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Analysis and study on volatile flavor compounds of three Yunnan cultivated cigars based on headspace-gas chromatography-ion mobility spectrometry

  • Ziyun Yu , Fuying He , Xingbo Gu , Shaobin Sun , Yirong Liao , Yanbi Yu , Lili Gu and Xiao Zhou EMAIL logo
Published/Copyright: October 22, 2025
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Open Chemistry
From the journal Open Chemistry Volume 23 Issue 1

Abstract

To investigate the differences in the volatile compounds of three main cultivars, Yunxue No. 1, Yunxue No. 36, and Yunxue No. 39, from Yunnan, the volatile flavor components were analyzed by headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). Principal component analysis and orthogonal partial least squares discriminant analysis were applied to investigate the differences in volatile flavor compounds among the three cigar tobacco varieties. GC-IMS detection results indicate that a total of 74 volatile organic compounds were detected in the cigars , including 11 types of aldehydes, ketones, alcohols, acids, esters, hydrocarbons, thiols, benzene derivatives, heterocycles, ethers, and terpenoids. Among the three cigar tobacco samples, esters, hydrocarbons, ketones, and aldehydes are the primary contributors to the overall flavor, though significant variations exist in their types and contents. This study showed that HS-GC-IMS was efficient in analyzing the composition and content of aroma components in cigar leaves. This technology can be used to establish relationships between aroma component composition/content and variety. This research provides data support for the style positioning and quality improvement of Yunnan cigars and further achieve the purpose of identifying and tracing the origin of cigars.

Graphical abstract

Keywords: cigar; headspace-gas chromatography-ion mobility spectrometry; volatile flavor compounds; fingerprint maps

1 Introduction

Cigars are renowned for their rich aroma, rich taste, and less harmful to the human body than ordinary cigarettes [1]. Cigar tobacco originated from Latin America and is currently cultivated mainly in tropical and subtropical countries such as Cuba, the Dominican Republic, Nicaragua, Indonesia, Brazil, and China. Cigar tobacco has high economic value and plays an important role in agricultural development. With the development of the global economy and culture, the cigar consumption market at home and abroad continues to expand [2]. Unlike traditional cigarettes, finished cigars are entirely made from cigar leaf fermentation and rolls and do not need to go through the baking process [3,4,5,6]. Therefore, the flavor and aroma of cigars directly determine their sensory characteristics, which are critical factors influencing the quality of cigars [7,8]. The flavor of a cigar is formed by the combination of volatile flavor compounds, which is closely related to the source, type, and processing technology of cigar, and directly affects its sensory characteristics, quality, and consumer purchasing intentions [9]. Thus, Yunnan Province, as an important cigar tobacco production area in China, is of great significance to analyze the volatile flavor substances in the main varieties and establish an accurate, simple, economical, and efficient analysis method for maintaining the flavor stability and improving the quality of cigar formulation.

Liquid chromatography (LC) and gas chromatography (GC) can quantitatively or qualitatively determine organic compounds [10,11], but it is difficult to accurately and quickly measure volatile aroma components in different varieties of cigar smoke because of complex sample preparation and large equipment used. Gas chromatography-ion mobility spectrometry (GC-IMS) is a detection technology that combines the high separation ability of GC with the rapid response characteristics of ion mobility spectrometry [12,13,14]. This technology has the advantages of high sensitivity, high accuracy, detection of trace samples, no need for complex sample pretreatment, and maximum retention of the original flavor of samples. Automatic headspace sampling realizes a simple and repeatable sampling process by controlling the incubation time and temperature [15]. Headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) is not only applied to the analysis of characteristic aroma components in complex foods and agricultural products such as grains [16,17,18], fruits and vegetables [19,20], tea [21,22,23], aquatic products [24,25], seasonings [26,27], and traditional Chinese medicinal materials [28,29], but also provides a feasible scheme for the determination of the composition of volatile and semi-volatile compounds in tobacco [30]. Liu et al. [16] used GC-IMS technology to evaluate the effects of different soil conditions on volatile organic compounds (VOCs) in “Mianhua” and identified specific VOCs that can be used to distinguish soil types. Zhong et al. [17] employed GC-IMS technology and found that hot air drying (HA) and microwave-hot air drying had significant effects on the flavor of wheat germ. Yang et al. [19] used GC-IMS technology to differentiate three asparagus varieties (Paladin, Grace, and Ggang Red). Guo et al. [23] identified 27 volatile flavor compounds in oolong tea through GC-IMS and GC-MS technologies. Sun et al. [25] reduced the trimethylamine content in tilapia by 56% through ultrasonic-assisted enzymatic deodorization technology and comprehensively analyzed the effects of ultrasonic-compound enzyme deodorization on flavor by combining GC-IMS technology. Miao et al. [27] comprehensively analyzed the flavor differences of fermented black beans from different origins using HS-GC-IMS technology and GC-O-QTOF/MS technology. Parastar et al. [28] developed a new non-targeted volatilities method based on HS-GC-IMS for the identification of saffron and the discrimination of its geographical origins. Zhu et al. [30] used HS-GC-IMS to analyze the changes in volatile flavor compounds in cigars under different aging conditions and identified a total of 82 volatile flavor compounds.

In this study, representative tobacco leaves of Yunxue No. 1, Yunxue No. 36, and Yunxue No. 39, which are the main cultivars of cigar tobacco in Yunnan Province, were selected, and their volatile flavor substances were analyzed by HS-GC-IMS technique, and the differences among the three cigar samples were resolved. This study provides a certain theoretical basis for the style orientation and quality improvement of the Yunnan cigar.

2 Experimental

2.1 Materials and instruments

2.1.1 Materials

Yunxue No. 1 (Central Grade II), Yunxue No. 36 (Central Grade II), and Yunxue No. 39 (Central Grade II) cigar tobacco leaves used in this study were all collected by professional technical personnel from the Tobacco Industry Service Center of Yuanjiang County, Yuxi City, Yunnan Province. Each type of cigar tobacco leaf was sampled at 500 × g and stored at room temperature.

2.1.2 Instruments

The following instruments are used: FlavourSpec® flavor analyzer (GC-IMS, G.A.S., Germany); chromatographic column type: MXT-WAX (30 m × 0.53 mm × 1.0 μm) (RESTEK, USA); and JE203G electronic analytical balance (METTLER, Switzerland).

2.2 Sample pretreatment

Three kinds of cigar tobacco leaf samples were harvested and dried according to the technical procedures for drying high-quality cigar tobacco. After drying, the tobacco leaves were placed in a constant temperature and humidity box at 45°C and 80% humidity for the initial fermentation, and 1.00 kg of each sample was randomly selected from each variety. The three samples were frozen and ground by liquid nitrogen to a solid powder and then passed through a 40-mesh sieve, and then, the grounded samples were vacuum-sealed and preserved in a storage bag and kept at room temperature for the subsequent analysis. Three parallel tests were done for each sample, and a blank bottle without a sample was used as a blank control.

2.3 Experimental methods

2.3.1 HS-GC-IMS detection conditions

GC-IMS analysis conditions are as follows: chromatographic column: MXT-WAX (30 m × 0.53 mm × 1.0 μm); column temperature: 60°C; carrier gas/drift gas: N2 (99.99%); IMS temperature: 45°C; analysis time: 40 min.

Headspace sampling conditions are as follows: accurately weigh 0.5 g of three cigar samples, respectively, Yunxue No. 1, Yunxue No. 36, and Yunxue No. 39, and then transfer them into a 20 mL headspace vial. Incubate at 80°C for 15 min before conducting three cigar sample injection tests: sample injection volume: 200 μL; incubation time: 15 min; incubation temperature: 80°C; injection needle temperature: 85°C; and incubation rotation speed: 500 rpm.

2.3.2 HS-GC-IMS detection data processing

The FlavourSpec® flavor analyzer, along with the VOCal software and the qualitative software GC × IMS Library Search (which includes built-in NIST and IMS databases), was used to identify volatile flavor compounds in three types of cigars. The Reporter plugin was used for sample GC-IMS spectrum comparison, the dynamic principal component analysis (PCA) plugin for PCA, and the Gallery Plot plugin for GC-IMS fingerprint comparison. SIMCA 14.1 software was used for orthogonal partial least squares discriminant analysis (OPLS-DA).

3 Results and discussion

3.1 GC-IMS spectral analysis

The GC-IMS two-dimensional spectrum of volatile flavor compounds in the three varieties of Yunnan-produced cigars is shown in Figure S1. Each point on both sides of the RIP peak in the spectrum represents a VOC, in which the concentration of aroma components is displayed through the difference in color. The red region indicates that the concentration of aroma components is higher, and the darker the color, the higher the concentration, while the blue region is the opposite. Based on this rule, the distribution and concentration information of aroma components in cigar samples can be directly observed [31]. As shown in Figure S1, the VOCs in the three kinds of cigars completed GC separation within 2,000 s, with most organic compounds eluting within 800 s and achieving good resolution. The types of volatile flavor compounds detected in the three cigar samples were generally similar, but there were some differences in the concentration of aroma components.

To compare this difference more clearly, the spectrogram of Yunxue No. 1 was selected as the reference, and the spectrograms of the other two samples were deducted from the reference ratio to obtain Figure S2. As shown in Figure S2, there are more red areas in the spectrum information of Yunxue 36 and Yunxue 39, indicating that the concentrations of some volatile flavor compounds in Yunxue No. 36 and Yunxue No. 39 were higher than those in Yunxue No. 1. The blue area of Yunxue No. 39 showed a relatively large value, suggesting that the content of some volatile flavor compounds in Yunxue No. 39 was lower than that in Yunxue No. 1.

3.2 Qualitative analysis of VOCs in cigars

Two-dimensional characterization of the three cigar volatile compounds was performed using the built-in NIST and IMS databases of GC-IMS. Some of these compounds generated dimer and trimer peaks during ionization, resulting in multiple peaks [32]. As previously mentioned, with Yunxue No. 1 as the reference sample, 80 peak points were obtained in the GC-IMS, as shown in Figure S3, where the horizontal axis represents drift time and the vertical axis represents the retention time. Among which, 74 peaks (including monomers and dimers) could be qualitatively analyzed. It should be noted that some peaks correspond to the same compound due to the presence of monomers and dimers, which may be attributed to the high concentration of the compound in the sample [33].

A total of 74 VOCs were detected in the three cigar smoke samples, details of which are shown in Table 1, which revealed the organic compounds and their corresponding peak positions in the ion mobility spectrum. As shown in Table 1, the VOCs in the three cigar tobaccos are compositionally diverse, including 11 types of aldehydes, ketones, alcohols, acids, esters, hydrocarbons, thiols, benzene derivatives, heterocycles, ethers, and terpenoids. Among all compounds, esters had the highest content, which was primarily attributed to the high content of γ-butyrolactone (>60.00%) in the sample. The compounds with higher content (>3.00%) also included 1-chlorobutane and citronellyl formate. The proportion of other compounds was lower (<3.00%). The peak positions and migration times of specific compounds in Table 1 can serve as references for qualitative analysis of tobacco samples of unknown origin.

Table 1

The volatile organic compounds in three types of cigars

No. Categories of compounds Compound Retention index Retention time (s) Drift time (ms) Average peak volume ± SD VIP value
Yunxue No. 1 Yunxue No. 36 Yunxue No. 39
1 Terpenoids (+)-Sabinene 1,111.7 442.974 1.09506 0.08 ± 0.02 0.07 ± 0.00 0.05 ± 0.00 0.79
2 Ketones 2-Methyl-2-hepten-6-one 1,347.1 848.66 1.17462 0.26 ± 0.05 0.25 ± 0.02 0.15 ± 0.00 0.79
3 Cyclohexanone 1,295.1 756.369 1.16092 0.16 ± 0.05 0.10 ± 0.01 0.07 ± 0.00 0.95
4 2-Butanone, 3-hydroxy 1,294.9 756.143 1.05886 0.02 ± 0.00 0.02 ± 0.00 0.02 ± 0.00 0.82
5 3-Octanone 1,236.7 655.686 1.29971 0.21 ± 0.03 0.29 ± 0.02 0.24 ± 0.01 1.49
6 2,3-Hexanedione 1,137.5 485.121 1.15796 0.97 ± 0.38 0.69 ± 0.14 0.49 ± 0.24 0.84
7 3-Heptanone 1,137.6 485.227 1.23798 0.34 ± 0.14 0.15 ± 0.03 0.11 ± 0.05 1.03
8 1-Penten-3-one (D) 1,036.5 352.527 1.31103 0.07 ± 0.01 0.12 ± 0.01 0.09 ± 0.00 1.44
9 4-Methyl-2-pentanone 1,023.6 339.584 1.18219 0.27 ± 0.05 0.25 ± 0.03 0.15 ± 0.01 0.78
10 1-Penten-3-one (M) 1,036.7 352.768 1.07994 0.19 ± 0.04 0.21 ± 0.01 0.12 ± 0.01 0.83
11 2-Pentanone 1,024 339.955 1.10596 0.19 ± 0.05 0.22 ± 0.02 0.10 ± 0.00 0.86
12 2,3 Butanedione 990.5 309.523 1.16564 1.46 ± 0.27 1.01 ± 0.09 0.85 ± 0.01 1.03
13 2,3-Pentadione 1,074 393.297 1.22188 0.05 ± 0.01 0.05 ± 0.01 0.03 ± 0.00 0.83
14 2-Propanone 828.9 216.913 1.10909 0.71 ± 0.19 1.18 ± 0.14 0.22 ± 0.00 1.02
15 2-Butanone 906 256.998 1.24842 0.33 ± 0.06 0.32 ± 0.03 0.16 ± 0.00 0.83
16 Hydrocarbons 3-Aminoheptane 1,137.5 485.086 1.31723 0.53 ± 0.05 0.29 ± 0.02 0.49 ± 0.04 1.35
17 β-Myrcene 1,168.5 541.088 1.212 0.17 ± 0.03 0.12 ± 0.01 0.12 ± 0.00 1.06
18 1,3-Cyclooctadiene 1,097.6 421.449 1.34358 0.46 ± 0.06 0.55 ± 0.05 0.43 ± 0.02 1.1
19 1-Chlorohexane 1,047 363.49 1.15666 0.12 ± 0.04 0.07 ± 0.02 0.05 ± 0.01 0.96
20 Acrylonitrile 1,008.6 325.023 1.08586 0.06 ± 0.01 0.08 ± 0.01 0.03 ± 0.00 0.93
21 (Z)-3-Nonene 947.6 281.681 1.27732 0.06 ± 0.01 0.06 ± 0.01 0.03 ± 0.00 0.8
22 2,4-Octadiene 891.1 248.71 1.33476 0.26 ± 0.05 0.19 ± 0.01 0.13 ± 0.00 0.98
23 1-Chlorobutane 837.3 220.986 1.15434 7.36 ± 1.22 3.18 ± 0.21 5.22 ± 0.05 1.35
24 1-Heptene 785.3 197.071 1.08774 0.46 ± 0.09 0.37 ± 0.03 0.25 ± 0.01 0.89
25 Cyclohexane 724 172.226 1.04166 0.05 ± 0.01 0.03 ± 0.00 0.03 ± 0.00 1.11
26 3,3,4-Trimethylhexane 821.5 213.438 1.54804 0.44 ± 0.06 0.15 ± 0.00 0.85 ± 0.03 1.16
27 Heterocyclic compounds 2-Methylpyrazine 1,276 721.87 1.09041 0.12 ± 0.02 0.08 ± 0.01 0.06 ± 0.00 1.08
28 2-Methylpyridine 1,228.1 642.002 1.0506 0.16 ± 0.02 0.14 ± 0.01 0.13 ± 0.00 1
29 1,4-Dioxan 1,080.7 401.071 1.12755 0.07 ± 0.01 0.05 ± 0.00 0.04 ± 0.00 1.13
30 2,3,5-Trimethylfuran (M) 1,056.2 373.432 1.14416 0.03 ± 0.01 0.03 ± 0.00 0.02 ± 0.00 0.81
31 2,3,5-Trimethylfuran (D) 1,057.7 375.083 1.20588 0.06 ± 0.01 0.07 ± 0.01 0.05 ± 0.00 0.91
32 2,6-Lupetidine 1,008.8 325.198 1.19319 0.09 ± 0.02 0.09 ± 0.01 0.04 ± 0.00 0.79
33 2,5-Dimethylfuran 923.2 266.907 1.3422 0.22 ± 0.04 0.21 ± 0.02 0.12 ± 0.00 0.82
34 Ethers Allyl sulfide 1,139.2 487.952 1.11986 0.25 ± 0.08 0.60 ± 0.05 0.13 ± 0.04 1.18
35 Ethyl methyl disulfide 1,136.6 483.463 1.89017 0.78 ± 0.21 0.43 ± 0.07 0.86 ± 0.02 1.31
36 1,2-Dimethoxyethane 929.1 270.417 1.28458 0.39 ± 0.07 0.26 ± 0.02 0.24 ± 0.00 1.1
37 Esters γ-Butyrolactone 1,663.5 1,709.317 1.322 61.89 ± 4.11 63.43 ± 0.07 64.5 ± 0.39 0.95
38 Methyl pentanoate 1,098.7 423.089 1.5562 0.12 ± 0.02 0.15 ± 0.02 0.09 ± 0.01 0.94
39 Butanoic acid ethyl ester 1,046 362.451 1.21407 0.10 ± 0.03 0.09 ± 0.01 0.04 ± 0.01 0.77
40 2-Methyl-1-propyl acetate 1,027.1 343.074 1.23677 0.11 ± 0.03 0.65 ± 0.03 0.07 ± 0.01 1.29
41 Ethyl propanoate 945.1 280.115 1.14928 2.79 ± 0.51 1.71 ± 0.13 1.64 ± 0.03 1.15
42 Propyl formate 938.2 275.884 1.2374 0.17 ± 0.03 0.17 ± 0.01 0.09 ± 0.00 0.83
43 Methyl acetate 839.2 221.912 1.19297 0.24 ± 0.05 0.3 ± 0.03 0.12 ± 0.00 0.9
44 Ethyl formate 814.5 210.164 1.06922 0.6 ± 0.11 0.48 ± 0.04 0.24 ± 0.01 0.9
45 Diethyl carbonate 1,101.7 427.537 1.17506 0.05 ± 0.01 0.06 ± 0.00 0.03 ± 0.01 0.87
46 Citronellyl formate 1,662.9 1,706.83 1.94009 6.46 ± 1.79 10.37 ± 2.08 13.6 ± 0.41 0.99
47 Ethyl 2-methy lpropionate 958.1 288.214 1.1892 0.33 ± 0.06 0.41 ± 0.04 0.19 ± 0.00 0.91
48 Benzene derivatives Propylbenzene 1,191.1 585.945 1.24236 0.08 ± 0.02 0.06 ± 0.00 0.05 ± 0.00 0.94
49 Ethyl benzene 1,137.1 484.388 1.44046 0.07 ± 0.04 0.12 ± 0.03 0.04 ± 0.03 0.97
50 Aldehydes 1-Nonanal 1,407 968.925 1.48975 0.06 ± 0.02 0.06 ± 0.00 0.04 ± 0.00 0.79
51 ( E)-2-Hexen-1-al 1,217.4 625.425 1.19766 0.17 ± 0.03 0.19 ± 0.02 0.10 ± 0.00 0.84
52 1-Hexanal 1,080.5 400.823 1.25673 0.03 ± 0.00 0.03 ± 0.00 0.02 ± 0.00 0.78
53 Crotonaldehyde 1,036.8 352.872 1.14016 0.06 ± 0.01 0.03 ± 0.00 0.03 ± 0.00 1.11
54 1-Pentanal 997.1 314.286 1.41911 0.57 ± 0.14 0.67 ± 0.06 0.27 ± 0.00 0.86
55 3-Methyl butanal 922.7 266.664 1.40787 1.15 ± 0.22 1.15 ± 0.09 0.77 ± 0.02 0.77
56 2-Methyl-2-propenal 889.7 247.97 1.21897 0.29 ± 0.06 0.35 ± 0.04 0.13 ± 0.00 0.89
57 Butanal 891 248.678 1.2773 0.18 ± 0.04 0.17 ± 0.01 0.08 ± 0.00 0.83
58 Acrolein 858.5 231.54 1.05982 0.04 ± 0.01 0.07 ± 0.01 0.02 ± 0.00 1.01
59 2-Methyl propanal 824.2 214.677 1.28555 0.06 ± 0.02 0.23 ± 0.02 0.02 ± 0.00 1.23
60 Propanal 752.8 183.499 1.13665 0.05 ± 0.01 0.03 ± 0.00 0.03 ± 0.00 1.04
61 Heptaldehyde 1,178.7 560.786 1.32294 1.68 ± 0.21 2.67 ± 0.12 2.00 ± 0.10 1.41
62 Alcohols 1-Pentanol-4-methyl 1,347.3 849.08 1.32263 0.28 ± 0.04 0.28 ± 0.02 0.27 ± 0.01 1.01
63 1- Butanol 1,152.6 511.639 1.1825 0.03 ± 0.01 0.07 ± 0.00 0.02 ± 0.00 1.16
64 2-Pentanol 1,109.9 440.177 1.20286 0.12 ± 0.02 0.11 ± 0.02 0.06 ± 0.01 0.82
65 1-Propanol (M) 1,048 364.606 1.11075 0.65 ± 0.23 0.46 ± 0.08 0.25 ± 0.12 0.84
66 1-Propanol (D) 1,047.4 363.963 1.25572 0.20 ± 0.11 0.15 ± 0.05 0.05 ± 0.04 0.78
67 1-Penten-3-ol 1,168.3 540.609 0.94381 0.44 ± 0.11 0.16 ± 0.03 0.14 ± 0.01 1.18
68 2-Propanol 908.7 258.562 1.08522 0.39 ± 0.08 0.34 ± 0.03 0.20 ± 0.00 0.83
69 Acids 1-Butanoic acid 1,607.8 1,510.944 1.15335 0.94 ± 0.1 0.87 ± 0.05 0.71 ± 0.05 0.88
70 2-Methyl propanoic acid 1,538.3 1,295.658 1.16062 0.23 ± 0.04 0.24 ± 0.02 0.15 ± 0.01 0.81
71 Acetic acid (M) 1,490.2 1,164.822 1.05689 1.09 ± 0.15 0.93 ± 0.07 1.20 ± 0.11 1.29
72 Acetic acid (D) 1,489.9 1,163.963 1.16194 0.12 ± 0.02 0.09 ± 0.00 0.15 ± 0.02 1.16
73 Thiols Ethyl mercaptan 727.2 173.448 1.23001 0.01 ± 0.00 0.01 ± 0.00 0.01 ± 0.00 1.07
74 Other Dibutylamine 1,098.2 422.399 1.27161 0.33 ± 0.08 0.38 ± 0.03 0.20 ± 0.03 0.85

M represents a monomer; D represents dimer. The data are expressed as the mean ± standard deviation (SD).

3.3 Fingerprint profiling of cigars

To further clarify the differences of VOCs in the three types of cigars, the Gallery Plot plugin was used to draw the fingerprint spectra of the volatile flavor compounds of the cigars. As shown in Figure 1, the background color of the figure is blue, and the concentration differences of volatile flavor compounds are displayed through color differences. The higher the concentration, the darker the color, indicating the higher the content of VOCs. To more clearly visualize the differences between similar substances in different cigars, a percentage stacked bar chart comparing various substances across the three types of cigars was created (Figure 2). As can be seen from Figures 1 and 2, the VOCs of the three types of cigars include 11 categories in total, namely aldehydes, ketones, alcohols, acids, esters, hydrocarbons, thiols, benzene derivatives, heterocycles, ethers, and terpenoids. Among which, the contents of esters, hydrocarbons, ketones, and aldehydes are relatively high in the three samples, and the sum of the four items is more than 90%. The specific differences among the three samples are as follows: among alcohols, the contents of most components of Yunxue No. 36 are higher than those of Yunxue No. 1 and Yunxue No. 39, such as 1-butanol, 2-propanol, and 2-pentanol. Among ketones, Yunxue No. 1 and Yunxue No. 39 had little difference, while Yunxue No. 36 had relatively high content of most components, such as 1-penten-3-one, 2-butanone, 2-propanone, 2-pentanone, and 2,3-pentanedione. Among the aldehydes, Yunxue No. 1 and Yunxue No. 39 had little difference, but they had a great difference from Yunxue No. 36. Almost all components of Yunxue No. 36 were larger in the three different samples. Among acids, there were obvious differences among the three samples, which showed that almost all acid substances in Yunxue No. 1 were less than the other two samples. 1-Butanoic acid and 2-methylpropionic acid were the highest in Yunxue No. 36, followed by Yunxue No. 39, and the lowest in Yunxue No. 1. Acetic acid was the highest in Yunxue No. 39, followed by Yunxue No. 36, and the lowest in Yunxue No. 1. Among the esters, the contents of most components in Yunxue No. 36 were relatively high, such as methyl acetate, butanoic acid ethyl ester, diethyl carbonate, 2-methyl-1-propyl acetate, propyl formate, methyl pentanoate, ethyl 2-methylpropionate, etc. There were little differences between Yunxue No. 1 and Yunxue No. 39 in terms of benzene ring compounds, heterocyclic compounds, ethers, and terpenoids, but they had a large difference from Yunxue No. 36, and almost all components in Yunxue No. 36 were higher than those in Yunxue No. 1 and Yunxue No. 39, such as ethyl benzene, 2,3,5-trimethylfuran, 2,5-dimethylfuran, (+)-sabinene, allyl sulfide, and so on.

Figure 1 Gallery plot of VOCs in three types of cigars.
Figure 1

Gallery plot of VOCs in three types of cigars.

Figure 2 A percentage stacked bar chart of the various substances between the three types of cigars.
Figure 2

A percentage stacked bar chart of the various substances between the three types of cigars.

3.4 “Nearest neighbor” fingerprint analysis of VOCs in cigars

To better illustrate the differences among different samples, a “nearest neighbor” fingerprint analysis was carried out for the volatile flavor compounds of the three types of cigars, as shown in Figure 3. The figure allows for a rapid comparison of samples based on the intensities of compounds within the selected evaluation area. It retrieves the “nearest neighbor” by calculating the similarity index between the spectra to find the minimum distance. The bottom area of the figure shows the normal distribution of VOCs in the three samples. This figure can intuitively display the differences among the three types of cigars. If the distance between the three samples is short and the peak intensity ratios are similar, it indicates a small difference. If the distance is long and the peak intensity ratios vary significantly, this means that the difference is obvious. As can be seen from Figure 3, there is a large gap between Yunxue No. 36, Yunxue No. 1, and Yunxue No. 39 is large, indicating that the volatile flavor substance contents of the three samples are significantly different. The difference between Yunxue No. 1, Yunxue No. 39 is small, indicating that the difference in volatile flavor substance content between the two samples is small.

Figure 3 “Nearest neighbor” fingerprint analysis map of three cigar samples.
Figure 3

“Nearest neighbor” fingerprint analysis map of three cigar samples.

3.5 PCA and OPLS-DA

To further analyze the differences among the three cigar samples, PCA and OPLS-DA analyses were performed based on the information on volatile flavor substances. PCA is a multivariate statistical analysis technique used to assess the regularity and variability among samples based on the contribution of principal component factors in different samples. It identifies several principal component factors to represent the many complex and difficult-to-identify variables in the original samples [34]. The results are shown in Figure 4, where the contribution rate of the first principal component is 25%, and that of the second principal component is 66%. The cumulative contribution rate of the two principal components reaches 91%, indicating that the data after PCA transformation can characterize most of the original data information. It shows that the PCA transformed by dimensionality reduction can characterize most of the information of the original data, thus obtaining the visualization of the data. The samples in the figure are effectively clustered and have good regional attribution, indicating that there are differences in the volatile compounds among different samples.

Figure 4 PCA plot of three types of cigars.
Figure 4

PCA plot of three types of cigars.

OPLS-DA is a multivariate statistical analysis method with supervised pattern recognition [35], which realizes the classification of the samples by establishing a relationship model between the experimental data and the sample categories, and its analysis results are shown in Figure 5. As depicted in Figure 5, the three groups of samples achieved significant separation, consistent with the results from the PCA classification model analysis. The three key indicators for the OPLS-DA model: R 2 X (proportion of information explaining the X matrix), R 2 Y (proportion of information explaining the Y matrix), and Q 2 (model predictive ability) were 0.981, 0.993, and 0.964, respectively, indicating good predictive performance and data interpretation of the model. Following 200 times of cross-validation permutation tests, the results showed R 2 = (0.0, 0.65) and Q 2 = (0.0, −0.128), confirming that no over fitting occurred in the OPLS-DA model (Figure 6). This demonstrates that the model possesses good stability and predictive capability, making it suitable for the discrimination of the three types of cigar cigarettes.

Figure 5 OPLS-DA plot of three types of cigars.
Figure 5

OPLS-DA plot of three types of cigars.

Figure 6 Cross-validation plot of OPLS-DA with 200 permutation tests.
Figure 6

Cross-validation plot of OPLS-DA with 200 permutation tests.

After normalizing the data, the higher the value of variable importance projection (VIP) in the OPLS-DA model, the higher its contribution to the classification of the samples [36], and the volatile flavor compounds were only considered key difference compounds related to the odor if VIP > 1, P < 0.05. As shown in Table 1, 29 volatile compounds in the three cigar cigarettes contributed significantly to the odor. They mainly include ester, hydrocarbon, ketone, and aldehyde compounds. Among them, three volatile compounds, 3-octanone, 1-penten-3-one (D), and heptaldehyde, contributed more to the sample differentiation.

4 Conclusion

In this study, the composition of VOCs of three major cigar tobacco cultivars in Yunnan Province was systematically analyzed for the first time based on HS-GC-IMS technology, and differentiated GC-IMS spectra, fingerprints, and nearest neighbor analyses were established among different cultivars. The main differences in volatile flavor substances among the three cigar tobacco cultivars were also analyzed by PCA and partial least squares discriminant analysis (OPLS-DA). The 74 VOCs identified in the study and their content differences among varieties provide intuitive chemical markers for the variety identification of cigar tobacco leaves and lay a foundation for the development of origin traceability technologies. Additionally, the study revealed that “Yunxue No. 1 and Yunxue No. 39 have high similarity in VOCs, while showing significant differences from Yunxue No. 36.” This feature can directly serve the optimization of variety layout and fragrance style positioning in Yunnan’s cigar tobacco production areas. For example, by regulating the high-content aldehydes (e.g., butanal, 1-pentanal) and esters (e.g., methyl acetate, butyric acid ethyl acetate) in Yunxue No. 36, its unique aroma style can be further strengthened, providing clear chemical targets for cigar quality improvement. This research will play an active role in promoting quality control, quality evaluation, and sensory quality management of cigar tobacco in Yunnan.

Acknowledgments

The authors gratefully thank the Tobacco Industry Service Center of Yuanjiang County, Yuxi City, for its assistance in the collection of cigar samples.

  1. Funding information: This work was supported by the Scientific Research Fund Project of the Education Department of Yunnan Province, titled “Research on the Component Analysis of the Main Cultivated Varieties of Cigars Produced in Yunnan Province” (2025J1768), and the Scientific Research Project for Young Talents under the Xingyu Talents Support Program, titled “Study on the Correlation between the Characterization of Microbial Community Diversity of Yunnan-produced Cigar Tobacco Leaves at Different Altitudes and the Neutral Aroma Components & Intrinsic Sensory Quality” (2023yn001).

  2. Author contributions: Z.Y.Y. – funding acquisition, writing – original draft; F.Y.H. – methodology; X.B.G. – resources; S.B.S. – data curation; Y.R.L. – investigation; Y.B.Y. – project administration; L.L.G. – software; X.Z. – funding acquisition, writing – review and editing. All the authors agreed on the final version of the manuscript.

  3. Conflict of interest: The authors state no conflict of interest.

  4. Ethical approval: The conducted research is not related to either human or animal use.

  5. Data availability statement: All data generated or analyzed during this study are included in this published article (and its supplementary information files).

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Received: 2025-04-22
Revised: 2025-07-10
Accepted: 2025-08-07
Published Online: 2025-10-22

© 2025 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/chem-2025-0197
Keywords for this article
cigar; headspace-gas chromatography-ion mobility spectrometry; volatile flavor compounds; fingerprint maps
Creative Commons
BY 4.0

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  57. Protective effect of green synthesized nanoceria on retinal oxidative stress and inflammation in streptozotocin-induced diabetic rat
  58. Evaluation of the antianxiety activity of green zinc nanoparticles mediated by Boswellia thurifera in albino mice by following the plus maze and light and dark exploration tests
  59. Yeast as an efficient and eco-friendly bifunctional porogen for biomass-derived nitrogen-doped carbon catalysts in the oxygen reduction reaction
  60. Novel descriptors for the prediction of molecular properties
  61. Synthesis and characterization of surfactants derived from phenolphthalein: In vivo and in silico studies of their antihyperlipidemic effect
  62. Turmeric oil-fortified nutraceutical-SNEDDS: An approach to boost therapeutic effectiveness of dapagliflozin during treatment of diabetic patients
  63. Analysis and study on volatile flavor compounds of three Yunnan cultivated cigars based on headspace-gas chromatography-ion mobility spectrometry
  64. Near-infrared IR780 dye-loaded poloxamer 407 micelles: Preparation and in vitro assessment of anticancer activity
  65. Study on the influence of the viscosity reducer solution on percolation capacity of thin oil in ultra-low permeability reservoir
  66. Detection method of Aristolochic acid I based on magnetic carrier Fe3O4 and gold nanoclusters
  67. Juglone’s apoptotic impact against eimeriosis-induced infection: a bioinformatics, in-silico, and in vivo approach
  68. Potential anticancer agents from genus Aerva based on tubulin targets: an in-silico integration of quantitative structure activity relationship (QSAR), molecular docking, simulation, drug-likeness, and density functional theory (DFT) analysis
  69. Hepatoprotective and PXR-modulating effects of Erodium guttatum extract in propiconazole-induced toxicity
  70. Studies on chemical composition of medicinal plants collected in natural locations in Ecuador
  71. A study of different pre-treatment methods for cigarettes and their aroma differences
  72. Cytotoxicity and molecular mechanisms of quercetin, gallic acid, and pinocembrin in Caco-2 cells: insights from cell viability assays, network pharmacology, and molecular docking
  73. Choline-based deep eutectic solvents for green extraction of oil from sour cherry seeds
  74. Green-synthesis of chromium (III) nanoparticles using garden fern and evaluation of its antibacterial and anticholinesterase activities
  75. Innovative functional mayonnaise formulations with watermelon seeds oil: evaluation of quality parameters and storage stability
  76. Molecular insights and biological evaluation of compounds isolated from Ferula oopoda against diabetes, advanced glycation end products and inflammation in diabetics
  77. Removal of cytotoxic tamoxifen from aqueous solutions using a geopolymer-based nepheline–cordierite adsorbent
  78. Unravelling the therapeutic effect of naturally occurring Bauhinia flavonoids against breast cancer: an integrated computational approach
  79. Characterization of organic arsenic residues in livestock and poultry meat and offal and consumption risks
  80. Synthesis and characterization of zinc sulfide nanoparticles and their genotoxic and cytotoxic effects on acute myeloid leukemia cells
  81. Activity of Coriandrum sativum methanolic leaf extracts against Eimeria papillata: a combined in vitro and in silico approach
  82. Special Issue on Advancing Sustainable Chemistry for a Greener Future
  83. One-pot fabrication of highly porous morphology of ferric oxide-ferric oxychloride/poly-O-chloroaniline nanocomposite seeded on poly-1H pyrrole: Photocathode for green hydrogen generation from natural and artificial seawater
  84. High-efficiency photocathode for green hydrogen generation from sanitation water using bismuthyl chloride/poly-o-chlorobenzeneamine nanocomposite
  85. Innovative synthesis of cobalt-based catalysts using ionic liquids and deep eutectic solvents: A minireview on electrocatalytic water splitting
  86. Special Issue on Phytochemicals, Biological and Toxicological Analysis of Plants
  87. Comparative analysis of fruit quality parameters and volatile compounds in commercially grown citrus cultivars
  88. Total phenolic, flavonoid, flavonol, and tannin contents as well as antioxidant and antiparasitic activities of aqueous methanol extract of Alhagi graecorum plant used in traditional medicine: Collected in Riyadh, Saudi Arabia
  89. Study on the pharmacological effects and active compounds of Apocynum venetum L.
  90. Chemical profile of Senna italica and Senna velutina seed and their pharmacological properties
  91. Essential oils from Brazilian plants: A literature analysis of anti-inflammatory and antimalarial properties and in silico validation
  92. Toxicological effects of green tea catechin extract on rat liver: Delineating safe and harmful doses
  93. Unlocking the potential of Trigonella foenum-graecum L. plant leaf extracts against diabetes-associated hypertension: A proof of concept by in silico studies
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