Metabolomics analysis of substance differences and biomarkers in cigar tobacco leaves from Yunnan and Foreign

Metabolomics analysis of substance differences and biomarkers in cigar tobacco leaves from Yunnan and Foreign | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Metabolomics analysis of substance differences and biomarkers in cigar tobacco leaves from Yunnan and Foreign Guanghai Zhang, Gaokun Zhao, Guanghui Kong, Mengxia Li, Yuping Wu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5777714/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract There is still a lack of systematic identification of the substance components of cigar tobacco leaves (CTLs) produced under different ecological conditions. To clarify the substance differences between cigar tobacco leaves from Yunnan and Foreign and explore potential discriminant biomarkers. A total of 25 CTL samples were selected as materials from 5 production areas abroad and 4 production areas in Yunnan. The metabolic components in CTLs were determined by continuous flow method, ion chromatography, GC-MS and MS/MS. Compared with other countries, the CTLs produced in Yunnan has the characteristics of lower sugar-nicotine ratio, higher starch, pectin and lignin content. The phenolic acids, alkaloids, flavonoids, amino acids and derivatives, terpenoids, lipids and heterocyclic compounds were the main components of CTLs. A total of 248 and 150 volatile and non-volatile differential metabolites were obtained, which were amino acids and their derivatives, alkaloids, terpenoids and lipids. A total of 12 potential biomarkers were identified for distinguishing Yunnan and Foreign CTLs. KEGG enrichment and MetOrigin analysis showed that the differential metabolites were mainly involved in the biosynthesis of alkaloids, amino acids, flavonoids, lipids and terpenes. The carbon and nitrogen coupling metabolism mediated by microorganisms and enzymes influenced the composition and content of CTLs. This study provided reference for the improvement of production technology and the analysis of related substances of style characteristics of CTLs in Yunnan. Cigar tobacco leaves Aroma components Metabolomics Differential metabolites Biomarkers Metabolic pathways MetOrigin Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction The production of high-quality cigar tobacco leaves(CTLs) requires a very high standard of ecological environment, with climate, water and soil being the key ecological factors that determine the style and characteristics of CTLs. Therefore, the world's high-quality cigar tobacco production areas are only distributed between the Tropic of cancer and the tropic of capricorn, especially around latitude 23 °N; the main foreign CTLs production areas include Cuba, the Dominican Republic, Indonesia, Nicaragua, Brazil, Mexico, the United States, Honduras, Ecuador, the Philippines, Jamaica, and Cameroon, etc (Zhang et al. 2023a ; Wang et al. 2020 ). Currently, the main producing areas of CTLs in China are Hainan, Sichuan, Hubei, and Yunnan, while Yunnan is mainly distributed in Pu'er, Lincang, Dehong, and Yuxi. The results from the cigar manufacturing industry and consumer feedback both indicate that the Chinese cigar tobacco leaves have prominent characteristics and broad application prospects. However, compared to foreign cigar leaf, there are still sensory quality defects such as slightly weaker aroma characteristics and taste characteristics, slightly poorer aroma richness, slightly poorer smoke mellowness and sweetness (Xu et al. 2024 ). Sensory quality is determined by chemical substances, and the relationship between the two is extremely complex. Recently, studies have reported on the differences in aroma components, key aroma substances, and sensory flavor characteristics of CTLs from different production areas (Wang et al. 2024 ; Zhang et al. 2024 ). As well as the sensory quality characteristics of cigar at home and abroad (Xu et al. 2024 ). This laid a solid data foundation for the analysis of the material composition of cigar tobacco leaves. However, there is still a lack of systematic identification of the metabolic profiles and differences of cigar tobacco produced under different ecological conditions. Metabolomics has been widely applied to study various small molecules or metabolites in cells, tissues, or organisms, enabling comprehensive characterization of small molecules present in biological samples. Complex and high-throughput analysis platforms such as gas chromatography (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) are commonly used, greatly facilitating our understanding of global metabolomics and metabolic pathway networks (Pavagadhi and Swarup 2020 ). The objective of this study was to uncover the global metabolome in CTLs samples using unbiased profiling and metabolic fingerprinting methods to evaluate metabolites of multiple chemical classes associated with different pathways and to screen for core biomarker between Foreign and Yunnan. To provide basic data reference for the optimization and improvement of the CTLs production technology of Yunnan, such as cultivation, air-curing and fermentation, as well as the analysis of quality style characteristics. 2. Materials and Methods 2.1 CTLs samples We selected the raw CTLs of four main producing areas in Yunnan Province, as well as the imported CTLs of five foreign producing areas including Cuba, Dominican Republic, Indonesia, Brazil, and Mexico as the experimental materials. All 25 samples were selected from tobacco leaves with relatively consistent position and leaf thickness. The main differences among the samples lie in the types (wrapper/binder/filler) and grades. The detailed information on the samples is listed in Table S1 . 2.2 Determination of chemical compositions The content of each index was determined by using a continuous flow analytical system or ion chromatography (Zhang et al. 2023b ). The contents of total sugar, reducing sugar, magnesium, polyphenol, total nitrogen, total nicotine, chlorine, potassium, pectin, starch, pH, petroleum ether extract, xylogen were separately determined in accordance with the standards in the tobacco industry YC/T 159–2019、YC/T 161–2002、YC/T 249–2008、YC/T 468–2013、YC/T 217–2007、YC/T 162–2011、YC/T 175–2003、YC/T176-2003、YC/T 346–2010、YC/T 347–2010、YC/T 222–2007. 2.3 Determination of volatile aroma compounds The indices of 44 volatile aroma compounds were detected by steam distillation extraction and combined gas chromatography/mass spectrometry (GC-MS) (Li et al. 2015 ; Zhu et al. 2009 ). Quantitation based on the quantitation ion which is unique in the coeluted components increased the accuracy of peak integration. 2.4 UPLC-ESI-MS/MS analysis Dissolve 50 mg of lyophilized powder with 1.2 mL 70% methanol solution, vortex 30 seconds every 30 minutes for 6 times in total. Following centrifugation at 12000 rpm for 3 min, the extracts were filtrated before UPLC-MS/MS analysis. The extracts were analyzed using an UPLC–ESI– MS/MS system (UPLC, ExionLC™ AD, https://sciex.com.cn/ ; MS, Applied Biosystems 6500 Q TRAP, https://sciex.com.cn/ ). The analytical conditions were as follows, UPLC: column, Agilent SB-C18. Mobile phase: Phase A is ultrapure water (with 0.1% formic acid), Phase B is acetonitrile (with 0.1% formic acid). Gradient program: The proportion of phase B was 5% at 0 min. Within 9.00 min, the proportion of phase B linearly increased to 95% and remained at 95% for 1min. From 10 to 11.1 min, the proportion of phase B decreased to 5% and balanced at 5% until 14 min. The ESI source operation parameters were as follows: source temperature 500°C: ion spray voltage (IS) 5500 V (positive ion mode)/-4500 V (negative ion mode), and oteher operation parameters were performed according to previously reported (Zhang et al. 2023a ). A specific set of MRM transitions were monitored for each period according to the metabolites eluted within this period. 2.5 HS-SPME-GC-MS analysis Chromatography and mass spectrometry were performed with the assistance of Wuhan Metware Biotechnology Co., Ltd. (Wuhan, China). Volatile metabolites in CTLs were analyzed by HS-SPME-GC-MS. A total of 1.5 g CTLs power was placed in a 10 ml glass vial and extracted by headspace solid phase microextraction (120 µm DVB/CAR/PDMS fiber, Supelco, Bellefonte, USA) at 60 ℃ for 15 min. After extraction, volatile metabolites were identified according to previously reported (Fan et al. 2023 ). The MS was operated in selected ion monitoring (SIM) mode for the identification and quantification of metabolites. 2.6 Statistical analysis and visualization The mass spectrometry data were processed using the software Analyst 1.6.3. The chromatographic peaks of the mass spectrometry data were integrated and corrected using the MultiOuant software. Principal component analysis (PCA), hierarchical cluster analysis (HCA), orthogonal partial least square discriminant analysis (OPLS-DA) and KEGG enrichment analysis were used to analyze non-volatile and volatile differential metabolites. The screening criteria for differential metabolites were VIP > 1, P value < 0.05, Fold change ≥ 2. The trace analysis of characteristic metabolites was performed by MetOringin (Yu et al. 2022 ), Simole MetOrigin analysis (SMOA) model was selected, and common tobacco ( Nicotiana tabacum ) was selected as host. ROC curve analysis was performed with pROC 1.16 in R software (version 3.5.1) to screen for potential biomarkers. Two-way ANOVA was used to analyze the difference of chemical composition indexes between Foreign and Yunnan. Fisher's LSD was used to compare the level of difference in variables between groups. A probability of P < 0.05 indicated that the differences were significant. The SPSS 24.0 (IBM) was used for descriptive statistical analysis and analysis of variance. OriginPro 2024b (10.1.5.132) was used to perform hierarchical cluster analysis and draw polar heat maps. 3. Results 3.1 Conventional chemical composition In addition to total nitrogen, chlorine and potassium, there were significant differences in total sugar, reducing sugar and nicotine between Yunnan and foreign countries (among groups), while there were only significant differences in pH and polyphenol among 9 different producing unit (within groups) (Table 1 ). The results indicated that the differences between groups were significantly greater than the differences within groups, and the grouping Settings were reasonable. The results of the two-way ANOVA test showed that the contents of total sugar, reducing sugar, magnesium and polyphenol in Yunnan CTLs were significantly lower than those in Foreign CTLs. The contents of total nicotine, pH, pectin, starch, lignin and petroleum ether extract were significantly higher than those of CTLs from Foreign. There were no significant differences in total nitrogen, potassium and chlorine (Table 1 ). The nicotine, reducing sugar and pectin were selected as biomarkers of conventional chemical components, which had great influence on quality. Table 1 Two-way ANOVA of conventional chemical components of cigar tobacco leaves between Foreign and Yunnan. Origins Producing unit Total sugar Reducing sugar Total nitrogen Total nicotine Chlorine Potassium Magnesium Pectin Starch pH petroleum ether extract Xylogen Polyphenol Foreign ID 0.40 ± 0.05 0.33 ± 0.07 3.88 ± 0.34 1.22 ± 0.17 1.14 ± 0.21 4.14 ± 0.03 1.36 ± 0.04 4.37 ± 0.59 0.71 ± 0.29 6.08 ± 0.08 3.49 ± 0.71 1.59 ± 0.08 3.78 ± 0.55 BR 0.34 ± 0.07 0.31 ± 0.07 3.87 ± 0.28 1.47 ± 0.5 0.74 ± 0.21 4.84 ± 0.62 1.3 ± 0.13 4.93 ± 0.67 0.70 ± 0.06 6.45 ± 0.12 3.24 ± 0.37 2.13 ± 0.25 3.72 ± 0.28 DO 0.40 ± 0.06 0.35 ± 0.05 4.05 ± 0.32 4.50 ± 0.58 1.58 ± 0.95 3.79 ± 0.89 0.89 ± 0.24 4.69 ± 0.47 1.08 ± 0.24 6.06 ± 0.46 5.89 ± 0.06 1.56 ± 0.3 4.48 ± 1.58 MEX 0.41 0.31 4.77 3.1 0.27 4.71 1.06 4.2 0.45 6.01 1.95 1.83 3.42 CU 0.38 ± 0.1 0.22 ± 0.21 3.54 ± 0.11 2.07 ± 0.04 1.04 ± 0.43 4.24 ± 0.18 0.91 ± 0.25 3.85 ± 0.57 0.97 ± 0.51 6.38 ± 0.11 4.24 ± 0.26 1.76 ± 0.34 3.82 ± 0.67 Yunnan PE 0.30 ± 0.06 0.1 ± 0.04 4.04 ± 0.44 5.12 ± 1.2 0.74 ± 0.05 4.9 ± 0.19 0.61 ± 0.08 6.13 ± 0.89 1.10 ± 0.23 6.83 ± 0.1 5.78 ± 0.14 2.24 ± 0.59 2.99 ± 0.31 LC 0.37 ± 0.05 0.18 ± 0.01 4.63 ± 0.25 3.07 ± 0.39 0.41 ± 0.18 5.89 ± 0.22 0.82 ± 0.1 6.24 ± 0.51 1.19 ± 0.1 6.10 ± 0.1 3.66 ± 0.69 1.83 ± 0.24 3.88 ± 0.5 DH 0.34 ± 0.02 0.08 ± 0.01 3.95 ± 0.56 3.53 ± 0.84 2.65 ± 0.53 4.58 ± 0.81 1.24 ± 0.09 7.63 ± 0.9 1.31 ± 0.25 6.61 ± 0.15 4.57 ± 0.2 2.20 ± 0.3 3.23 ± 0.24 YX 0.27 ± 0.05 0.07 ± 0.03 3.97 ± 0.19 3.23 ± 0.13 1.14 ± 0.23 3.83 ± 0.03 0.88 ± 0.05 4.95 ± 0.49 1.16 ± 0.06 6.76 ± 0.1 6.99 ± 0.5 3.43 ± 0.21 2.54 ± 0.15 Two-way ANOVA P-Values Origins 0.018 <0.0001 0.169 0.011 0.57 0.090 0.039 <0.0001 0.003 0.007 0.031 0.005 0.015 Producing unit 0.37 0.74 0.85 0.83 0.06 0.07 0.25 0.57 0.84 0.012 0.34 0.90 0.014 Origins × Producing unit 0.43 0.82 0.56 0.80 0.20 0.29 0.60 0.58 0.61 0.018 0.29 0.91 0.049 Note: The unit of each index in the table is %, P ≤ 0.05. 3.2 Volatile aroma compound Through quantitative analysis of 44 volatile aroma components detected in foreign and Yunnan CTLs, the results showed that 15 aroma components such as valeraldehyde, pyridine and megastigmatrienone were significantly higher in Yunnan, while 29 aroma components such as 1-Penten-3-one, 2,3-Pentanedione and β-cyclocitral were significantly higher in Foreign. The results of hierarchical cluster analysis showed that 44 volatile aroma components were divided into 2 categories, which could distinguish Yunnan CTLs from Foreign CTLs. Further by OPLS-DA by variable importance projection (VIP) > 1 material has 18 kinds, in which pyridine and beta-damascone and megastigmatrienone content is higher in Yunnan CTLs. The contents of 1-penten-3-one; 2,3-pentanedione; 3-methyl-2-butena; n-hexanal; 2-acetyl-1; pyrroline; β-cyclocitral; indole; 6,8-nonadien-2-one,8-methyl-5-(1-methylethyl)-, (6e); cyclohexanone, 2,3-dimethyl-2-(3-oxobutyl); beta-ionone 5,6-epoxide and 2(4h)-benzofuranone,5,6,7,7a-tetrahydro-4,4,7a-trimethyl were relatively high in Foreign CTLs. Secondly, the types of odorant substances with relatively high content in CTLs abroad were significantly more than those in Yunnan (Fig. 1 ). According to the clustering results, pyridine, megastigmatrienone and β-cyclocitral with VIP value > 1.6 and relative content difference were selected as the characteristic aroma substances to distinguish Foreign and Yunnan CTLs. 3.3 Volatile and non-volatile metabolic profiles 3.3.1 Metabolic profiling A total of 1,105 non-volatile metabolites were identified in the 27 CTLs samples, of which 541 were annotated by KEGG. The Pearson correlation coefficient R2 > 95%, which met the requirements for subsequent analysis. Ten classes of metabolites were detected in all CTLs, including phenolic acids (16.56%), alkaloids (14.12%), flavonoids (14.03%), amino acids and derivatives (12.58%), lipids (12.04%), and organic acids (10.41%) (Fig. 2 A). Accounted for 79.76% of total tissue metabolites. A total of 615 volatile metabolites were detected based on GC-MS, 223 of which were annotated to the KEGG pathway. The detected metabolites were divided into 15 categories, of which terpenoids (22.44%), lipids (14.63%), heterocyclic compounds (14.15%), ketones (9.92%) and hydrocarbons (8.94%) accounted for more than 70% (Fig. 2 B). The non-volatile metabolites PCA analysis showed that samples in the same group could not only be clustered together, but also separated from samples in other groups, and the trend of metabolite separation among groups was obvious (Fig. 3 A). Importantly, the CTLs of YNLC and YNYX were more similar, while the clustering of YNDH and YNPE was similar. For foreign samples, except BR-3, the other origin units are close to each other. Similarly, the HCA analysis showed that samples from foreign also clustered together (Fig. 3 B). These results indicate that the non-volatile metabolites of CTLs from foreign countries and Yunnan are significantly different. The PCA analysis of volatile metabolites in the two groups showed a weak separation trend between the groups (Fig. 3 C). The HCA analysis results showed that the CTLs samples were grouped into four categories, among which YNDH, YNPE and YNLC were grouped into one category, Cuba, Mexico, Indonesia and Brazil were grouped into one category, and YNYX and Dominica were grouped separately into one category. One sample from Indonesia is similar to that from Dominica (Fig. 3 D). In conclusion, there are some differences in the volatile metabolites of CTLs produced under different ecological conditions, but they have the general characteristics of convergence. 3.3.2 Differential metabolites The OPLS-DA was used for quantitative and modeling analysis of metabolites, the score map was drawn, and VIP values were obtained. R 2 Y represents the interpretation rate of the model, Q 2 is used to evaluate the prediction ability of the model, and R 2 Y > Q 2 indicates that the model is well established. According to cross-validation, the interpretation of PC1 and PC2 of non-volatile metabolites to the dataset was 23.2% and 9.01%, respectively, indicating large differences between groups and small differences between samples within groups. The prediction model R 2 Y = 0.989, Q 2 = 0.953, R 2 Y > Q 2 (Fig. 4 A, B). The interpretation of PC1 and PC2 of volatile metabolites were 23.9% and 16.3%, respectively, and the prediction model R 2 Y = 0.977, Q 2 = 0.945, and R 2 Y > Q 2 (Fig. 4 C, D). The results show that the two metabolomic models are well established and have good predictive ability and interpretation effect for CTLs from Yunnan and Foreign. We further selected VIP > 1, Log 2 FC > 2 and P < 0.5 as differential metabolites, and Fig. 5 shows the relative content difference and statistical significance of metabolites in Foreign vs Yunnan. A total of 248 volatile differential metabolites were identified, of which 88 were significantly down-regulated, the “N, N'-diferuloylputrescine, retusin and 5-hydroxyl-3',4',7,8-tetramethoxyl flavone N, N' -diferuloylutamide” were the top3 differentially varied substances. And 160 were significantly up-regulated, and the “tomatine, N-(3-indolylacetyl)-L-alanine and 4,4,5-trihydroxy-l, l'-di-2-propenylbiphenyl” were the three substances with the largest Log 2 FC (Fig. 5 A). A total of 150 volatile metabolites were identified, of which 107 were significantly down-regulated, “p-menth-8-en-3-ol-acetate, benzoic acid, 1-methylethyl ester and phenol, 3,5-dimethyl” were the Top3, and 43 were significantly up-regulated. “bornyl acetate, (R) -3, 7-dimethyl-6-octenol and styrene were the largest differential multiples (Fig. 5 B). The differential metabolites were classified and counted based on substance classification. The results showed that the primary classification of non-volatile differential metabolites mainly belonged to amino acids and their derivatives (28%), alkaloids (18.2%), phenolic acids (11.9%), lipids (11.9%) and organic acids (10.2%) (Fig. 6 A). The alkaloids were mainly alkaloids, phenolamines and indole alkaloids (12.6%), and the lipids were mainly free fatty acids (7.6%). The main volatile differential metabolites were terpenoids (19.5%), lipids (14.8%), heterocyclic compounds (13.40%) and aromatics (10.1%) (Fig. 6 B). In summary, the main metabolites of CTLs from Foreign vs Yunnan were amino acids and their derivatives, alkaloids, terpenoids and lipids. 3.3.3 Metabolic pathways of differential metabolites KEGG pathway enrichment analysis showed that non-volatile differential metabolites were significantly enriched in 11 metabolic pathways (P < 0.05), including “aminoacyl-tRNA biosynthesis, tropinane, piperidine and pyridine alkaloid biosynthesis, tryptophan metabolism, biosynthesis of amino acid, D-amino acid metabolism, glucosinolide biosynthesis, arginine biosynthesis” (Fig. 7 A). The volatile differential metabolites are mainly involved in “biosynthesis of various plant secondary metabolites, monoterpenoid biosynthesis and phenylalanine metabolism” (Fig. 7 B). 3.4 Characteristic metabolites and potential markers ROC curve analysis is widely recognized as an objective and effective method to evaluate biomarker performance. To identify the characteristic metabolites of CTLs in Yunnan, strict screening criteria were applied: AUC = 1.0, Log 2 FC > 2, VIP > 1, P < 0.01. These criteria enabled us to identify the characteristic metabolites more accurately. For non-volatile metabolites, a total of 11 metabolites with distinctive characteristics were identified, mainly steroid alkaloids, amino acids and their derivatives, lignans, phenolic acids, free fatty acids and coumarins (Table 2 ). Based on ROC curves and AUC values, we suggest that tomatine, n-(3-indolyl) -L-alanine, and 5,6,7-trimethoxycoumarin may be potential non-volatile biomarkers. Table 2 The non-volatile biomarkers of cigar tobacco leaves between Foreign and Yunnan Metabolites AUC VIP P-value Log 2 FC Formula Class II Tomatidine 1 2.06 3.27E-05 17.89 C27H45NO2 Steroid alkaloids N-(3-Indolylacetyl)-L-alanine 1 2.06 0.000111 17.77 C13H14N2O3 Amino acids and derivatives 4,4,5-Trihydroxy-l,l'-di-2-propenylbiphenyl 1 2.05 0.004481 17.52 C18H18O3 Lignans L-γ-Glutamyl-L-leucine 1 1.89 0.000522 4.98 C11H20N2O5 Amino acids and derivatives L-Prolyl-L-Leucine 1 1.12 3.2E-06 3.28 C11H20N2O3 Amino acids and derivatives Dihydrocaffeic acid 1 1.40 1.4E-08 -2.30 C9H10O4 Phenolic acids 12,13-DHOME; (9Z)-12,13- Dihydroxyoctadec-9-enoic acid 1 1.93 8.82E-06 -2.32 C18H34O4 Free fatty acids Hexadecanedioic acid 1 1.90 3.05E-05 -2.40 C16H30O4 Free fatty acids 9,16-Dihydroxypalmitic acid 1 1.83 2.6E-05 -2.58 C16H32O4 Free fatty acids 9-Hydroperoxy-9Z,11E-Octadecadienoic Acid 1 2.00 3.24E-07 -2.83 C18H32O4 Free fatty acids Benzoyltartaric acid 1 1.48 1.86E-08 -2.91 C11H10O7 Phenolic acids 7S,8S-DiHODE; (9Z,12Z)-(7S,8S)- Dihydroxyoctadeca-9,12-dienoic acid 1 2.00 3.1E-07 -2.92 C18H32O4 Free fatty acids 10,16-Dihydroxypalmitic acid 1 1.79 0.000178 -2.95 C16H32O4 Free fatty acids 5,6,7-Trimethoxycoumarin 1 1.78 0.002976 -5.18 C12H12O5 Coumarins As for volatile metabolites, a total 10 characteristic compounds were identified, belonging to terpenes, aromatics, heterocyclic compounds, hydrocarbons, ethers and lipids (Table 3 ). The bornyl acetate, 5,6,7, 8-tetrahydroquinoxaline and benzoic acid, 1-methylethyl ester could be defined as biomarkers for volatile metabolites. Table 3 The volatile biomarkers of cigar tobacco leaves between Foreign and Yunnan Metabolites AUC VIP P-value Log 2 FC Formula Class I Bornyl acetate 1 1.95 1.62E-06 12.68 C12H20O2 Terpenoids Octen-1-ol, 3,7-dimethyl-, (R)- 1 1.59 7.41E-06 3.79 C10H20O Terpenoids Ethylbenzene 1 1.83 1.15E-07 3.00 C8H10 Aromatics Ethanone, 1-(3,5-dimethylpyrazinyl)- 1 1.80 1.15E-07 2.92 C8H10N2O Heterocyclic compound Benzene, 1,3-dimethyl- 1 1.81 1.15E-07 2.10 C8H10 Aromatics Trimethyltridecane 1 1.90 1.15E-07 -2.32 C16H34 Hydrocarbons Benzene, 1-methoxy-4-(1-methylpropyl)- 1 1.46 1.15E-07 -3.29 C11H16O Ether 5,6,7,8-Tetrahydroquinoxaline 1 1.96 1.15E-07 -3.40 C8H10N2 Heterocyclic compound Benzoic acid, 1-methylethyl ester 1 1.97 1.15E-07 -3.77 C10H12O2 Ester p-Menth-8-en-3-ol, acetate 1 1.50 1.15E-07 -5.24 C12H20O2 Ester 3.5 MetOrigin analysis of the differential metabolites MetOrigin analyses were performed for 11 non-volatile and 10 volatile characteristic metabolites, using sankey networks to integrate statistical and biological associations. The results showed that non-volatile characteristic metabolites were involved in the “degradation of flavonoids”, “linoleic acid metabolism”, and “the biosynthesis of cutin, xylocin, and wax”, “suberine and wax biosynthesis”. The volatile metabolites are enriched into two metabolic pathways “ethylbenzene degradation” and “xylene degradation”. Further identification of the microbial communities related to non-volatile metabolites showed that the microorganisms closely related to flavonoid degradation were mainly Pseudomonadota and Bacillota. The phloretin hydrolase was involved in degradation of flavonoid. The 3-Hydroxyphloretin is a metabolic substrate, while dihydrocaffeicacid and phloroglucinol are metabolites (Fig. 8 A). Associated with linoleic acid metabolism are the microorganisms of Podospora and Pyricularia , and linoleate lipoxygenase and hydroperoxidase isomerase are involved in linoleic acid metabolism (Fig. 8 B). The microbial communities associated with xylene degradation, a volatile characteristic metabolite, were Novosphingobium , Sphingobium , Pseudoxanthomonas , Rhodococcus , and Pseudonocardia , ferridoxin reductase and toluene monooxygenase participate in the degradation of xylene, and the metabolite is 3-methylbenzyl alcohol (Fig. 8 C). The microorganisms associated with the degradation metabolism of ethylbenzene were pseudomonas , Bacillus , Actinomycetota, Bacteroidota and Euryarchaeota. Many oxygenases, such as ferredoxin NAD(P) reductase, naphthalene dioxygenase, arylnitro and dinitrotoluene, participate in ethylbenzene degradation, and the metabolite is 1-phenylethanol (Fig. 8 D). 4. Discussion This study selected 5 different ecological and climatic conditions Foreign CTLs and 4 Yunnan CTLs as experimental materials, and the results of PCA and HCA analysis (Fig. 3 ) show that the sample clustering is obvious, and the separation trend of the two groups is obvious. In terms of metabolic composition, the selected samples and group setting have scientific basis, which can objectively analyze and compare the differences in two types of CTLs raw materials with different ecological types. The conventional chemical components of the representative Foreign CTLs generally have high sugar, protein and polyphenol content, while nicotine content in Yunnan was significantly higher than that of Foreign, previous research results also show that the nicotine content of domestic CTLs is significantly higher than that of South America and Southeast Asia (Sun et al. 2020 ). Our previous research also indicated that the contents of starch, nicotine and pectin in CTL produced in the Dominica and Indonesia were significantly lower than those in CTL from China (Wu et al. 2023 ). The comparative analysis of volatile aroma components shows that 6 metabolites, including pyridine, megastigmatrienone, and β-damascenone, have higher quality fractions in Yunnan CTLs, while 12 metabolites, including β-cyclocitral, 1-penten-3-one, and 6,8-nonadien-2-one,8-methyl-5-(1-methylethyl), have higher content in Foreign CTLs (Fig. 1 ). The research results are basically consistent with the trend of the types and contents of aromatic substances in Cuba cigar, non-Cuba cigar, and Chinese cigar (Yu et al. 2021 ). Combining the extensive targeted metabolomics identification of differential metabolites, classification (Table 3 ), and participation of biogenic amines, various amino acids, lipids, and flavonoids and related metabolic pathways (Fig. 7 ), it can be found that the main reasons for the low sugar-to-alkaloid ratio, high pectin, starch, and petroleum ether extract content in Yunnan CTLs are probably that many large molecular substances are not fully hydrolyzed or metabolic pathway are interrupted, resulting in low aromatic substance activity values (Zhang et al. 2024 ; Yu et al. 2021 ), which are missing or relatively low, leading to differences in sensory quality. Currently, over 6,000 chemical components have been identified and reported in tobacco (Gui et al. 2024 ), and the basic skeleton substances in tobacco leaves consist of sugars, nitrogen-containing compounds, alkaloids, pigments, and mineral elements, as well as a variety of secondary metabolites. In this study, we conducted a non-targeted metabolomics analysis to comprehensively characterize the metabolites in different CTLs, and found that phenolic acids, alkaloids, flavonoids, amino acids and their derivatives, lipids, and organic acids are the main components of non-volatile metabolites in CTLs (Fig. 2 A). The proportion of various metabolites in CTLs is dynamic during the air-curing and fermentation process (Zhang et al. 2023a ; Zhao et al. 2023 ). The main components of volatile metabolites are terpenes, lipids, heterocyclic compounds, ketones, and hydrocarbons (Fig. 2 B). Previous analysis of the significant differentially abundant metabolites before and after fermentation indicated that the enriched metabolic pathways were various amino acid metabolism, monoterpene, sesquiterpene, and triterpene biosynthesis (Fan et al. 2023 ), indicating that terpene compounds are mainly synthesized during fermentation. Based on the methods described by Dan et al. ( 2021 ) and Steinfath et al. ( 2010 ), this study selected the top three metabolites in terms of AUC and VIP values to significantly improve the diagnostic performance of biomarkers. This indicates that using multiple metabolites as discriminating substances may be more effective than using a single metabolite. Therefore, it is suggested that the top three feature metabolites of conventional chemical components, aroma-forming substances, non-volatile metabolites, and volatile metabolites be combined as discriminating substances for potential biomarkers of Yunnan and Foreign CTLs, and that the production processes of cultivation, air-curing, and fermentation be directed based on the differences in the signature metabolites. The biomarkers discovered in this study could be used for the accurate identification and traceability of CTL origin, and also as quality control indicators for tobacco leaf quality during the fermentation process. MetOrigin is an interactive web server that can distinguish metabolites from the microbiome, perform origin-based metabolic pathway enrichment analysis, and use the Sankey network to trace metabolites with statistical correlation and biology (Yu et al. 2022 ), which can not only rapidly identify microbial metabolites and their metabolic functions, but also identify microbial metabolites. It can also facilitate the discovery of specific microorganisms that are closely related to metabolites. The MetOrigin analysis showed that non-volatile characteristic metabolites were involved in three metabolic pathways of flavonoid degradation, linoleic acid metabolism, and keratin, xylocin and wax biosynthesis, while volatile metabolites were enriched in ethylbenzene degradation and xylene degradation pathways (Fig. 8 A). The closely related microorganisms are mainly Pseudomonas , Bacillus and Sphingomonas . Previous studies have shown that the main functional microbial community in the air-curing and fermentation CTLs is bacteria, which includes but is not limited to the key microbial genera obtained from this study (Zhang et al. 2023c ). The results also indicate that the flavor formation of CTLs were closely related to the microbial community. During the long production process, CTLs serve as the host to provide nutrition and habitat for the growth and reproduction of functional microorganisms, a variety of enzymes produced in tobacco cells or microbial metabolism as the main catalytic factors to mediate the various metabolic pathways of carbohydrates and nitrogen compounds, the whole process is also affected by geographical conditions, ecological climate, technology and other external environmental factors. 5. Conclusion In this study, through multi-omics and multi-dimensional qualitative and quantitative detection and analysis of CTLs from different producing areas, it was found that compared with Foreign CTLs, Yunnan CTLs had a lower sugar-base ratio and higher content of starch, pectin and lignin, which had adverse effects on sensory quality. The metabolome showed that phenolic acids, alkaloids, flavonoids, amino acids and derivatives, lipids, organic acids, terpenoids, lipids, heterocyclic compounds, ketones and hydrocarbons were the main components of secondary metabolites in cigar tobacco. A variety of analytical methods were used to screen and verify 12 potential markers of Yunnan and Foreign CTLs, which were nicotine, reducing sugar and pectin. The pyridine, megastigmatrienone, and β-damascenone three kinds of aroma substances; tomatine, n-(3-indolyl) -L-alanine, and 5,6,7-trimethoxycoumarin were three non-volatile metabolites. Three volatile metabolites bornyl acetate, 5,6,7, 8-tetrahydroquinoxaline and benzoic acid, 1-methylethyl ester. The differences in the material components of CTLs between Yunnan and Foreign are determined by soil, climate and process technology. However, in the production process, the use of microorganisms and enzymes to cooperatively regulate the carbon and nitrogen coupling metabolic pathways in CTLs to achieve raw material homogenization is a systematic study that needs to be carried out in the next step. Declarations Ethics approval and consent to participate Not applicable. Author contributions ZG (Guanghai Zhang) and LY: writing—original draft; methodology; validation; investigation; data curation. ZG (Gaokun Zhao) and KG: funding acquisition; resources; writing—review & editing; supervision. LM, WY, YH, LW and XH: validation, investigation, discussed the results and commented on the manuscript. All authors have read and agreed to the published version of the manuscript. Funding This work was supported by the China Tobacco Monopoly Bureau Grants and Yunnan Provincial Tobacco Monopoly Bureau Grants (110202201037（XJ-08）/2023530000241001, 110202103018/2022530000241002), Project of Yunnan Daguan Laboratory (YNDG202402XJ01). Availability of data and materials Data and materials described in this study are available from the authors upon reasonable request and availability. Consent for publication All authors approved the consent for publishing the manuscript to Bioresources and Bioprocessing. Competing interests The authors declare that they have no competing interests. Author details Yunnan Academy of Tobacco Agricultural Sciences, Yuantong Road 33#, Kunming 650021, Yunnan Province, China. References Dan Z, Chen Y, Li H, Zeng Y, Xu W, Zhao W, He R, Huang W (2021) The metabolomic landscape of rice heterosis highlights pathway biomarkers for predicting complex phenotypes. Plant Physiology 187(2):1011-1025. https:// doi:10.1093/plphys/kiab273 Fan J, Kong G, Yao H, Wu Y, Zhao G, Li F, Zhang G (2023) Widely targeted metabolomic analysis reveals that volatile metabolites in cigar tobacco leaves dynamically change during fermentation. Biochemistry and Biophysics Reports 35. https:// doi:10.1016/j.bbrep.2023.101532 Gui Z, Yuan X, Yang J, Du Y, Zhang P (2024) An updated review on chemical constituents from Nicotiana tabacum L.: Chemical diversity and pharmacological properties. Industrial Crops and Products 214:118497. https:// doi:org/10.1016/j.indcrop.2024.118497 Li Y, Lin Q, Pang T, Shi J, Kong G, Mu M (2015) Qualititative and quantitative analysis of volative flavor components in tobacco leaf by gas chromatography-mass spectrometry. Journal of Analytical Science 31(3):372-378. https:// doi:10.13526/j.issn.1006-6144.2015.03.016 Pavagadhi S, Swarup S (2020) Metabolomics for Evaluating Flavor-Associated Metabolites in Plant-Based Products. Metabolites 10(5):1-21. https:// doi:10.3390/metabo10050197 Steinfath M, Strehmel N, Peters R, Schauer N, Groth D, Hummel J, Steup M, Selbig J, Kopka J, Geigenberger P, Van Dongen JT (2010) Discovering plant metabolic biomarkers for phenotype prediction using an untargeted approach. Plant Biotechnol J 8(8):900-911. https:// doi:10.1111/j.1467-7652.2010.00516.x Sun Y, Zhao Y, Zhou D, et al (2020) Analysis on TSNAs and alkaloid contents in different types of cigar tobacco from different regions at home and abroad. Acta Tabacaria Sinica 26(05):30-38. https:// doi:DOI: 10.16472/j.chinatobacco.2020.029 Wang H, Guo D, Zhang M, Wu G, Shi Y, Zhou J, Ding N, Chen X, Li X (2024) Correlation study on microbial communities and volatile flavor compounds in cigar tobacco leaves of diverse origins. Applied Microbiology and Biotechnology 108(1). https:// doi:10.1007/s00253-024-13032-6 Wang Y , Liu G , Xiang X , et al (2020) Overview of main cigar production areas and variety resources at domestic and overseas. Chinese Tobacco Science 41(03):93-98. https:// doi:10.13496/j.issn.1007-5119.2020.03.016 Wu Y, Huang D, Kong G, Zhang C, et al (2023). Geographical origin determination of cigar at different spatial scales based on C and N metabolites and mineral elements combined with chemometric analysis. Biological Trace Element Research 201(8):4191-4201. https://doi.org/10.1007/s12011-022-03499-7 Xu B, Zi C, Wang J, et al (2024) Comparison of sensory quality characteristics of representative domestic and imported cigars. Tobacco Science & Technology 57(06):64-74. https:// doi:10.16135/j.issn1002-0861.2024.0154 Yu G, Xu C, Zhang D, Ju F, Ni Y (2022) MetOrigin: discriminating the origins of microbial metabolites for integrative analysis of the gut microbiome and metabolome. iMeta 1(1):e10. https:// doi:10.1002/imt2.10 Yu H, Liu Y, Shang M, et al (2021) Cigar leaf differences from different producing areas based on aroma component analysis. Tobacco Science & Technology 54(09):58-71. https:// doi:10.16135/j.issn1002-0861.2021.0003 Zhang G, Yao H, Zhao G, Wu Y, Xia H, Li Y, Kong G (2023a) Metabolomics reveals the effects producing region and fermentation stage on substance conversion in cigar tobacco leaf. Chemical and Biological Technologies in Agriculture 10(1):66. https:// doi:10.1186/s40538-023-00444-1 Zhang G, Zhao L, Li W, Yao H, Lu C, Zhao G, Wu Y, Li Y, Kong G (2023b) Changes in physicochemical properties and microbial community succession during leaf stacking fermentation. AMB Express 13(1):132. https:// doi:10.1186/s13568-023-01642-8 Zhang M, Guo D, Wang H, Wu G, Ding N, Shi Y, Zhou J, Zhao E, Li X (2024) Integrated characterization of filler tobacco leaves: HS–SPME–GC–MS, E-nose, and microbiome analysis across different origins. Bioresources and Bioprocessing 11(1). https:// doi:10.1186/s40643-024-00728-w Zhang Q, Kong G, Zhao G, Liu J, Jin H, Li Z, Zhang G, Liu T (2023c) Microbial and enzymatic changes in cigar tobacco leaves during air-curing and fermentation. Applied Microbiology and Biotechnology 107:5789–5801. https:// doi:10.1007/s00253-023-12663-5 Zhao G, Zhang Q, Kong G, Yao H, Wu Y, Cai B, Liu T, Zhang G (2023) Identification of physiological and metabolic networks involved in postharvest browning of cigar tobacco leaves. Chemical and Biological Technologies in Agriculture 10(135). https:// doi:10.1186/s40538-023-00509-1 Zhu X, Gao Y, Chen Z, Su Q (2009) Development of a chromatographic fingerprint of tobacco flavor by use of GC and GC-MS. Chromatographia 69(7):735-742. https:// doi:10.1365/s10337-009-0968-4 Supplementary Files GraphicalAbstract.jpeg TableS1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5777714","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":448505084,"identity":"4dd2a991-b79f-4a75-866d-c360e904f702","order_by":0,"name":"Guanghai Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYFACHiC2seHhZ28gSUtamoxkzwHStBy2MbjhQKQG+f61hz/zJJznYbjBwPjhYw4RWhhnvEswnJFwm4dxdgOz5MxtRGhhljhjkPDxx20eZpkDbMy8xGhhA2o5kJBwjodNIoFILTz8PYYNHxIO8PAQrUVCgseYcUZCMo8Ez8Fm4vwi33/GGBhidvb2x5sPfvhIjBYGiQQYi7GBGPVAwH+ASIWjYBSMglEwcgEAzP4ygvP9nU4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1326-8570","institution":"Yunnan Academy of Tobacco Science","correspondingAuthor":true,"prefix":"","firstName":"Guanghai","middleName":"","lastName":"Zhang","suffix":""},{"id":448505085,"identity":"addc12bb-7912-4e9a-ad43-e38037a559d6","order_by":1,"name":"Gaokun Zhao","email":"","orcid":"","institution":"Yunnan Academy of Tobacco Science","correspondingAuthor":false,"prefix":"","firstName":"Gaokun","middleName":"","lastName":"Zhao","suffix":""},{"id":448505086,"identity":"e4226735-e59d-4b89-aa79-aa6ea3046130","order_by":2,"name":"Guanghui Kong","email":"","orcid":"","institution":"Yunnan Academy of Tobacco Science","correspondingAuthor":false,"prefix":"","firstName":"Guanghui","middleName":"","lastName":"Kong","suffix":""},{"id":448505087,"identity":"31825e39-8981-43c7-a455-92e75abd0fe8","order_by":3,"name":"Mengxia Li","email":"","orcid":"","institution":"Yunnan Academy of Tobacco Science","correspondingAuthor":false,"prefix":"","firstName":"Mengxia","middleName":"","lastName":"Li","suffix":""},{"id":448505088,"identity":"ddd489e2-fef9-414e-8e7d-378c972819a0","order_by":4,"name":"Yuping Wu","email":"","orcid":"","institution":"Yunnan Academy of Tobacco Science","correspondingAuthor":false,"prefix":"","firstName":"Yuping","middleName":"","lastName":"Wu","suffix":""},{"id":448505089,"identity":"fa4abcf8-ee86-4be0-b816-15c449a4bc77","order_by":5,"name":"Heng Yao","email":"","orcid":"","institution":"Yunnan Academy of Tobacco Science","correspondingAuthor":false,"prefix":"","firstName":"Heng","middleName":"","lastName":"Yao","suffix":""},{"id":448505090,"identity":"0e231fd6-6fc0-4f14-a980-dd2d4a998858","order_by":6,"name":"Wei Li","email":"","orcid":"","institution":"Yunnan Academy of Tobacco Science","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Li","suffix":""},{"id":448505091,"identity":"fb9c0df8-a054-422e-b959-384f2f5d4cd7","order_by":7,"name":"Huachang Xia","email":"","orcid":"","institution":"Yunnan Academy of Tobacco Science","correspondingAuthor":false,"prefix":"","firstName":"Huachang","middleName":"","lastName":"Xia","suffix":""},{"id":448505092,"identity":"f5ee953c-1afc-426b-92a8-422b7ff3d581","order_by":8,"name":"Yongping Li","email":"","orcid":"","institution":"Yunnan Academy of Tobacco Science","correspondingAuthor":false,"prefix":"","firstName":"Yongping","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-01-07 04:19:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5777714/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5777714/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81686117,"identity":"2a9882f3-612a-4bf6-ab67-cf146b8e52f7","added_by":"auto","created_at":"2025-04-30 10:27:24","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5926934,"visible":true,"origin":"","legend":"\u003cp\u003ePolar Heatmap of volatile aroma compounds in cigar tobacco leaves. Chemical names in red font indicate VIP \u0026gt; 1.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5777714/v1/993d026a6ebb3f7b36af5a9f.jpg"},{"id":81686118,"identity":"1a24af20-ff90-4c90-8d77-08180050b59a","added_by":"auto","created_at":"2025-04-30 10:27:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2535933,"visible":true,"origin":"","legend":"\u003cp\u003eThe composition of non-volatile (A) and volatile (B) metabolites in cigar tobacco leaves.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5777714/v1/e06fc2af013f9c25d185a0bb.jpg"},{"id":81686119,"identity":"04a9100e-49fe-457a-a9b6-3258f38eb24d","added_by":"auto","created_at":"2025-04-30 10:27:24","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5307478,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate statistical analysis of metabolites in cigar tobacco leaves (PCA, HCA). 1105 non-volatile metabolites were analyzed by PCA (A) and HCA (B). PCA (C) and HCA (D) analysis of 615 volatile metabolites.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5777714/v1/e911e4bbff91336ed671665d.jpg"},{"id":81686115,"identity":"4a409151-2322-4d03-bb75-af3dec824a96","added_by":"auto","created_at":"2025-04-30 10:27:24","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1897576,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of volatile (A, B) and non-volatile (C, D) metabolites OPLS-DA in cigar tobacco leaves.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5777714/v1/e554a1f1c648177160c50c53.jpg"},{"id":81686257,"identity":"9c00857c-2cff-49f3-93e4-dce88b546f86","added_by":"auto","created_at":"2025-04-30 10:35:24","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1851382,"visible":true,"origin":"","legend":"\u003cp\u003eVolcanic plots of nonvolatile (A) and volatile (B) differential metabolites of Foreign vs Yunnan.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5777714/v1/1877e3205d2af927849cbc16.jpg"},{"id":81686121,"identity":"20596de1-6627-4992-ae07-28cb1d187ce5","added_by":"auto","created_at":"2025-04-30 10:27:24","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1781290,"visible":true,"origin":"","legend":"\u003cp\u003eClassification and pie chart of nonvolatile (A) and volatile (B) differential metabolites of Foreign vs Yunnan.\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5777714/v1/41025b47dd0026987a95c25c.jpg"},{"id":81686938,"identity":"842d1e00-3fc6-4eab-b4a2-4aa3023cddea","added_by":"auto","created_at":"2025-04-30 10:43:24","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2168141,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG enrichment diagram of nonvolatile (A) and volatile (B) differential metabolites of Foreign vs Yunnan.\u003c/p\u003e","description":"","filename":"Fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5777714/v1/74c2cb1327d5afe7f2a8081f.jpg"},{"id":81686132,"identity":"051cee91-bdf2-47c1-ad28-5dfff51d96fb","added_by":"auto","created_at":"2025-04-30 10:27:25","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3836908,"visible":true,"origin":"","legend":"\u003cp\u003eBIO-Sankey network diagram of characteristic metabolite traceability analysis.\u003c/p\u003e","description":"","filename":"Fig8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5777714/v1/702518584ffda6ce49224a05.jpg"},{"id":83152003,"identity":"e3021e93-8ef4-4723-a64b-d13c9241cdc4","added_by":"auto","created_at":"2025-05-20 14:00:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16691934,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5777714/v1/c61933be-d273-47e3-ab67-29af9dfea025.pdf"},{"id":81687184,"identity":"809735a5-b236-4a84-87c9-2496e65658fd","added_by":"auto","created_at":"2025-04-30 10:51:24","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2383264,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5777714/v1/c7a62582b04166a04c4f25fa.jpeg"},{"id":81686114,"identity":"8553779f-4bdd-4a8c-8ef3-8b2304dbfe10","added_by":"auto","created_at":"2025-04-30 10:27:24","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21020,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5777714/v1/2ec5cc0f1f80390f6009ab1a.docx"}],"financialInterests":"","formattedTitle":"Metabolomics analysis of substance differences and biomarkers in cigar tobacco leaves from Yunnan and Foreign","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe production of high-quality cigar tobacco leaves(CTLs) requires a very high standard of ecological environment, with climate, water and soil being the key ecological factors that determine the style and characteristics of CTLs. Therefore, the world's high-quality cigar tobacco production areas are only distributed between the Tropic of cancer and the tropic of capricorn, especially around latitude 23 \u0026deg;N; the main foreign CTLs production areas include Cuba, the Dominican Republic, Indonesia, Nicaragua, Brazil, Mexico, the United States, Honduras, Ecuador, the Philippines, Jamaica, and Cameroon, etc (Zhang et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Currently, the main producing areas of CTLs in China are Hainan, Sichuan, Hubei, and Yunnan, while Yunnan is mainly distributed in Pu'er, Lincang, Dehong, and Yuxi. The results from the cigar manufacturing industry and consumer feedback both indicate that the Chinese cigar tobacco leaves have prominent characteristics and broad application prospects. However, compared to foreign cigar leaf, there are still sensory quality defects such as slightly weaker aroma characteristics and taste characteristics, slightly poorer aroma richness, slightly poorer smoke mellowness and sweetness (Xu et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSensory quality is determined by chemical substances, and the relationship between the two is extremely complex. Recently, studies have reported on the differences in aroma components, key aroma substances, and sensory flavor characteristics of CTLs from different production areas (Wang et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As well as the sensory quality characteristics of cigar at home and abroad (Xu et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This laid a solid data foundation for the analysis of the material composition of cigar tobacco leaves. However, there is still a lack of systematic identification of the metabolic profiles and differences of cigar tobacco produced under different ecological conditions.\u003c/p\u003e \u003cp\u003eMetabolomics has been widely applied to study various small molecules or metabolites in cells, tissues, or organisms, enabling comprehensive characterization of small molecules present in biological samples. Complex and high-throughput analysis platforms such as gas chromatography (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) are commonly used, greatly facilitating our understanding of global metabolomics and metabolic pathway networks (Pavagadhi and Swarup \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The objective of this study was to uncover the global metabolome in CTLs samples using unbiased profiling and metabolic fingerprinting methods to evaluate metabolites of multiple chemical classes associated with different pathways and to screen for core biomarker between Foreign and Yunnan. To provide basic data reference for the optimization and improvement of the CTLs production technology of Yunnan, such as cultivation, air-curing and fermentation, as well as the analysis of quality style characteristics.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 CTLs samples\u003c/h2\u003e \u003cp\u003eWe selected the raw CTLs of four main producing areas in Yunnan Province, as well as the imported CTLs of five foreign producing areas including Cuba, Dominican Republic, Indonesia, Brazil, and Mexico as the experimental materials. All 25 samples were selected from tobacco leaves with relatively consistent position and leaf thickness. The main differences among the samples lie in the types (wrapper/binder/filler) and grades. The detailed information on the samples is listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Determination of chemical compositions\u003c/h2\u003e \u003cp\u003eThe content of each index was determined by using a continuous flow analytical system or ion chromatography (Zhang et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). The contents of total sugar, reducing sugar, magnesium, polyphenol, total nitrogen, total nicotine, chlorine, potassium, pectin, starch, pH, petroleum ether extract, xylogen were separately determined in accordance with the standards in the tobacco industry YC/T 159\u0026ndash;2019、YC/T 161\u0026ndash;2002、YC/T 249\u0026ndash;2008、YC/T 468\u0026ndash;2013、YC/T 217\u0026ndash;2007、YC/T 162\u0026ndash;2011、YC/T 175\u0026ndash;2003、YC/T176-2003、YC/T 346\u0026ndash;2010、YC/T 347\u0026ndash;2010、YC/T 222\u0026ndash;2007.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Determination of volatile aroma compounds\u003c/h2\u003e \u003cp\u003eThe indices of 44 volatile aroma compounds were detected by steam distillation extraction and combined gas chromatography/mass spectrometry (GC-MS) (Li et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Quantitation based on the quantitation ion which is unique in the coeluted components increased the accuracy of peak integration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 UPLC-ESI-MS/MS analysis\u003c/h2\u003e \u003cp\u003eDissolve 50 mg of lyophilized powder with 1.2 mL 70% methanol solution, vortex 30 seconds every 30 minutes for 6 times in total. Following centrifugation at 12000 rpm for 3 min, the extracts were filtrated before UPLC-MS/MS analysis. The extracts were analyzed using an UPLC\u0026ndash;ESI\u0026ndash; MS/MS system (UPLC, ExionLC\u0026trade; AD, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sciex.com.cn/\u003c/span\u003e\u003cspan address=\"https://sciex.com.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; MS, Applied Biosystems 6500 Q TRAP, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sciex.com.cn/\u003c/span\u003e\u003cspan address=\"https://sciex.com.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe analytical conditions were as follows, UPLC: column, Agilent SB-C18. Mobile phase: Phase A is ultrapure water (with 0.1% formic acid), Phase B is acetonitrile (with 0.1% formic acid). Gradient program: The proportion of phase B was 5% at 0 min. Within 9.00 min, the proportion of phase B linearly increased to 95% and remained at 95% for 1min. From 10 to 11.1 min, the proportion of phase B decreased to 5% and balanced at 5% until 14 min. The ESI source operation parameters were as follows: source temperature 500\u0026deg;C: ion spray voltage (IS) 5500 V (positive ion mode)/-4500 V (negative ion mode), and oteher operation parameters were performed according to previously reported (Zhang et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). A specific set of MRM transitions were monitored for each period according to the metabolites eluted within this period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 HS-SPME-GC-MS analysis\u003c/h2\u003e \u003cp\u003eChromatography and mass spectrometry were performed with the assistance of Wuhan Metware Biotechnology Co., Ltd. (Wuhan, China). Volatile metabolites in CTLs were analyzed by HS-SPME-GC-MS. A total of 1.5 g CTLs power was placed in a 10 ml glass vial and extracted by headspace solid phase microextraction (120 \u0026micro;m DVB/CAR/PDMS fiber, Supelco, Bellefonte, USA) at 60 ℃ for 15 min. After extraction, volatile metabolites were identified according to previously reported (Fan et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The MS was operated in selected ion monitoring (SIM) mode for the identification and quantification of metabolites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis and visualization\u003c/h2\u003e \u003cp\u003eThe mass spectrometry data were processed using the software Analyst 1.6.3. The chromatographic peaks of the mass spectrometry data were integrated and corrected using the MultiOuant software. Principal component analysis (PCA), hierarchical cluster analysis (HCA), orthogonal partial least square discriminant analysis (OPLS-DA) and KEGG enrichment analysis were used to analyze non-volatile and volatile differential metabolites. The screening criteria for differential metabolites were VIP\u0026thinsp;\u0026gt;\u0026thinsp;1, P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fold change\u0026thinsp;\u0026ge;\u0026thinsp;2. The trace analysis of characteristic metabolites was performed by MetOringin (Yu et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Simole MetOrigin analysis (SMOA) model was selected, and common tobacco (\u003cem\u003eNicotiana tabacum\u003c/em\u003e) was selected as host. ROC curve analysis was performed with pROC 1.16 in R software (version 3.5.1) to screen for potential biomarkers.\u003c/p\u003e \u003cp\u003eTwo-way ANOVA was used to analyze the difference of chemical composition indexes between Foreign and Yunnan. Fisher's LSD was used to compare the level of difference in variables between groups. A probability of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated that the differences were significant. The SPSS 24.0 (IBM) was used for descriptive statistical analysis and analysis of variance. OriginPro 2024b (10.1.5.132) was used to perform hierarchical cluster analysis and draw polar heat maps.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Conventional chemical composition\u003c/h2\u003e \u003cp\u003eIn addition to total nitrogen, chlorine and potassium, there were significant differences in total sugar, reducing sugar and nicotine between Yunnan and foreign countries (among groups), while there were only significant differences in pH and polyphenol among 9 different producing unit (within groups) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The results indicated that the differences between groups were significantly greater than the differences within groups, and the grouping Settings were reasonable. The results of the two-way ANOVA test showed that the contents of total sugar, reducing sugar, magnesium and polyphenol in Yunnan CTLs were significantly lower than those in Foreign CTLs. The contents of total nicotine, pH, pectin, starch, lignin and petroleum ether extract were significantly higher than those of CTLs from Foreign. There were no significant differences in total nitrogen, potassium and chlorine (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The nicotine, reducing sugar and pectin were selected as biomarkers of conventional chemical components, which had great influence on quality.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTwo-way ANOVA of conventional chemical components of cigar tobacco leaves between Foreign and Yunnan.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrigins\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProducing unit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal sugar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReducing sugar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal nitrogen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal nicotine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChlorine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMagnesium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePectin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eStarch\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003epetroleum ether extract\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eXylogen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003ePolyphenol\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eForeign\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e 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\u003cp\u003e0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e4.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eYunnan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eTwo-way ANOVA P-Values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOrigins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eProducing unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOrigins \u0026times; Producing unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003eNote: The unit of each index in the table is %, P\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Volatile aroma compound\u003c/h2\u003e \u003cp\u003eThrough quantitative analysis of 44 volatile aroma components detected in foreign and Yunnan CTLs, the results showed that 15 aroma components such as valeraldehyde, pyridine and megastigmatrienone were significantly higher in Yunnan, while 29 aroma components such as 1-Penten-3-one, 2,3-Pentanedione and β-cyclocitral were significantly higher in Foreign. The results of hierarchical cluster analysis showed that 44 volatile aroma components were divided into 2 categories, which could distinguish Yunnan CTLs from Foreign CTLs. Further by OPLS-DA by variable importance projection (VIP)\u0026thinsp;\u0026gt;\u0026thinsp;1 material has 18 kinds, in which pyridine and beta-damascone and megastigmatrienone content is higher in Yunnan CTLs. The contents of 1-penten-3-one; 2,3-pentanedione; 3-methyl-2-butena; n-hexanal; 2-acetyl-1; pyrroline; β-cyclocitral; indole; 6,8-nonadien-2-one,8-methyl-5-(1-methylethyl)-, (6e); cyclohexanone, 2,3-dimethyl-2-(3-oxobutyl); beta-ionone 5,6-epoxide and 2(4h)-benzofuranone,5,6,7,7a-tetrahydro-4,4,7a-trimethyl were relatively high in Foreign CTLs. Secondly, the types of odorant substances with relatively high content in CTLs abroad were significantly more than those in Yunnan (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). According to the clustering results, pyridine, megastigmatrienone and β-cyclocitral with VIP value\u0026thinsp;\u0026gt;\u0026thinsp;1.6 and relative content difference were selected as the characteristic aroma substances to distinguish Foreign and Yunnan CTLs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Volatile and non-volatile metabolic profiles\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Metabolic profiling\u003c/h2\u003e \u003cp\u003eA total of 1,105 non-volatile metabolites were identified in the 27 CTLs samples, of which 541 were annotated by KEGG. The Pearson correlation coefficient R2\u0026thinsp;\u0026gt;\u0026thinsp;95%, which met the requirements for subsequent analysis. Ten classes of metabolites were detected in all CTLs, including phenolic acids (16.56%), alkaloids (14.12%), flavonoids (14.03%), amino acids and derivatives (12.58%), lipids (12.04%), and organic acids (10.41%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Accounted for 79.76% of total tissue metabolites.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of 615 volatile metabolites were detected based on GC-MS, 223 of which were annotated to the KEGG pathway. The detected metabolites were divided into 15 categories, of which terpenoids (22.44%), lipids (14.63%), heterocyclic compounds (14.15%), ketones (9.92%) and hydrocarbons (8.94%) accounted for more than 70% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThe non-volatile metabolites PCA analysis showed that samples in the same group could not only be clustered together, but also separated from samples in other groups, and the trend of metabolite separation among groups was obvious (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Importantly, the CTLs of YNLC and YNYX were more similar, while the clustering of YNDH and YNPE was similar. For foreign samples, except BR-3, the other origin units are close to each other. Similarly, the HCA analysis showed that samples from foreign also clustered together (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). These results indicate that the non-volatile metabolites of CTLs from foreign countries and Yunnan are significantly different.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe PCA analysis of volatile metabolites in the two groups showed a weak separation trend between the groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The HCA analysis results showed that the CTLs samples were grouped into four categories, among which YNDH, YNPE and YNLC were grouped into one category, Cuba, Mexico, Indonesia and Brazil were grouped into one category, and YNYX and Dominica were grouped separately into one category. One sample from Indonesia is similar to that from Dominica (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). In conclusion, there are some differences in the volatile metabolites of CTLs produced under different ecological conditions, but they have the general characteristics of convergence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Differential metabolites\u003c/h2\u003e \u003cp\u003eThe OPLS-DA was used for quantitative and modeling analysis of metabolites, the score map was drawn, and VIP values were obtained. R\u003csup\u003e2\u003c/sup\u003eY represents the interpretation rate of the model, Q\u003csup\u003e2\u003c/sup\u003e is used to evaluate the prediction ability of the model, and R\u003csup\u003e2\u003c/sup\u003eY\u0026thinsp;\u0026gt;\u0026thinsp;Q\u003csup\u003e2\u003c/sup\u003e indicates that the model is well established. According to cross-validation, the interpretation of PC1 and PC2 of non-volatile metabolites to the dataset was 23.2% and 9.01%, respectively, indicating large differences between groups and small differences between samples within groups. The prediction model R\u003csup\u003e2\u003c/sup\u003eY\u0026thinsp;=\u0026thinsp;0.989, Q\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.953, R\u003csup\u003e2\u003c/sup\u003eY\u0026thinsp;\u0026gt;\u0026thinsp;Q\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). The interpretation of PC1 and PC2 of volatile metabolites were 23.9% and 16.3%, respectively, and the prediction model R\u003csup\u003e2\u003c/sup\u003eY\u0026thinsp;=\u0026thinsp;0.977, Q\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.945, and R\u003csup\u003e2\u003c/sup\u003eY\u0026thinsp;\u0026gt;\u0026thinsp;Q\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D). The results show that the two metabolomic models are well established and have good predictive ability and interpretation effect for CTLs from Yunnan and Foreign.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further selected VIP\u0026thinsp;\u0026gt;\u0026thinsp;1, Log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;\u0026gt;\u0026thinsp;2 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.5 as differential metabolites, and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the relative content difference and statistical significance of metabolites in Foreign vs Yunnan. A total of 248 volatile differential metabolites were identified, of which 88 were significantly down-regulated, the \u0026ldquo;N, N'-diferuloylputrescine, retusin and 5-hydroxyl-3',4',7,8-tetramethoxyl flavone N, N' -diferuloylutamide\u0026rdquo; were the top3 differentially varied substances. And 160 were significantly up-regulated, and the \u0026ldquo;tomatine, N-(3-indolylacetyl)-L-alanine and 4,4,5-trihydroxy-l, l'-di-2-propenylbiphenyl\u0026rdquo; were the three substances with the largest Log\u003csub\u003e2\u003c/sub\u003eFC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of 150 volatile metabolites were identified, of which 107 were significantly down-regulated, \u0026ldquo;p-menth-8-en-3-ol-acetate, benzoic acid, 1-methylethyl ester and phenol, 3,5-dimethyl\u0026rdquo; were the Top3, and 43 were significantly up-regulated. \u0026ldquo;bornyl acetate, (R) -3, 7-dimethyl-6-octenol and styrene were the largest differential multiples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThe differential metabolites were classified and counted based on substance classification. The results showed that the primary classification of non-volatile differential metabolites mainly belonged to amino acids and their derivatives (28%), alkaloids (18.2%), phenolic acids (11.9%), lipids (11.9%) and organic acids (10.2%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The alkaloids were mainly alkaloids, phenolamines and indole alkaloids (12.6%), and the lipids were mainly free fatty acids (7.6%). The main volatile differential metabolites were terpenoids (19.5%), lipids (14.8%), heterocyclic compounds (13.40%) and aromatics (10.1%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). In summary, the main metabolites of CTLs from Foreign vs Yunnan were amino acids and their derivatives, alkaloids, terpenoids and lipids.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Metabolic pathways of differential metabolites\u003c/h2\u003e \u003cp\u003eKEGG pathway enrichment analysis showed that non-volatile differential metabolites were significantly enriched in 11 metabolic pathways (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including \u0026ldquo;aminoacyl-tRNA biosynthesis, tropinane, piperidine and pyridine alkaloid biosynthesis, tryptophan metabolism, biosynthesis of amino acid, D-amino acid metabolism, glucosinolide biosynthesis, arginine biosynthesis\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The volatile differential metabolites are mainly involved in \u0026ldquo;biosynthesis of various plant secondary metabolites, monoterpenoid biosynthesis and phenylalanine metabolism\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Characteristic metabolites and potential markers\u003c/h2\u003e \u003cp\u003eROC curve analysis is widely recognized as an objective and effective method to evaluate biomarker performance. To identify the characteristic metabolites of CTLs in Yunnan, strict screening criteria were applied: AUC\u0026thinsp;=\u0026thinsp;1.0, Log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;\u0026gt;\u0026thinsp;2, VIP\u0026thinsp;\u0026gt;\u0026thinsp;1, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01. These criteria enabled us to identify the characteristic metabolites more accurately. For non-volatile metabolites, a total of 11 metabolites with distinctive characteristics were identified, mainly steroid alkaloids, amino acids and their derivatives, lignans, phenolic acids, free fatty acids and coumarins (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Based on ROC curves and AUC values, we suggest that tomatine, n-(3-indolyl) -L-alanine, and 5,6,7-trimethoxycoumarin may be potential non-volatile biomarkers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe non-volatile biomarkers of cigar tobacco leaves between Foreign and Yunnan\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLog\u003csub\u003e2\u003c/sub\u003eFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eClass II\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTomatidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.27E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC27H45NO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSteroid alkaloids\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-(3-Indolylacetyl)-L-alanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC13H14N2O3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAmino acids and derivatives\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4,4,5-Trihydroxy-l,l'-di-2-propenylbiphenyl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC18H18O3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLignans\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-γ-Glutamyl-L-leucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC11H20N2O5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAmino acids and derivatives\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Prolyl-L-Leucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC11H20N2O3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAmino acids and derivatives\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDihydrocaffeic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC9H10O4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePhenolic acids\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12,13-DHOME; (9Z)-12,13- Dihydroxyoctadec-9-enoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.82E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC18H34O4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFree fatty acids\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHexadecanedioic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.05E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC16H30O4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFree fatty acids\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9,16-Dihydroxypalmitic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC16H32O4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFree fatty acids\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-Hydroperoxy-9Z,11E-Octadecadienoic Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.24E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC18H32O4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFree fatty acids\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenzoyltartaric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.86E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC11H10O7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePhenolic acids\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7S,8S-DiHODE; (9Z,12Z)-(7S,8S)- Dihydroxyoctadeca-9,12-dienoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.1E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC18H32O4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFree fatty acids\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10,16-Dihydroxypalmitic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC16H32O4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFree fatty acids\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5,6,7-Trimethoxycoumarin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC12H12O5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCoumarins\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs for volatile metabolites, a total 10 characteristic compounds were identified, belonging to terpenes, aromatics, heterocyclic compounds, hydrocarbons, ethers and lipids (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The bornyl acetate, 5,6,7, 8-tetrahydroquinoxaline and benzoic acid, 1-methylethyl ester could be defined as biomarkers for volatile metabolites.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe volatile biomarkers of cigar tobacco leaves between Foreign and Yunnan\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLog\u003csub\u003e2\u003c/sub\u003eFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eClass I\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBornyl acetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.62E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC12H20O2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTerpenoids\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOcten-1-ol, 3,7-dimethyl-, (R)-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.41E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC10H20O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTerpenoids\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthylbenzene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC8H10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAromatics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthanone, 1-(3,5-dimethylpyrazinyl)-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC8H10N2O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHeterocyclic compound\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenzene, 1,3-dimethyl-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC8H10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAromatics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrimethyltridecane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC16H34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHydrocarbons\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenzene, 1-methoxy-4-(1-methylpropyl)-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC11H16O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEther\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5,6,7,8-Tetrahydroquinoxaline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC8H10N2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHeterocyclic compound\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenzoic acid, 1-methylethyl ester\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC10H12O2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEster\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-Menth-8-en-3-ol, acetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC12H20O2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEster\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 MetOrigin analysis of the differential metabolites\u003c/h2\u003e \u003cp\u003eMetOrigin analyses were performed for 11 non-volatile and 10 volatile characteristic metabolites, using sankey networks to integrate statistical and biological associations. The results showed that non-volatile characteristic metabolites were involved in the \u0026ldquo;degradation of flavonoids\u0026rdquo;, \u0026ldquo;linoleic acid metabolism\u0026rdquo;, and \u0026ldquo;the biosynthesis of cutin, xylocin, and wax\u0026rdquo;, \u0026ldquo;suberine and wax biosynthesis\u0026rdquo;. The volatile metabolites are enriched into two metabolic pathways \u0026ldquo;ethylbenzene degradation\u0026rdquo; and \u0026ldquo;xylene degradation\u0026rdquo;.\u003c/p\u003e \u003cp\u003eFurther identification of the microbial communities related to non-volatile metabolites showed that the microorganisms closely related to flavonoid degradation were mainly Pseudomonadota and Bacillota. The phloretin hydrolase was involved in degradation of flavonoid. The 3-Hydroxyphloretin is a metabolic substrate, while dihydrocaffeicacid and phloroglucinol are metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Associated with linoleic acid metabolism are the microorganisms of \u003cem\u003ePodospora\u003c/em\u003e and \u003cem\u003ePyricularia\u003c/em\u003e, and linoleate lipoxygenase and hydroperoxidase isomerase are involved in linoleic acid metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe microbial communities associated with xylene degradation, a volatile characteristic metabolite, were \u003cem\u003eNovosphingobium\u003c/em\u003e, \u003cem\u003eSphingobium\u003c/em\u003e, \u003cem\u003ePseudoxanthomonas\u003c/em\u003e, \u003cem\u003eRhodococcus\u003c/em\u003e, and \u003cem\u003ePseudonocardia\u003c/em\u003e, ferridoxin reductase and toluene monooxygenase participate in the degradation of xylene, and the metabolite is 3-methylbenzyl alcohol (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). The microorganisms associated with the degradation metabolism of ethylbenzene were \u003cem\u003epseudomonas\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, Actinomycetota, Bacteroidota and Euryarchaeota. Many oxygenases, such as ferredoxin NAD(P) reductase, naphthalene dioxygenase, arylnitro and dinitrotoluene, participate in ethylbenzene degradation, and the metabolite is 1-phenylethanol (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study selected 5 different ecological and climatic conditions Foreign CTLs and 4 Yunnan CTLs as experimental materials, and the results of PCA and HCA analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) show that the sample clustering is obvious, and the separation trend of the two groups is obvious. In terms of metabolic composition, the selected samples and group setting have scientific basis, which can objectively analyze and compare the differences in two types of CTLs raw materials with different ecological types. The conventional chemical components of the representative Foreign CTLs generally have high sugar, protein and polyphenol content, while nicotine content in Yunnan was significantly higher than that of Foreign, previous research results also show that the nicotine content of domestic CTLs is significantly higher than that of South America and Southeast Asia (Sun et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our previous research also indicated that the contents of starch, nicotine and pectin in CTL produced in the Dominica and Indonesia were significantly lower than those in CTL from China (Wu et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The comparative analysis of volatile aroma components shows that 6 metabolites, including pyridine, megastigmatrienone, and β-damascenone, have higher quality fractions in Yunnan CTLs, while 12 metabolites, including β-cyclocitral, 1-penten-3-one, and 6,8-nonadien-2-one,8-methyl-5-(1-methylethyl), have higher content in Foreign CTLs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The research results are basically consistent with the trend of the types and contents of aromatic substances in Cuba cigar, non-Cuba cigar, and Chinese cigar (Yu et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Combining the extensive targeted metabolomics identification of differential metabolites, classification (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and participation of biogenic amines, various amino acids, lipids, and flavonoids and related metabolic pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), it can be found that the main reasons for the low sugar-to-alkaloid ratio, high pectin, starch, and petroleum ether extract content in Yunnan CTLs are probably that many large molecular substances are not fully hydrolyzed or metabolic pathway are interrupted, resulting in low aromatic substance activity values (Zhang et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which are missing or relatively low, leading to differences in sensory quality.\u003c/p\u003e \u003cp\u003eCurrently, over 6,000 chemical components have been identified and reported in tobacco (Gui et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and the basic skeleton substances in tobacco leaves consist of sugars, nitrogen-containing compounds, alkaloids, pigments, and mineral elements, as well as a variety of secondary metabolites. In this study, we conducted a non-targeted metabolomics analysis to comprehensively characterize the metabolites in different CTLs, and found that phenolic acids, alkaloids, flavonoids, amino acids and their derivatives, lipids, and organic acids are the main components of non-volatile metabolites in CTLs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The proportion of various metabolites in CTLs is dynamic during the air-curing and fermentation process (Zhang et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The main components of volatile metabolites are terpenes, lipids, heterocyclic compounds, ketones, and hydrocarbons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Previous analysis of the significant differentially abundant metabolites before and after fermentation indicated that the enriched metabolic pathways were various amino acid metabolism, monoterpene, sesquiterpene, and triterpene biosynthesis (Fan et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), indicating that terpene compounds are mainly synthesized during fermentation.\u003c/p\u003e \u003cp\u003eBased on the methods described by Dan et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Steinfath et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), this study selected the top three metabolites in terms of AUC and VIP values to significantly improve the diagnostic performance of biomarkers. This indicates that using multiple metabolites as discriminating substances may be more effective than using a single metabolite. Therefore, it is suggested that the top three feature metabolites of conventional chemical components, aroma-forming substances, non-volatile metabolites, and volatile metabolites be combined as discriminating substances for potential biomarkers of Yunnan and Foreign CTLs, and that the production processes of cultivation, air-curing, and fermentation be directed based on the differences in the signature metabolites. The biomarkers discovered in this study could be used for the accurate identification and traceability of CTL origin, and also as quality control indicators for tobacco leaf quality during the fermentation process.\u003c/p\u003e \u003cp\u003eMetOrigin is an interactive web server that can distinguish metabolites from the microbiome, perform origin-based metabolic pathway enrichment analysis, and use the Sankey network to trace metabolites with statistical correlation and biology (Yu et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which can not only rapidly identify microbial metabolites and their metabolic functions, but also identify microbial metabolites. It can also facilitate the discovery of specific microorganisms that are closely related to metabolites. The MetOrigin analysis showed that non-volatile characteristic metabolites were involved in three metabolic pathways of flavonoid degradation, linoleic acid metabolism, and keratin, xylocin and wax biosynthesis, while volatile metabolites were enriched in ethylbenzene degradation and xylene degradation pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). The closely related microorganisms are mainly \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e and \u003cem\u003eSphingomonas\u003c/em\u003e. Previous studies have shown that the main functional microbial community in the air-curing and fermentation CTLs is bacteria, which includes but is not limited to the key microbial genera obtained from this study (Zhang et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023c\u003c/span\u003e). The results also indicate that the flavor formation of CTLs were closely related to the microbial community. During the long production process, CTLs serve as the host to provide nutrition and habitat for the growth and reproduction of functional microorganisms, a variety of enzymes produced in tobacco cells or microbial metabolism as the main catalytic factors to mediate the various metabolic pathways of carbohydrates and nitrogen compounds, the whole process is also affected by geographical conditions, ecological climate, technology and other external environmental factors.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, through multi-omics and multi-dimensional qualitative and quantitative detection and analysis of CTLs from different producing areas, it was found that compared with Foreign CTLs, Yunnan CTLs had a lower sugar-base ratio and higher content of starch, pectin and lignin, which had adverse effects on sensory quality. The metabolome showed that phenolic acids, alkaloids, flavonoids, amino acids and derivatives, lipids, organic acids, terpenoids, lipids, heterocyclic compounds, ketones and hydrocarbons were the main components of secondary metabolites in cigar tobacco. A variety of analytical methods were used to screen and verify 12 potential markers of Yunnan and Foreign CTLs, which were nicotine, reducing sugar and pectin. The pyridine, megastigmatrienone, and β-damascenone three kinds of aroma substances; tomatine, n-(3-indolyl) -L-alanine, and 5,6,7-trimethoxycoumarin were three non-volatile metabolites. Three volatile metabolites bornyl acetate, 5,6,7, 8-tetrahydroquinoxaline and benzoic acid, 1-methylethyl ester. The differences in the material components of CTLs between Yunnan and Foreign are determined by soil, climate and process technology. However, in the production process, the use of microorganisms and enzymes to cooperatively regulate the carbon and nitrogen coupling metabolic pathways in CTLs to achieve raw material homogenization is a systematic study that needs to be carried out in the next step.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZG (Guanghai Zhang) and LY: writing\u0026mdash;original draft; methodology; validation; investigation; data curation. ZG (Gaokun Zhao) and KG: funding acquisition; resources; writing\u0026mdash;review \u0026amp; editing; supervision. LM, WY, YH, LW and XH: validation, investigation, discussed the results and commented on the manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the China Tobacco Monopoly Bureau Grants and Yunnan Provincial Tobacco Monopoly Bureau Grants (110202201037（XJ-08）/2023530000241001, 110202103018/2022530000241002), Project of Yunnan Daguan Laboratory (YNDG202402XJ01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData and materials described in this study are available from the authors upon reasonable request and availability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors approved the consent for publishing the manuscript to Bioresources and Bioprocessing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYunnan Academy of Tobacco Agricultural Sciences, Yuantong Road 33#, Kunming 650021, Yunnan Province, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDan Z, Chen Y, Li H, Zeng Y, Xu W, Zhao W, He R, Huang W (2021) The metabolomic landscape of rice heterosis highlights pathway biomarkers for predicting complex phenotypes. 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Tobacco Science \u0026amp; Technology 54(09):58-71. https:// doi:10.16135/j.issn1002-0861.2021.0003\u003c/li\u003e\n \u003cli\u003eZhang G, Yao H, Zhao G, Wu Y, Xia H, Li Y, Kong G (2023a) Metabolomics reveals the effects producing region and fermentation stage on substance conversion in cigar tobacco leaf. Chemical and Biological Technologies in Agriculture 10(1):66. https:// doi:10.1186/s40538-023-00444-1\u003c/li\u003e\n \u003cli\u003eZhang G, Zhao L, Li W, Yao H, Lu C, Zhao G, Wu Y, Li Y, Kong G (2023b) Changes in physicochemical properties and microbial community succession during leaf stacking fermentation. AMB Express 13(1):132. https:// doi:10.1186/s13568-023-01642-8\u003c/li\u003e\n \u003cli\u003eZhang M, Guo D, Wang H, Wu G, Ding N, Shi Y, Zhou J, Zhao E, Li X (2024) Integrated characterization of filler tobacco leaves: HS\u0026ndash;SPME\u0026ndash;GC\u0026ndash;MS, E-nose, and microbiome analysis across different origins. Bioresources and Bioprocessing 11(1). https:// doi:10.1186/s40643-024-00728-w\u003c/li\u003e\n \u003cli\u003eZhang Q, Kong G, Zhao G, Liu J, Jin H, Li Z, Zhang G, Liu T (2023c) Microbial and enzymatic changes in cigar tobacco leaves during air-curing and fermentation. Applied Microbiology and Biotechnology 107:5789\u0026ndash;5801. https:// doi:10.1007/s00253-023-12663-5\u003c/li\u003e\n \u003cli\u003eZhao G, Zhang Q, Kong G, Yao H, Wu Y, Cai B, Liu T, Zhang G (2023) Identification of physiological and metabolic networks involved in postharvest browning of cigar tobacco leaves. Chemical and Biological Technologies in Agriculture 10(135). https:// doi:10.1186/s40538-023-00509-1\u003c/li\u003e\n \u003cli\u003eZhu X, Gao Y, Chen Z, Su Q (2009) Development of a chromatographic fingerprint of tobacco flavor by use of GC and GC-MS. Chromatographia 69(7):735-742. https:// doi:10.1365/s10337-009-0968-4\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cigar tobacco leaves, Aroma components, Metabolomics, Differential metabolites, Biomarkers, Metabolic pathways, MetOrigin","lastPublishedDoi":"10.21203/rs.3.rs-5777714/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5777714/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThere is still a lack of systematic identification of the substance components of cigar tobacco leaves (CTLs) produced under different ecological conditions. To clarify the substance differences between cigar tobacco leaves from Yunnan and Foreign and explore potential discriminant biomarkers. A total of 25 CTL samples were selected as materials from 5 production areas abroad and 4 production areas in Yunnan. The metabolic components in CTLs were determined by continuous flow method, ion chromatography, GC-MS and MS/MS. Compared with other countries, the CTLs produced in Yunnan has the characteristics of lower sugar-nicotine ratio, higher starch, pectin and lignin content. The phenolic acids, alkaloids, flavonoids, amino acids and derivatives, terpenoids, lipids and heterocyclic compounds were the main components of CTLs. A total of 248 and 150 volatile and non-volatile differential metabolites were obtained, which were amino acids and their derivatives, alkaloids, terpenoids and lipids. A total of 12 potential biomarkers were identified for distinguishing Yunnan and Foreign CTLs. KEGG enrichment and MetOrigin analysis showed that the differential metabolites were mainly involved in the biosynthesis of alkaloids, amino acids, flavonoids, lipids and terpenes. The carbon and nitrogen coupling metabolism mediated by microorganisms and enzymes influenced the composition and content of CTLs. This study provided reference for the improvement of production technology and the analysis of related substances of style characteristics of CTLs in Yunnan.\u003c/p\u003e","manuscriptTitle":"Metabolomics analysis of substance differences and biomarkers in cigar tobacco leaves from Yunnan and Foreign","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-30 10:27:19","doi":"10.21203/rs.3.rs-5777714/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5dceab67-03bf-4922-9039-d608feab7ed2","owner":[],"postedDate":"April 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-20T13:52:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-30 10:27:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5777714","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5777714","identity":"rs-5777714","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}