A Comparison of Volatile Organic Compounds in Puerariae Lobatae Radix and Puerariae Thomsonii Radix Using Gas Chromatography–Ion Migration Spectrometry

Abstract

Puerariae Radix is one of the most widely used ancient traditional Chinese medicines and is also consumed as food, which has rich edible and medicinal value. Puerariae Radix can be divided into Puerariae Lobatae Radix (PL) and Puerariae Thomsonii Radix (PT). These two medicinal materials are very similar, and they are often mixed up or misused. In this study, gas chromatography–ion migration spectrometry (GC-IMS) was used to analyze the volatile organic compounds (VOCs) of PL and PT, and the differences in VOCs were analyzed using fingerprint, principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA). The results showed that a total of 173 VOCs were obtained from PL and PT, and 149 were qualitatively identified, including 38 aldehydes, 22 alcohols, 22 ketones, 19 esters, 13 esters, 10 acids, 10 pyrazines, 6 terpenes, 4 furans, and 2 pyridines. The characteristic VOCs of PL and PT were clarified by constructing GC-IMS fingerprints. PL and PT can be effectively distinguished, and five characteristic VOCs were screened using PCA and OPLS-DA analysis methods. This study identified and evaluated the types and differences in VOCs in PL and PT. The established method is simple, rapid, accurate, and sensitive, and it provides theoretical guidance for the identification, tracing, and quality evaluation of PL and PT.

1. Introduction

Puerariae Lobatae Radix (PL) and Puerariae Thomsonii Radix (PT) are traditional medicinal materials. They are included in the “List of Items Both Food and Drugs” by the Ministry of Health of China, detailing their widespread use in medicine, health products, food, and so on [1,2]. In order to improve their taste, efficacy, and convenience, they are usually eaten in powder form. Although China has formulated systems and regulations for the application of powdered traditional Chinese medicine and food, the application of powdered Chinese medicine faces obstacles. Furthermore, powdered Chinese medicine does not display the obvious characteristics of conventional Chinese medicine, so it is relatively difficult to identify. The authenticity of the powder of traditional Chinese medicine directly affects the safety and efficacy of traditional Chinese medicine, so the scientific identification of the powder of Chinese medicine is very important. PL and PT are derived from the dried roots of leguminous plants Pueraria lobata (Willd.) Ohwi and Puerariathomsonii Benth., respectively. They can release muscles and subside fever, encourage the production of body fluids, induce eruptions, and elevate spleen yang to arrest diarrhea [3], and they both contain flavonoids, starch, dietary fiber, and a variety of trace elements and other components. Flavonoids such as puerarin, daidzin, and daidzein have significant effects on improving microcirculation and lowering blood pressure [4]. Dietary fibers such as cellulose and lignin have anti-cancer effects and regulate blood sugar [5]. Starch is the main component of PL and PT, which contains trace isoflavone compounds, which are rich in calcium, phosphorus, potassium, iron, zinc, and other mineral elements essential for the human body [6] and is often used as raw material for new health food [7]. Although PL and PT contain similar components, the content of pueraria is greatly different due to the influence of the growing environment and variety. The 2020 edition of the Chinese Pharmacopoeia stipulates that the contents of puerarin in PL and PT are not less than 2.4% and 0.3%, respectively. Puerarin is a special isoflavone compound in PL and PT that can be used to treat cardiovascular and cerebrovascular diseases [8], diabetes and complications of diabetes [9], osteonecrosis [10], Parkinson’s disease [11], Alzheimer’s disease [12], endometriosis [13], cancer [14], etc.

Most of the original plants of PL are wild, while most of the original plants of PT are cultivated. At present, there are many cultivated varieties of PL in various places, and the sources of artificially cultivated varieties are complex, resulting in an uneven quality and yield of medicinal materials. In addition, low-cost PT and PL are often mixed and sold to obtain higher profits [15], which seriously affects the safety and effectiveness of drugs [16]. At present, the identification studies of PL and PT mostly adopt trait identification, microscopic identification, high-performance liquid fingerprints [17], gene sequencing [18], etc. Although they are provided more choices for identification, there are also some limitations, such as the character identification is subjective, the microscopic identification is not specific, the HPLC identification operation is cumbersome and time-consuming, and the gene sequencing technology is relatively difficult. Therefore, an efficient, rapid, and intuitive method is urgently needed for the analysis and identification of PL and PT.

Volatile organic compounds (VOCs) are important indicators for the identification and quality evaluation of traditional Chinese medicine. At present, gas chromatography–ion migration spectrometry (GC-IMS) is a new analysis technology for VOC detection that has been widely used in the separation, identification, and quantification of VOCs. It has a high separation capability for GC and fast response, high resolution, and high sensitivity for IMS [19]. In the process of substance analysis, the sample requires limited pretreatment to retain the sample’s smell to the maximum extent, and via signal integration in the spectrum, the visualization of flavor substances can be realized, and the types of VOCs in the sample can be rapidly analyzed [20,21,22,23]. It has been widely used in the analysis of food flavor [24,25,26,27,28,29]. He Jia used HS-GC-IMS technology to analyze VOCs in Ophiopogon from different producing areas, and these characteristics could effectively identify Ophiopogon from Sichuan and Zhejiang, as well as the two traditional main producing areas of Cixi City and Sanmen County, providing a scientific basis for the identification of Ophiopogon origin [30]. Zhen–Zhou Wang used GC-IMS technology to identify Ginseng Radix ET Rhizoma Rubra, Panacis Quniquefolii Radix, and Ginseng, and realized the origin traceability of Ginseng via a gas-phase ion migration system combined with data analysis software, providing reference for the identification of Ginseng and clinical use accuracy [31]. Fengliu Guo used GC-IMS to identify Fritillariae Cirrhosae Bulbus and other Fritillaria, providing new ideas and data support for the rapid authenticity identification of Fritillariae Cirrhosae Bulbus [32].

At present, there are almost no reports on the identification of PL and PT using GC-IMS. Therefore, in this study, we analyzed and identified the VOCs of PL and PT using GC-IMS technology and established the fingerprints of VOCs. Additionally, the differences between the VOCs of PL and PT were explored by combining principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), which provides technical support for VOCs’ rapid analysis and the identification of variety for PL and PT.

2. Materials and Methods

2.1. Materials

The powders of Puerariae Lobatae Radix were purchased from the National Institutes for Food and Drug Control, Beijing, China (No. 121551-201805, named PL); Puerariae Lobatae Radix is the dried root of Pueraria lobata (Willd.) Ohwi, commonly known as “ye ge”.

The powders of Puerariae Thomsonii Radix were purchased from Yifeng Pharmacy, Changsha, China (No. 220801, named PT). Puerariae Thomsonii Radix is the dried root of Pueraria thomsonii Benth., commonly known as “fen ge”.

2.2. Analysis Using GC–IMS

PL and PT were ground into powders. After passing through a 65-mesh sieve, 1 g of the powders was accurately weighed into a 20 mL headspace bottle. Then, they were incubated at 80 °C for 20 min, and the samples were injected. Three parallel samples were included for each group.

Headspace sampling conditions: the sample incubation temperature was 80 °C, the incubation speed was 500 r/min, the incubation time was 20 min, the injection volume was 500 µL, the needle temperature was 85 °C, and splitless injection was performed.

Chromatographic conditions: FlavourSpec^® gas-phase ion mobility spectrometer (G.A.S., Dortmund, Germany); CTC-PAL 3 static headspace automatic sampling device (CTC Analytics AG, Zwingen, Switzerland); 20 mL headspace bottle (Shandong Haineng Scientific Instrument Co., Ltd., Jinan, China); the chromatographic column was MXT-WAX capillary chromatography column (15 m × 0.53 mm × 1 μm, Restek Company of the United States, Bellefonte, PA, USA); temperature: 60 °C; carrier gas: high-purity nitrogen (purity ≥ 99.999%); programmed pressure increase: initial flow rate of 2.00 mL/min maintained for 2 min, linearly increased to 10.00 mL/min, linearly increased to 100.00 mL/min within 10 min, and maintained for 40 min. Chromatography running time: 60 min; injection port temperature: 80 °C.

IMS conditions: drift tube temperature was 45 °C, drift gas was N_2, and drift gas velocity was 75 mL/min.

2.3. Data Analysis

The software configured by GAS company and the built-in NIST gas chromatography retention index database and IMS migration time database were used to characterize the VOCs in the sample. The plug-in of VOCal data processing software, such as Reporter, Gallery Plot, and Dynamic PCA, was used to generate the 3D spectrum, 2D spectrum, difference spectrum, fingerprints, and PCA map of VOCs, respectively, to compare VOCs between samples. SIMCA software was used for OPLS-DA to calculate the projected importance of variables (VIP).

3. Results

3.1. GC-IMS Analysis of VOCs in PT and PL

GC-IMS was used to analyze the VOCs of PL and PT, and a three-dimensional spectrum was obtained, in which the X axis represents the ion drift time, the Y axis represents the retention time of the gas chromatograph, and the Z axis represents the peak intensity used for quantification, as shown in Figure 1. We can observe the difference in VOCs in the PL and PT samples. To facilitate observation, the following two-dimensional top view is used for comparison. As shown in Figure 2, the horizontal coordinate is ion drift time, the red vertical line at 1.0 is the normalized reactive ion peak (RIP peak), and the vertical coordinate is the retention time of gas chromatography. Each point on either side of the RIP represents a volatile organic compound. The color represents the peak strength of the substance. From blue to red, the darker the color, the greater the peak intensity. There are certain differences in VOCs in PT and PL samples, as can be seen in Figure 2.

In order to further visually compare the differences in VOCs, the spectra of PL samples were selected as the reference, and the spectra of the PT samples were deducted from the reference ratio to obtain the difference comparison diagram of different samples, as shown in Figure 3. If the two volatile substances are consistent, the deducted background is white, while red means that the concentration of the substance is higher than the reference, and blue means that the concentration of the substance is lower than the reference. It is easier to distinguish the difference between two samples using contrast atlases.

3.2. Fingerprints of VOCs in PL and PT

The construction of characteristic flavor fingerprints of PL and PT can provide an effective means for quality evaluation the and identification of the variety. To find the exact difference in VOCs between samples of PL and PT, the GC-IMS results of the two samples were further analyzed using the Gallery Plot plug-in, and the VOCs detected in each sample were selected for a fingerprint comparison (Figure 4). Each row in the diagram represents all of the selected signal peaks in the sample, and each column represents the signal peaks of the same volatile organic compound in a different sample. Some substances are followed by _M, D, and T, which are monomers, dimers, and trimers of the same substance, respectively. The numbers are unidentified peaks, and the darker the color of each bright spot, the greater the compound content. The complete volatile information for each sample and the differences in volatiles between the samples are outlined in Figure 4.

A comprehensive analysis of Figure 4 showed that the contents of VOCs such as 3-methylbutyraldehyde, 1-octene-3-ol, e-2-hexene-1-ol, isovalerate leaf alcohol ester, butyl acetate, 2,3-dimethyl-5-ethylpyrazine, 2-hexanone, and 1,8-cineulin were high in PL. The contents of VOCs such as delta-decalactone, citronellal, Z-6-nonenal, (E, E)-2, 4-decadienal, camphor, α-terpinol, and α-pinene were high in PT.

3.3. Identification of VOCs in Different PL and PT

A total of 173 VOCs were detected from PL and PT using GC-IMS analysis, as shown in Table 1. A total of 149 VOCs (monomers, dimers, or trimers) were identified by comparing the NIST2020 vapor phase retention index database built into the practical Vocal software with the IMS migration time database of G.A.S. Among them, there were 38 aldehydes, 22 alcohols, 22 ketones, 19 esters, 13 terpenes, 10 acids, 10 pyrazines, 6 terpenes, 4 furans, and 2 pyridines. In addition, the peak areas of PL and PT show significant differences in the content of VOCs, such as 3-methylbutyraldehyde, 1-octene-3-ol, e-2-hexene-1-ol, isovalerate leaf alcohol ester, butyl acetate, 2,3-dimethyl-5-ethylpyrazine, 2-hexanone, 1,8-cineole, delta-decenolactone, citronellal, Z-6-nonenal, (E, E)-2,4-decenal, camphor, alpha-terpinol, α-pinene, etc.

3.4. Chemometrics Analysis

3.4.1. PCA of VOCs in Samples

Principal component analysis (PCA) is a multi-variable data analysis tool that converts and reduces the dimensions of the information collected by the sensor to obtain the most important factor with the largest contribution rate, and it reflects the difference in the test samples on the PCA diagram [33]. In order to distinguish the difference between PL and PT, PCA was performed on all samples of PL and PT. As shown in Figure 5, there are clear differences between PL and PT. If the distance between the samples is close then the difference is small. If the distance is long then the difference is obvious. As can be seen from Figure 5, the distance between PL and PT is very long, which means that the VOC contents of them are significantly different.

3.4.2. Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA)

PCA focuses on describing the classification trend of samples. Unlike PCA analysis, OPLS-DA is a supervised analysis that can statistically analyze complex data dimensionality reduction, visualize the data, and then build a model to predict the data. In order to further explore and judge the differences and accuracy of VOCs in PL and PT, we further evaluated the feasibility of GC-IMS technology for rapid authenticity identification. The peak volume of 149 VOCs with large differences in selected content was taken as a variable, and the OPLS-DA scores were obtained with partial least squares discriminant analysis. The results are shown in Figure 6, which are consistent with the results of PCA, and different Pueraria samples are clearly distinguished. According to the data processed by SIMCA software, the model can relatively accurately summarize, explain, and predict; the VOC composition of PL and PT is identifiable according to this study; and different varieties can be distinguished to clarify the differences between PL and PT. Figure 6 shows the verification of the OPLS-DA model by using permutation testing. It can be seen from Figure 7 that R² intersects the vertical axis (0, 0.842), Q² intersects the vertical axis (0, 0.0186), and the slope of the two regression lines is large. It was confirmed that the model could be used to study the classification and discrimination of VOCs in two different varieties of PL and PT via verification.

The variable importance projection (VIP) of the OPLS-DA model with different peak volumes of VOCs is highlighted in Figure 8. The larger the VIP value, the more significant the difference. By observing the VIP value, potential markers can be analyzed. The results showed that there were five VOCs with a VIP value > 1 and p < 0.05, including 2-methyl-3-furanthiol, 1-propanol, ethyl acetate, gamma-butyrolactone-M, and methyl hexanoate-D. The above five VOCs are important indicators for the classification and identification of PL and PT, and they can provide a reference for the rapid authenticity identification of the two pieces.

4. Discussion

In this study, we used GC-IMS for the first time to analyze and identify VOCs in PL and PT. A total of 173 VOCs were detected, and 149 of them were identified, mainly including aldehydes, alcohols, ketones, lipids, and other components, by rapidly comparing the types and contents of VOCs in PL and PT by observing the size and color changes in the sample points representing compound information. By constructing GC-IMS fingerprints, it was shown that the VOCs of PL and PT have extremely high similarity, but the content differences between the groups are obvious. Using principal component analysis and partial least squares discrimination, the distribution of VOCs of PL and PT samples occupies a relatively independent space in the diagram, which can be easily distinguished. Then, the VIP value and p-value were used to identify five different markers of PL and PT, which provided a scientific basis for rapid identification. Compared with traditional analytical methods such as enthrone colorimetry and high-performance liquid chromatography for the identification of PL and PT, GC-IMS technology has great room for the development of identifying the origin of Chinese medicinal materials and counterfeit and shoddy materials. Not only can the composition differences in VOCs of Chinese medicinal materials be analyzed, but samples with similar compositions of VOCs can also be accurately classified according to the content differences in characteristic volatile substances. The experimental results of this study show that GC-IMS can effectively analyze and identify the VOCs in PL and PT, detect the difference between PL and PT, and reach scientific judgments. Moreover, this method requires less sample dosage and is simple in the process of drug pretreatment, which has great application potential, and it provides a scientific basis for the research and development of PT and PL identification in the future.

5. Conclusions

The rapid identification of traditional Chinese medicines based on “odor” information is an important part of the traditional identification method of traditional Chinese medicines [34]. For example, Houttuynia Cordata has a strong fishy smell, and Xiangjiapi has a special fragrance. Experienced pharmacists can directly and quickly identify authenticity and even evaluate quality based on the unique smell and odor thickness of traditional Chinese medicine. With its fast and convenient advantages, up until now, this method has spread as a traditional identification approach. However, for some decoction pieces with insufficient odor information or even weak odor, it may be difficult to quickly realize the identification of traditional Chinese medicine using the traditional “sniffing” method. As a trace detection technology for VOCs, GC-IMS technology cleverly combines the advantages of the rapid identification of traditional traits with the accuracy and quantification of modern instrument analysis. It can be used to quickly and accurately detect information on VOCs in traditional Chinese medicine to allow the inheritance and development of traditional skills. At present, this technology is widely used in food, agriculture, medicine, and other fields. It is mainly used for the rapid detection and characterization of VOCs in samples, as well as the comparative analysis of the differences in VOCs in different samples, and many studies have shown that GC-IMS technology can be used for the identification or classification of two/multiple types of samples [35,36].

Author Contributions

Conceptualization, Y.M. and L.Z. (Lingfeng Zhu); methodology, Y.M. and L.Z. (Lingfeng Zhu); software, F.F. and L.Z. (Lijun Zhu); validation, J.C. and J.L.; formal analysis, Y.M. and L.Z. (Lingfeng Zhu); investigation, L.Z. (Lijun Zhu); resources, J.C.; data curation, J.L.; writing—original draft preparation, Y.M. and L.Z. (Lingfeng Zhu); writing—review and editing, C.L.; visualization, Y.M. and L.Z. (Lingfeng Zhu); supervision, F.F.; project administration, D.H.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

This published article includes all data generated or analyzed during this study.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Figures and Table

Figure 1. GC-IMS three-dimensional spectrum of PL and PT.

Figure 2. GC-IMS two-dimensional spectrum of PL and PT.

Figure 3. Comparison of GC-IMS differences between PL and PT.

Figure 4. Fingerprints of VOCs in PL and PT.

Figure 5. PCA analysis of PL and PT.

Figure 6. OPLS-DA analysis of VOCs in PT and PL.

Figure 7. Permutation test results of VOCs in PT and PL.

Figure 8. VIP value of the characteristic variables.

Table 1

Results of VOC analysis of PL and PT.

Count	Compound	CAS	MolecularFormula	RI	Rt (s)	Dt (RIPrel)	Comment
1	delta-Decalactone	C705862	C10H18O2	1589.8	1686.545	1.43476
2	Decanoic acid	C334485	C10H20O2	1516.9	1544.131	1.56651
3	(E)-2-Undecenal	C53448070	C11H20O	1413.4	1341.792	1.56651
4	(Z)-3-Hexenyl isovalerate	C35154451	C11H20O2	1238.1	999.416	1.45514
5	2-Heptylfuran	C3777717	C11H18O	1215.1	954.414	1.40119
6	beta-Citronellol	C106229	C10H20O	1205.2	935.127	1.35083
7	Decanal	C112312	C10H20O	1206.2	936.964	1.55586
8	2,6-Nonadien-1-ol	C7786449	C9H16O	1174.2	874.511	1.3814
9	Ethyl octanoate	C106321	C10H20O2	1169.3	864.971	1.47097
10	(E)-2-Nonenal	C18829566	C9H16O	1151.6	830.265	1.40739	Monomer
11	(E)-2-Nonenal	C18829566	C9H16O	1152.2	831.527	1.96372	Dimer
12	Camphor	C76222	C10H16O	1127.7	783.568	1.34735	Monomer
13	Camphor	C76222	C10H16O	1128.0	784.199	1.84539	Dimer
14	Phenylethanol	C60128	C8H10O	1116.4	761.482	1.29436
15	Nonanal	C124196	C9H18O	1105.5	740.324	1.49251	Monomer
16	Nonanal	C124196	C9H18O	1105.5	740.324	1.93345	Dimer
17	Linalool	C78706	C10H18O	1099.8	729.114	1.20504
18	2-Ethyl-3,5-dimethylpyrazine	C13925070	C8H12N2	1090.6	710.618	1.23206	Monomer
19	(Z)-3-Hexenyl propionate	C33467742	C9H16O2	1091.9	713.358	1.36119
20	2-Ethyl-3,5-dimethylpyrazine	C13925070	C8H12N2	1090.6	710.618	1.73805	Dimer
21	1-Octanol	C111875	C8H18O	1087.9	705.137	1.47079
22	2,3-Dimethyl-5-ethylpyrazine	C15707343	C8H12N2	1073.2	674.995	1.24107	Monomer
23	2,3-Dimethyl-5-ethylpyrazine	C15707343	C8H12N2	1073.2	674.995	1.73655	Dimer
24	delta-Hexalactone	C823223	C6H10O2	1083.6	696.232	1.15249
25	(E)-2-Octenal	C2548870	C8H14O	1062.8	653.758	1.33116	Monomer
26	(E)-2-Octenal	C2548870	C8H14O	1062.8	653.758	1.81162	Dimer
27	Acetophenone	C98862	C8H8O	1061.1	650.333	1.19153
28	2,3-Dihydro-4-hydroxy-2,5-dimethyl-3-furanone	C3658773	C6H8O3	1054.9	637.628	1.19698
29	2-Ethyl-1-hexanol	C104767	C8H18O	1051.3	630.284	1.4224
30	2-Phenylacetaldehyde	C122781	C8H8O	1036.2	599.333	1.52577	Monomer
31	2-Phenylacetaldehyde	C122781	C8H8O	1036.5	599.858	1.25729	Dimer
32	Methyl heptanoate	C106730	C8H16O2	1030.3	587.268	1.37645
33	1.8-Cineole	C470826	C10H18O	1023.9	574.043	1.28893	Monomer
34	1.8-Cineole	C470826	C10H18O	1023.9	574.043	1.72454	Dimer
35	Trimethylpyrazine	C14667551	C7H10N2	1016.8	559.507	1.16495
36	(E,E)-2,4-Heptadienal	C4313035	C7H10O	1009.7	544.971	1.19845	Monomer
37	(E,E)-2,4-Heptadienal	C4313035	C7H10O	1009.9	545.49	1.61061	Dimer
38	Hexanoic acid	C142621	C6H12O2	1003.3	531.992	1.31071	Monomer
39	Hexanoic acid	C142621	C6H12O2	1003.3	531.992	1.64244	Dimer
40	2-Pentylfuran	C3777693	C9H14O	1006.9	539.26	1.24872
41	2,4-Heptadienal	C5910850	C7H10O	997.8	520.571	1.20683	Monomer
42	2,4-Heptadienal	C5910850	C7H10O	998.0	521.09	1.61899	Dimer
43	2,4,6-Trimethylpyridine	C108758	C8H11N	995.5	516.418	1.15322
44	6-Methyl-5-hepten-2-one	C110930	C8H14O	991.0	508.935	1.17184
45	2-Octanol	C123966	C8H18O	988.4	504.689	1.44167
46	1-Octen-3-one	C4312996	C8H14O	981.4	493.365	1.27916	Monomer
47	1-Octen-3-ol	C3391864	C8H16O	971.4	477.086	1.15651
48	4,5-Dihydro-3(2H)-thiophenone	C1003049	C4H6OS	962.1	461.87	1.19024
49	Benzaldehyde	C100527	C7H6O	947.6	438.16	1.14731	Monomer
50	Benzaldehyde	C100527	C7H6O	947.8	438.514	1.46313	Dimer
51	(E)-2-Heptenal	C18829555	C7H12O	952.2	445.591	1.66244	Dimer
52	3-Methylbutyl propanoate	C105680	C8H16O2	953.9	448.422	1.84641
53	Dihydro-5-methyl-2(3H)-furanone	C108292	C5H8O2	940.5	426.482	1.12278
54	Methyl hexanoate	C106707	C7H14O2	928.4	406.869	1.28653	Monomer
55	Methyl hexanoate	C106707	C7H14O2	928.8	407.425	1.67625	Dimer
56	2,3-Dimethylpyrazine	C5910894	C6H8N2	925.4	401.866	1.12795
57	2,5-Dimethylpyrazine	C123320	C6H8N2	915.3	385.36	1.11464
58	Ethylpyrazine	C13925003	C6H8N2	919.9	392.988	1.15016
59	2,6-Dimethylpyrazine	C108509	C6H8N2	915.2	385.167	1.14652
60	2-Butoxyethanol	C111762	C6H14O2	907.1	372.015	1.1975
61	(E,E)-2,4-Hexadienal	C142836	C6H8O	905.1	368.815	1.12104
62	Heptanal	C111717	C7H14O	901.9	363.483	1.35407	Monomer
63	Heptanal	C111717	C7H14O	901.9	363.483	1.6939	Dimer
64	2-Acetylfuran	C1192627	C6H6O2	903.6	366.327	1.44145
65	Pentanoic acid	C109524	C5H10O2	897.5	356.374	1.23876	Monomer
66	Pentanoic acid	C109524	C5H10O2	897.3	356.018	1.51063	Dimer
67	2-Heptanone	C110430	C7H14O	892.9	348.909	1.26061	Monomer
68	Cyclohexanone	C108941	C6H10O	892.7	348.553	1.46815
69	2-Heptanone	C110430	C7H14O	893.4	349.62	1.62351	Dimer
70	(Z)-4-Heptenal	C6728310	C7H12O	898.6	358.151	1.14288
71	gamma-Butyrolactone	C96480	C4H6O2	887.8	343.221	1.08341	Monomer
72	gamma-Butyrolactone	C96480	C4H6O2	887.8	343.221	1.29945	Dimer
73	1-Hexanol	C111273	C6H14O	878.4	333.623	1.32615	Monomer
74	1-Hexanol	C111273	C6H14O	878.8	333.979	1.64535	Dimer
75	2-Methyl-3-furanthiol	C28588741	C5H6OS	866.6	321.537	1.13803
76	Isovaleric acid	C503742	C5H10O2	857.6	312.295	1.2157
77	(E)-2-Hexen-1-ol	C928950	C6H12O	862.5	317.271	1.5434
78	(E)-2-Hexenal	C6728263	C6H10O	843.7	298.076	1.17808	Monomer
79	(E)-2-Hexenal	C6728263	C6H10O	844.0	298.431	1.50942	Dimer
80	Ethyl 2-methylbutanoate	C7452791	C7H14O2	849.3	303.763	1.22784
81	2-Methyl-2-pentenal	C623369	C6H10O	826.7	280.657	1.16109	Monomer
82	2-Methyl-2-pentenal	C623369	C6H10O	826.7	280.657	1.49486	Dimer
83	Furfural	C98011	C5H4O2	819.7	273.548	1.08098	Monomer
84	Furfural	C98011	C5H4O2	819.7	273.548	1.33101	Dimer
85	2-Hexanone	C591786	C6H12O	809.0	262.528	1.19143	Monomer
86	Dihydro-2-methyl-3(2H)furanone	C3188009	C5H8O2	807.9	261.462	1.42567
87	2-Hexanone	C591786	C6H12O	809.7	263.239	1.50092	Dimer
88	Butyl acetate	C123864	C6H12O2	805.1	258.618	1.62836
89	4-Methyl-3-penten-2-one	C141797	C6H10O	796.4	249.731	1.44267
90	1-Pentanol	C71410	C5H12O	767.6	225.559	1.51549
91	3-Methyl-2-butenal	C107868	C5H8O	775.7	231.246	1.35164
92	(E)-2-Pentenal	C1576870	C5H8O	745.3	209.918	1.35164
93	3-Hydroxy-2-butanone	C513860	C4H8O2	728.2	197.93	1.07394	Monomer
94	1-Penten-3-one	C1629589	C5H8O	705.6	182.083	1.32811
95	2-Pentanone	C107879	C5H10O	692.3	172.762	1.3669
96	Propanoic acid	C79094	C3H6O2	701.1	178.976	1.27192
97	1-Butanol	C71363	C4H10O	662.0	160.022	1.37359
98	2,3-Pentadione	C600146	C5H8O2	651.4	155.983	1.29466
99	3-Methylbutanal	C590863	C5H10O	638.4	151.011	1.19835
100	Acetic acid	C64197	C2H4O2	634.4	149.458	1.15019
101	Butanal	C123728	C4H8O	620.6	144.175	1.1114
102	2-Butanone	C78933	C4H8O	589.7	132.368	1.24517
103	Acetone	C67641	C3H6O	525.5	107.821	1.1221
104	1-Propanol	C71238	C3H8O	571.8	125.532	1.1114
105	Ethyl acetate	C141786	C4H8O2	619.7	143.865	1.3348
106	2-Butanol	C78922	C4H10O	601.9	137.029	1.33747
107	2-Methylpropanoic acid	C79312	C4H8O2	788.3	241.431	1.36155
108	3-Hydroxy-2-butanone	C513860	C4H8O2	729.9	199.173	1.32944	Dimer
109	Pentanal	C110623	C5H10O	712.6	187.055	1.20638
110	Propanal	C123386	C3H6O	517.3	104.714	1.15822
111	3-Methyl-1-pentanol	C589355	C6H14O	853.3	307.925	1.30403
112	2,6-Dimethylpyridine	C108485	C7H9N	884.9	340.195	1.45516
113	gamma-Decalactone	C706149	C10H18O2	2242.0	2960.963	1.45582
114	beta-Caryophyllene	C87445	C15H24	1960.3	2410.574	1.45774
115	(E,E)-alpha-Farnesene	C502614	C15H24	1837.5	2170.534	1.44026
116	Geranyl acetate	C105873	C12H20O2	1440.1	1394.063	1.22263
117	(E,E)-2,4-Decadienal	C25152845	C10H16O	1317.5	1154.45	1.39648
118	Thymol	C89838	C10H14O	1294.4	1109.284	1.24831
119	Geraniol	C106241	C10H18O	1268.2	1058.254	1.21258
120	Nerol	C106252	C10H18O	1228.2	980.031	1.22169
121	alpha-Terpineol	C98555	C10H18O	1186.1	897.749	1.21972	Monomer
122	alpha-Terpineol	C98555	C10H18O	1186.4	898.355	1.30566	Dimer
123	alpha-Terpineol	C98555	C10H18O	1186.4	898.355	1.78253	Trimer
124	4-Terpinenol	C562743	C10H18O	1170.9	868.05	1.21804
125	Citronelal	C106230	C10H18O	1158.8	844.413	1.21804
126	Methyl salicylate	C119368	C8H8O3	1174.9	875.93	1.1658
127	2,3-Diethyl-5-methylpyrazine	C18138040	C9H14N2	1138.3	804.41	1.27364
128	Linalool	C78706	C10H18O	1107.9	745.013	1.21804
129	(Z)-6-Nonenal	C2277192	C9H16O	1093.7	716.947	1.16944
130	Linalool oxide	C60047178	C10H18O2	1083.0	695.065	1.2611
131	2-Methylphenol	C95487	C7H8O	1067.1	662.538	1.11611
132	beta-Ocimene	C13877913	C10H16	1033.1	592.989	1.68091
133	Limonene	C138863	C10H16	1025.8	578.047	1.22338
134	Methyl 3-(methylthio)propionate	C13532188	C5H10O2S	1025.0	576.309	1.60088
135	Octanal	C124130	C8H16O	1010.9	547.468	1.42572
136	alpha-Terpinene	C99865	C10H16	1011.2	548.163	1.7398
137	(Z)-3-Hexenyl acetate	C3681718	C8H14O2	1009.1	543.875	1.78589
138	Dimethyl trisulfide	C3658808	C2H6S3	987.7	503.647	1.29904
139	1-Heptanol	C111706	C7H16O	985.3	499.658	1.40408	Monomer
140	1-Octen-3-one	C4312996	C8H14O	981.8	494.006	1.67918	Dimer
141	(E)-2-Heptenal	C18829555	C7H12O	952.9	446.797	1.25569	Monomer
142	beta-Pinene	C127913	C10H16	971.6	477.383	1.22734
143	1-Heptanol	C111706	C7H16O	985.5	499.99	1.77422	Dimer
144	3-Hepten-2-one	C1119444	C7H12O	935.4	418.205	1.204
145	alpha-Pinene	C80568	C10H16	939.9	425.519	1.30737	Monomer
146	alpha-Pinene	C80568	C10H16	940.3	426.184	1.68418	Dimer
147	2-Methylbutanoic acid	C116530	C5H10O2	870.2	325.194	1.22302
148	Isomenthone	C491076	C10H18O	1149.4	826.025	1.33262
149	Hexanal	C66251	C6H12O	799.2	252.51	1.56289

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