Data supporting metabolite profiles of essential oils and SSR molecular markers in Juniperus rigida Sieb. et Zucc. from different regions: A potential source of raw materials for the perfume and healthy products.

The data presented in this article afford insight into how high-quality origins were basically evaluated viewed from yields of essential oils and how GC-MS fingerprint constructed and analyzed as supplementary materials supporting the results displayed in the article of metabolite profiles of essential oils and SSR molecular markers in Juniperus rigida Sieb. et Zucc. from different regions: A potential source of raw materials for the perfume and healthy products Liu et al., 2019. The presented data demonstrate the supplementary instruction of the GC-MS fingerprint analysis results of Juniperus rigida from different origins Meng et al., 2016. The data of essential oils yields, similarities and correlation coefficients of GC-MS fingerprint and principal component analysis (PCA) supported the results of high-quality J. rigida provenance selection.

Data

Data presented in this article displays a drawing showing the supplementary information for the GC-MS fingerprint analysis. The yields of essential oils of J. rigida in different origins were displayed in Fig. 1. The similarities comparison of GC-MS chromatography of J. rigida essential oils from different origins was showed in Table 1. The correlation coefficients among different groups were assessed to support the GC-MS fingerprint analysis as well as quality evaluation of J. rigida origins (Table 2). The scores plot generated from principal component analysis (PCA) of J. rigida variables (S1-10) supported the results of hierarchical clustering analysis in GC-MS chromatography for screening the high-quality origins (Fig. 2).

Fig. 1

Yields of essential oils of J. rigida samples in different regions.

Table 1

Similarities of the GC-MS chromatograms of J. rigida samples based on the correlation.

No.	S1	S2	S3	S4	S5	S6	S7	S8	S9	S10
S1	1.000
S2	0.901	1.000
S3	0.833	0.824	1.000
S4	0.983	0.906	0.903	1.000
S5	0.921	0.945	0.929	0.950	1.000
S6	0.987	0.914	0.805	0.960	0.911	1.000
S7	0.951	0.929	0.761	0.917	0.907	0.976	1.000
S8	0.935	0.883	0.679	0.878	0.833	0.960	0.955	1.000
S9	0.892	0.881	0.638	0.832	0.808	0.929	0.929	0.978	1.000
S10	0.906	0.888	0.646	0.844	0.820	0.938	0.949	0.995	0.982	1.000

Table 2

Correlation coefficients between individual chromatograms within a group and the group simulative mean chromatogram, and between the group simulative mean chromatogram.

Group	G1	G2	G3
G1	0.985 ± 0.008a(n = 3)	0.658b	0.924b
G2		1a(n = 1)	0.862b
G3			0.937 ± 0.029a(n = 6)

Correlation coefficient of individual chromatograms to the simulative mean chromatogram of the corresponding group. Values are the mean ± SD.

Correlation coefficient between simulative mean chromatograms.

Fig. 2

The scores plot generated from principal component analysis (PCA) of variables (S1-10).

Experimental design, materials and methods

Extraction of essential oils

All J. rigida needles were air-dried and powdered, and were stored in the dark at −20 °C for further analysis. The essential oils of J. rigida was isolated by supercritical CO2 fluid extraction technology used by Meng et al. [2]. The optimum condition is at a pressure of 18 MPa and a temperature of 40 °C and an extraction time of 120 min. The essential oil was stored in tightly closed dark vials and covered with aluminum foil at 4 °C until further analysis. The essential oil was obtained as a light yellow liquid and had specific aroma.

Principal component analysis (PCA)

Principal component analysis (PCA) was performed using SPSS software (SPSS for Windows 19.0, SPSS Inc., USA) for the chemometrics of essential oils [3]. Differences in chromatograms of samples mainly existed due to variations in the common peaks. To evaluate the discrimination capacity of the common constituents, PCA was conducted using the RPAs of common peaks using HCA input data. The first two principal components contained the most information of all variables, accounting more than 85% of total variability. The score plot of the first three principal components, PC1 and PC2, visually revealed a positive influence on quality evaluation of J. rigida from different regions.

Specifications table

Subject area	Agricultural and Biological Sciences
More specific subject area	Plant Science
Type of data	Table, figure
How data was acquired	The similarities, correlation coefficients and principal components analysis of GC-MS chromatograms of J. rigida were investigated using SPSS Package 21.The yields of essential oils of J. rigida were draw by Sigma plot 12.0.
Data format	Analyzed, raw
Experimental factors	The essential oils of J. rigida was isolated by supercritical CO2fluid extraction technology used by[2].
Experimental features	The components of J. rigida essential oils were identified by GC-MS and the fingerprint chromatography were collected and analyzed by chemometrics methods.
Data source location	Northwest A&F University, Shaanxi, China
Data accessibility	Data are available with this article
Related research article	[1] Z. Liu, S. Kuang, M. Qing, D. Wang, D. Li. Metabolite profiles of essential oils and SSR molecular markers in Juniperus rigida Sieb. et Zucc. from different regions: A potential source of raw materials for the perfume and healthy products. J. Ind. Crop. Prod., 133, 2019, 424.

Value of the data• The data of essential oil yields provided basic information for screening J. rigida which contains enrich essential oils, the data can also be used for displays as windows for screening high-quality J. rigida provenance. • The data of similarities and correlation coefficients of chromatograms appeared characteristic of the specific essential oils, supporting disparities among plants derived from different regions. This data helped with the classification of the chromatography of GC-MS fingerprint into different groups and provided auxiliary information for quality evaluation of J. rigida provenances. • The principal component analysis (PCA) of different J. rigida origins confirmed the results in hierarchical clustering analysis. Chromatograms were similar within a particular group while significantly distinct between groups. The first two principal components contained the most information of all variables, accounting more than 85% of total variability. These data helped with high-quality J. rigida provenance selection and were in supplementary materials for the indicating components screen.