| Literature DB >> 25816078 |
Hainan Sun1,2, Ting Zhang3, Qingqing Fan4, Xiangyu Qi5, Fei Zhang6, Weimin Fang7, Jiafu Jiang8, Fadi Chen9,10, Sumei Chen11,12.
Abstract
The objective of this study was to identify the major volatile compounds and their relative concentrations in flowers of different chrysanthemum cultivars and their wild relatives. The volatile organic components of fresh flowers were analyzed using a headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry. In total, 193 volatile organic components were detected; the major scent components were monoterpenoids and oxygenated monoterpenoids, which accounted for 68.59%-99.93% of the total volatiles in all tested materials except for Chrysanthemum indicum collected from Huangshan, in which they accounted for only 37.45% of total volatiles. The major volatile compounds were camphor, α-pinene, chrysanthenone, safranal, myrcene, eucalyptol, 2,4,5,6,7,7ab-hexahydro-1H-indene, verbenone, β-phellandrene and camphene. In a hierarchical cluster analysis, 39 accessions of Chrysanthemum and its relatives formed six clusters based on their floral volatile compounds. In a principal component analysis, only spider type flowers were located closely on the score plot. The results of this study provide a basis for breeding chrysanthemum cultivars which desirable floral scents.Entities:
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Year: 2015 PMID: 25816078 PMCID: PMC6272594 DOI: 10.3390/molecules20045346
Source DB: PubMed Journal: Molecules ISSN: 1420-3049 Impact factor: 4.411
Result of L16 (44) orthogonal test for optimization of the HS-SPME parameters.
| Code | Sample Weight (A) | Extraction Time (B) | Handle Scale (C) | Desorption Temperature (D) | Peak Area |
|---|---|---|---|---|---|
| 1 | 1 | 1 | 1 | 1 | 2.09 × 109 |
| 2 | 1 | 2 | 2 | 2 | 1.49 × 109 |
| 3 | 1 | 3 | 3 | 3 | 1.36 × 109 |
| 4 | 1 | 4 | 4 | 4 | 1.01 × 109 |
| 5 | 2 | 1 | 2 | 4 | 2.90 × 109 |
| 6 | 2 | 2 | 1 | 3 | 2.55 × 109 |
| 7 | 2 | 3 | 4 | 2 | 3.22 × 109 |
| 8 | 2 | 4 | 3 | 1 | 1.54 × 109 |
| 9 | 3 | 1 | 3 | 2 | 2.01 × 109 |
| 10 | 3 | 2 | 4 | 1 | 1.27 × 109 |
| 11 | 3 | 3 | 1 | 4 | 2.09 × 109 |
| 12 | 3 | 4 | 2 | 3 | 3.31 × 109 |
| 13 | 4 | 1 | 4 | 3 | 1.34 × 109 |
| 14 | 4 | 2 | 3 | 4 | 3.95 × 109 |
| 15 | 4 | 3 | 2 | 1 | 8.50 × 108 |
| 16 | 4 | 4 | 1 | 2 | 9.70 × 108 |
| Mean value 1 | 1.49 × 109 | 2.09 × 109 | 1.93 × 109 | 1.44 × 109 | |
| Mean value 2 | 2.14 × 109 | 1.92 × 109 | |||
| Mean value 3 | 2.17 × 109 | 1.88 × 109 | 2.14 × 109 | ||
| Mean value 4 | 1.78 × 109 | 1.71 × 109 | 1.71 × 109 | ||
| Range | 1.07 × 109 | 6.08 × 108 | 5.05 × 108 | 1.05 × 109 | |
| Optimization level | A2 | B2 | C3 | D4 |
Figure 1Representative different flower types of Chrysanthemum and chemical composition of flower headspace volatiles. (a) Wild relatives; (b) spider type; (c) pompon type; (d) anemone type; (e) single-flower; (f) double-flowers. Individual stacked bars reflect the relative composition of flower volatiles by chemical group.
Quantity of floral aroma volatiles in 39 accessions of Chrysanthemum.
| Quantity | 10–20 | 20–29 | 30–39 | ≥40 |
|---|---|---|---|---|
| Plant accessions | 9 | 19 | 8 | 2 |
| Percent | 24% | 50% | 21% | 5% |
Figure 2PC1 vs. PC2 Eigenvector values of flower volatile values from chrysanthemum cultivars and wild relatives. Chrysanthemum wild relatives (◊); spider type (×); pompon type (∆); anemone type (○); single-flower (+); double-flowers (□).
Figure 3Loading plot of Eigenvector load values of 193 volatile components from PC1 and PC2.
Figure 4Hierarchical cluster dendrogram of 39 plant materials.
Chrysanthemum cultivars and wild relatives included in this study.
| Code | Accessions | Collection Locality |
|---|---|---|
| 1 | Nanjing, Jiangsu province, China | |
| 2 | Nanjing, Jiangsu province, China | |
| 3 | Nanjing, Jiangsu province, China | |
| 4 | Nanjing, Jiangsu province, China | |
| 5 | Nanjing, Jiangsu province, China | |
| 6 | Nanjing, Jiangsu province, China | |
| 7 | Nanjing, Jiangsu province, China | |
| 8 | Nanjing, Jiangsu province, China | |
| 9 | Nanjing, Jiangsu province, China | |
| 10 | Nanjing, Jiangsu province, China | |
| 11 | Nanjing, Jiangsu province, China | |
| 12 | Nanjing, Jiangsu province, China | |
| 13 | Nanjing, Jiangsu province, China | |
| 14 | Nanjing, Jiangsu province, China | |
| 15 | Nanjing, Jiangsu province, China | |
| 16 | Nanjing, Jiangsu province, China | |
| 17 | Nanjing, Jiangsu province, China | |
| 18 | Nanjing, Jiangsu province, China | |
| 19 | Nanjing, Jiangsu province, China | |
| 20 | Nanjing, Jiangsu province, China | |
| 21 | Nanjing, Jiangsu province, China | |
| 22 | Nanjing, Jiangsu province, China | |
| 23 | Nanjing, Jiangsu province, China | |
| 24 | Nanjing, Jiangsu province, China | |
| 25 | Nanjing, Jiangsu province, China | |
| 26 | Nanjing, Jiangsu province, China | |
| 27 | Nanjing, Jiangsu province, China | |
| 28 | Nanjing, Jiangsu province, China | |
| 29 | Nanjing, Jiangsu province, China | |
| 30 | Tsukuba, Japan | |
| 31 | Jinchuan, Sichuan province, China | |
| 32 | Tsukuba, Japan | |
| 33 | Tsukuba, Japan | |
| 34 | Nanjing, Jiangsu province, China | |
| 35 | Hangzhou, Zhejiang province China | |
| 36 | Huangshan, Anhui province, China | |
| 37 | Tokyo, Japan | |
| 38 | Nanjing, Jiangsu province, China | |
| 39 | Tsukuba, Japan |
L16 (44) orthogonal test to optimized HS-SPME parameters.
| Level | Factor | |||
|---|---|---|---|---|
| Sample Weight (A/g) | Extraction Time (B/min) | Handle Graduation (C/cm) | Desorption Temperature (D/°C) | |
| 1 | 2.5 | 40 | 1 | 220 |
| 2 | 2.0 | 30 | 2 | 230 |
| 3 | 1.5 | 20 | 3 | 240 |
| 4 | 1.0 | 10 | 4 | 250 |