(GC-MS) combined with Kovat's retention index. The relative content of each component was calculated by normalization method. RESULTS: 29 and 17 volatile components were identified from the fruits and leaves respectively, representing the relative content of 95. 30% and 96. 91% of the volatile components. 13 common components were identified in both the fruits and leaves. CONCLUSION: The volatile components in the fruits vary from that in the leaves in type and content, terpenoids are major components in the fruits and leaves of Pistacia chinesis in Shaanxi. Monoterpenes(76. 32%) are the major components of the fruits, while sesquiterpenes(65. 42%) are the major components of the leaves.

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

OBJECTIVE: To analyze and compare the volatile components in fruits and leaves of Pistacia chinesis. METHODS: The volatile components were extracted from the fruits and leaves of Pistacia chinesis by solid-phrase microextration, and were analyzed and identified by gas chromatography-mass spectrometry(GC-MS) combined with Kovat's retention index. The relative content of each component was calculated by normalization method. RESULTS: 29 and 17 volatile components were identified from the fruits and leaves respectively, representing the relative content of 95. 30% and 96. 91% of the volatile components. 13 common components were identified in both the fruits and leaves. CONCLUSION: The volatile components in the fruits vary from that in the leaves in type and content, terpenoids are major components in the fruits and leaves of Pistacia chinesis in Shaanxi. Monoterpenes(76. 32%) are the major components of the fruits, while sesquiterpenes(65. 42%) are the major components of the leaves.

Key concepts: Pistacia, Gas chromatography–mass spectrometry, Kovats retention index, Chemistry, Terpenoid, Volatile organic compound, Botany, Chromatography

Related papers

Back to paper searchBrowse research topicsOriginal source
[SPME-GC-MS combined with Kovat's retention index analysis for volatile components in Pistacia chinensis]. — Research Paper | ScholarLens