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Article

The Discrimination and Characterization of Volatile Organic Compounds in Different Areas of Zanthoxylum bungeanum Pericarps and Leaves by HS-GC-IMS and HS-SPME-GC-MS

by
Xinlong Wu
1,
Jiaxin Yin
1,
Hui Ding
1,
Wei Li
1,
Lifeng Han
2,3,
Wenzhi Yang
2,3,
Fangyi Li
1,2,3,
Xinbo Song
1,2,3,
Songtao Bie
1,2,3,
Xingchu Gong
4,
Heshui Yu
1,2,3,* and
Zheng Li
1,2,3
1
College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
2
State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
3
Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
4
Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
*
Author to whom correspondence should be addressed.
Foods 2022, 11(22), 3745; https://doi.org/10.3390/foods11223745
Submission received: 25 October 2022 / Revised: 15 November 2022 / Accepted: 18 November 2022 / Published: 21 November 2022
(This article belongs to the Special Issue Application of Gas Chromatography/Mass Spectrometry-Based Metabolomics for Food Authentication and Quality Assessment)
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Abstract

:
The pericarps of Zanthoxylum bungeanum (ZBP) and leaves of Zanthoxylum bungeanum (ZBL) are popular spices in China, and they have pharmacological activities as well. In this experiment, the volatile organic compounds (VOCs) of the pericarps of Zanthoxylum bungeanum in Sichuan (SJ) and its leaves (SJY) and the pericarps of Zanthoxylum bungeanum in Shaanxi (SHJ) and its leaves (SHJY) were analyzed by headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). The fingerprint of HS-GC-IMS and the heat maps of HS-SPME-GC-MS were established. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed. The results showed that a total of 95 components were identified, 62 components identified by HS-SPME-GC-MS and 40 components identified by HS-GC-IMS, of which 7 were the same. The analysis found that SJ and SHJ were obviously distinguished, while SJY and SHJY were not. There were considerably fewer VOCs in the leaves than in the pericarps. In the characterization of the VOCs of ZBL and ZBP, the flavor of ZBP was more acrid and stronger, while the flavor of ZBL was lighter and slightly acrid. Thirteen and eleven differential markers were identified by HS-GC-IMS and HS-SPME-GC-MS, respectively. This is helpful in distinguishing between SHJ and SJ, which contributes to their quality evaluation.

Graphical Abstract

1. Introduction

The pericarps of Zanthoxylum bungeanum (ZBP) are an important seasoning in China [1]. ZBP has a special flavor and is often used in Sichuan dishes. In addition to that, it is in great demand in traditional medicine [2]. Essential oils are its main active ingredients. It has anti-inflammatory [3], antiseptic [4] and other effects. Zanthoxylum bungeanum is grown in southwest China in Sichuan, Shaanxi, Yunnan and so on [5]. Its wide range of cultivation leads to different quality. It is necessary to differentiate ZBP from different regions. Aroma has an important influence on food evaluation results [6], and the volatile terpenoids are the main source of its aroma [7]. People have different opinions about ZBP from different producing areas: its color, taste, smell and even its medicinal effects. For the evaluation of the quality of pepper, experienced operators are often required. However, there is also subjectivity in judgment that affects the stability and accuracy of quality evaluation. Therefore, it is necessary to find a suitable approach to identifying it. Other than that, ZBL has special aroma. It is also used as a condiment and has some antioxidant [8] and antibacterial activity [9]. Currently, the study of volatile organic compounds (VOCs) of ZBP is abundant, but the study of VOCs of Leaves of Zanthoxylum bungeanum (ZBL) is less. It is interesting to characterize the VOCs of ZBL and investigate the similarities and differences of their composition with ZBP.
GC-MS is undoubtedly a good choice for the detection of VOCs, high sensitivity, accuracy and wide detection range, but it is a complex treatment of samples [10]. Solid-phase microextraction (SPME) has the advantages of good extraction effect, selectivity, environmental friendliness and convenience [11]. The SPME combination with GC-MS can make up for some of its shortcomings, mainly the complex preprocessing steps. It was reported that SPME-GC-MS combined with chemometrics could be used to analyze the quality of strong aroma base liquor at different grades [12].
Ion mobility spectrometry (IMS), as a high-sensitivity, fast detection method, was used in the military early [13]. GC has good separation performance. By coupling GC with IMS, HS-GC-IMS ushered in a new development in food [14], cosmetics [15] and medicine [16]. In HS-GC-IMS, VOCs form into ions when they pass through the ionization source, and the ions drift in a weak electric field at atmospheric pressure; depending on the differences in structure, mass, charge and volume of each ion, the ions are detected at different times, providing information on the type and concentration of the analyte [17]. In addition, HS-GC-IMS also has the advantages of high sensitivity and environmental friendliness and no sample pretreatment [18]. Previously, it has been reported that HS-GC-IMS can identify Pericarpium Citri Reticulatae and its counterfeits [19]. GC-MS tends to be qualitative and quantitative. HS-GC-IMS tends to identify samples and can also detect small odor molecules that are not detected by GC-MS. GC-MS and HS-GC-IMS each have their own advantages. The combination of these two approaches can achieve better evaluation results [20]. It has reported that the Liuyang Douchi was determined by HS-GC-IMS and HS-SPME-GC-MS [21].
Maoxian and Hancheng are historical pepper production areas in China. Maoxian pepper has been appraised as one of China’s national geographical indication products. The purpose of this study was to characterize the VOCs of ZBP of Hancheng (SHJ), ZBL of Hancheng (SHJY), ZBP of Maoxian (SJ) and ZBL of Maoxian (SJY) based on HS-GC-IMS and HS-SPME-GC-MS and investigate their differential components. Laying the foundation for the study of its active ingredients, the fingerprint of HS-GC-IMS data and the heat map of HS-SPME-GC-MS data were established. The PCA and OPLS-DA were used to distinguish the samples and find the differences in VOCs between them. Differential marker screening of ZBP and ZBL by one-way ANOVA and variable importance in projection (VIP) evaluation for the differential marker were calculated.

2. Materials and Methods

2.1. Materials and Chemicals

In this study, ZBP and ZBL taken from Maoxian, Sichuan, China (longitude 102.22 east latitude 31.90 north), and Hancheng, Shaanxi, China (east longitude 110.43 north latitude 35.48) (Figure S1) were selected (for detailed information, refer to Table S1). Six batches were selected from each producing area to pick leaves and fruits in July. The N-ketone C4-C9 standard mix purchased from Sinopharm Chemical Co., Ltd. (Shanghai, China) was used to calculate retention index (RI) of HS-GC-IMS, N-alkane C8-C20 standard mix for GC–MS was purchased from Sigma-Aldrich Chemical Co., Ltd. (Saint Louis, MO, USA). The standard compounds are as follows: linalool (CAS registry No. 78-70-6, purity ≥ 98%), linalyl acetate (CAS registry No. 115-95-7, purity ≥ 98%), l-menthol (CAS registry No. 2216-51-5, purity ≥ 98%), myrcene (CAS registry No. 123-35-3, purity ≥ 98%,) all purchased from Nature Standard (Shanghai, China).

2.2. HS-GC-IMS Analysis

The GC-IMS system (FlavourSpec®, Gesellschaft für Analytische Sensorsysteme mbH, Dortmund, Germany) with an autosampler (CTC Analytics AG, Zwingen, Switzerland) was used to detect the VOCs of that ZBP and ZBL in Sichuan and Shaanxi. Weigh three samples from each batch for experiment. Weigh 0.01 g of each batch of samples and add them into a 20 mL headspace glass sampling bottle (Zhejiang HAMAG technology, Ningbo, China). Set the relevant parameters of the instrument as follows.
Conditions of HS-GC-IMS: incubation temperature was 75 °C, the incubation time was 20 min, the oscillation rate was 500 rpm, headspace injection was used, the injection temperature was 85 °C, the injection volume was 300 µL, and the carrier gas was N2 (purity ≥ 99.999%). The column was FS-SE-54-CB-1 capillary column (15 m × 0.53 mm ID, 1 µm, Beijing, China), the column temperature was 60 °C, the programmed flow was as follows: 2 mL/min initially for 2 min, increased to 10 mL/min over 10 min, increased to 150 mL/min over 20 min, and keep the flow rate of 150 mL/min over 30 min. The drift tube is 98 mm long, the temperature is 45 °C, the voltage is 500 kV, and the drift gas is nitrogen (purity ≥ 99.999%). The gas flow of drift tube was 150 mL/min.

2.3. SPME Optimization

SPME fiber (Supelco, Bellefonte, Penn.) was installed on a MultiPurpose sampler (Gerstel, GER) and combined with 7890B-7000D triple quadrupole gas chromatography mass spectrometry (Agilent Technologies, Palo Alto, CA, USA) to detect VOCs that ZBP and ZBL. Weigh three samples from each batch for experiment. weigh 0.01g for 20 mL headspace vials for each batch of samples. Incubation temperature was 75 °C, incubation time was 5 min, extraction time was 40 min, vial penetration was 21.00 mm, injection penetration was 54.00 mm desorption time was 5 min, desorption temperature was 250 °C.

2.4. Conditions of GC-MS

The chromatographic column was HP-5MS phenyl methyl siloxane (30 m × 0.25 mm × 0.25 µm, Agilent Technologies, Palo Alto, CA, USA) elastic quartz capillary column; The initial temperature was 50 °C for 2 min, then 20 °C/min to 80 °C, and 2 °C/min to 155 °C, finally 30 °C/min to 220 °C and held for 5 min; The carrier gas was Helium (purity 99.999%) with a flow rate of 1.0 mL/min; The split ratio was 1:1; The injection port temperature is 250 °C. The conditions of MS were set as follow: the ion source temperature was 230 °C, ionization energy was 70 eV with a scan range of m/z 30ߝ650.

2.5. Statistical Analysis

Weighed an equal amount of sample as QC, each take 6 QC measured by HS-GC-IMS and HS-SPME-GC-MS, and selected 9 and 6 major components to calculate the relative standard deviation to verify the reproducibility, RSD < 10.47% and 6.67%, respectively (Table S2). Analysis of HS-GC-IMS data based on Vocal, drift time and RI were used as criteria for component identification. Analysis of GC-MS data based on Masshunter. NIST17 (match > 80, RI) as well as standard compounds were used for identification analysis. Multivariate statistical analysis by simca 14.1 (Umetrics, Malmo, Sweden). Data were normalized by peak area and normalization and scaled using pareto scaling. Plotting heat maps on MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/, accessed on 11 June 2022). Compound odor and partial activity query from ChemicalBook (www.chemicalbook.com, accessed on 15 June 2022).

3. Results and Discussion

3.1. HS-GC-IMS Topographic Plots in SHJY, SHJ, SJY and SJ

In HS-GC-IMS, when the concentration of components in the sample is high, the compounds will form dimers or even multimers with protons or electrons. And the presence of multimers contributes more to qualitative accuracy. The X-axis was the drift time, the Y-axis was the retention time, and the Z-axis was the ion peak intensity. The color represented the peak intensity. The peak intensity decreased from red to white and then to blue. It can be seen that most of the ion peaks appear at the retention time of 100–1600 s and the drift time of 1.0–2.5 (Figure 1A). Identification of differences in SHJY, SHJ, SJY, SJ from three dimensions of retention time, drift time and peak intensity. The overall number of peaks of ZBP was more than that of ZBL, and the peak intensity was greater than that of ZBL. Compared with the other three samples, SHJ had two very obvious ion peaks. There was little difference in the peaks of the same part from different places, but the difference in the peak can also be seen.

3.2. Fingerprint Analysis of VOCs in SHJ, SJ, SHJY, SJY

The aroma is usually composed of small molecule volatile aldehydes, ketones, organic acids, esters and monoterpenes [22]. There were 5 aldehydes, 5 olefins, 10 alcohols, 5 esters, 4 acids, 6 ketones, 1 ether, 3 thioethers and 1 phenol detected by HS-GC-IMS (Table 1). Figure 1B shows the fingerprint of SHJ, SJ, SHJY, and SJY. The peak intensity decreased from red to white, and then to blue. It can be seen that there are more components in ZBL in Figure 1B-a. E-2-hexenal (strong aroma of fresh fruit and green leaves), diethyl butanedioate (pleasant smell), ethyl acetate (strong etherlike smell, clear and fruity aroma). The components of the pericarps and its leaves in Figure 1B-b were both obvious. Such as pentanal (special fragrance), α-terpinene (citrus and lemonlike aroma, antibacterial and antioxidant), ethanol, 1,8-cineole (camphor smell and cool herbal flavor), β-pinene (resin and rosin aroma), α-pinene (anti tumor, antifungal, anti allergy and ulcer improvement). The components of the pericarps in area Figure 1B-c were more significant, Such as 2-hexanol, acetic acid, 4-terpineol (aroma of pepper, light earth and stale wood), furaneol (sweet, toasty, bread, cooking, fruit and caramel frangrance), δ-octalactone (cocoa, coconut and creamy aromas). These ingredients are also a little in ZPL. Methyl-5-hepten-2-one (fruit aroma and fresh fragrance), (E,E)-2,4-hexadienal, furfural (special smell similar to Benzaldehyde), (E)-2-pentenal, neryl acetate (aromas of orange blossom and rose, honey and raspberry), butanoic acid, nerol (sweet smell of fresh roses, with a slight smell of lemon), 2-butoxyethanol (moderate ether flavor), phenylacetic acid, 2,3-butanedione (creamy aroma after dilution), acetone, allyldisulfide (special smell of garlic), carveol. Nerol has antioxidant, antimicrobial, antispasmodic, insect repellent and antiarrhythmic effects. A certain dose of nerol could protect the liver of rats [23]. 4-terpineol had strong antifungal activity and was one of the main antifungal components of tea tree oil [24]. There were more components of SJ in Figure 1B-d, such as linalool oxide, isopropyl acetate (sruity), 2-Cyclohexen-1-one, 2,3-Butanediol, and β-ocimene was also obvious in ZBL. The components of SHJ pericarps in Figure 1B-e were relatively high. Such as pentanol, and it has a certain content in SHJY, SJY and SJ, 2,5-dimethylfuran, dimethyl disulfide, 2-methyl butanol, 2,3-Butanediol, 2-acetylfuran (slmonds, nuts, fermented, milk and sweet caramel-like aroma), dipropyl sulfide, phenylacetic acid, 4-methylguaiacol (aroma of spices, cloves, vanillin and smoke).

3.3. Component Content Analysis of HS-SPME-GC-MS Results

The overall peak intensity of ZBP was higher than ZBL, the overall peak intensity of SJ was higher than that of SHJ, and SHJ has more peak than SJ. The top three VOCs in the peak area of each sample were as follows: linalool 25.54%, linalyl acetate 18.56%, and terpinyl acetate 4.89% of SJY; linalyl acetate 21.26%, linalool 13.6%, terpinyl acetate 12.16% of SHJY; Dl-limonene 3.99%, piperitone 3.67% and sabinene 3.22% of SHJ; linalyl acetate 45.47%, linalool 7.74%, γ-terpineol 6.40% of SJ (Figure 2). Terpinyl acetate had anti-cholinesterase, anti-oxidation, anti-amyloidosis and neuroprotective effects, which may be used in the treatment of Alzheimer’s disease [25]. Linalyl acetate could prevent rheumatoid arthritis by antagonizing related muscle atrophy [26]. α-phellandrene, limonene, 1,8-cineole, α-terpinene and linalool are the main contributing components of ZBP odor [27]. Limonene could inhibit anxiety-related behavior [28]. Linalool and 1,8-cineole were plant-derived isoprenoids with anticancer activities in lung cancer cells with chronic nicotine exposure [29]. Myrcene could produce pine leaf flavor [30]. Although the smells of ZBP and ZBL were clearly different, they also had some similar flavor.
A total of 62 VOCs were measured, including 1 acid, 31 alkenes, 14 alcohols, 1 phenol, 2 aldehydes, 3 ketones, 9 esters and 1 alkane (Table S3). The color indicated the logarithm of the peak area at the bottom of 10, which increased from blue to white and then to red. More VOCs of ZBP were detected by HS-SPME-GC-MS than of ZBL (Figure 3). More (1E,4E)-germacrene B, α-bisabolol (antiinflammatory, antibacterial) were detected in SJ. In SHJ, more 4-isopropylcyclohex-2-en-1-one, cumic alcohol, cuminaldehyde (at high concentration, strong special smell of dry tea gas, which is unpleasant; sweet smell at low concentration) and so on were detected. 1,8-cineole was detected in SHJ, SJ, SJY and SHJY, but more in SHJ and SJ. More VOCs were detected in ZBP than in its leaves, such as γ-terpineol (pine- and clovelike aroma), α-terpinene, α-phellandrene (black pepper aroma and sweet lotus aroma). γ-terpineol was heavily detected in SHJ and SJ; γ-terpineol had bactericidal and anticancer potential [31]. SJY’s specific VOCs were (+)-citronellal (lemon flavor, anthelmintic and antifungal). β-lonone, (-)-humulene epoxide II could be detected in SJY and SHJY. α-terpineol (clove aroma; it has strong antibacterial activity against periodontal disease and cariogenic bacteria), spathulenol, caryophyllene oxide (anti-inflammatory, anti-cancer and enhance skin permeability); all of them were detected except SJ. Terpinyl acetate (lemon and lavender fragrance), linalool (the sweet, tender and fresh flowers smell like lily of the valley), linalyl acetate (light and sweet aroma, like orange leaves, terpene-free lemon and raw pear, and like lavender flowers) were detected in all samples, and their content was higher. There was more terpinyl acetate in SHJ; more linalool and linalyl acetate in SJ, and more total ZBP than leaves.

3.4. Multivariate Statistical Analysis

In statistics, too many variables can increase the complexity of the analysis. PCA is an unsupervised multivariate statistical analysis method. By comparing the principal component factors, the dimensionality of the data is reduced and regularity and difference between samples are revealed [32]. Multivariate statistics of HS-GC-SPME-MS and HS-GC-IMS were obtained for model A and model B. SHJ, SJ, SHJY and SJY were clustered in the PCA of A and B, respectively. In the PCA of model A, Q2 = 0.855, indicating that the model was good. The contribution of PC1 was 72.1%, and the contribution of PC2 was 16.5%. The positive part of PC1 could well distinguish the pericarps, and the negative part of PC1 could well distinguish the leaves. In the direction of PC2, SHJ and SJ could be distinguished (Figure 4A). The PCA of model A was 0.924, indicating an excellent model fit. The contribution of PC1 was 62.7%; the contribution of PC2 was 34.7%; the distinction between SHJ and SJ was obvious; and the difference between SHJY and SJY was not obvious. The overall results were similar to HS-GC-IMS (Figure 4D). Both HS-GC-IMS and HS-SPME-GC-MS can significantly distinguish between SHJ and SJ, but the distinction between ZBL of different origins is not obvious. The reason may be due to climatic and geographical factors. Maoxian is a plateau monsoon climate with sufficient sunshine, little precipitation, a dry climate and large temperature differences; Hancheng is a continental monsoon climate with mild climate, sufficient light and more rainfall. Additionally, there were relatively fewer VOCs of ZBL, which makes the difference between SHJY and SJY appear less obvious.
OPLS-DA is a supervised method for identifying differences in samples from different categories, eliminating data that are not relevant to category information [33], here, the less characteristic components of ZBL, mostly small-molecule components detected by HS-GC-IMS. To exclude the risk of overfitting, HS-GC-IMS and HS-SPME-GC-MS were cross-validated 200 times. Q2 were less than 0.05, indicated that the results are not over-fitted (Figure 4C,F). Compounds with VIP values of >1 and p < 0.05 were screened by one-way ANOVA as differential markers, and components with large errors were sieved out, the differential markers marked in red in the graph (Figure 4G,H). The peak area is used as a reference to indicate the content, The content ranges from high to low corresponding to warm to cool tones. In HS-GC-IMS, 1,8-cineole for SHJ, neryl acetate, nerol, (E,E)-2,4-hexadienal, 4-terpineol, α-pinene, linalool oxide, acetone, β-ocimene, furaneol, α-terpinene, 2-butoxyethanol for SJ, and diethyl butanedioate for SJY were differential marker (Figure 4G). In HS-SPME-GC-MS, α-caryophyllene, linalool, linalyl acetate, DL-limonene for determining SJ, terpinyl acetate, α-thujene, piperitone, γ-terpineol, α-terpineol, sabinene for SHJ, and (+)-citronellal for CJY were differential marker (Figure 4H).

3.5. Comprehensive Analysis

The peak areas were used as a reference for the content of the components and were analyzed for all identified components. It was discovered that the VOC content of ZBL was significantly less than that of ZBP, and the content of SHJ was slightly less than that of SJ, regardless of HS-GC-IMS or HS-SPME-GC-MS. The results of HS-SPME-GC-MS showed that SHJ has more ketones than SJ but much less esters (Figure 5A,C). Among the VOCs detected by HS-GC-IMS, alcohols accounted for the largest proportion of both ZBL and ZBP, followed by alkenes. In addition, more abundant esters were detected in ZBP (Figure 5B). In HS-SPME-GC-MS, more ester components were detected in all samples. In SJ, the ester component was even more than 50%. The alcohol component was less and the ester component was more than in Zanthoxylum schinifolium Sieb. et Zucc [34]. Alcohols and alkenes still account for a high proportion of all samples (Figure 5D). Linalool, myrcene, 1,8-cineole, limonene and 3-nonanone were identified as the five predominant components [35]. A total of 95 components were identified by HS-GC-IMS and HS-SPME-GC-MS. α-Terpinene, 1.8-cineole, linalool, 4-terpineol, neryl acetate, acetic acid, α-pinene and β-pinene were the common components detected by those two approaches. HS-SPME-GC-MS detected more middle molecular alkenes, alcohols and esters, and HS-GC-IMS detected more small molecular alcohols, aldehydes and esters.

4. Conclusions

HS-GC-IMS and HS-SPME-GC-MS were used to characterize the VOCs of SHJY, SJY, SHJ and SJ rapidly, accurately, comprehensively and without contamination. The foundation was laid for the study of active ingredients. Moreover, the differences in VOCs were compared between SHJY, SHJ, SJY and SJ. Combined with multivariate statistics, thirteen and eleven differential markers were screened from HS-GC-IMS and HS-SPME-GC-MS, respectively, which contributed to the quality evaluation of ZBP and ZBL. Similarly, HS-GC-IMS and HS-SPME-GC-MS could also be applied to other VOCs of food, spices, traditional Chinese medicine and so on. Regrettably, the VOCs of ZBL are much less than ZBP. The flavor of ZBP is more acrid and strong, while the flavor of ZBL is lighter and slightly acrid, which is a valuable flavoring. Some active VOCs are present in ZBL and ZBP, and the results show the medical value of ZBP and ZPL. The different components of ZBP from different areas enriched the flavor of different regions but also led to its unstable efficacy. It is necessary to strictly control the origin and other factors to ensure that its components and properties are stable and controllable. In terms of quality control, HS-GC-IMS and HS-SPME-GC-MS will be good detection means.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/foods11223745/s1, Table S1: Detailed information of all the samples; Table S2: Reproducibility; Table S3: HS-SPME-GC-MS integration parameters of VOCs in SHJ, SJ, SHJY, and SJY; Figure S1: Different areas of Zanthoxylum bungeanum pericarps and leaves.

Author Contributions

Conceptualization, F.L.; Data curation, W.L.; Formal analysis, H.Y.; Funding acquisition, H.D.; Investigation, S.B.; Methodology, Z.L.; Project administration, X.W.; Resources, L.H.; Software, X.W.; Supervision, X.S.; Validation, W.Y.; Visualization, X.G.; Writing—original draft, X.W.; Writing—review & editing, J.Y. and X.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Science and Technology Program of Tianjin (No. 22ZYJDSS00100) and Innovation Team and Talents Cultivation Program of National Ad-ministration of Traditional Chinese Medicine (No.ZYYCXTD-D-202002).

Data Availability Statement

Data is contained within the article and supplementary material.

Conflicts of Interest

The authors declare no conflict of interest.

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Foods 11 03745 g001 550
Figure 1. (A): topographic of SHJ, SJ, SHJY, SJY detected by HS-GC-IMS; (B,C): gallery plot of SHJ, SJ, SHJY, SJY detected by HS-GC-IMS. The a, b, c, d, and e are significant components of ZBL, ZBP and ZBL, ZBP, SHJ and SJ, respectively, among the identified components. (The codes of the compounds correspond to those in Table 1.)
Figure 1. (A): topographic of SHJ, SJ, SHJY, SJY detected by HS-GC-IMS; (B,C): gallery plot of SHJ, SJ, SHJY, SJY detected by HS-GC-IMS. The a, b, c, d, and e are significant components of ZBL, ZBP and ZBL, ZBP, SHJ and SJ, respectively, among the identified components. (The codes of the compounds correspond to those in Table 1.)
Foods 11 03745 g001
Foods 11 03745 g002 550
Figure 2. GC-MS total ion chromatogram ((A–E) corresponded to SJ, SHJ, SHJY, SJY, Reference compounds).
Figure 2. GC-MS total ion chromatogram ((A–E) corresponded to SJ, SHJ, SHJY, SJY, Reference compounds).
Foods 11 03745 g002
Foods 11 03745 g003 550
Figure 3. Heatmap of cluster analysis of content detected by HS-SPME-GC-MS. The a, b, c, d, e, f and g correspond to alkene, other, phenol, acid, aldehyde, ketone, ester and alcohol, respectively (the codes of the compounds correspond to those in Supplementary Table S3).
Figure 3. Heatmap of cluster analysis of content detected by HS-SPME-GC-MS. The a, b, c, d, e, f and g correspond to alkene, other, phenol, acid, aldehyde, ketone, ester and alcohol, respectively (the codes of the compounds correspond to those in Supplementary Table S3).
Foods 11 03745 g003
Foods 11 03745 g004 550
Figure 4. PCA, OPLS-DA, Permutation and VIP of HS-SPME-GC-MS correspond to (A–C,G); PCA, OPLS-DA, Permutation and VIP of HS-GC-IMS correspond to (D–F,H). (The codes of the compounds correspond to those in Supplementary Table S3).
Figure 4. PCA, OPLS-DA, Permutation and VIP of HS-SPME-GC-MS correspond to (A–C,G); PCA, OPLS-DA, Permutation and VIP of HS-GC-IMS correspond to (D–F,H). (The codes of the compounds correspond to those in Supplementary Table S3).
Foods 11 03745 g004
Foods 11 03745 g005 550
Figure 5. Percentage of component content of SHJ, SJ, SHJY, SJY. (A,B): the absolute and percentage contents of HS-GC-IMS; (C,D): the absolute and percentage contents of HS-SPME-GC-MS.
Figure 5. Percentage of component content of SHJ, SJ, SHJY, SJY. (A,B): the absolute and percentage contents of HS-GC-IMS; (C,D): the absolute and percentage contents of HS-SPME-GC-MS.
Foods 11 03745 g005
Table
Table 1. HS-GC-IMS integration parameters of VOCs in SHJ, SJ, SHJY, and SJY.
Table 1. HS-GC-IMS integration parameters of VOCs in SHJ, SJ, SHJY, and SJY.
NumberCompoundCASFormulaRIRt [s]Dt [a.u.]
1E-2-Hexenal monomer6728-26-3C6H10O840.4338.4591.18769
2E-2-Hexenal dimer6728-26-3C6H10O839336.8451.52614
3Methyl-5-hepten-2-one110-93-0C8H14O986.2573.3541.18323
4α-Terpinene monomer99-86-5C10H161018.9634.3861.22448
5α-Terpinene dimer99-86-5C10H161018.1632.8921.72976
61,8-Cineole monomer470-82-6C10H18O1033.2661.0431.29673
71,8-Cineole dimer470-82-6C10H18O1032.9660.4451.73863
82-Hexanol monomer626-93-7C6H14O784.9274.171.28425
92-Hexanol dimer626-93-7C6H14O783.5272.7361.57117
102,3-Butanedione431-03-8C4H6O2564.4132.7991.17274
11Linalool oxide monomer60047-17-8C10H18O21078.7746.1511.26752
12Linalool oxide dimer60047-17-8C10H18O21079.1746.9321.82662
132,3-Butanediol513-85-9C4H10O2771.4262.2861.36835
14Acetic acid monomer64-19-7C2H4O2578.8139.5081.04966
15Acetic acid dimer64-19-7C2H4O2579.4139.8041.15007
16Pentanol monomer71-41-0C5H12O786275.4771.25575
17Pentanol dimer71-41-0C5H12O784.9274.1961.50928
182-Acetylfuran monomer1192-62-7C6H6O2884.9389.9971.12002
192-Acetylfuran dimer1192-62-7C6H6O2884.9389.9971.44292
20β-Ocimene monomer13877-91-3C10H161050.1692.6661.25509
21β-Ocimene dimer13877-91-3C10H161050692.5221.69652
224-Terpineol monomer562-74-3C10H18O1168.6914.1981.2266
234-Terpineol dimer562-74-3C10H18O1169.1915.081.73235
24Nerol monomer106-25-2C10H18O1232.21033.0821.2273
25Nerol dimer106-25-2C10H18O1228.91026.7871.73416
26Phenylacetic acid103-82-2C8H8O21250.71067.5541.32855
27Acetone67-64-1C3H6O529.6116.5591.1205
28Ethanol64-17-5C2H6O509.4107.1071.12947
29Ethyl acetate monomer141-78-6C4H8O2603.3150.9511.09808
30Ethyl acetate dimer141-78-6C4H8O2601.6150.1631.34211
31Furfural98-01-1C5H4O2846.1345.0371.08274
32Neryl acetate141-12-8C12H20O21367.51285.921.22894
33δ-Octalactone698-76-0C8H14O21272.21107.7511.28219
34Furaneol3658-77-3C6H8O31083.7755.421.19676
35Diethyl butanedioate123-25-1C8H14O41193.7961.0411.29436
36Isopropyl acetate108-21-4C5H10O2658.7176.8191.16357
372,5-Dimethylfuran625-86-5C6H8O690.4192.3471.35536
382-Methyl butanol137-32-6C5H12O740.3235.4251.24002
39(E)-2-Pentenal1576-87-0C5H8O790280.0731.10915
40Butanoic acid107-92-6C4H8O2854.4354.7321.1635
41(E,E)-2,4-Hexadienal monomer142-83-6C6H8O909.9431.4141.11381
42(E,E)-2,4-Hexadienal dimer142-83-6C6H8O909.9431.4141.45388
43Pentanoic acid109-52-4C5H10O2898.2409.6941.23289
44Dipropyl sulfide111-47-7C6H14S879.3383.5461.15847
45Dimethyl disulfide624-92-0C2H6S2751.4245.0561.06575
462-Butoxyethanol111-76-2C6H14O2898.4410.0861.20642
47α-Pinene monomer80-56-8C10H16922.1454.0741.22494
48α-Pinene dimer80-56-8C10H16922.3454.51.67418
492-Cyclohexen-1-one930-68-7C6H8O941.1489.491.11358
50β-Pinene monomer127-91-3C10H16970.5544.161.22525
51β-Pinene dimer127-91-3C10H16970.2543.6051.64775
52β-Pinene tripolymer127-91-3C10H16969541.3831.73717
534-Methylguaiacol93-51-6C8H10O21186.6947.8461.17166
54Carveol99-48-9C10H16O1232.91034.2871.19266
55Allyldisulfide2179-57-9C6H10S21066722.3331.19546
56Pentanal monomer110-62-3C5H10O689.4191.4441.18336
57Pentanal dimer110-62-3C5H10O689.9191.8771.43071
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Wu, X.; Yin, J.; Ding, H.; Li, W.; Han, L.; Yang, W.; Li, F.; Song, X.; Bie, S.; Gong, X.; et al. The Discrimination and Characterization of Volatile Organic Compounds in Different Areas of Zanthoxylum bungeanum Pericarps and Leaves by HS-GC-IMS and HS-SPME-GC-MS. Foods 2022, 11, 3745. https://doi.org/10.3390/foods11223745

AMA Style

Wu X, Yin J, Ding H, Li W, Han L, Yang W, Li F, Song X, Bie S, Gong X, et al. The Discrimination and Characterization of Volatile Organic Compounds in Different Areas of Zanthoxylum bungeanum Pericarps and Leaves by HS-GC-IMS and HS-SPME-GC-MS. Foods. 2022; 11(22):3745. https://doi.org/10.3390/foods11223745

Chicago/Turabian Style

Wu, Xinlong, Jiaxin Yin, Hui Ding, Wei Li, Lifeng Han, Wenzhi Yang, Fangyi Li, Xinbo Song, Songtao Bie, Xingchu Gong, and et al. 2022. "The Discrimination and Characterization of Volatile Organic Compounds in Different Areas of Zanthoxylum bungeanum Pericarps and Leaves by HS-GC-IMS and HS-SPME-GC-MS" Foods 11, no. 22: 3745. https://doi.org/10.3390/foods11223745

APA Style

Wu, X., Yin, J., Ding, H., Li, W., Han, L., Yang, W., Li, F., Song, X., Bie, S., Gong, X., Yu, H., & Li, Z. (2022). The Discrimination and Characterization of Volatile Organic Compounds in Different Areas of Zanthoxylum bungeanum Pericarps and Leaves by HS-GC-IMS and HS-SPME-GC-MS. Foods, 11(22), 3745. https://doi.org/10.3390/foods11223745

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