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. 2024 Dec 13;29(24):5883. doi: 10.3390/molecules29245883

Utilizing Headspace–Gas Chromatography–Ion Mobility Spectroscopy Technology to Establish the Volatile Chemical Component Fingerprint Profiles of Schisandra chinensis Processed by Different Preparation Methods and to Perform Differential Analysis of Their Components

Yiping Yan 1,†, Bowei Sun 1,2,†, Mengqi Wang 1, Yanli Wang 1, Yiming Yang 1, Baoxiang Zhang 1, Yining Sun 1, Pengqiang Yuan 1, Jinli Wen 1, Yanli He 1, Weiyu Cao 1, Wenpeng Lu 1,3,*, Peilei Xu 1,3,*
Editors: Irene Panderi, Antoni Szumny
PMCID: PMC11677488  PMID: 39769970

Abstract

In order to characterize the volatile chemical components of Schisandra chinensis processed by different Traditional Chinese Medicine Processing methods and establish fingerprint profiles, headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS) technology was employed to detect, identify, and analyze Schisandra chinensis processed by five different methods. Fingerprint profiles of volatile chemical components of Schisandra chinensis processed by different methods were established; a total of 85 different volatile organic compounds (VOCs) were detected in the experiment, including esters, alcohols, ketones, aldehydes, terpenes, olefinic compounds, nitrogen compounds, lactones, pyrazines, sulfur compounds, thiophenes, acid, and thiazoles. Principal component analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), and Pearson correlation analysis methods were used to cluster and analyze the detected chemical substances and their contents. The analysis results showed significant differences in the volatile chemical components of Schisandra chinensis processed by different methods; the Variable Importance in Projection (VIP) values of the OPLS-DA model and the P values obtained from one-way ANOVA were used to score and screen the detected volatile chemical substances, resulting in the identification of five significant chemical substances with the highest VIP values: Alpha-Farnesene, Methyl acetate,1-octene, Ethyl butanoate, and citral. These substances will serve as marker compounds for the identification of Schisandra chinensis processed by different methods in the future.

Keywords: Schisandra chinensis, HS-GC-IMS, traditional Chinese medicine processing, volatile components, OPLS-DA

1. Introduction

Traditional Chinese Medicine Processing is a traditional method of processing most herbs and medicinal materials in China, widely used in traditional Chinese medicine. The main purpose of processing is to reduce toxic substances in certain medicinal materials or increase pharmacologically active substances. Processing can also alter the properties of medicinal materials to make them easier to digest or swallow [1,2].

Schisandra chinensis is a climbing plant of the Magnoliaceae family, and its fruit, “Northern Schisandra”, is a traditional herbal medicine in Asia, also considered a palatable fruit and condiment [3]. Schisandra chinensis contains natural chemical constituents with medicinal activity such as lignans [4]. Lignans have been proven to possess hepatoprotective effects [5]. Qu Zhongyuan compared steamed and raw Schisandra chinensis in the treatment of allergic asthma symptoms and, using a rat model, validated that wine-steamed Schisandra chinensis processed by Traditional Chinese Medicine Processing has better pharmacological effects than raw Schisandra chinensis [6]. Schisandra chinensis has been proven to effectively prevent cell damage caused by UV radiation and effectively inhibit the spread of skin cancer [7]; its volatile oil components have antidepressant effects [8]. Schisandra chinensis polysaccharides have been shown to alleviate Parkinson’s disease by activating the autophagy signal MCL-1 [9]; the main constituent of Schisandra chinensis, schisandrin A, can alleviate non-alcoholic fatty liver disease [10]. Processing can increase the medicinally active substances in Schisandra chinensis or promote the absorption of active substances by the human body. Several Chinese scientists have conducted experiments on the widely recognized lignan medicinal components of Schisandra chinensis, proving that different processing methods significantly affect the content of various lignans in Schisandra chinensis. Ge conducted a systematic study evaluating the effects of vinegar-processed Schisandra chinensis on acute liver injury. The results showed that vinegar processing not only identified the main active components for treating acute liver injury but also isolated the by-product 5-Hydroxymethylfurfural (5-HMF), produced during processing due to the Maillard reaction, which has a therapeutic effect on liver injury, indicating that processing can increase the active substances in Schisandra chinensis and enhance its efficacy [11]. Dong studied the addition of salt to the processing of compound medicines with Schisandra chinensis as the main medicinal material. By examining the changes in chemical components before and after processing, it was verified that adding salt during processing can improve the total apoptosis rate of spermatogenic cells and increase the activity of compound medicines [12].

The characterization of volatile chemical substances in herbal medicines has received increasing attention in recent years. Yan identified the gas-phase components of Schisandra chinensis fruits and branch sap of different colors and established fingerprint profiles for Schisandra chinensis of different colors [13]. Although significant differences exist in the compositional profiles resulting from various preparation methods for the same medicinal plant, these differences are often not discernible through visual or olfactory cues after the herbs have been processed and dried. Traditional methods of distinguishing Schisandra chinensis preparations involve assessing color and odor. However, it has been observed that the color of Schisandra chinensis fruit can vary considerably, not only due to genetic diversity but also in response to harvest timing. In practical production applications, relying solely on color is insufficient for distinguishing Schisandra chinensis preparations. Furthermore, the aroma of processed Schisandra chinensis tends to diminish during the drying process, making accurate differentiation based on smell challenging. Therefore, it is essential to develop a non-destructive and rapid method for assessing volatile component differences and establish volatile compound fingerprint profiles for different Schisandra chinensis preparations. Several research teams have used gas chromatography–mass spectrometry (GC-MS), gas chromatography–ion mobility spectrometry (GC-IMS), and other volatile chemical substance detection methods to identify and establish fingerprint profiles for various Traditional Chinese Medicine Processing products: Xing characterized the volatile chemical components of Polygonum multiflorum processed by different methods using GC-MS and GC-IMS technologies, identifying characteristic substances of the processed products [14]; Fu used GC-IMS technology to identify the chemical components of nine processed tangerine peels, analyzing the volatile chemical components at different processing stages in detail [15]; Gao used GC-IMS technology to study different processing methods of pomegranate seeds, and the results showed the fingerprint profiles and key identification substances of processed pomegranate seeds [16].

Headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS) technology is a newly emerging sensitive and rapid gas-phase separation detection technology of recent years. This technology can quickly and accurately distinguish and identify volatile substances [17,18]. It combines ion mobility spectrometry and gas chromatography analysis methods, producing a two-dimensional spectrum of volatile compounds. This is based on the retention characteristics of the gas chromatography column and the ion mobility of the ion mobility spectrometer, providing a more convenient, faster, and accurate analysis method [19]. Compared to GC-MS detection, HS-GC-IMS technology has a better ability to identify isomers and similar chemical substances [20]. Additionally, samples do not require complex concentration and enrichment, which helps maintain the stability of flavor substances. Therefore, GC-IMS can be widely used to distinguish volatile components and isomers, analyze trace components, and facilitate rapid on-site detection [21].

Currently, research on the chemical composition changes in Schisandra chinensis due to processing mainly focuses on liquid-phase components using techniques such as High Performance Liquid Chromatography (HPLC) or Liquid Chromatography–Mass Spectrometry (LC-MS). However, the study of changes and differences in the volatile chemical components and contents of Schisandra chinensis processed by different methods remains an unexplored area. This study starts with Schisandra chinensis processed by different methods, using steam-processed Schisandra chinensis as the control group and vinegar, honey, wine, and salt-processed Schisandra chinensis as the experimental groups. It discusses the changes in volatile components and their differential analysis after adding different substances and processing.

2. Results and Discussion

2.1. Spectrum Analysis

The results detected by the HS-GC-IMS machine were output using VoCal 0.4.03 software and its plugins (Figure 1). The vertical axis of the spectrum represents the gas chromatography retention time of the chemical substances, while the horizontal axis represents the ion migration time of the chemical substances. The red vertical lines in the ion mobility spectrum are the normalized reaction ion peaks (RIP peaks), and the other bright spots in the spectrum are the detected volatile chemical substances. The concentration of the chemical substances can be preliminarily determined based on the color; the redder the color, the higher the concentration of the detected substance. To better observe the differences in volatile substances processed by different methods, steam-processed Schisandra chinensis was used as the control group to create differential spectra (Figure 2). The background color of the differential spectrum is nearly white. After subtracting the peaks of the control group, the ion peaks of Schisandra chinensis processed by other methods were added. The different colors reflect the concentration differences of the chemical substances. Red peaks indicate that the concentration of the detected volatile chemical substances in the experimental group is higher than that in the control group; blue peaks indicate that the concentration of the detected volatile chemical substances in the experimental group is lower than that in the control group.

Figure 1.

Figure 1

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Ion mobility spectra of Schisandra chinensis processed by different methods (PAO01–03 for steam processing, PAO11–13 for wine processing, PAO21–23 for vinegar processing, PAO31–33 for honey processing, and PAO41–43 for salt processing; same for the figures below).

Figure 2.

Figure 2

Figure 2

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Differential spectra of Schisandra chinensis processed by different methods compared to steam processing (red indicates increased substances; blue indicates decreased substances).

2.2. Qualitative Analysis of Volatile Chemical Substances

The VoCal software was used to identify and select the ion peaks of the chemical substances detected in the spectra. The selected ion peaks were integrated to calculate the peak volumes, and the different ion peaks were summarized to obtain the volatile component fingerprint profiles of Schisandra chinensis processed by different methods (Figure 3). The fingerprint profiles can display the composition of volatile chemical substances in different processed products and the differences in the content of various components. Qualitative analysis was performed using the NIST database included in the software. The chemical substances were identified based on Retention Time and other detected data, resulting in the fingerprint profiles and list of chemical substances for the experimental groups (Table 1) [22].

Figure 3.

Figure 3

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Fingerprint profiles of volatile chemical substances of Schisandra chinensis processed by different methods (red and white dots in the fingerprint map represent the peaks of chemical substances detected in the spectrum. Each row of the spectrum represents a different treatment and its replicates, and each column represents a different chemical substance).

Table 1.

List of volatile chemical substances of Schisandra chinensis processed by different methods. (In the comments, M stands for monomers of chemical substances, D stands for dimers, T stands for trimers, and P for Polymers. RI for Retention Index, RT for Retention Time, DT for Drift Time.)

Count Compound CAS# Category MW RI RT [s] DT [a.u.] Comment
1 Alpha-Farnesene C502614 Olefinic Compound 204.4 1729.9 1592.47 1.43239 M
2 Alpha-Farnesene C502614 Olefinic Compound 204.4 1733.1 1602.809 1.45975 D
3 Bornyl acetate C76493 Terpene 196.3 1591.6 1201.681 1.21588 M
4 Bornyl acetate C76493 Terpene 196.3 1588.5 1194.162 2.18565 D
5 1-Octanol C111875 Alcohol 130.2 1578.4 1169.837 1.4701
6 gamma-Butyrolactone C96480 Lactone 86.1 1632.4 1305.957 1.31604
7 2-Furaldehyde C98011 Aldehyde 96.1 1486 969.453 1.08465 M
8 2-Furaldehyde C98011 Aldehyde 96.1 1481.8 961.064 1.33283 D
9 Acetic acid C64197 Acid 60.1 1511.4 1020.842 1.05214 M
10 Acetic acid C64197 Acid 60.1 1499.5 996.469 1.15284 D
11 2-Decanone C693549 Ketone 156.3 1473.5 944.998 1.46981
12 (Z)-3-Hexen-1-ol butanoate C16491364 Ester 170.3 1471.2 940.538 1.43841
13 1-(2-furanyl)ethanone C1192627 Ketone 110.1 1506 1009.703 1.43757
14 octyl acetate C112141 Ester 172.3 1469.5 937.334 1.51274
15 2(3H)-Furanone, 5-methyl- C591128 Lactone 98.1 1437.6 878.501 1.1238
16 1-Octen-3-ol C3391864 Alcohol 128.2 1441.5 885.344 1.16758
17 2-ethyl hexanol C104767 Alcohol 130.2 1479.7 957.044 1.79682
18 2-Nonanone C821556 Ketone 142.2 1398.3 810.918 1.4062 M
19 citral C5392405 Terpene 152.2 1660.2 1381.817 1.05235
20 2-Nonanone C821556 Ketone 142.2 1399.6 813.097 1.87918 D
21 1-hexanol C111273 Alcohol 102.2 1369.3 764.385 1.32648 M
22 1-hexanol C111273 Alcohol 102.2 1361.3 752.049 1.98575 D
23 Acetic acid C64197 Acid 60.1 1379.4 780.371 1.04326 T
24 1-Hydroxy-2-propanone C116096 Ketone 74.1 1323.4 696.175 1.04187
25 (Z)-3-Hexen-1-yl acetate C3681718 Ester 142.2 1304.6 670.04 1.04198 M
26 4-Methylthiazole C693958 Thiazole 99.2 1314.6 683.84 1.05121
27 2-Butanone, 3-hydroxy C513860 Ketone 88.1 1299.2 662.804 1.05893 M
28 2-Butanone, 3-hydroxy C513860 Ketone 88.1 1296.7 659.415 1.33055 D
29 2-methyl-3-ketotetrahydrofuran C3188009 Others 100.1 1281 630.767 1.07369
30 2,6-Dimethyl pyrazine C108509 Pyrazine 108.1 1359.9 749.95 1.14014 M
31 2,6-Dimethyl pyrazine C108509 Pyrazine 108.1 1359.4 749.188 1.53791 D
32 2-Ethylpyrazine C13925003 Pyrazine 108.1 1321 692.806 1.13081 M
33 2-Ethylpyrazine C13925003 Pyrazine 108.1 1312.8 681.445 1.15845 D
34 2-Acetyl-1-pyrroline C85213225 Nitrogen Compound 111.1 1304.7 670.276 1.13019
35 (Z)-3-hexen-1-ol C928961 Alcohol 100.2 1391.2 799.339 1.21841
36 2,6-Dimethyl-5-heptenal C106729 Aldehyde 140.2 1351.2 736.742 1.17582
37 2-methyl-2-hepten-6-one C110930 Ketone 126.2 1328.6 703.666 1.17597
38 p-cymene C99876 Terpene 134.2 1289.1 646.579 1.18955 D
39 p-cymene C99876 Terpene 134.2 1288.1 644.675 1.29912 T
40 p-cymene C99876 Terpene 134.2 1281.8 632.44 1.16475 M
41 1-Hydroxy-2-propanone C116096 Ketone 74.1 1313.8 682.714 1.23109
42 1-Octen-3-one C4312996 Ketone 126.2 1320.6 692.273 1.26847
43 alpha-Terpinolene C586629 Terpene 136.2 1285.1 638.776 1.22435
44 gamma-Terpinene C99854 Terpene 136.2 1274.2 617.855 1.21313 M
45 gamma-Terpinene C99854 Terpene 136.2 1255.1 583.1 1.70573 D
46 p-cymene C99876 Terpene 134.2 1278.7 626.399 1.18673 P
47 OCIMENE C13877913 Olefinic Compound 136.2 1255.5 583.868 1.21385 M
48 OCIMENE C13877913 Olefinic Compound 136.2 1267.4 605.266 1.24943 D
49 Isoamyl alcohol C123513 Alcohol 88.1 1222.3 527.826 1.24303 M
50 Isoamyl alcohol C123513 Alcohol 88.1 1221.7 526.879 1.49185 D
51 (Z)-3-Hexen-1-yl acetate C3681718 Ester 142.2 1320.5 692.192 1.30909 D
52 Geranyl formate C105862 Ester 182.3 1297.1 659.974 1.21875
53 OCIMENE C13877913 Olefinic Compound 136.2 1246.3 567.768 1.25028 T
54 2-hexanol C626937 Alcohol 102.2 1248.4 571.328 1.28294
55 (Z)-Ocimene C470826 Olefinic Compound 154.3 1223.2 529.281 1.2945
56 1,4-Cineol C470677 Olefinic Compound 154.3 1215.8 517.403 1.72909
57 Limonene C138863 Terpene 136.2 1207.5 504.535 1.29179 D
58 Limonene C138863 Terpene 136.2 1209.3 507.432 1.65853 T
59 2-Heptanone C110430 Ketone 114.2 1190.6 479.421 1.72137
60 3-Octanone C106683 Ketone 128.2 1261.9 595.194 1.32308
61 1-Octen-3-one C4312996 Ketone 126.2 1328.8 703.948 1.69984
62 Heptanal C111717 Aldehyde 114.2 1169.7 450.987 1.68416
63 Phenyl methyl carbinyl acetate C93925 Ester 164.2 1199.1 491.838 1.04076
64 2-Methylthiophene C554143 Thiophene 98.2 1102.1 370.101 1.0448
65 (E)-2-Pentenal C1576870 Aldehyde 84.1 1147.3 422.443 1.11402
66 Allyl sulfide C592881 Sulfur Compound 114.2 1146.5 421.389 1.13021
67 Isobutanol C78831 Alcohol 74.1 1105.8 374.067 1.17171 M
68 Isobutanol C78831 Alcohol 74.1 1103.4 371.475 1.36373 D
69 2-Hexenal C6728263 Aldehyde 98.1 1187.9 475.688 1.16633 M
70 2-Hexenal C6728263 Aldehyde 98.1 1190.1 478.769 1.18993 D
71 2-Methylpropyl butanoate C539902 Ester 144.2 1141 414.682 1.81094
72 beta-Pinene C127913 Terpene 136.2 1121.5 391.699 1.28509 M
73 beta-Pinene C127913 Terpene 136.2 1133.8 406.006 1.64008 D
74 beta-Pinene C127913 Terpene 136.2 1131.2 402.979 1.73325 T
75 Myrcene C123353 Terpene 136.2 1157.9 435.653 1.21677 M
76 Myrcene C123353 Terpene 136.2 1156.6 434.049 1.29248 D
77 4-methyl-2-pentanol C108112 Internal standard 102.2 1180 464.726 1.54846
78 2-Heptanone C110430 Ketone 114.2 1178.8 463.14 1.64267
79 1-Penten-3-ol C616251 Alcohol 86.1 1208.3 505.831 1.34755
80 Mesityl oxide C141797 Olefinic Compound 98.1 1145.2 419.816 1.44772
81 Limonene C138863 Terpene 136.2 1196.2 487.488 1.21606 M
82 2-Methyl-1-butanol C137326 Alcohol 88.1 1218.5 521.686 1.21862
83 delta3-carene C13466789 Terpene 136.2 1130.1 401.634 1.22054 M
84 3-Pentanol C584021 Alcohol 88.1 1083.5 353.037 1.21457
85 Myrtenol C19894974 Olefinic Compound 152.2 1175.1 458.244 2.16023
86 delta 3-carene C13466789 Terpene 136.2 1129.6 401.134 2.18758 D
87 Acetic acid butyl ester C123864 Ester 116.2 1105.2 373.512 1.24908
88 hexanal C66251 Aldehyde 100.2 1099.2 366.986 1.26079 M
89 hexanal C66251 Aldehyde 100.2 1100.7 368.604 1.56086 D
90 Pentyl isopentanoate C25415627 Ester 172.3 1101 368.867 1.4703
91 2-formyl-5-methylthiophene C13679704 Thiophene 126.2 1114 383.224 1.58825
92 (Z)-6-nonenal C2277192 Aldehyde 140.2 1100.8 368.713 1.77005
93 Dimethyl disulfide C624920 Sulfur Compound 94.2 1057.2 331.445 1.13176
94 2-Butanone C78933 Ketone 72.1 880.8 243.241 1.05576
95 Ethanol C64175 Alcohol 46.1 988.2 283.215 1.14115
96 Acetic acid propyl ester C109604 Ester 102.1 996.2 286.709 1.47075
97 n-Pentanal C110623 Aldehyde 86.1 1005.9 293.016 1.42287
98 2-Pentanone C107879 Ketone 86.1 1003.3 291.202 1.36813 M
99 2-Pentanone C107879 Ketone 86.1 1003.9 291.678 1.39543 D
100 alpha-Pinene C80568 Terpene 136.2 1026.5 307.94 1.21178 M
101 alpha-Pinene C80568 Terpene 136.2 1031.1 311.329 1.29451 D
102 alpha-Pinene C80568 Terpene 136.2 1041.2 318.956 1.67437 T
103 alpha-Pinene C80568 Terpene 136.2 1037.8 316.408 1.73311 P
104 Propanoic acid propyl ester C106365 Ester 116.2 1051.2 326.765 1.21666
105 2-Hexanone C591786 Ketone 100.2 1054.3 329.179 1.19327
106 4-Methyl-2-pentanone C108101 Ketone 100.2 1022.8 305.173 1.17392
107 1-octen-3-one C4312996 Ketone 126.2 965 273.178 1.67708
108 3-Hepten-2-one C1119444 Ketone 112.2 927.2 257.647 1.63179
109 ethyl 2-methylpentanoate C39255328 Ester 144.2 957.7 270.115 1.77095
110 Isopentyl propanoate C105680 Ester 144.2 975.1 277.524 1.85323
111 Pyrrolidine C123751 Nitrogen Compound 71.1 1007.2 293.963 1.29013
112 Propanal C123386 Aldehyde 58.1 826.7 229.208 1.06079 M
113 Ethyl formate C109944 Aldehyde 74.1 779.9 217.74 1.08903 M
114 Ethyl formate C109944 Aldehyde 74.1 877.4 242.324 1.07505 D
115 Acetic acid ethyl ester C141786 Ester 88.1 914.3 252.582 1.10692 M
116 Acetic acid ethyl ester C141786 Ester 88.1 903.1 249.256 1.33781 D
117 1-octene C111660 Ester 112.2 845 233.862 1.15705
118 Propanal C123386 Olefinic Compound 58.1 807.6 224.455 1.13918 D
119 2-Butanone C78933 Aldehyde 72.1 922.5 255.798 1.24818
120 Butanal C123728 Ketone 72.1 917.5 253.829 1.2913
121 Methyl acetate C79209 Aldehyde 74.1 841 232.836 1.19564
122 Ethyl butanoate C105544 Ester 116.2 1001.3 289.855 1.21108
123 2,5-Dimethylfuran C625865 Ester 96.1 931.7 259.447 1.37262
124 2-methyl butanal C96173 Others 86.1 938.5 262.234 1.40162
125 Propionaldehyde C123386 Aldehyde 58.1 826.7 229.208 1.06079 M
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The qualitative results of volatile chemical substances showed that 125 volatile chemical substances were identified in Schisandra chinensis processed by different methods, with an additional 4-methyl-2-pentanol added as an internal standard in the experiment. By combining the monomers and polymers of the same substance for classification, it was found that the experimental samples of Schisandra chinensis processed by different methods contained 85 successfully identified volatile chemical substances, including 16 esters, 12 alcohols, 15 ketones, 12 aldehydes, 10 terpenes, 6 olefinic compounds, 2 nitrogen compounds, 2 lactones, 2 pyrazines, 2 sulfur compounds, 2 thiophenes, 1 acid, 1 thiazole, and 2 unclassified chemical substances. Due to the need for further enrichment and supplementation of the database, 15 unknown chemical substances were not identified.

2.3. Differential Analysis of Volatile Chemical Substances

2.3.1. Classification Analysis of Chemical Substance Peak Volumes

VoCal software can calculate the peak volume based on the ion reaction peak height and peak area of the selected region. By combining and statistically analyzing the peak volumes of monomers and polymers of the same substance, the total peak volumes of different categories of volatile chemical substances were obtained (Figure 4).

Figure 4.

Figure 4

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Differences in peak volumes of Schisandra chinensis processed by different methods (summed by chemical substance categories). The vertical axis represents the total peak volume, and the horizontal axis represents different categories of chemical substances. The table below provides detailed explanations of the peak volume values represented in the figure.

Classification analysis revealed significant differences in the content of volatile chemical substance categories in Schisandra chinensis samples processed by different methods: Schisandra chinensis processed by vinegar steaming had higher peak volumes of various components than other groups, especially alcohols and acids, which is related to the volatile components of added rice vinegar; Schisandra chinensis processed by wine steaming had much higher levels of acid chemical substances than other groups, which is related to the treatment with rice wine. Excluding the abnormally high data from these three groups, it was observed that salt-processed Schisandra chinensis contained more aldehydes and the least esters; water-processed Schisandra chinensis had the highest content of ketones; pyrazines reached the highest expression after vinegar processing. The five different processing methods of Schisandra chinensis showed significant differences in the content of chemical substance categories, with good differentiation, indicating further research value.

2.3.2. PCA (Principal Component Analysis)

Through preliminary data statistics, we found that Schisandra chinensis processed by different methods has abundant volatile chemical substances. By summing the peak volumes of chemical substance categories, samples from different groups can be preliminarily distinguished. To further identify the key components of processed Schisandra chinensis and the differences in components after different processing methods, we combined the peak volumes of monomers and dimers, obtaining 85 chemical substance peak volumes as dependent variables, with three repetitions of different processing methods as independent variables. PCA (principal component analysis) is a multivariate statistical analysis method often used to discuss the correlation between multiple variables and perform data dimensionality reduction. In the analyzed graph, the differences and similarities between samples can be directly observed through the distances between samples [23,24].

After PCA (principal component analysis), it is evident in the image that the processed Schisandra chinensis clusters separately (Figure 5). The PCA results show that the PAO2 group differs more significantly from the other four processing methods. The similarity between PAO0, PAO1, PAO3, and PAO4 is high, and PCA cannot directly cluster these four groups, requiring further Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA).

Figure 5.

Figure 5

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PCA (principal component analysis) of Schisandra chinensis processed by different methods (PC1 = 0.287, PC2 = 0.198; the PAO2 group is clearly distinguished from the other groups).

2.3.3. OPLS-DA Analysis

To further distinguish and analyze the differences in volatile chemical components of Schisandra chinensis processed by different methods, OPLS-DA can more accurately differentiate between groups [25] and directly identify characteristic components of different processing methods through subsequent Variable Importance in Projection (VIP) value calculations [26].

In the OPLS-DA (Figure 6), we can see that the independent variable fitting indices (R2X = 0.921; R2Y = 0.988; Q2 = 0.749) are all greater than 0.5, indicating an acceptable model fit [27]. To further verify whether the model is overfitted, 200 permutation analyses were performed using the model data (Figure 7). The permutation analysis results showed that the intersection of the Q2 regression line with the vertical axis was less than 0, indicating that the analysis was valid.

Figure 6.

Figure 6

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OPLS-DA of Schisandra chinensis processed by different methods (R2X1 = 0.276, R2X2 = 0.141; different groups are clustered separately and are well distinguished).

Figure 7.

Figure 7

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Analysis of 200 permutations.

2.3.4. VIP Value Analysis and One-Way Analysis of Variance (ANOVA)

OPLS-DA can establish a model to screen the VIP values of various volatile chemical substances, identifying differential variables and locating key differential chemical substances. A VIP value > 1 indicates that the chemical substance is a key volatile chemical substance (Figure 8) [28].

Figure 8.

Figure 8

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VIP value analysis (the red sections represent chemical substances with VIP > 1, the green sections represent chemical substances with VIP < 1).

SPSS software 27 was used to perform one-way ANOVA on the peak volumes of all detected volatile chemical substances, calculating the significance P of all single chemical components. A data table with P < 0.05 and VIP > 1 was established for further analysis and discussion. After calculation, it was found that 36 chemical substances had VIP values greater than 1 (Table 2). Combining with P values for joint screening, 24 chemical substances met the screening criteria. The five substances with the highest VIP values were Alpha-Farnesene, Methyl acetate,1-octene, Ethyl butanoate, and citral. The P values of these substances were all <0.05, indicating significant key differential volatile chemical substances in the experiment.

Table 2.

VIP values and P values from ANOVA analysis of Schisandra chinensis processed by different methods.

No. Compounds VIP P No. Compounds VIP P
1 Alpha-Farnesene 1.273 0.001 44 OCIMENE 0.983 0.119
2 Methyl acetate 1.254 0.000 45 Ethanol 0.978 0.000
3 1-octene 1.253 0.000 46 2-Butanone, 3-hydroxy 0.977 0.064
4 Ethyl butanoate 1.219 0.022 47 Propanal 0.975 0.247
5 Pentyl isopentanoate 1.210 0.142 48 2-Ethylpyrazine 0.973 0.000
6 citral 1.191 0.000 49 2-formyl-5-methylthiophene 0.969 0.404
7 2-Hexenal 1.175 0.001 50 3-Hepten-2-one 0.966 0.457
8 1-(2-furanyl)ethanone 1.165 0.013 51 (Z)-3-Hexen-1-ol butanoate 0.962 0.122
9 4-Methylthiazole 1.149 0.026 52 2-hexanol 0.959 0.028
10 3-Octanone 1.148 0.165 53 (Z)-Ocimene 0.957 0.163
11 2-Methyl-1-butanol 1.144 0.019 54 Myrtenol 0.956 0.240
12 Butanal 1.140 0.003 55 2-Butanone 0.953 0.322
13 Acetic acid 1.136 0.000 56 2-Nonanone 0.947 0.580
14 3-Pentanol 1.122 0.251 57 1-Hydroxy-2-propanone 0.946 0.020
15 Limonene 1.120 0.142 58 gamma -Butyrolactone 0.945 0.220
16 Acetic acid ethyl ester 1.107 0.004 59 2-Acetyl-1-pyrroline 0.934 0.002
17 2-methyl-3-ketotetrahydrofuran 1.102 0.001 60 Heptanal 0.933 0.343
18 1-hexanol 1.099 0.188 61 2-Decanone 0.915 0.158
19 alpha-Pinene 1.084 0.185 62 2-Methylpropyl butanoate 0.911 0.575
20 Isobutanol 1.080 0.191 63 2-Heptanone 0.910 0.529
21 2,5-Dimethylfuran 1.080 0.013 64 Acetic acid propyl ester 0.908 0.090
22 Geranyl formate 1.072 0.061 65 1-Octanol 0.904 0.136
23 2-methyl-2-hepten-6-one 1.071 0.037 66 n-Pentanal 0.902 0.040
24 hexanal 1.066 0.001 67 (Z)-6-nonenal 0.901 0.596
25 alpha-terpinolene 1.061 0.025 68 Mesityl oxide 0.900 0.310
26 2,6-Dimethyl pyrazine 1.060 0.001 69 1-Octen-3-one 0.897 0.484
27 1-Octen-3-ol 1.056 0.118 70 2-Pentanone 0.888 0.058
28 2(3H)-Furanone, 5-methyl- 1.048 0.034 71 Propanoic acid propyl ester 0.887 0.276
29 (E)-2-Pentenal 1.043 0.137 72 2-methyl butanal 0.885 0.284
30 p-cymene 1.042 0.032 73 octyl acetate 0.882 0.469
31 2-Furaldehyde 1.036 0.079 74 (Z)-3-hexen-1-ol 0.879 0.071
32 (Z)-3-Hexen-1-yl acetate 1.028 0.001 75 Isopentyl propanoate 0.869 0.609
33 Pyrrolidine 1.026 0.004 76 Myrcene 0.865 0.636
34 gamma -Terpinene 1.025 0.110 77 ethyl 2-methylpentanoate 0.846 0.597
35 Acetic acid butyl ester 1.023 0.000 78 delta 3-carene 0.844 0.832
36 Dimethyl disulfide 1.006 0.000 79 4-Methyl-2-pentanone 0.800 0.389
37 Allyl sulfide 1.000 0.000 80 Ethyl formate 0.774 0.972
38 2-Methylthiophene 0.998 0.000 81 beta-Pinene 0.772 0.942
39 1-Penten-3-ol 0.998 0.000 82 1,4-Cineol 0.764 0.857
40 Isoamyl alcohol 0.996 0.128 83 Bornyl acetate 0.759 0.811
41 Phenyl methyl carbinyl acetate 0.996 0.000 84 Propionaldehyde 0.729 0.986
42 2,6-Dimethyl-5-heptenal 0.995 0.064 85 2-ethyl hexanol 0.681 0.471
43 2-Hexanone 0.993 0.000
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2.3.5. Heatmap Analysis

After screening for volatile chemical substances with VIP > 1 and P < 0.05, the peak volumes of the chemical substances were analyzed using the Heatmap plugin in Origin software 2021.

To clearly and identify the correlation between Schisandra chinensis processed by different methods and different categories of chemical substances (after screening), the peak volumes of different categories of chemical substances were summed, and the data were normalized using the logarithm log10. The data were then subjected to clustering analysis using Pearson correlation analysis, clustering rows and columns separately. Pearson correlation analysis can be used to evaluate the correlation between different types of volatile chemical substances to explore potential key marker compounds (Figure 9) [29].

Figure 9.

Figure 9

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Heatmap clustering of the amounts of different types of volatile chemical substances and different processing methods. (The closer the color of the block is to pink, the higher the Z-score; the closer to olive, the lower the Z-score; and the closer to white, the closer to 0.)

In the figure, it can be observed that steam-processed Schisandra chinensis is positively correlated with olefinic compounds, with the highest correlation; wine-processed Schisandra chinensis is most positively correlated with aldehydes and positively correlated with terpenes, but almost not correlated with thiazole; vinegar-processed Schisandra chinensis is positively correlated with various substances, with the highest correlations being sulfur compounds, pyrazines, esters, and acid; honey-processed Schisandra chinensis is most positively correlated with thiazoles and most negatively correlated with olefinic compounds; salt-processed Schisandra chinensis is positively correlated with ketones and other substances, and most negatively correlated with alcohol. Schisandra chinensis processed by different methods shows significant differences in volatile component content and clusters separately.

In the data heatmap (Figure 10), it is evident that Schisandra chinensis processed by different methods shows good data separation, and the repeated experiments cluster separately, indicating good repeatability. By adding the peak volumes of individual volatile chemical substances to the analysis, we can directly observe the positive and negative correlations between different processing methods and peak volumes. According to the data model calculations, the key chemical substances screened with VIP > 1 and P < 0.05 were clustered separately. Vinegar processing was most positively correlated with acetic acid ethyl ester and acetic acid butyl ester; honey processing was most positively correlated with ethyl butanoate and butanal; wine processing was most positively correlated with 1-octene, methyl acetate, and 2-hexenal; salt processing was most positively correlated with 2-methyl-3-ketotetrahydrofuran, 4-methylthiazole, 2(3H)-Furanone, 5-methyl-, and butanal; steam processing was most positively correlated with 1-(2-furanyl) ethanone and hexanal. In the clustering analysis of different processing methods and key volatile chemical substances, it can be seen that honey-processed Schisandra chinensis and salt-processed Schisandra chinensis cluster together, but their correlation with individual chemical substances is significantly different and can be clearly distinguished. Vinegar-processed Schisandra chinensis clusters separately, showing the greatest difference in key differential component content compared to other processed Schisandra chinensis.

Figure 10.

Figure 10

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Heatmap clustering of the amounts of different volatile chemical substances and different processing methods. (The closer the color of the block is to pink, the higher the Z-score; the closer to olive, the lower the Z-score; and the closer to white, the closer to 0.)

The above heatmap clustering analysis demonstrates that Schisandra chinensis processed by different methods shows significant differences in volatile components based on their similarities and can be accurately clustered separately. The experiments show good repeatability, accurate results, good model fit, and no overfitting.

2.4. Methodological Discussion

Currently, most studies on the chemical components of Schisandra chinensis focus on the determination of non-volatile organic compounds, while the measurement of volatile components in Schisandra chinensis processing for medicinal purposes lacks relevant research. The apparent differences in Schisandra chinensis before and after processing cannot be distinguished with the naked eye. This experiment utilized HS-GC-IMS technology to characterize and analyze the differences in volatile chemical components of Schisandra chinensis processed by different methods. Key differential components with VIP values greater than 1, such as Alpha-Farnesene, Methyl acetate,1-octene, Ethyl butanoate, and citral, were identified in Schisandra chinensis processed by different methods. The quantities of these key differential components can be used in the future for rapid identification of the processing methods of Schisandra chinensis products. The proposed methodology resolves the difficulty in distinguishing between different processed products of Schisandra chinensis; however, it does not fully characterize the component differences in these products. The pharmacological activity of non-volatile organic compounds is significant, and this method does not account for non-volatile substances. Further research is needed to explore the differences in non-volatile organic compound content in Schisandra chinensis processed by different methods.

3. Material and Methods

The entire process of the Materials and Methods can be simplified into a diagram for easier understanding (Figure 11).

Figure 11.

Figure 11

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Experimental research diagram.

3.1. Materials and Experimental Equipment

3.1.1. Experimental Materials

Schisandra chinensis was collected from Wangqing and identified by Associate Researcher Peilei Xu from the Institute of Special Animal and Plant Sciences of the Chinese Academy of Agricultural Sciences as Northern Schisandra (Schisandra chinensis) of the Magnoliaceae family; edible rice vinegar (Haitian, Zhejiang, China), edible yellow wine (Shaoxing, Zhejiang, China), edible refined honey (Wang’s, Jiangxi, China), analytical pure NaCl (Aladdin, New York, NY, USA), chromatographic pure 4-methyl-2-pentanol (Sigma-Aldrich, St. Louis, MO, USA), and chromatographic methanol (Fisher, Rochester, NY, USA) were also obtained.

3.1.2. Experimental Equipment

FlavourSpec® flavor analyzer; analytical balance (Mettler Toledo, Columbus, OH, USA).

3.2. Processing Methods

In 2022, Schisandra chinensis was collected from Wangqing County, Jilin Province. After air-drying indoors at 21 °C to a constant weight, it was considered dry Schisandra chinensis and vacuum-sealed and stored in a refrigerator at 4 °C. The experimental methods were adapted from those of Zhou at Shenyang Pharmaceutical University, with appropriate modifications for our study.

3.2.1. Steamed Schisandra chinensis

Take 100 g of dry Schisandra chinensis, remove impurities, rinse it with distilled water, place it in a steamer after the water boils, steam it for 2 h, label it as PAO0, and cool it to 21 °C (room temperature).

3.2.2. Wine-Steamed Schisandra chinensis

Take 100 g of dry Schisandra chinensis and mix with 20 mL of yellow wine; let it sit briefly, place it in a steamer after the water boils using an induction heater, steam it for 2 h, labeled it as PAO1, and dry it at room temperature until no further weight change occurs after being cooled to 21 °C (room temperature).

3.2.3. Vinegar-Steamed Schisandra chinensis

Take 100 g of dry Schisandra chinensis, mix it with 15 mL of rice vinegar, place it in a steamer after the water boils using an induction heater, steam it for 2 h, label it as PAO2, and dry it at room temperature until no further weight change occurs after being cooled to 21 °C (room temperature).

3.2.4. Honey-Steamed Schisandra chinensis

Take 100 g of dry Schisandra chinensis, mix it with 15 g of refined honey, place it in a steamer after the water boils using an induction heater, steam it for 2 h, label it as PAO3, and dry it at room temperature until no further weight change occurs after being cooled to 21 °C (room temperature).

3.2.5. Salt-Steamed Schisandra chinensis

Take 100 g of dry Schisandra chinensis, mix it with 15 mL of water and 2 g NaCl, place it in a steamer after the water boils using an induction heater, steam it for 2 h, label it as PAO4, and dry it at room temperature until no further weight change occurs after being cooled to 21 °C (room temperature).

3.3. Detection Method for Volatile Substances

Grind the Schisandra chinensis processed by the above five methods to the same level using a mortar; accurately weigh 0.5 g with an analytical balance, place it in a 20 mL headspace vial, and add 20 μg of 4-methyl-2-pentanol methanol solution with a concentration of 10−5 g/mL as an internal standard.

HS-GC-IMS analysis conditions: The experimental instrument is the FlavourSpec® flavor analyzer (Haineng G.A.S company, Qingdao, China). GC-IMS unit analysis time: 45 min. The chromatographic column is a WAX-Columm (15 m long, 0.53 mm inner diameter, 1 μm film thickness; column temperature: 60 °C; carrier/drift gas: N2; analysis temperature: 60 °C). Automatic headspace sampling (HS) unit: sample volume: 500 μL; incubation time: 10 min; incubation temperature: 60 °C; injection needle temperature: 85 °C; incubation speed: 500 rpm; analysis temperature: 60 °C.

Carrier gas flow rate setting: 0–2 min at 2 mL/min; 2–20 min increase in carrier gas flow rate to 100 mL/min; maintain the flow rate until the analysis is completed.

Using the same Schisandra chinensis and extraction medium, three independent biological repetitions were performed, with each sample loaded separately (Detailed test conditions and RAW spectral data will be provided in the Supplementary Materials section of the article).

3.4. Data Analysis Methods

3.4.1. Preliminary Data Analysis Methods

The preliminary analysis of the detected experimental data was conducted using the specialized data processing software VoCal, equipped with the experimental apparatus. The main functions of the software include the establishment of peak maps, integration calculation of peak volumes, and the creation of fingerprint profiles.

3.4.2. Principal Component Analysis (PCA)

PCA is a mainstream feature extraction algorithm and the most common dimensionality reduction method. It can be used to interpret intergroup differences in multidimensional samples. SIMCA 16.0 software was used to perform dimensionality reduction calculations on the peak volumes of volatile components from different samples, with group settings at A = 3.

3.4.3. OPLS-DA, VIP Value Analysis, and 200-Time Permutation Analysis

SIMCA 16.0 software was used to establish an OPLS-DA model based on the peak volumes of the samples, with group settings at autofit and A = 4 + 5 + 0. The resulting model underwent VIP value screening, and a 200-time permutation analysis was performed on all data to verify the model’s validity.

3.4.4. One-Way ANOVA

The peak volumes of volatile chemical components from the samples were input into IBM SPSS 27 software to conduct one-way ANOVA. The results were presented in the form of P values.

3.4.5. Correlation Clustering Analysis

The average peak volumes of volatile components from three repeated detections within the same treatment group were calculated. Depending on the model requirements, peak volumes of the same chemical category were combined, or average peak volumes of volatile chemical compounds with VIP > 1 and P < 0.05 were screened. The data were normalized using log10 logarithms. After normalization, heatmap clustering was performed using Origin 2021 software, with Pearson correlation analysis as the clustering calculation method. The results were visualized using the www.omicshare.com (accessed on 6 December 2024) platform.

4. Conclusions

This study is the first to use HS-GC-IMS technology to detect and compare the volatile components of Schisandra chinensis processed by different Traditional Chinese Medicine Processing methods. The fingerprint profiles can accurately distinguish Schisandra chinensis processed by different methods. The detection results showed that 125 volatile chemical substances were qualitatively identified in the five different experimental groups, along with 15 unidentified chemical substances. After combining the polymers of the chemical substances, a total of 85 different volatile chemical substances were obtained.

This study used various mathematical analysis methods such as PCA-X, OPLS-DA, one-way ANOVA, and Pearson clustering analysis to establish models and compare and analyze the similarities and differences in the volatile components of Schisandra chinensis processed by different methods. By establishing screening models based on VIP values and P values, 24 key significant volatile chemical substances were identified for processed Schisandra chinensis. For different preparation procedures of natural products used for medicinal purposes, it is essential to establish a rapid and accurate non-destructive identification method.

Heatmap clustering analysis was performed based on these substances and the total chemical substance category peak volumes, with peak volumes normalized for Pearson correlation analysis. The analysis results showed significant differences in the chemical composition of Schisandra chinensis processed by different methods, which can be clearly distinguished from each other. This study identified the five chemical substances with the highest VIP values—Alpha-Farnesene, Methyl acetate,1-octene, Ethyl butanoate, and citral—based on the peak volumes of chemical substances. These substances will serve as marker volatile chemical substances for processed Schisandra chinensis, providing a basis for identification and differentiation along with the fingerprint profiles.

Acknowledgments

We would like to express our gratitude to all members of the Northern Berry Team for their strong support of this research. We also appreciate the suggestions made by team members on experimental design and data processing methods.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/molecules29245883/s1, Raw data of the detected volatile chemical components from the experiments; Basic parameters of the experimental equipment.

molecules-29-05883-s001.zip (148.1KB, zip)

Author Contributions

Y.Y. (Yiping Yan) and B.S. have contributed equally and are co-first authors. Y.Y. (Yiping Yan), Y.H. and P.X. contributed to the conceptualization. Y.Y. (Yiping Yan), Y.S., P.Y. and P.X. contributed to data curation. Y.Y. (Yiping Yan), B.S., M.W. and P.Y. contributed to formal analysis. Y.Y. (Yiping Yan), Y.W., M.W. and Y.S. contributed to the methodology. Y.Y. (Yiping Yan) wrote the original draft. Y.W., Y.H. and P.X. administered the project. B.S., Y.W., M.W. and Y.Y. (Yiming Yang) conducted the investigation. B.S., B.Z., M.W. and Y.H. were responsible for visualization. B.S., W.L. and P.X. reviewed and edited the writing. Y.W., Y.Y. (Yiming Yang), J.W. and W.C. provided the resources. Y.Y. (Yiming Yang), B.Z., J.W. and W.L. supervised the project. Y.Y. (Yiming Yang), B.Z. and J.W. validated the project. Y.S., W.C. and P.Y. provided the software. P.X. and W.L. acquired the funding. P.X. and W.L. are the co-corresponding authors of the article. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article and Supplementary Materials.

Conflicts of Interest

The authors declare no conflict of interest.

Funding Statement

This research and the experiments were funded by the 20th Batch of Innovation and Entrepreneurship Talent Support from the Jilin Provincial Department of Human Resources and Social Security. Project Title: Study on the Mechanism of Schisandra chinensis Essential Oil on Lung Cancer Cells through the PI3K-AKT Signaling Pathway.

Footnotes

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References

  • 1.Wu X., Wang S., Lu J., Jing Y., Li M., Cao J., Bian B., Hu C. Seeing the Unseen of Chinese Herbal Medicine Processing (Paozhi): Advances in New Perspectives. Chin. Med. 2018;13:4. doi: 10.1186/s13020-018-0163-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Yang B., Zhang Z., Song J., Qi T., Zeng J., Feng L., Jia X. Interpreting the Efficacy Enhancement Mechanism of Chinese Medicine Processing from a Biopharmaceutic Perspective. Chin. Med. 2024;19:14. doi: 10.1186/s13020-024-00887-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Chan A.S.L., Yip E.C.H., Yung L.Y., Pang H., Luk S.C.W., Pang S.F., Wong Y.H. CKBM Stimulates MAPKs but Inhibits LPS-Induced IFN-γ in Lymphocytes. Phytother. Res. 2006;20:725–731. doi: 10.1002/ptr.1943. [DOI] [PubMed] [Google Scholar]
  • 4.Fu K., Dai S., Ma C., Zhang Y., Zhang S., Wang C., Gong L., Zhou H., Li Y. Lignans Are the Main Active Components of Schisandrae Chinensis Fructus for Liver Disease Treatment: A Review. Food Sci. Hum. Wellness. 2024;13:2425–2444. doi: 10.26599/FSHW.2022.9250200. [DOI] [Google Scholar]
  • 5.Wang O., Cheng Q., Liu J., Wang Y., Zhao L., Zhou F., Ji B. Hepatoprotective Effect of Schisandra Chinensis (Turcz.) Baill. Lignans and Its Formula with Rubus Idaeus on Chronic Alcohol-Induced Liver Injury in Mice. Food Funct. 2014;5:3018–3025. doi: 10.1039/C4FO00550C. [DOI] [PubMed] [Google Scholar]
  • 6.Qu Z. Screening of Q-Markers for the Wine-Steamed Schisandra Chinensis Decoction Pieces in Improving Allergic Asthma. Chin. Med. 2023;18:10. doi: 10.1186/s13020-023-00712-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Gao C., Chen H., Niu C., Hu J., Cao B. Protective Effect of Schizandrin B against Damage of UVB Irradiated Skin Cells Depend on Inhibition of Inflammatory Pathways. Bioengineered. 2017;8:36–44. doi: 10.1080/21655979.2016.1227572. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Tan L., Yang Y., Peng J., Zhang Y., Wu B., He B., Jia Y., Yan T. OSchisandra Chinensis (Turcz.) Baill. Essential Oil Exhibits Antidepressant-like Effects and against Brain Oxidative Stress through Nrf2/HO-1 Pathway Activation. Metab. Brain Dis. 2022;37:2261–2275. doi: 10.1007/s11011-022-01019-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Wang S.-Y., Li M.-M., Wang L., Pan J., Sun Y., Wu J.-T., Naseem A., Jiang Y.-K., Kuang H.-X., Yang B.-Y., et al. Schisandra Chinensis (Turcz.) Baill Neutral Polysaccharides Alleviate Parkinson’s Disease via Effectively Activating MCL-1 Expression Regulation of Autophagy Signaling. Int. J. Biol. Macromol. 2024;279:134952. doi: 10.1016/j.ijbiomac.2024.134952. [DOI] [PubMed] [Google Scholar]
  • 10.Zong L., Xu R., Sun Y., Qiu J., Tao H., Shentu C., Niu H., Xu T., Zhou Y. Schisandrin A Alleviates Non-Alcoholic Fatty Liver Disease by Improving Liver Metabolic Disorders and Suppressing the GSK3β Signaling Pathway. Food Biosci. 2024;62:105245. doi: 10.1016/j.fbio.2024.105245. [DOI] [Google Scholar]
  • 11.Dong B., Jiang Y., Jiang Y., Wu J., Cao M., Hu C., Yu L., Chen Z. Mechanism of Efficacy Enhancing by Salt-Processing in Wuzi Yanzong Pills Based on UPLC-Q-TOF-MS Combined with Network Pharmacology and Molecular Docking. Pharmacol. Clin. Chin. Mater. Medica. 2023;39:15–24. doi: 10.13412/j.cnki.zyyl.20230711.007. [DOI] [Google Scholar]
  • 12.Ge H.X., Mei J.C., Wang Y., Chen D.Z., Li S.Q. Study on relationship of vinegar Schisandra chinensis on acute liver injury based on theory of “vinegar into liver” in traditional Chinese medicine processing. Drug Eval. Res. 2023;46:1897–1907. [Google Scholar]
  • 13.Yan Y., Lu W., Tian T., Shu N., Yang Y., Fan S., Han X., Ge Y., Xu P. Analysis of Volatile Components in Dried Fruits and Branch Exudates of Schisandra Chinensis with Different Fruit Colors Using GC-IMS Technology. Molecules. 2023;28:6865. doi: 10.3390/molecules28196865. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Xing Y., Yu Z., Hu X., Yin J., Fan T., Fu Z., Pan G., Liu E., Zhou J., Han L. Characterization of Volatile Organic Compounds in Polygonum Multiflorum and Two of Its Processed Products Based on Multivariate Statistical Analysis for Processing Technology Monitoring. J. Chromatogr. A. 2022;1680:463431. doi: 10.1016/j.chroma.2022.463431. [DOI] [PubMed] [Google Scholar]
  • 15.Fu M., Wang Y., Yu Y., Wen J., Cheong M.S., Cheang W.S., Wu J. Changes of Volatile Substance Composition during Processing of Nine-Processed Tangerine Peel (Jiuzhi Chenpi) Determined by Gas Chromatography-Ion Mobility Spectrometry. Front. Nutr. 2022;9:963655. doi: 10.3389/fnut.2022.963655. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Gao L., Zhang L., Liu J., Zhang X., Lu Y. Analysis of the Volatile Flavor Compounds of Pomegranate Seeds at Different Processing Temperatures by GC-IMS. Molecules. 2023;28:2717. doi: 10.3390/molecules28062717. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Liu H., Xu Y., Wu J., Wen J., Yu Y., An K., Zou B. GC-IMS and Olfactometry Analysis on the Tea Aroma of Yingde Black Teas Harvested in Different Seasons. Food Res. Int. 2021;150:110784. doi: 10.1016/j.foodres.2021.110784. [DOI] [PubMed] [Google Scholar]
  • 18.Song Z., Cao Y., Qiao H., Wen P., Sun G., Zhang W., Han L. Analysis of the Effect of Tenebrio Molitor Rennet on the Flavor Formation of Cheddar Cheese during Ripening Based on Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) Food Res. Int. 2023;171:113074. doi: 10.1016/j.foodres.2023.113074. [DOI] [PubMed] [Google Scholar]
  • 19.Vautz W., Franzke J., Zampolli S., Elmi I., Liedtke S. On the Potential of Ion Mobility Spectrometry Coupled to GC Pre-Separation—A Tutorial. Anal. Chim. Acta. 2018;1024:52–64. doi: 10.1016/j.aca.2018.02.052. [DOI] [PubMed] [Google Scholar]
  • 20.Xuan X., Sun R., Zhang X., Cui Y., Lin X., Sun Y., Deng W., Liao X., Ling J. Novel Application of HS-GC-IMS with PCA for Characteristic Fingerprints and Flavor Compound Variations in NFC Chinese Bayberry (Myrica Rubra) Juice during Storage. LWT. 2022;167:113882. doi: 10.1016/j.lwt.2022.113882. [DOI] [Google Scholar]
  • 21.Gu S., Zhang J., Wang J., Wang X., Du D. Recent Development of HS-GC-IMS Technology in Rapid and Non-Destructive Detection of Quality and Contamination in Agri-Food Products. TrAC Trends Anal. Chem. 2021;144:116435. doi: 10.1016/j.trac.2021.116435. [DOI] [Google Scholar]
  • 22.Zhu W., Benkwitz F., Kilmartin P.A. Volatile-Based Prediction of Sauvignon Blanc Quality Gradings with Static Headspace–Gas Chromatography–Ion Mobility Spectrometry (SHS–GC–IMS) and Interpretable Machine Learning Techniques. J. Agric. Food Chem. 2021;69:3255–3265. doi: 10.1021/acs.jafc.0c07899. [DOI] [PubMed] [Google Scholar]
  • 23.Feng T., Sun J., Song S., Wang H., Yao L., Sun M., Wang K., Chen D. Geographical Differentiation of Molixiang Table Grapes Grown in China Based on Volatile Compounds Analysis by HS-GC-IMS Coupled with PCA and Sensory Evaluation of the Grapes. Food Chem. X. 2022;15:100423. doi: 10.1016/j.fochx.2022.100423. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Zhou Y., Wang D., Duan H., Zhou S., Guo J., Yan W. Detection and Analysis of Volatile Flavor Compounds in Different Varieties and Origins of Goji Berries Using HS-GC-IMS. LWT. 2023;187:115322. doi: 10.1016/j.lwt.2023.115322. [DOI] [Google Scholar]
  • 25.Yin H., Wang L., Su H., Liang Y., Ji P., Wang X., Xi Z. Effects of Ultraviolet and Infrared Radiation Absence or Presence on the Aroma Volatile Compounds in Winegrape during Veraison. Food Res. Int. 2023;167:112662. doi: 10.1016/j.foodres.2023.112662. [DOI] [PubMed] [Google Scholar]
  • 26.Yang X., Chen Q., Liu S., Hong P., Zhou C., Zhong S. Characterization of the Effect of Different Cooking Methods on Volatile Compounds in Fish Cakes Using a Combination of GC–MS and GC-IMS. Food Chem. X. 2024;22:101291. doi: 10.1016/j.fochx.2024.101291. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Li Z., Wang T., Jiang H., Wang W.-T., Lan T., Xu L., Yun Y.-H., Zhang W. Comparative Key Aroma Compounds and Sensory Correlations of Aromatic Coconut Water Varieties: Insights from GC × GC-O-TOF-MS, E-Nose, and Sensory Analysis. Food Chem. X. 2024;21:101141. doi: 10.1016/j.fochx.2024.101141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Xi B.-N., Zhang J.-J., Xu X., Li C., Shu Y., Zhang Y., Shi X., Shen Y. Characterization and Metabolism Pathway of Volatile Compounds in Walnut Oil Obtained from Various Ripening Stages via HS-GC-IMS and HS-SPME-GC–MS. Food Chem. 2024;435:137547. doi: 10.1016/j.foodchem.2023.137547. [DOI] [PubMed] [Google Scholar]
  • 29.Zhang T., Ayed C., Fisk I.D., Pan T., Wang J., Yang N., Sun Q. Evaluation of Volatile Metabolites as Potential Markers to Predict Naturally-Aged Seed Vigour by Coupling Rapid Analytical Profiling Techniques with Chemometrics. Food Chem. 2022;367:130760. doi: 10.1016/j.foodchem.2021.130760. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

molecules-29-05883-s001.zip (148.1KB, zip)

Data Availability Statement

Data are contained within the article and Supplementary Materials.

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