The portable GC-MS and HS/SPME-GC×GC-TOF MS-Based Analysis of the Difference of Citri Grandis Exocarpium from Different Regions

doi:10.21203/rs.3.rs-3612603/v1

Download PDF

Research Article

The portable GC-MS and HS/SPME-GC×GC-TOF MS-Based Analysis of the Difference of Citri Grandis Exocarpium from Different Regions

https://doi.org/10.21203/rs.3.rs-3612603/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

In this study, the portable GC-MS and comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOF MS) combined with headspace solid-phase microextraction (HS/SPME) combined with chemometric were firstly used to analyze the volatile components of Citri Grandis Exocarpium that were from Huazhou (CGEH) and Guangxi (CGEG), respectively. The results from portable GC-MS only can identified some differences between samples. Compared with results from HS/SPME-GC×GC-TOF MS we found that portable GC-MS could not analyze the mark of CGEH.From identification of GC×GC-TOF MS we got that total 304 chemicals among which 261 were firstly reported were identified. Among them 52 compounds were only detected in CGEH, 81 were unique in CGEG and 171 were in both CGEH and CGEG. The volatile components in CGEH were classified into 17 and CGEG into 19 types. The contents of alkene aromatics, hydrocarbons and alcohols in both CGEH and CGEG were relatively high. These results fully demonstrated the detection ability of GC×GC-TOF MS. Principal Component Analysis (PCA) and Orthogonal Partial Least Squares (OPLS-DA) were performed. The results indicated that there was basically no difference from different batches of the same origin. However, there were obvious differences between CGEH and CGEG. 10 compounds in CGEH were screened out the values of |p(corr)|>0.9, |p|>0.06 and VIP value > 1.1. These results proved portable GC-MS could not find marks of Citri Grandis Exocarpium and GC×GC-TOF MS could find it.

Citri Grandis Exocarpium

portable GC-MS Comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry

Chemometric analysis

Citri Grandis Exocarpium is a traditional Chinese herbal medicine in Huazhou city, Guangdong province. It is derived from Citrus grandis 'Tomentosa' or Citrus grandis (L.) Osbeck [1-3]. Symptoms such as food accumulation, alcohol injury, wind-cold cough can be relieved by application of it [4]. However, there are many kinds of Citri Grandis Exocarpium from different area in the medicinal material market, and the chemical components from different areas are quite different, which may result in the different medical treatment efficiency. The further identification of the components of Citri Grandis Exocarpium is obviously significant.

The main active ingredients in Citri Grandis Exocarpium are volatile oil, flavonoids, coumarin and polysaccharides [5], and the content of the ingredients varies with different growing area. Lu Haisheng et al. used DNA barcode technology and ultra-high performance liquid chromatography (UPLC) to distinguish two types of Citri Grandis Exocarpium from two origins[6]. Gao Zelin used HPLC to investigate the difference between samples by determining the content of naringin and rhoifolin [7]. The species in Luchuan County, Baise City, Nanning City and Huazhou City, were identified by steam distillation and gas chromatograph mass spectrometer (GC-MS), respectively. The results indicated that the difference of the volatile substance in the Huazhou, Luchuan, and Nanning was little, but much more with that from Baise [8]. Zhou Weiming used near-infrared spectroscopy technology to directly detect the original samples (Maojuhong, Guangjuhong, counterfeit products) without any pre-treatment. The rapid and non-destructive identification of Citri Grandis Exocarpium was carried out by the pattern recognition technology of cluster analysis and principal component analysis [9].

The ultra-high performance liquid chromatography (UPLC) and DNA barcode technology are usually complex and time-consuming. The low sensitivity and relatively large relative error are the demerits of near-infrared spectroscopy technology. There are some disadvantages such as large matrix interference and serious co-elution when GC-MS is employed in the identification of complex systems.. With strong separation ability, high sensitivity, accurate qualitative, the comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOF MS) is a suitable technology for the identification of Citri Grandis Exocarpium and the merits of portable GC-MS is saving time[11]. In this study, portable GC-MS and GC×GC-TOF MS were firstly used to analyze Citri Grandis Exocarpium from Guangxi and Huazhou.

Instruments and Chemicals

Smart SpmeArrow 1.10 mm DVB/Carbon WR/PDMS (Phase length 20mm, Phase thickness 120 µm, CTC). High speed pulverizer (Wuyi County Yili Tools Co., Ltd). C7-C30 n-paraffin standard solution (1mg·L^− 1, Sigma-Aldrich).

Samples and Sample Preparation

Samples including Citri Grandis Exocarpium from Huazhou city, Guangdong province (CGEH) and Guangxi province (CGEG) were provided by Sinopharm Group Fengliaoxing (Foshan) Pharmaceutical Co., Ltd and were identified by Xiaodan Zhang, an associate professor from Zhejiang Sci-Tech University. After that, CGEH and CGEG were conserved at the Laboratory of Samples, Guangzhou Hexin Analytical Instrument Company Limited, Guangdong Province, China. The information about these samples was shown in Table 1. The samples were grounded into fine powder and took out 0.5 g of the powder that put it in a 20 mL headspace bottle.

Table 1

The samples source information of Citri Grandis Exocarpium
Samples	Sample numbers	Sources
CGEH	CGEH-1	Huazhou city, Maoming city, Guangdong Province, China
	CGEH-2
	CGEH-3
	CGEH-4
	CGEH-5
	CGEH-6
CGEG	CGEG-1	Guangxi Province, China
	CGEG-2
	CGEG-3
	CGEG-4
	CGEG-5
	CGEG-6

Portable GC- MS Conditions

Samples of CGEH and CGEG were detected by Portable GC- MS (Guangzhou Hexin Instrument Co., Ltd, HSP 1000) and the injection method was headspace injection. The temperature of the front inlet was set at 250°C, and the split ratio was 50:1. DB-5 (6 m×0.1 mm×0.4 µm, Agilent Technologies) was chosen to analyze the components. The carrier gas was helium (99.999%) and the flow rate was 0.2 mL/min. The initial oven of gas chromatography was maintained at 50°C for1 min, then rose to 120°C with the rate of 35°C/min and rose to 260°C with the rate of 120°C/min. The temperature of the interface was 250°C and the ion source were set at 200°C. The mass spectrometry detector (MSD) voltage was − 1250 V.

GC×GC-TOF MS Conditions

Samples of CGEH and CGEG were detected by comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOF-MS, Guangzhou Hexin Instrument Co., Ltd, GGT 0620) coupled with a fully automatic multi-functional samples preparation platform (CTC PALRTC) which can complete the process of headspace solid phase microextraction (HS/SPME).The temperature of the front inlet was set at 240°C, and the split ratio was 5:1. Chromatographic columns of DB-WAX (60 m×0.25 mm×0.25 µm, Agilent Technologies) and DB-17MS (2.3 m×0.18 mm×0.18 µm, Agilent Technologies) were chosen as the primary dimension column and the secondary dimension column, respectively. The carrier gas was helium (99.999%) and the flow rate was 1 mL/min. The initial oven of gas chromatography was maintained at 40°C for1 min and then rose to 241°C using the rate of 3°C/min, with the total run time of 78 min. A HV modulation column (1.3 m × 0.25 mm, Agilent Technologies) as the modulation column was chosen and 6 s was set as the modulation period. The temperature of the interface was 250°C and the ion source were set at 230°C. The mass spectrometry detector (MSD) voltage was − 1850 V. The ionization energy was70 eV. The MS acquisition mass range was 50–500 amu and the param of MS data acquisition rate was 100 spectra/s.

Data Processing

By using software GC-MS Prtb (Guangzhou Hein, V1.0) and matching the spectral library (NIST 17), we got the data of portable GC-MS. The comprehensive two-dimensional data processing workstation software Canvas (Xuejing Technology, Version 2.5.0) was used to automatically identify peaks with a signal-to-noise ratio greater than 20, and qualitative identification was performed by comparing and searching the mass spectrum of each compound in the spectral library (NIST 17). Among them, the substances, positive and negative matching degrees greater than 800, were chosen. At the same time, the peak area normalization method was used to determine the relative content of every component. The analysis was performed by unsupervised pattern recognition principal component analysis (PCA) and orthogonal partial least squares discrimination analysis (OPLS-DA) models of SIMCA 14.1 software to find differential compounds between CGEH and CGEG.

Retention Index Determination

A blank sample was tested after each six detected samples. Before the samples were measured, a syringe of C7-C30 n-paraffin standard solution was collected under the same conditions with samples to obtain the retention index (RI), which confirmed the condition of instrument system was working well and helped the characterization of unknown substances.

Results of portable GC-MS

In this experiment, portable GC-MS was used to detect CGEH and CGEG, and the result shown that the spectra of each origin were basically consistent. Comparing the spectra of CGEG (see Fig. 1), it was found that more components in CEGH was detected. The retention time of highest peaks of samples were similar that appear at about 3.1 minutes and the substance of it was limonene. But the intensity of CGEH was significantly higher than that in CGEG. From this, it could be inferred that the limonene content of CGEH was higher thanCGEG. After 4 minutes of retention time, (Z)-5-[(1R,3R,6S)-2,3-dimethyl-3-tricyclo[2.2.1.0^2,6]heptanyl]-2-methylpent-2-en-1-ol was detected in CGEH, but it was not detected in CGEG. After qualitative analysis of the data (see Table 2), it could be seen that there were five substances: (3E)-3,7-dimethylocta-1,3,6-triene, 3,7,7-trimethylbicyclo[4.1.0]hept-3-ene, (1S,5S)-6,6-dimethyl-2-methylidenebicyclo[3.1.1]heptane, methyl octadeca-2,5-diynoate, and (Z)-5-[(1R,3R,6S)-2,3-dimethyl-3-tricyclo[2.2.1.02,6]heptanyl]-2-methylpent-2-en-1-ol. They were all detected in CGEH. It could be seen that these substances could be used as differential compounds to distinguish CGEH and CGEG.

Table 2

The Volatile Components of Citri Grandis Exocarpium
Category	Names	CAS	RT/min	Area
CGEH	7-methyl-3-methylideneocta-1,6-diene	123-35-3	3.163	134523
	1-methyl-2-propan-2-ylbenzene	527-84-4	3.311	13813
	1-methyl-4-prop-1-en-2-ylcyclohexene	138-86-3	3.328	493213
	(3E)-3,7-dimethylocta-1,3,6-triene	13877-91-3	3.368	10057
	3,7,7-trimethylbicyclo[4.1.0]hept-3-ene	13466-78-9	3.420	56508
	(1S,5S)-6,6-dimethyl-2-methylidenebicyclo[3.1.1]heptane	18172-67-3	3.466	10796
	methyl octadeca-2,5-diynoate	57156-91-9	3.509	27755
	(Z)-5-[(1R,3R,6S)-2,3-dimethyl-3-tricyclo[2.2.1.02,6]heptanyl]-2-methylpent-2-en-1-ol	115-71-9	4.098	12801
CGEH	7-methyl-3-methylideneocta-1,6-diene	123-35-3	3.159	5703
	7-methyl-3-methylideneocta-1,6-diene	527-84-4	3.305	5912
	1-methyl-2-propan-2-ylbenzene	138-86-3	3.324	180133
	1-methyl-2-prop-1-en-2-ylbenzene	7399-49-7	3.507	7247

Results of GC×GC-TOF MS

Total 304 chemicals among which 261 were firstly reported were identified. There were 223 volatile substances in CGEH, among which the content of olefins was the highest. The proportion of olefins was 25.56%-47.55% with the average content 43.09% in 6 batches, alcohols were16.05%-27.96% with the average content 19.04%, aromatics were 15.44%-19.27% with the average 18.19%. Total 252 volatile substances were detected in CGEG, among which the content of olefins was also the highest. Olefins were 43.07%-45.62% with the average content 44.4% in 6 batches, aromatics were 21.44%-22.49% with the average22.1%, alcohols were11.65%-12.68% with the average 12.25%. The volatile components were shown in Table S1. Compared with the literatures [12–15], 50 volatile compounds, such as6-methylhept-5-en-2-one(1),1, 3,3-trimethylbicyclo[2.2.1]heptan-2-ol(2),2-methylhexanoic acid(3),were firstly detected.

Compared with chromatograms of GC×GC-TOF MS, we got that components in CGEH and CGEG were similar and the proportion of olefins was highest in samples. It was equal with result of portable GC-MS. But, compared with the result of GC×GC-TOF MS, we figured that five substances only detected in CGEH by portable GC-MS were analyzed in CGEG. It approved that portable GC-MS could only be used to distinguish CGEH and CGEG and not find the markers.

The chromatograms and Venn diagram of CGEH and CGEG were shown in Fig. 2 and Fig. 3. Total 52 compounds in CGEH did not detect in CGEG, and 81 were unique in CGEG. Many compounds with medicinal function were identified. With anti-asthma, antitussive, anti-degenerative inflammation and phlegm-reducing function, (4R)-1-methyl-4-prop-1-en-2-ylcyclohexene (4) was clinically used for treating cholecystitis [16, 17]. The average content of (4R)-1-methyl-4-prop-1-en-2-ylcyclohexene (4) in CGEH was 8.58%, and that in CGEG was only 5.64%, so the better clinical performance like relieving cough from CGEH than that from CGEG may result from the higher content of (4R)-1-methyl-4-prop-1-en-2-ylcyclohexene (4). The content of (1S,8aR)-4,7-dimethyl-1-propan-2-yl-1,2,3,5,6,8a-hexahydronaphthalene (5) that was an effective component for relieving cough and reducing phlegm in both CGEH and CGEG was about 3.3%.2,6,6-trimethylbicyclo [3.1.1] hept-2-ene (6) had antitussive and expectorant effects [18, 16]. (1R,4E,9S)-4,11,11-trimethyl-8-methylidenebicyclo [7.2.0] undec-4-ene (7) that had a lively and violent clove smell was used for the anti-inflammatory [19], pain relief, paralysis, warming body, and relief of gastritis, etc. In addition, soothing effect on the skin and tissues could be achieved by using (1R,4E,9S)-4,11,11-trimethyl-8-methylidenebicyclo [7.2.0] undec-4-ene (7)[20]. 2,3,5-trimethylpyrazine (8) in CGEG was a high-grade spice with a strong aroma of roasted peanuts or potatoes and acute toxicity (Mouse oral LD50: 806mg/kg), which may result in less effective of CGEG than that of CGEH [21].

Compared with traditional GC-MS [22–25], GC×GC-TOFMS had a better separation ability that was used to solved the problems of co-elution of components, and provided more comprehensive and high-quality information for the complex aroma components. It was seen from Fig. 4 that only one chromatographic peak was observed in the one-dimensional chromatogram near the 35.5 min, while in the two-dimensional three chromatographic peaks, (1,7,7-trimethyl-2-bicyclo [2.2.1] heptanyl) acetate, 1,3,3-trimethylbicyclo[2.2.1]heptan-2-ol,1-ethenyl-1-methyl-2,4-bis(prop-1-en-2-yl)cyclohexane,were observed at the same retention time in the chromatogram. Three aroma components, were separated and identified with GC×GC-TOFMS instead one with GC-MS.

Chemometric Analysis

Chemometrics that is applied to optimize the chemical measurement process and maximally to extract the useful information from chemical measurement data is a branch of chemical disciplines arising from the intersection of chemistry, statistics, mathematics, and computer science[26, 27].Principal component analysis (PCA) is an unsupervised multivariate statistical method that can distinguish samples on the basis of retaining the original information of the data to the greatest extent[28].It is also a common and effective way to compress information by which the complexity can be simplified and a lot of information can be compressed (variables) into several new dummy variables to simplify the problem. It is based on the variable covariance matrix composed of multiple samples, and adopts the method of eigendecomposition to get the virtual principal component that can replace the original variable. The new variables called principal components score are independent and uncorrelated with each other, which can eliminate the correlation and information redundancy in the original data [29].

As shown in the Fig. 5, every sample concludes many indicators and each indicator represents a dimension. In this case, each sample is a multidimensional vector. So, it looks like a high-dimensional data cloud from the perspective of three-dimensional space. PCA will complete the process of dimension reduction by means of projection, which could achieve the purpose of establishing a suitable model for interpretation and prediction. The PCA score plot shows the projection on the plane formed by the two directions PC1 (first principal component) and PC2 (second principal component). The two directions of PC1 and PC2 are virtual rather than specific variables, and are contributed by all variables [30].

Orthogonal partial least squares discrimination analysis (OPLS-DA) is a supervised multivariate statistical method that tends to extract variable information that is beneficial to sample classification, which greatly reduces system noise interference and improves classification efficiency [31].

Variable Importance in The Projection (VIP) defined that the VIP value of the independent variable can reflect the role of the independent variable in the prediction of the dependent variable. Much larger the VIP value of the independent variable play more important role in the prediction of the dependent variable, it reflects the importance of the compound.

The formula of VIP of the j_th (j = 1,...,p) compound is as follows:

$$\text{V}\text{I}\text{P}\text{j}=\sqrt{\frac{p\sum _{a=1}^{A}\left({q}_{a}^{2}{t}_{a}^{T}{t}_{a}\right)({w}_{ja}/{‖{w}_{a}‖}^{2})}{\sum _{a=1}^{A}{q}_{a}^{2}{t}_{a}^{T}{t}_{a}}}$$

In the formula, w_a and t_aare the a_th column of the model coefficient matrix W and the score matrix T, respectively.q_a is the a_th element of the matrix Q. w_jais the weight value of the j_th compound in, and t_ais composed of a linear combination of all compounds, which can be used to directly predict the sample property matrix Y. It can be seen that the j_th compound is the sample property matrix through the first A latent variables Y to explain.

In the formula, ${q}_{a}^{2}{t}_{a}^{T}{t}_{a}$reflects the ability of t_a to explain the sample property matrix Y, so it can be analyzed that if ${q}_{a}^{2}{t}_{a}^{T}{t}_{a}$is larger than others which indicated the prediction of t_a on the sample property matrix Y is important. Meanwhile, if w_jais also larger, it reflects that the j_th compound plays a more important role in the calculation of t_a, which means that the j_th compound is more important for the prediction of the model.

As the mean value of the square of the importance coefficient of all compound point variable projections is 1. Some scholars proposed to use the VIP value as the importance index of differential compounds, and then screen out characteristic compounds and eliminate unimportant compounds[32].

To get the characteristic components between CGEH and CGEG, PCA was performed on the components detected in twelve samples, and the results were shown in Fig. 4. There was basically no difference in the composition of Citri Grandis Exocarpium from different batches of the same origin, but there were some fluctuations in the relative content of the components in different batches of samples. However, there were obvious differences between the volatile components in CGEH and CGEG.

The OPLS-DA was applied to extract the VIP of the model, the compounds with |p(corr)|>0.9 and |p|>0.06 combined with VIP value > 1.1 were selected (Table 3), which can help us distinguish CGEH from others. After passed 200 permutation test, the simulated values of the model parameters R2 were greater than the simulated values of Q2, and the intercept of the Q2 regression line was − 0.298 less than 0.652 (see Fig. 7), indicating that the model does not have overfitting phenomenon.

Table 3

Specific Compounds in CGEH
Peak number	Names	CAS	VIP value
1	1,2-diethyl-4-phenylbenzene	61141-66-0	1.56
2	1-methyl-4-prop-1-en-2-ylcyclohexane-1,2-diol	1946-00-5	1.50
3	dodecan-1-ol	112-53-8	1.42
4	(E)-2-phenylbut-2-enal	4411-89-6	1.37
5	3,8-dimethylundecane	17301-30-3	1.33
6	tetradecanal	124-25-4	1.27
7	2-methyltridecane	1560-96-9	1.26
8	2-methyl-2H-furan-5-one	591-11-7	1.22
9	2-[2-[2-(2-hydroxyethoxy)ethoxy]ethoxy]ethanol	112-60-7	1.22
10	(1S,3S,5S)-4-methylidene-1-propan-2-ylbicyclo[3.1.0]hexan-3-ol	3310-02-9	1.10

The Permutations Plot helps to assess the risk that the current model is spurious. In other words, the model just fits the training set well but does not predict Y well for new observations. The idea of this validation is to compare the goodness of fit (R2 and Q2) of the original model with the goodness of fit of several models based on data where the order of the Y-observations has been randomly permuted, while the X-matrix has been kept intact.

The plot shows, for a selected Y-variable, on the vertical axis the values of R2 and Q2 for the original model (far to the right) and of the Y-permuted models further to the left. The horizontal axis shows the correlation between the permuted Y-vectors and the original Y-vector for the selected Y. The original Y has the correlation 1.0 with itself, defining the high point on the horizontal axis.

The plot (Fig. 7) strongly indicates that the original model is valid. The criteria for validity are: All blue Q2-values to the left are lower than the original points to the right or the blue regression line of the Q2-points intersects the vertical axis (on the left) at, or below zero. The R2-values always show some degree of optimism. However, when all green R2-values to the left are lower than the original point to the right, this is also an indication for the validity of the original model.

In this study, portable GC-MS and comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry were firstly used to analyze CGEH and CGEG. The results shown that portable GC-MS was not suitable to use to find marks in CGEH and CGEG and only could be distinguished it. The GC×GC-TOF MS could be used to achieved it. The result of GC×GC-TOF MS was that 304 chemicals among which 261 were firstly reported were identified. There were 52 compounds only in CGEH, 81 were unique in CGEG and 171 were identified in both CGEH and CGEG. The contents of esters, alcohols and aromatics in both CGEH and CGEG were higher than that of other compounds. The results from chemometric method showed that10 characteristic components in CGEH based on |p(corr)|>0.9, |p|>0.06 and VIP value > 1.1 were selected. The results can provide a helpful reference for the application of Citri Grandis Exocarpium.

Funding This study was supported by the Science and Technology Planning Project of Guangzhou Development Zone (2020GH16), China.

Conflict of Interest The authors declare no competing interests.

Ethical approval： This study is not involved with human participants and human tissue.

Author Contribution

Guobin Tan and Zhouzhen: conceived and designed the experiments. Jianqun Luo and Sifan Luo: performed the experiments. Manman Wu: supervised the project. Luoxu Huang, Kaixuan Li and Jingwei Xu: wrote the manuscript

FanshengKong ,Zhendong Ding, Kai Zhang, Weijie Duan, Yaru Qin, Zhipeng Su, Yongguang Bi (2020) https://doi.org/10.1016/j.jep.2020.113178
Chinese Pharmacopoeia Commission,Pharmacopoeia of the People's Republic of China. vol 1, China Medical Science and Technology Press, Beijing (2020)
Zhong Chu-Chu, Wu Meng-Hua, Yu Pin-Hao, Li Feng, Zhang Ying, Ma Zhi-Guo, Xie Chao-Jie, Cao Hui (2021) DOI:10.19540/j.cnki.cjcmm.20210622.101.
Yuzhen Wang, Liwen Chen, Lijuan Wang. Practical Journal of Medicine Pharmacy 31: 823-824．(2014)
Xiaolu Mo, Yuewen Cai, Qingqian Cai. Food and Drug 6: 39-41. (2007)
LU Hai-sheng, LI Ting, JIANG Dan, SUN Yu-feng. China Journal of Chinese Materia Medica 44: 4419-4425 (2019).
GAO Zelin, ZHANG Xiaoying, MAI Baoyu, LIN Xiating. China Pharmacy 33: 825-829. (2022)
Xiaofeng Wang, Debin Chen. Popular Science & Technology 21: 32-34+76. (2019)
Zhou weiming, Liu hao. Channel Pharmacy (32) 11: 55-58. (2020)
Li Woying, Lu Sijie. Guangdong Chemical Industry 49: 223-226. (2022)
Li Woying, Lu Sijie. Guangdong Chemical Industry 49: 223-226. (2022)
Xiaofeng Wang, Debin Chen. Popular Science & Technology 21: 32-34+76. (2019)
Zhou weiming, Liu hao. Heilongjiang Medicine Journal 35: 745-747. (2022)
Wu Hong, Shen Yonggen, Cai Zhipeng, Fan Shaowu. Academic Periodical of Farm Products Processing. 5: 90-91. (2011)
XIE Z, LIU Q, LIANG Z, et al (2013) https://doi.org/10.1155/2013/918406
Jiang Jiwu. Handbook of plant active ingredients( People's Medical Publishing House,1986).
VIEIRA A J, BESERRA F P, SOUZA MC, et al (2018) https://doi.org/10.1016/j.cbi.2018.02.007
Fan Yuhu, Zhou Gang, Zhang Lu,Chen Xiaoyan, Li Cuiping. Chinese Journal of Experimental Traditional Medical Formulae, 16: 41-44 (2010)
Xiaojiang Tang, Huarong Huang. China Journal of Chinese Materia Medica, 5: 46-49. (2003)
Qinghan Tang, Fei Xu, Xuchao Wei, Jingyue Gu, Pengli Qiao, XueminZhu,et al (2023) https://doi.org/10.1016/j.ejps.2023.106401
Wei jie, Sun bin, Lizhi Wang, Guangjun Zhang. SHANDONG CHEMICAL INDUSTRY, 43: 32-34. (2014)
CC Zhong, MH Wu, PH Yu, F Li, Y Zhang, ZG Ma, CJ Xie, H Cao (2021) DOI:10.19540/j.cnki.cjcmm.20210622.101.
Fansheng Kong, Zhendong Ding, Kai Zhang, Weijie Duan, Yaru Qin, Zhipeng Su, Yongguang Bi (2020) https://doi.org/10.1016/j.jep.2020.113178.
W Sun, S Wu, P Zhu, S Deng, X Zhou, X Liu, J Zhao, W Zhang, X Gao, X Chen(2022) https://doi.org/10.1080/00032719.2022.2075006
NWOSU L C, EDO G I, ÖZGÖR E (2022) https://doi.org/10.1016/j.fbio.2022.101551.
JALALVAND A R (2021) https://doi.org/10.1016/j.ijbiomac.2021.03.184
Yipeng Huang, Zhenwei Wu, RihuiSu, Guihua Ruan, Fuyou Du, Gongke Li (2016) https://doi.org/10.1016/j.jchromb.2015.12.050
COZZOLINO D, POWER A, CHAPMAN J (2019) https://doi.org/10.1007/s12161-019-01605-5
Feng, CM., Gao, YL., Liu, JX., Zheng, CH., Li, SJ., Wang, D. (2016). https://doi.org/10.1007/978-3-319-42294-7_33
SALEM N, HUSSEIN S (2019) https://doi.org/10.1016/j.procs.2019.12.111
MEHMOOD T, AHMED B (2016) https://doi.org/10.1002/cem.2762
Afanador N L, Tran T N. Buydens L M C (2013) https://doi.org/10.1016/j.aca.2013.01.004

Supplementary Table S1 is not available with this version

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

The portable GC-MS and HS/SPME-GC×GC-TOF MS-Based Analysis of the Difference of Citri Grandis Exocarpium from Different Regions

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

Instruments and Chemicals

Samples and Sample Preparation

Portable GC- MS Conditions

GC×GC-TOF MS Conditions

Data Processing

Retention Index Determination

Results and Discussion

Results of portable GC-MS

Results of GC×GC-TOF MS

Chemometric Analysis

Conclusions

Declarations

References

Supplementary Tables

Additional Declarations

Status:

Version 1