Potential antioxidant compounds in Mallotus species fingerprints. Part II: Fingerprint alignment, data analysis and peak identification

Analytica Chimica Acta 721 (2012) 35–43

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Potential antioxidant compounds in Mallotus species fingerprints. Part II: Fingerprint alignment, data analysis and peak identification C. Tistaert a , B. Dejaegher a , G. Chataigné b , C. Rivière b , N. Nguyen Hoai c , M. Chau Van c , J. Quetin-Leclercq b , Y. Vander Heyden a,∗ a Department of Analytical Chemistry and Pharmaceutical Technology, Center for Pharmaceutical Research (CePhaR), Vrije Universiteit Brussel – VUB, Laarbeeklaan 103, B-1090 Brussels, Belgium b Analytical Chemistry, Drug Analysis and Pharmacognosy Unit, Université Catholique de Louvain, Avenue E. Mounier 72, B-1200 Brussels, Belgium c Institute of Natural Products Chemistry, Vietnamese Academy of Science and Technology, 18 Hoang Quoc Viet Road, Hanoi, Viet Nam

a r t i c l e

i n f o

Article history: Received 19 April 2011 Received in revised form 10 November 2011 Accepted 27 January 2012 Available online 6 February 2012 Keywords: Fingerprints Alignment Peak identification Multivariate calibration

a b s t r a c t Some Mallotus species are commonly used as traditional medicine (TM) ingredients in Vietnam and China, but only a few are studied for their activities. In Part I, high-performance liquid chromatography (HPLC) fingerprints of 39 Mallotus samples (17 species) were developed and, because of the complexity of and the large differences between the samples, it was chosen to analyse the unaligned fingerprints. The peaks, potentially responsible for the antioxidant activity in given Mallotus species, were indicated by the regression coefficients from an orthogonal projections to latent structures (O-PLS) model. In the present study, an in depth discussion on the need for alignment of the Mallotus fingerprints for the indication of the potentially active compounds is made, as well as an experimental analysis and identification of the previously indicated peaks by HPLC–mass spectrometry (HPLC–MS). Additionally, to thoroughly study and discuss the alignment problem, the modelling and prediction of the antioxidant activity of green tea samples based on HPLC fingerprints were also considered. © 2012 Elsevier B.V. All rights reserved.

1. Introduction In the larger part of the world, traditional medicines (TM) are the primary source of healthcare. In Africa, Asia and Latin America up to 80% of the population relies on TM to meet their primary health care needs, or uses TM due to historical and cultural influences [1]. Also in the developed countries, an increasing interest in the potential benefits of complementary and alternative medicines (CAM) is observed. Although TM are becoming an economically important industry, many issues are yet to be addressed. The quality, safety, efficacy and rational use of TM occasionally remain problematic [1,2]. Quality control of TM is usually done by identifying and assaying just a few compounds, i.e. the so-called biomarkers. This hardly describes the complex composition of natural products and ignores all synergic interactions between the different constituents [3–6]. The World Health Organisation (WHO) has introduced chromatographic fingerprint techniques as a methodology for the assessment of natural products. Fingerprints obtained by (hyphenated) chromatographic instruments reflect the complex composition of the analysed natural products. The results can be used not

∗ Corresponding author. Tel.: +32 2 477 47 34, fax: +32 2 477 47 35. E-mail addresses: [email protected], [email protected] (Y. Vander Heyden). 0003-2670/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.aca.2012.01.058

only for authentication of the samples, but also for chemometric treatment of the data allowing to retrieve additional information, such as indication of peaks with interesting activities [7,8] or discrimination between samples, species, etc. [9]. Despite the recent advances, many TM are not yet officially recognised because their quantity, quality, efficacy and safety cannot be sufficiently guaranteed to meet the criteria set by the local authorities [1,2]. Most species belonging to the Mallotus genus (family Euphorbiaceae) and their derived natural products are in the latter situation. Spread throughout South-East and North Asia, the Mallotus genus comprises over 140 species. A number of them are used in Chinese and Vietnamese TM [10,11] since hundreds of years. The concentrations of the herbal constituents may vary significantly depending on the harvest season, the cultivation conditions, the drying processes and the collected part of the plant, making it not only extremely difficult to assess their quality, but this variability also has its influence on the determination and isolation of compounds of interest [12,13]. Nevertheless, the Mallotus genus provides a broad basis for researchers searching for potentially interesting active compounds. Over the years, many studies on Mallotus species have been published and several pharmacologically active constituents were determined [14–25]. Reported activities include anti-inflammatory [14], hepatoprotective [15], antioxidant [16], antimicrobial [17,18], cytotoxic [19,20] and retroviral [21]. However, most of these

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studies focus on a very limited number of compounds analysed in one particular species and hereby ignore the complex composition of herbal samples, or fail to discriminate between related species. A review of the past and current research on the genus Mallotus has been written by Rivière et al. [26]. In a previous study [27], a data set containing the highperformance liquid chromatography (HPLC) fingerprints of 39 Mallotus samples, from at least 17 different species, was developed. From some species different samples were analysed, but all varied in origin, harvesting period, or collected part of the plant. For all samples, the antioxidant activity was determined with the 1,1diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity assay [28]. Nine samples were found to be highly antioxidant and two as intermediately active. Modelling the DPPH results as a function of the HPLC fingerprints allowed indicating the peaks potentially responsible for the antioxidant activity of the samples [27]. For this purpose, the regression coefficients of the orthogonal projections to latent structures (O-PLS) technique [29] were evaluated. Because of the complexity of and the large differences between the samples, alignment proved to be problematic and the data analysis was performed on the unaligned fingerprints. Alignment corrects for the retention time shifts between the fingerprints and ensures correspondence between the chromatographic peaks prior to chemometric treatment of the data. Currently, several alignment techniques are described in the literature. Amongst them are the well-established techniques of dynamic time warping [30], parametric time warping [31], fuzzy warping [32] and correlation optimised warping (COW) [30,31,33], as well as more recent developments, such as the automated alignment described by Daszykowski et al. [34]. This study mainly addresses the alignment of the complex and diverse Mallotus fingerprints and provides an in depth discussion on fingerprint alignment for the indication of potentially active compounds in biological samples. To provide additional information for the alignment procedure, 16 Mallotus samples were analysed by HPLC–mass spectrometry (HPLC–MS). The results were used to monitor the alignment procedure and avoid matching noncorresponding peaks. Additionally, the compounds corresponding to the peaks indicated by the regression coefficients without and with fingerprint alignment were identified and evaluated. To critically assess the influence of alignment on the data analysis of complex samples in multivariate calibration, a second case study was opted. In contrast with the Mallotus fingerprints, the alignment of homogeneous green tea fingerprints for the construction of prediction models for future samples was considered. For a thorough discussion on the development of both data sets, the reader is referred to references [8,27]. 2. Theory 2.1. DPPH radical scavenging assay The antioxidant capacity of the 39 Mallotus samples was measured by the DPPH radical scavenging assay, which measures the capacity of a compound or a sample to scavenge the DPPH radical. The radical has an absorption band at 515 nm which disappears upon reduction by the antioxidant compounds present in the samples, resulting in an inverse correlation of the remaining absorbance at 515 nm to the antioxidant activity of the sample [28]. 2.2. Correlation optimised warping Correlation optimised warping is used to correct for the occurring peak shifts. COW aligns two signals by means of piecewise linear stretching and compressing a profile P to match it as good as

possible with the target profile T. The optimised solution is achieved by dividing both profiles into a user-defined number of segments, N. Starting from the last section, each section of profile P is individually stretched or compressed by moving the section’s end point by a limited number of data points, defined as the slack parameter t. For every possible end point ranging from −t to t, the correlation coefficient to the corresponding section of the target T is calculated. The solution with the highest correlation coefficient is stored and interpolated to the length of the corresponding section of the target profile. The global warping solution is then defined as the highest cumulative sum of the correlation coefficients for all sections [33]. 2.3. Principal component analysis A common way to gain insight in a multivariate data table and reveal potentially underlying structures is by exploratory analysis. For this purpose, principal component analysis (PCA) was applied to the data set. PCA creates linear combinations of the original variables, the principal component (PC), which describe the systemic patterns of variation between the samples. The PCs are orthogonal and can be defined until a maximal number of PCs, equal to n − 1 (with n ≤ p), is reached. The projections of the n objects from the original data space on a PC are called the scores, while the contribution of each original variable to the score on a PC is reflected by its loading. Both scores and loadings are valuable for the exploratory analysis of the original data as they can be visualised easily: a score plot representing the scores on two PCs reflects the (dis)similarity of the objects, while a loading plot provides information on the contribution of the original variables to the considered PC [35–37]. 2.4. Orthogonal projections to latent structures O-PLS [29,38] is a multivariate calibration technique which allows modelling a continuous property as a function of the recorded fingerprints. O-PLS is a modification of the partial least squares (PLS) algorithm studying the relationship between an n × p data matrix X and an n × 1 response vector y. It removes the information that is not correlated to the response by subtracting orthogonal components from the original data. Consequently, the data is split into two data sets containing the y-relevant information and the y-orthogonal information. By removing the orthogonal information from the original data, the model complexity can be reduced to a single factor, improving the interpretability of the regression coefficients without compromising the predictive power of the model [29]. 3. Experimental 3.1. Sample preparation 39 Mallotus samples, from at least 17 different species, were collected in different Vietnamese regions. Five samples were unidentified. The antiradical activity was measured using the stable 1,1-diphenyl-2-picrylhydrazyl radical [28]. Nine samples possessed high antioxidant activity (%[DPPHrem ◦ ] < 30), two samples were intermediately active (30 < %[DPPHrem ◦ ] < 50), while the others were considered as inactive (%[DPPHrem ◦ ] > 50). Depending on the species and their applicable nature conservation laws (which are issued to preserve and protect the environment), the leaves, roots and/or bark were used (Table 1). All samples were authenticated by Professor Nguyen Nghia Thin (Hanoi National University, Vietnam). Extracts were prepared by weighing 2.5 g plant sample and extracting three times with 25 mL methanol in an ultrasonic bath (Branson Ultrasonic Corporation, Connecticut, USA) at a temperature between 40 and 50 ◦ C during 1 h. The extract was filtered through a 240 nm pore size filter paper (Whatman, Hanoi,

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Table 1 The Mallotus samples with their voucher numbers, species, origin, collection time, used part of the plant and the DPPH scavenging activities results indicated. The samples analysed by LC–MS are marked in bold. Voucher number

Species

Origin

Collection time

Part of plant

%DPPHrem a

01 02 03 MA07 NT01 NT02 NT03 MA01 MA02 MA03 SP4 SP5 MA11 MA12 MA13 MA14 MA15 MA16 MA17 MA18 MA19 MA20 MA22 MA23 MA24 M25 MA28 MA29 MP31L MP32R MP33L MP34R MP35R MP36L MN37R MN37L MN39C M40L M41C

Mallotus luchenensis Mallotus microcarpus Mallotus barbatus Mallotus sp1 Mallotus barbatus Mallotus paniculatus Mallotus metcalfianus Mallotus apelta (Ma1) Mallotus apelta (Ma2) Mallotus paniculatus Mallotus sp2 Mallotus philippinensis Mallotus macrostachyus Mallotus microcarpus Mallotus pallidus Mallotus oblongifolius Mallotus floribundus Mallotus cuneatus Mallotus cuneatus Mallotus sp3 Mallotus yunnanensis Mallotus poilanei Mallotus hookerianus Mallotus nanus Mallotus sp4 Mallotus oreophilus Mallotus philippinensis Mallotus barbatus Mallotus paniculatus Mallotus paniculatus Mallotus paniculatus Mallotus paniculatus Mallotus paniculatus Mallotus paniculatus Mallotus nanus Mallotus nanus Mallotus nanus Mallotus sp5 Mallotus sp6

Son La Son La Son La Van Hoa Hagiang Hagiang Hagiang Tam Dao Tam Dao Tam Dao Langson Langson Langson Quangbinh Quangbinh Quangtri Langson Langson Quangbinh Quang tri Lang Son Ke Bang Dakrong Daclak Daclak LaoCai Cucphuong Cucphuong VQG Pumat VQG Pumat Bach Ma-TTH Bach Ma-TTH Cucphuong Cucphuong VQG-Bachma VQG-Bachma VQG-Bachma VQG Bavi VQG Bavi

July 2006 July 2006 July 2006 April 2006 November 2006 November 2006 November 2006 July 2006 December 2006 April 2006 March 2006 March 2006 March 2006 March 2006 March 2006 March 2006 November 2006 November 2006 December 2006 December 2006 November 2006 March 2006 March 2006 March 2006 March 2006 June 2006 December 2006 December 2006 September 2006 September 2006 October 2006 October 2006 December 2006 December 2006 May 2006 May 2006 May 2006 August 2006 August 2006

Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Leaves Roots Leaves Roots Roots Leaves Roots Leaves Bark Leaves Bark

82.0 63.6 79.4 113.0 77.2 82.2 51.1 94.5 92.5 58.4 56.8 98.9 75.7 83.1 65.3 6.7 6.4 86.9 10.3 91.6 91.6 90.5 50.0 78.4 56.9 88.8 22.3 11.3 73.5 91.5 81.5 83.5 27.9 75.3 12.2 4.5 27.1 73.7 65.6

a

% DPPH remaining at a concentration of 20 ␮g mL−1 .

Vietnam) and evaporated at reduced pressure (60 Pa) and elevated temperature (50 ◦ C). The obtained crude extracts were divided over three sample tubes, i.e. one for the DPPH radical scavenging assay, one for HPLC and MS analysis, and one as a voucher specimen. The voucher specimens were deposited at the Institute of Natural Products Chemistry, Hanoi, Vietnam. The samples for HPLC–MS analysis were prepared diluting 50.0 mg crude extract in 2.0 mL methanol. The solution was mixed during 15 min at 250 rpm on a shaking bath (Edmund Bühler, Hechingen, Germany) and afterwards filtered through a 2 ␮m pore size filter (Schleicher & Schuell, Dassel, Germany) followed by filtration through a 25 mm syringe polypropylene membrane with 0.2 ␮m pore size (VWR International, Leuven, Belgium). 3.2. Reference compounds The standard compounds mallonanoside A and mallonanoside B and the recently isolated cytotoxic benzopyrans 6-[l -oxo-3 (R)hydroxy - butyl] - 5,7 - dimethoxy - 2,2 - dimethyl -2H-l-benzopyran and 6-[l -oxo-3 (R)-methoxy-butyl]-5,7-dimethoxy-2,2-dimethyl2H-l-benzopyran [20] were provided by the Institute of Natural Products Chemistry (Vietnamese Academy of Science and Technology, Hanoi, Vietnam), quercitrin was obtained from Sigma–Aldrich (St. Louis, MO) and kaempferol-3-O-l-rhamnosyl was provided by the Analytical Chemistry, Drug Analysis and Pharmacognosy Unit (Université Catholique de Louvain, Brussels, Belgium). From all standard compounds, 1.0 mg was weighed and dissolved in 1.0 mL

methanol. Then, the same procedure was followed as for the crude extracts. 3.3. Chromatographic conditions The stationary phase consists of two coupled ChromolithTM Performance RP-18e columns (100 mm × 4.6 mm I.D.) with a ChromolithTM RP-18e guard column (5 mm × 4.6 mm I.D.) placed before the analytical ones. HPLC grade methanol, acetonitrile (both Fisher Scientific, Leicestershire, UK), trifluoroacetic acid (TFA) (Sigma–Aldrich, Steinheim, Germany) and MilliQ water, obtained from a MilliQ purification system (Millipore, Bedford, MA), were used to prepare the mobile phases. The mobile phase consisted of (A) 0.05% TFA in MilliQ water, and (B) 0.05% TFA in ACN water. A gradient elution was applied; 5–20% B in 0–25 min, 20–95% B in 25–50 min, and 95% B during 50–60 min. The column temperature was 25 ◦ C, the flow rate 1.0 mL min−1 , the injection volume 10 ␮L, and the detection wavelength was set at 254 nm. 3.4. HPLC–MS All experiments were executed on an Alliance HPLC (Waters, Milford, Massachusetts, US) equipped with an auto sampler and column oven. MS-detection was conducted using an ion trap LCQ-advantage system (Thermo Fisher Scientific, Waltham, Massachusetts, USA) equipped with an APCI interface. All MS analyses

C. Tistaert et al. / Analytica Chimica Acta 721 (2012) 35–43

Absorbance (mAU)

(a) unaligned fingerprints

(b) aligned fingerprints

1000

1000

800

800

600

600

400

400

200

200

0

O-PLS regress ion coefficients

4 3

0

O-PLS regress ion coefficients

6

4

1

0

1

4

5

6

8

2

10

Time (min)

0

2

6

3

1 2

4

Absorbance (mAU)

38

5

6

8

10

Time (min)

Fig. 1. Green tea fingerprints and regression coefficients as given by the O-PLS model, (a) without and (b) with alignment of the fingerprints. Numbers 1–6 indicate the corresponding peaks and coefficients without and with fingerprint alignment.

were performed with a mass precision of 0.5 atomic mass units (amu). The MS acquisitions were performed in both positive and negative atmospheric pressure ionisation modes. The following APCI inlet conditions were used. Nitrogen was used both as a nebulising gas at 450 ◦ C and an arbitrary flow of 70 and as a drying gas at 450 ◦ C and an arbitrary flow of 30. The capillary temperature was set at 200 ◦ C. In the positive mode, the capillary voltage was set to 26 V, the source voltage to 6 kV and the source current to 5 ␮A. In the negative mode, the capillary voltage was set to −4 V, the source voltage to 4.5 kV and the source current to 80 ␮A. In both modes 25 V of collision energy was applied. The MS analyses were performed in both the positive and negative modes. Due to the presence of TFA as mobile phase additive, many of the analysed compounds are bound to TFA in the MS spectra when analysed in the negative mode, causing a difference of +113 amu. During analyses in the positive mode, this problem does not occur. To avoid confusion, all values reported in this paper are corrected for the addition of TFA. 4. Results and discussion 4.1. Aligning complex biological fingerprints Because of the great diversity of the Mallotus samples, large differences between the constituents of the fingerprints are observed, resulting in a problematic alignment. Therefore to avoid the mismatching of non-corresponding information, it was chosen to perform the data analysis on the unaligned fingerprints [27]. This study evaluates the indication of potentially active compounds without and with alignment of the fingerprints. For this, an alignment procedure for the Mallotus fingerprints was developed based on COW, in synergy with an exploratory analysis and the available MS data. Then, O-PLS models were constructed for both the unaligned and the aligned fingerprints, and their performance (i.e. the prediction of the antioxidant samples) and regression coefficients were evaluated. To critically assess the alignment of complex biological samples and to contrast the diversity of the Mallotus fingerprints and the purpose of the data analysis, the

antioxidant activity of a homogenous set of green tea fingerprints was also modelled with as main objective the prediction of future unknown samples [8]. Again, the performance of the unaligned and aligned models and their regression coefficients were evaluated. The results of both contrasting case studies are considered to discuss the benefits and/or drawbacks of aligning diverse fingerprints versus the generally accepted strategy of aligning homogenous fingerprints for predictive purposes.

4.2. The green tea data For the green tea data, the O-PLS models were constructed without and with fingerprint alignment. As all samples belong to the same species, their fingerprints have similar profiles (representing identical constituents) and the alignment of the fingerprints did not pose any major problem. As the main purpose of the green tea data was the establishment of a predictive model for the antioxidant capacity of future green tea samples, the data were divided into a calibration set (40 duplicate measured samples) and a test set (12 duplicate measured samples). For the unaligned fingerprints, the optimised O-PLS model resulted in the removal of two orthogonal components, a root mean square error (RMSE) of 4.9% and a root mean square error of prediction (RMSEP) of 4.7%. For the aligned fingerprints, the RMSE and RMSEP decreased to 3.9% and 4.0%, respectively. When evaluating the regression coefficients of both models, the differences are clearly visible (Fig. 1). For the unaligned fingerprints, the regression coefficients show a number of split (peaks 1,4 and 6), broad (peaks 2 and 5), and composed peaks (peak 3) resulting from peak shifts occurring in the system during the recording of the data set. The split peaks cause difficulties to understand the contribution of the individual peaks and result in less performing models. These problems are resolved in the regression coefficients from the aligned fingerprints (Fig. 1b). The correction for the peak shifts removed the interpretational ambiguity of the model’s regression coefficients. However, one should notice that the alignment of the fingerprints did not result in the selection of different peaks by the modelling: the regression coefficients indicated the same six

5000

1

4000

0.8

39

(e)

0.6 3000

PC2 (14.4%)

Absorbance (mAU)

C. Tistaert et al. / Analytica Chimica Acta 721 (2012) 35–43

2000

0.4

(b)

(c)

0.2 MA01

1000

MA07

0

(d)

MA02

(a)

0 0

10

20

30

40

50

-0.2

60

Time (min) -0.4 -0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

PC1 (15.5%) unaligned

COW

COW -PCA - MS

MA16

MA11

M40L

MA16

MA11

53

M40L

MA16

MA11

SP4

SP4

SP4

NT01

NT01

NT01

60 46

Time (min)

01

01

01

46

Absorbance (mAU)

M40L

M41C

M41C

Absorbance (mAU)

Absorbance (mAU)

M41C

53

Time (min)

60 46

53

60

Time (min)

Fig. 2. Fingerprints of the unaligned Mallotus extracts. The alignment of the fingerprints by COW, with and without the information of PCA and MS (as described in Section 4.3.1), is visualised in the zoom plots.

Absorbance (mAU)

3000

2000

1000 MA02 MA01

0 0

MA07

10

20

30

40

50

60

Time (min) Fig. 3. PC1–PC2 score plot for the 60 min fingerprints of the 39 Mallotus samples, normalised and column centred. The unidentified sample MA07 is clustered together with samples MA01 and MA02, both belonging to the species Mallotus apelta (top) and have very similar fingerprints (bottom). Groups (a–e) are used in the warping strategy for the Mallotus data set.

chromatographic peaks to be important for the model, regardless of alignment. 4.3. The Mallotus data Contrary to the green tea data, the main purpose of the Mallotus data set was not to predict the activity of future samples. The fingerprints are not well suited to build prediction models because of the diversity of the fingerprints and the imbalance between non-active and active samples. However, based on the existing fingerprints, the compounds that are potentially responsible for the measured activity can be indicated by the regression coefficients. Accordingly, as no future samples are to be predicted, the Mallotus data were not split into a calibration and validation set. The focus on evaluation of the model was set to the interpretability of the regression coefficients, while the prediction error served as an indication of the reliability of the model, and thus of the indication by the regression coefficients. 4.3.1. The alignment procedure For the alignment, we deviated from the recommended COW procedure which aligns the samples based on a single selected target and the highest cumulative correlation coefficient [33]. This procedure was not suitable for the Mallotus fingerprints as the differences between the samples (Fig. 2) introduce a high probability of aligning non-corresponding information. Therefore, prior to the construction of an overall warping solution, the data were

divided into subsections based on the species and the findings of an exploratory analysis. Additionally, by taking into account a limited availability of MS spectra (16 out of 39 samples), the alignment procedure was monitored carefully. The zoom plots of Fig. 2 visualise the differences between the unaligned fingerprints, the fingerprints after COW, and the fingerprints after COW with consideration for the exploratory analysis and the available MS data. Based solely on the correlation coefficient, COW clearly aligns non-corresponding peaks. In a first step, all samples belonging to the same species were organised in separate groups and aligned. Next, the a priori information on the samples and the fingerprint profiles were combined depending on their proximity on the PC1–PC2 score plot. This resulted in the distinction of three subgroups consisting of (a) Mallotus apelta and an unknown sample, (b) Mallotus nanus, and (c) Mallotus paniculatus and two unknown samples. The remaining samples were divided over two additional clusters, (d) and (e), based on the similarity of the fingerprint profiles (Fig. 3a). Within each group, the correlation coefficients between the fingerprints were determined and the sample with the highest overall correlation coefficient was selected as the target profile for COW. As a final step, all groups were recombined and a final warping solution for the entire set of fingerprints was constructed based on similarities in the fingerprints profiles and MS spectra. Below a brief overview of the most important MS information that could be included in

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C. Tistaert et al. / Analytica Chimica Acta 721 (2012) 35–43 Table 2 Prediction of the antioxidant Mallotus samples without and with fingerprint alignment.

Absorbance (mAU)

a

MA29 03 NT01

b

MP35R MP35Rnew MP36L MP34R MP33L MP32R MP31L MA03 NT02

c MN37R MN37L MN39C

0

10

20

30

40

50

60

Time (min) Fig. 4. Fingerprints of (a) Mallotus barbatus, (b) Mallotus paniculatus, and (c) Mallotus nanus.

the alignment is given. Additionally, the data on the indicated compounds (as described in Section 4.3.2) was also taken into account. 4.3.2. Classification of the unidentified sample MA07 Exploratory analysis by PCA revealed three samples (MA01, MA02 and MA07) in close proximity on the PC1–PC2 score plot (Fig. 3 – group (a)). The samples MA01 and MA02 belong to the M. apelta species, while MA07 was unidentified. The proximity on the score plot and the highly similar chromatographic profiles (correlation coefficients 0.9001 and 0.9013, respectively – Fig. 3b), led to the assumption that MA07 belongs to the same species. LC–MS analyses confirmed the classification by the identification of two compounds representative for the M. apelta species, i.e. two cytotoxic benzopyrans 6-[l -oxo-3 (R)-hydroxy - butyl] - 5,7 - dimethoxy - 2,2 - dimethyl-2H-l-benzopyran and 6-[l -oxo-3 (R)-methoxy-butyl]5,7-dimethoxy-2,2-dimethyl-2H-l-benzopyran [20], in the three samples. 4.3.3. Differences in antioxidant activity within one species Within one species, noticeable differences in antioxidant activity may occur. The presence and/or concentrations of the herbal constituents may vary significantly depending on the harvest season, the cultivation conditions, the drying processes and the part of the plant used. As the data contain several samples from a given species, it is examined whether or not differences in the constituents are noticed. For Mallotus barbatus, sample MA29 possesses a high antioxidant activity (%[DPPHrem ◦ ] = 11.3), while samples 03 and NT01 are only slightly antioxidant (%[DPPHrem ◦ ] = 79.4 and 77.2, respectively). When examining the fingerprints, both visually (Fig. 4a) and using LC–MS, large differences are observed. Sample MA29 contains an unknown compound at 19.7 min, while sample 03 contains the flavonoid quercitrin (28.3 min). Sample NT01 contains glycosides of morin or quercetin (25.4 min) and glycosides of kaempferol and luteolin (28.3 min). Remarkably, the highly antioxidant sample MA29 does not contain any of the screened flavonoids, while the non-antioxidant ones do. This may be explained by the concentrations of the flavonoids which are too low to cause a real antioxidant effect for samples 03 and NT01. It would be interesting to determine the structure of the unknown compound in MA29, which may posses interesting antioxidant activity. The M. paniculatus species is represented by eight identified samples in the data. However, only MP35R possesses high antioxidant activity. Visual examination of its fingerprint reveals a very

Voucher number

%DPPHrem

O-PLS unaligned

O-PLS aligned

MA14 MA15 MA17 MA28 MA29 MP35R MN37R MN37L MN39C

6.7 6.4 10.3 22.3 11.3 27.9 12.2 4.5 27.1

0.5 15.5 28.7 44.8 47.3 48.8 9.1 24.5 38.0

−0.1 10.3 25.8 45.6 62.4 54.6 10.9 25.3 5.2

dissimilar profile compared to the fingerprints of the other M. paniculatus samples (Fig. 4b). LC–MS analyses revealed no analogy between the antioxidant and non-antioxidant samples. While sample MP35R contains six unknown compounds, the nonantioxidant samples all contain the same two main compounds at 17.7 and 20.6 min. Given these data, the voucher specimen of MP35R, stored at the Institute of National Products Chemistry (Hanoi, Vietnam) was requested and analysed. The activity of the library sample was closer to the non-antioxidant samples (%[DPPHrem ◦ ] = 79.8), while also no similarity was found between the fingerprints of MP35R and its voucher specimen. Most likely, a gross error has occurred prior to analysis and the originally included sample MP35R does not belong to M. paniculatus. The faulty sample did not have a high correlation to any of the other fingerprints nor their corresponding MS spectra and remained unidentified. The identification of this problematic sample demonstrates the potential risks of misalignment and the caution with which the current automated alignment procedures should be handled. Finally, the three samples of M. nanus are highly antioxidant, from the same origin and collected at the same time. The difference between these samples resides in the part of the plant that was used: the roots for MN37R, the leaves for MN37L and the bark for MN39C. All samples have similar fingerprints and the constituents potentially responsible for the antioxidant activity are identical (Fig. 4c). 4.3.4. Data analysis Without alignment, the Mallotus data resulted in an optimised O-PLS model with one orthogonal component removed and a RMSE of 13.8%. From the nine highly active antioxidant samples, five were predicted as highly active, and the four others as intermediately active (Table 2). The intermediately active samples were predicted as intermediately active, and none of the non-antioxidant samples were predicted as highly or intermediately active. With alignment, the optimal model resulted in a RMSE of 14.7%. Six out of nine highly antioxidant samples were predicted accordingly, while the other three samples were predicted as intermediately or not active. The model did not predict any antioxidant activity for the inactive samples. Compared to the green tea data, the regression coefficients without and with fingerprint alignment contain less pronounced but still visible differences (Fig. 5a). To evaluate whether the regression coefficients of both models indicate the same peaks as potentially responsible for the antioxidant activity, the LC–MS analyses of the nine antioxidant samples are compared with the negative regression peaks from both the unaligned and the aligned fingerprints. The analysed samples were Mallotus Oblongifolius (MA14), Mallotus floribundus (MA15), Mallotus cuneatus (MA17), Mallotus philippinensis (MA28), M. barbatus (MA29), M. paniculatus (MP35R) and M. nanus (MN37R, MN37L, MN39C). All samples were screened for known (mallonanoside A, mallonanoside B and

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a

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Fig. 5. (a) The regression coefficients of the Mallotus data obtained without and with fingerprint alignment. (b) Indication of the compounds of interest in the fingerprints of the nine highly active antioxidant samples results in the identification of the same compounds both from the unaligned and aligned fingerprints.

the flavonoids quercetin, quercitrin, myricetin, and kaempferol-3O-l-rhamnosyl) and unknown compounds, underlaying the peaks indicated by the regression coefficients (Fig. 5b).

analogue to mallonanoside A and B, to have antioxidant capacities [41–43]. Also, the flavonoids quercitrin (28.3 min) and kaempferol3-O-l-rhamnosyl (31.1 min) were identified in the three M. nanus samples.

5. Identified compounds LC–MS analyses of the indicated peaks in the nine highly active samples identified four known compounds, i.e. mallonanoside A, mallonanoside B, quercitrin and kaempferol-3-O-l-rhamnosyl. Two compounds, mallonanoside A (6.5 min) and mallonanoside B (10.5 min) were present in the three M. nanus samples and in sample MA28. Both compounds are C-glycosyl benzoic acid derivatives that were recently identified [39,40]. Little is known about their activity, but several studies determined benzoic acid structures,

5.1. Unknown compounds with important negative regression coefficients Sample MA14 presents a peak at 16.5 min. The negative mode MS spectrum reveals two co-eluting compounds, compound A (with [A−H]− at m/z 633) and compound B (with [B−H]− at m/z 463). The same compounds were observed in samples MA15, MA17 and MA28 eluting at 16.8, 16.5 and 16.3 min, respectively. Compound C can be found in samples MA14, MA15, MA17 and MA28

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eluting at 23.8, 23.8, 23.5 and 23.6 min, respectively. It is most probably the aglycon of compound B (with [C−H]− at m/z 301, 162 mass units less than [B−H]− ). Compounds D (with [D−H]− at m/z 211) and compound E (with [E−H]− at m/z 395 and [E+H]+ at m/z 397) co-elute at 24.5 min in MA14. Compound E was also observed in samples MA15, MA17 and MA28 at 24.5, 24.3 and 24.3 min, respectively. In addition, sample MA17 possesses a peak at 31.5 min corresponding to compound F (with [F+H]+ at m/z 373), whilst sample MA29 revealed compound K at 19.7 min (with [K−H]− at m/z 358 and [K+H]+ at m/z 360). Samples MN37R, MN37L and MN39C show a common peak at 23.8 min. Analysis indicated the presence of compound G (with [G−H]− at m/z 609, [G+H]+ at m/z 611, two successive losses of deoxyhexose) for MN37R, MN37L and MN39C. However, the corresponding peak in MN39C also contains compound H (with [H+H]+ at m/z 314). For sample MP35R, MS analysis showed the presence of compounds N (with [N+H]+ at m/z 405), O (with [O+H]+ at m/z 417), and P (with [P+H]+ at m/z 613) eluting at 31.5, 33.4 and 36.7 min, respectively.

5.2. Evaluation of the regression coefficients For both the unaligned and aligned fingerprints, the individual profiles are plotted against the regression coefficients. Based on the LC–MS results discussed above the analysed peaks were marked (Fig. 5). The observed differences in the regression coefficients correspond solely to the retention time shifting between the unaligned and the aligned fingerprints. No differences were found in the peaks indicated by the regression coefficients of both data sets. However, correction of the peak shifts removed interpretational ambiguities of the model’s regression coefficients and facilitates the evaluation of the model for the indication of the peaks of interest. On a side note, the existence of several co-eluting compounds underlying the indicated peaks should be pointed out, complicating the interpretation of the model as the true contribution of the individual compounds to the antioxidant activity remains unknown. To clarify this, additional analysis should be performed separating the co-eluting compounds (e.g. on a dissimilar chromatographic system) [44].

5.3. Unaligned versus aligned fingerprints From the results obtained for both data sets, one observes similar influences of the alignment of the fingerprints on the interpretability of the model, in which the differences reside in the simplicity of the regression coefficients, eliminating split and broad regression coefficients by correcting for the retention time shifts. When evaluating the green tea and the Mallotus models, those of the aligned fingerprints provide an improved simplicity and interpretability of the regression coefficients. Alignment did not result in the indication of different peaks. The peaks indicated by positive regression in the one model also have positive regression in the other model (Figs. 1 and 5). Oppositely, when considering the ability of the models to accurately predict the activity of future unknown samples, alignment of the homogenous green tea fingerprints resulted in improved prediction errors. Therefore, prior to data analysis one should consider the purpose of the data analysis, the complexity and diversity of the samples, and the a priori available information on the samples. Although we do not want to deny nor neglect the importance of fingerprint alignment, this study points out that, in case diverse fingerprints are modelled for indicative purposes, the trade-off between the interpretability of the model and the time-consuming alignment strategy should be considered.

6. Conclusions In an earlier study [27], HPLC fingerprints of 39 Mallotus samples were developed and linked to their antioxidant activity by O-PLS. The intention was to indicate the chromatographic peaks potentially responsible for the antioxidant activity of the samples. Because of the complexity and diversity of the biological samples, causing large differences between the fingerprints which were initially recorded without Diode Array or Mass Spectrometry detection, it was preferred to perform the chemometric analysis on the unaligned fingerprints to avoid the alignment of noncorresponding peaks. In the actual study, the latter problem of aligning complex biological fingerprints of highly diverse samples was addressed. Multivariate models were built using two different data sets: a heterogeneous data set consisting of 39 diverse Mallotus fingerprints, and a homogenous data set consisting of 50 duplicate measured green tea fingerprints. O-PLS models were built for both data sets without and with alignment of the fingerprints. For all models, their performance and the information of the regression coefficients were evaluated. Alignment of the green tea fingerprints did not pose any major difficulties because of the homogenous nature of the samples, resulting in very similar chromatographic profiles with identical compounds. Oppositely, the alignment of the diverse Mallotus fingerprints proved to be a time consuming challenge. To avoid mismatching of non-corresponding peaks, an alignment strategy combining COW with a priori available information on the data, the findings of the exploratory analysis and the LC–MS results, was applied to generate the best possible result. In a next step, the performance of the models and the regression coefficients obtained without and with fingerprint alignment were evaluated. For the green tea fingerprints, alignment resulted in an improved predictive model decreasing the prediction error. Evaluation of the regression coefficients resulted in an improved interpretability for the aligned fingerprints, but did not result in the indication of different peaks compared to the model of the unaligned fingerprints. Similar results were found when evaluating the regression coefficients of the Mallotus data. LC–MS analyses of the chromatographic peaks of the highly active antioxidant samples indicated without and with fingerprint alignment revealed identical results. Four known compounds were indicated as being potentially antioxidant. Two of them were the flavonoids quercitrin and kaempferol-3-0-l-rhamnosyl, while the others were the C-glycosyl benzoic acid analogues mallonanoside A and B. Furthermore, 16 unknown but potentially interesting compounds were observed underlying the peaks indicated by the regression coefficients of both O-PLS models. Taking into account the above results, one should carefully consider the purpose of the data analysis and the complexity of the data. For both case studies, alignment of the fingerprints resulted in an improved interpretability of the regression coefficients, whilst the indication of the peaks of interest was not influenced by the alignment. However, for the green tea fingerprints alignment led to a significant decrease of the prediction error, which is of primordial importance when focussing on the prediction of future unknown samples. On this behalf, one should consider the tradeoff between a time consuming alignment strategy and the purpose and interpretability of the model. Acknowledgements The authors gratefully thank the Belgian Science Policy Office (BELSPO) Bilateral Project (BIL/03/V09) between Belgium and Vietnam for financial support on this research. Bieke Dejaegher is a post-doctoral fellow of the FWO.

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