liquid chromatography/mass spectrometry-based metabolic profiling to elucidate chemical differences...

Research Article

Liquid chromatography/mass spectrometry-based metabolic profiling to elucidatechemical differences of tobacco leavesbetween Zimbabwe and China

An approach was developed for extracting and analyzing the chemical components of

tobacco leaves based on solvent extraction and rapid & resolution liquid chromatography/

quadrupole time-of-flight mass spectrometry analysis. Two solvents with different polari-

ties were used to extract hydrophilic components and hydrophobic components, respec-

tively, the combined analytical data can provide a ‘‘global’’ view of metabolites. Based on

the evaluation of parallel samples, it was found that this approach provided good

repeatability, accurate and reliable profiling data, and is suitable for the metabolomics

study of tobacco leaves. In order to find the chemical component differences of tobacco

leaves, 56 samples from Zimbabwe and China were analyzed using the developed

method. The metabolite data were processed by multivariate statistic technique; an

obvious group classification between Zimbabwe and China was observed, 14 significantly

changed compounds were found, and 9 of them were identified.

Keywords: Chemical components / Liquid chromatography / Mass spectrometry /Metabolic profiling / Tobacco leavesDOI 10.1002/jssc.201000652

1 Introduction

Plant metabolomics, as a novel experimental methodology,

aims at providing an essentially unbiased, comprehensive

qualitative and (semi)quantitative analysis of the metabolites

present in plant tissues, at a certain point in time [1, 2].

Metabolomic studies require reliable sensitivity, high accu-

racy and robustness of analytical methodology. Among

various techniques prevailing in metabolomics world, liquid

chromatography (LC) coupled to mass spectrometry (MS)

offers the best combination of sensitivity and selectivity, and

therefore is indispensable in most of metabolomic

approaches [3, 4]. It covers a wide mass range and targets

many compound classes, representing the overall biochem-

ical richness of plants. By using suitable type of stationary

phase, LC-MS can detect the large (semipolar) group of plant

secondary metabolites and various primary metabolites [5, 6].

Besides the analytical tools used, sample preparation

including the drying method, extraction method and the

type of extraction solvent also greatly affects the end

composition of the finished products in metabolomic

studies. As we know, the number of metabolites in the plant

kingdom is estimated to exceed 200 000, which consist of a

wide variety of compounds at very different levels with very

different polarities and molecular mass [2]. The chemical

diversity of the plant metabolome makes the selection of

sample preparation method become very important. The

extraction solvent is one of the most critical factors since it

greatly influences the range of the detectable metabolites [7].

At present, there is no single solvent composition capable to

dissolve the whole range of compounds. In fact, one prob-

ably needs to do several extractions with different solvents to

have a total view of the metabolome [8]. Thus, a suitable

extraction solvent and extraction method should be devel-

oped to assure the number and amount of metabolites

extracted are maximized, nevertheless with reproducible

operating procedures [7].

As the leading products of tobacco commodity, tobacco

leaves are the important material of tobacco industry, their

chemical components are closely related to the quality of

cigarette brand. Herein, the comprehensive analysis of

tobacco components is critical for the discrimination of

cigarette brand characteristics. For the volatile and semi-

volatile components in tobacco, gas chromatography (GC)-

based instrumental analysis methods have been developed

and applied in tobacco chemical fingerprinting and cigarette

brand characteristics investigation [9–13]. For LC-based

instrumental analysis of tobacco, most of the studies mainly

focused on the analysis of target compounds, such as

Qinghua Li1,2

Chunxia Zhao1

Yong Li1

Yuwei Chang1

Zeming Wu1

Tao Pang3

Xin Lu1

Yi Wu3

Guowang Xu1

1CAS Key Laboratory ofSeparation Science forAnalytical Chemistry, DalianInstitute of Chemical Physics,Chinese Academy of Science,Dalian, P. R. China

2Kunming Cigarette Factory ofHongyun Tobacco (Group) Co.,Ltd., Kunming, P. R. China

3Yunnan Academy of TobaccoAgricultural Sciences, Yuxi,P. R. China

Received September 8, 2010Revised November 2, 2010Accepted November 3, 2010

Abbreviations: DA, discriminant analysis; MFE, molecularfeatures extraction; PLS, partial least squares; Q-TOF,

quadrupole-TOF; RRLC, rapid & resolution LC

Correspondence: Dr. Chunxia Zhao, CAS Key Laboratory ofSeparation Science for Analytical Chemistry, Dalian Institute ofChemical Physics, Chinese Academy of Science, Dalian 116023,P. R. ChinaE-mail: [email protected]: 186-411-84379559

& 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.jss-journal.com

J. Sep. Sci. 2011, 34, 119–126 119

abscisic acid, glycosides and so on [14, 15]. To date, global

chemical fingerprinting of tobacco based on LC technique

has not ever been reported.

Here, we report the development of an approach to

extracting and analyzing the chemical components of

tobacco leaves by a rapid & resolution liquid chromato-

graphy (RRLC) followed by quadrupole time-of-flight MS

(Q-TOF MS). In order to get a ‘‘global’’ view of metabolites,

water and hexane were used to extract the hydrophilic

components and hydrophobic components, respectively.

Very rich information of chemical composition of tobacco

leaves was obtained. Based on the developed method, the

chemical components of tobacco leaves from Zimbabwe and

China were analyzed, group classification was performed by

multivariate statistical analysis and the important features

for the classification were identified.

2 Materials and methods

2.1 Chemicals

HPLC-grade acetonitrile was purchased from Merck (USA),

HPLC-grade hexane and formic acid were supplied by Tedia

(USA). Deionized water (18.2 O) was prepared using an in-

house water purification system (Millipore, USA). All other

reagents utilized in this study were of analytical grade.

Trigonelline, L-proline, nicotinic acid, chlorogenic acid,

nicotine and caffeic acid were purchased from Sigma-

Aldrich (USA).

2.2 Sample preparation

Fifty-six tobacco samples were provided by Yunnan academy

of tobacco agricultural sciences. These leaves samples were

obtained from the mid-part of tobacco stems, and were

oven-dried at 401C until a constant weight.

These tobacco samples were ground and passed

through a 40-mesh sieve before extraction. Five hundred

milligram powder was weighed into a 10-mL tube and

10 mL of extraction solvent was added. The extraction was

carried out for 20 min in an ultrasonic washer. After

centrifugation at 8000� g for 10 min, 1.5 mL of supernatant

was lyophilized in a Labconco Freezone 4.5 freeze dry

system (Kansas, USA). The dried extract was resolved in

300 mL extraction solvent. Prior to analysis by HPLC/MS,

the extract was filtered using a 0.22 mm-filter.

2.3 HPLC/Q-TOF MS analysis

The chromatographic separation of tobacco leaves was

performed with Agilent 1200 RRLC (Agilent, USA) on a

Zorbax SB-C18 column (1.8 mm, 3.0� 100 mm, Agilent) at

501C with consistent flow rate 0.3 mL/min after 1 mL

injection. The elutes after column were directly introduced

into Agilent 6510 Q-TOF mass spectrometer (Agilent)

equipped with dual electrospray ionization source without

splitting stream. About 0.1% formic acid–water (A) and

0.1% formic acid–acetonitrile (B) were employed to elute

column with following gradient: 0 min 5% B, 25 min

100% B, 32 min 100% B, 33 min 5% B. The column was

reconditioned with initial gradient for 7 min. The total

sample analysis time was 40 min, of which the first 25 min

were data acquired.

Mass spectrometer was operated with settings as

follows: ionization mode, positive; gas temperature, 3501C

drying gas, 11.0 L/min; nebulizer, 45 psig; ref nebulizer,

5 psig; Vcapillary, 4000 V; fragmentor, 230 V; skimmer,

65 V; OCT 1 RF Vpp, 750 V. Centroid mass data within

mass range 100–1000 m/z was acquired in 1 spectra/s rate

with Mass Hunter workstation (Agilent) during analysis.

During data acquisition, reference solution containing

purine (121.0509 m/z) and hexakis (1H,1H,3H-tetrafluoro

propoxy) phosphazine (922.0098 m/z) was constantly

nitrogen-pressed into MS to execute real-time mass axis

calibration through an exclusive reference electrospray

probe. Besides, as target MS/MS analysis was conducted,

product ion scan range was 50–500 Da. High-purity nitro-

gen was introduced into collision cell as fragmentation gas,

and the fragmentation voltage was set at 20 and 30 eV to

obtain MS/MS fragmentation patterns in different collision

levels.

2.4 Data preprocess

Data preprocessing consists of peak detection, chromato-

graphic alignment, normalization and masses filtering,

which converted spectrometric data into a complex dataset.

The raw data acquired from RRLC/Q-TOF MS were first

analyzed by the molecular features extraction software

(MFE, Agilent) for detection of the compounds. The

software operated on the raw mass spectral data (retention

time, m/z and abundance), and generated lists of chemically

qualified molecular features by eliminating interferences

and reducing data complexity. Masses were finally grouped

into ‘‘compounds’’ by their molecular features. Then, the

data were exported to the GeneSpring MS 1.1 software

(Agilent) for peak alignment. The following steps were used

for data normalization and producing relative quantities

about the detected metabolic features. Each value was

performed per-run normalization and per-mass normal-

ization. Per-run normalization is dividing each value by the

median of all values in that run to reduce the difference in

sample concentration, and per-mass normalization is divid-

ing each value by the median of that mass in all runs to

reduce the difference in detection efficiency between runs.

To eliminate the negative influence of redundant missing

value on multivariate analysis, an ‘‘80% rule’’ [16, 17] was

employed, only the variables with values above zero

presenting in at least 80% samples of either group were

kept for the following analysis.

J. Sep. Sci. 2011, 34, 119–126120 Q. Li et al.


2.5 Chemometrics analysis

Multivariate statistics was performed using soft indepen-

dent modeling of class analogy (SIMCA-P, version 11.0,

Umetrics AB, Umea, Sweden). All variables were pareto-

scaled prior to principal component analysis and partial

least squares-discriminant analysis (PLS-DA) for classifica-

tion. Variables with variable importance in the projection

41.0, which played important roles in the classification

were picked out, and the jack-knifing confidence interval,

was taken into account [18, 19]. In the meantime, the PLS-

DA S-plot reflecting both the covariance and correlation

information was used to reduce the risk of false positives

[20]. Independent unparametric test was also applied to

exclude the variables without significant differences

(p40.05) between different groups. Then, an integrative

method, including database search based on exact molecular

weight, MS/MS analysis and chromatographic behavior

comparison in accordance with hydrophobic constant, was

employed to conduct structure identification [21].

3 Results and discussion

3.1 Extraction method development

3.1.1 Extraction solvent selection

Solvent extraction combined with ultrasonication was used

here, which adds the required energy to the system and

facilitates the swelling and hydration of plant materials to

cause enlargement of the pores of the cell wall. Since the

selection of extraction solvent can limit the range of the

detectable metabolites and there is no single solvent

composition capable to dissolve the whole range of

compounds. In this study, six kinds of solvents were

studied for the extraction of hydrophilic components and

hydrophobic components in tobacco leaves, respectively.

Water, 20%-acetone water and 20%-methanol water were

tried as the extraction solvents for hydrophilic components,

hexane (Hex), dichloromethane (Dichlo) and isopropanol

(Isop) were tested as the extraction solvents for hydrophobic

components. The extraction efficiency of different solvents

was investigated and compared. The total number of peaks

and peak area of HPLC/MS analysis were calculated as an

evaluation criterion. For the extraction of hydrophobic

components, dichloromethane showed the highest extrac-

tion efficiency (Fig. 1A and B), in order to reduce the risk of

column pollution by strong retention components, hexane

was chosen as the suitable solvent for the extraction of

hydrophobic components. For the extraction of hydrophilic

components, water was selected as the best solvent (Fig. 1C

and D).

3.1.2 Extraction time

For ultrasonication extraction, increased swelling will

improve the rate of mass transfer and break the cell walls,

resulting in increased extraction efficiency [22]. Here

extraction efficiency of different extraction time (10, 20, 40

Figure 1. Total number ofpeaks and peak area of tobaccosample extracted with differenthydrophobic solvents (A andB) and hydrophilic solvents (Cand D).

J. Sep. Sci. 2011, 34, 119–126 Liquid Chromatography 121


and 60 min) was investigated, the total number of peaks and

peak area of HPLC/MS analysis were still used as the

evaluation criterion. From the extraction results of hydro-

phobic components with hexane (Fig. 2A and B), it can be

seen that extraction efficiency showed an improvement with

the increase of extraction time, especially from 10 to 20 min.

Since the total number of peaks and peak area with 20-min

extraction can achieve 80% efficiency of 60-min extraction,

in order to eliminate the interference of ultrasonic extrac-

tion procedure, here 20 min was chosen as the suitable

extraction time. For the extraction of hydrophilic compo-

nents with water, same results were obtained (Fig. 2C

and D).

3.2 Optimization of the HPLC/Q-TOF MS method

In order to obtain as much information as possible, several

different column systems were evaluated to improve the

chromatographic resolution of chemical components in

tobacco leaves (data not given), Zorbax SB-C18 column was

finally chosen in view of its good resolution capability and

excellent repeatability; a 100 mm-column was used here for

the same reason. As a result of utilizing 1.8 mm sub-two

micron and thermal stable particle, the so-called high-

resolution and rapid throughput were achieved just as the

name of RRLC went.

The MS parameters (ion mode, capillary voltage, frag-

mentor and skimmer voltage and collision energy) were

optimized to maximize the overall sensitivity of the analysis.

The positive ion mode showed more advantages for

measuring chemical components in tobacco leaves. Features

detection implemented with MFE algorithm resulted in

hundreds of compounds deconvoluted in the spectra of

tobacco leaves. Base peak chromatograms of hydrophilic

components and hydrophobic components from a repre-

sentative tobacco leaf sample were exemplified in Fig. 3.

3.3 Validation of the HPLC/Q-TOF MS method

3.3.1 Reproducibility of the extraction method

To evaluate the reproducibility of extraction method, five

parallel samples were accurately weighted and extracted

with water and hexane respectively according to Section 2.2.

After HPLC/Q-TOF MS analysis, the raw data set was

extracted with MFE and aligned with GeneSpring software

to provide a peak list file. For target compound analysis, the

FDA recommends a coefficient of variation (CV) of 15%

regarding the analytical variability (except for concentrations

close to the detection limit (LOQ) where a CV of 20% is

acceptable) [23]. Although metabolic profiling analysis is of a

whole different fundamental analytical nature, the FDA

guidance can also be used here as a referenced benchmark

toward the reproducibility evaluation. To determine whether

the reproducibility of this extraction method was acceptable,

the total number of peaks, total peak area and their CV were

calculated. Table 1 summarizes the evaluation results of the

method reproducibility. It was noted that a very high

percentage of metabolic features were with CV% o20%.

For hydrophilic components, 79.1% metabolic features were

Figure 2. Total number ofpeaks and peak area of tobac-co sample extracted by hexane(A and B) and water (C and D)with different extraction time.

J. Sep. Sci. 2011, 34, 119–126122 Q. Li et al.


with CV% o20%, which accounted for 96.3% of summed

responses. For hydrophobic components, the corresponding

values were 84.4 and 92.2%, respectively.

3.3.2 Instrument precision evaluation

Instrument precision experiment was carried out by using a

water-extracted sample. Intra- and inter-day accuracy were

evaluated by analyzing five duplicates of above sample on

the same day and on four consecutive days, the total number

of peaks, total peak area and their CV were calculated.

Intra- and inter-day reproducibility were described based on

the criteria mentioned above, as shown in Table 2. For the

intra-day examination, 74.2% metabolic features were with

CV% o20%, which accounted for 89.7% of summed

responses. For the inter-day examination, the corresponding

values were 73.1 and 88.1%, respectively. Moreover, the

repeatability in retention time and signal intensity seen here

was very satisfactory, giving further confidence that the

HPLC/Q-TOF MS system was operating robustly and the

method developed can be used for metabolic profiling

analysis.

3.4 Classification of tobacco leaves based on meta-

bolic profiling analysis

Sixty-three tobacco samples (including seven quality control

samples) were extracted with water and hexane, and

analyzed respectively, as mentioned in Sections 2.2 and

2.3. The MFE software was used for extracting chemical

components of tobacco leaves. After peak alignment and

data pretreatment, the raw data were normalized and zero

measurement values were deleted using the method

described. As we mentioned above, the combination of

hydrophilic components (291 variables) extracted by water

and hydrophobic components (365 variables) extracted by

hexane may help to get a ‘‘global’’ view of metabolic

profiling. The two datasets were then combined as the new

dataset and analyzed by the SIMCA-P software.

Figure 3. Typical base peakchromatograms (BPC) ofhydrophilic components (A)and hydrophobic components(B) of tobacco sample.



In order to thoroughly understand the difference in

chemical components of tobacco leaves from Zimbabwe and

China, the PLS-DA was utilized for the classification, and

Pareto scaling method was used for data scaling before the

PLS-DA. From the score plot of PLS-DA (Fig. 4A), we can

see that two groups of the samples, namely Zimbabwe

tobacco and Chinese tobacco, showed good separation with

this model. R2Y and Q2Y of the model were more than 0.93,

indicating good explanative ability of sample classification

information and cross-validated predictive capability. More-

over, the permutation test was operated to test the over-

fitting of PLS-DA after modeling the data, results showed

that this model was satisfactory since the R2-intercept was

0.4 and Q2-intercept was �0.3 [24–26]. Additionally, a new

successful PLS-DA model was built using random 90% of

raw data as a training set and the rest 10% data was used as

an independent test set to validate the results. Good

separation was obtained with the new model, R2Y and Q2Y

of the model were 0.97 and 0.91 respectively, permutation

test also showed low R2-intercept (0.4) and Q2-intercept

(�0.3). The validation result is shown in Fig. 4B and a good

predictive accuracy was obtained, which further proved the

reliability of this model.

Simultaneously, S-plot was mapped to visualize the

magnitude and reliability of differential features for the

classification. Based on the parameter VIP and S-plot, 14

compounds playing key roles in classification were found

(Table 3), including trigonelline, L-proline, d-valerolactam,

nicotinic acid, nicotine N-oxide, caffeic acid, 2-acetyl-

pyrrolidine, cholesterol glucoside and six unidentified

compounds. From the reports [27], a high similarity in

common components between China and Zimbabwei flue-

cured tobacco cultivars was observed, the main differences

were in the content of sugar, protein, malic acid, etc. In this

study, the chemical components of Chinese tobacco leaves

were characterized by higher content of caffeic acid, 2-

acetylpyrrolidine and lower content of nicotine analog and

glucoside. This kind of variation was consistent with the

reports [27, 28]. In addition, higher levels of d-valerolactam

and low levels of trigonelline and L-proline were also

observed in Chinese tobacco leaves with this method. These

component differences may play an important role in the

forming of special aroma character of different tobacco.

4 Concluding remarks

Metabolomic analysis aims at detecting all metabolites from

a plant or part of a plant by measuring an extract of the plant

containing the metabolites. But the choice of the extraction

Table 2. Instrument precision evaluation

No. of peaks No. (%) of peaks with PA CV% o20% Total PA of all peaks Total PA (%) with PA CV% o20%

Intra-day (n 5 5) 554 411 (74.2%) 1.55E108 1.39E108 (89.7%)

Inter-day (n 5 4) 554 405 (73.1%) 1.51E108 1.33E108 (88.1%)

PA, peak area.

Table 1. Reproducibility of the extraction method (n 5 5)

Extraction solvent No. of peaks No. (%) of peaks with PA CV% o20% Total PA of all peaks Total PA (%) with PA CV% o20%

Water 589 466 (79.1%) 1.87E108 1.80E108 (96.3%)

Hexane 935 789 (84.4%) 2.05E108 1.89E108 (92.2%)

PA, peak area.

Figure 4. (A) Score plot from PLS-DA model of the data. The R2Yand Q2Y value of the PLS-DA model, which was 0.975 and 0.937respectively, indicated that the model had good ability ofexplaining and predicting the variations in X and Y matrix. (B)Score plot from new PLS-DA model for prediction. The R2Y andQ2Y values were 0.972 and 0.909 respectively. (m) Zimbabwetobacco, (�) Chinese tobacco, (& ) predicted samples.

J. Sep. Sci. 2011, 34, 119–126124 Q. Li et al.


and analysis method give certain constraints for the study

results. In fact, there is not any general applicable extraction

protocol suited for all kinds of compounds in plant. We

developed here an approach to extracting and analyzing the

chemical components of tobacco leaves based on solvent

extraction and RRLC/Q-TOF MS analysis. In order to

provide a ‘‘global’’ view of metabolite, two solvents with

different polarity were used for the extraction of hydrophilic

components and hydrophobic components respectively, rich

information of chemical composition of tobacco leaves was

indeed obtained. From the evaluation results of parallel

samples, this new method appears to offer highly sensitive,

accurate and reliable profiling data, and is suitable for the

metabolomics study of tobacco leaves. With the developed

method, 56 samples of tobacco leaves from Zimbabwe and

China were analyzed and the component differences were

investigated, fourteen significantly distinguishable features

were discovered, including trigonelline, L-proline, d-valero-

lactam, nicotinic acid, nicotine N-oxide, caffeic acid, 2-

acetylpyrrolidine, cholesterol glucoside and six unidentified

compounds. As a typical character of Chinese tobacco,

higher content of caffeic acid, 2-acetylpyrrolidine, d-valero-

lactam and low content of nicotine analog, glucoside,

trigonelline and L-proline were found, which may contribute

to the forming of special aroma. Moreover, since the

growing conditions and harvesting time play an important

role in the forming of quality character, more samples of

tobacco leaves from different geographical origins with

different planting conditions and harvesting time should be

studied in the future to get more information on the

chemical composition, and further elucidate the difference

in quality and character of different tobacco.

This study has been supported by the Scientific Foundationof State Tobacco Monopoly Administration of China(110200701005) and the project (07A01) from China NationalTobacco Corporation Yunnan Provincial Company.

The authors have declared no conflict of interest.

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Table 3. Important compounds showing difference between

two groups

Peak

no.

tr

(min)

m/z P-value Compounds Variance

trend

1 1.52 138.0539 8.19E-7 Trigonellinea) m6.3

2 1.55 116.0695 4.01E-3 L-Prolinea) m1.3

3 1.58 100.0752 8.19E-7 d-Valerolactamb) k0.2

4 2.01 106.1920 2.20E-3 UN m1.6

5 2.12 124.0395 2.071E-3 Nicotinic acida) m1.2

6 2.27 179.1109 1.25E-8 Nicotine N-oxideb) m10

7 4.99 181.0495 6.24E-8 Caffeic acida) k0.3

8 7.24 114.0914 1.41E-7 2-Acetylpyrrolidineb) k0.3

9 7.90 566.4278 1.02E-3 UN m1.5

10 8.30 137.0923 1.02E-3 UN k0.2

11 8.39 435.3297 2.20E-3 UN k0.3

12 8.40 549.4036 1.58E-8 Cholesterol glucosideb) m5.0

13 15.57 302.2690 6.15E-7 UN k0.3

14 16.30 415.2120 4.16E-5 UN k0.3

tr, Retention time.

a) Identified based on retention time and MS2 spectrum of an

authentic standards.

b) Identified by the accurate mass and observed MS2 fragments

and literatures; UN, unidentified. m or k: higher or lower level

in Zimbabwe tobacco than that in the Chinese tobacco,

respectively.



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