olive oil traceability

Analytical & Bioanalytical Chemistry

Using Chemometric Resolution Methods For Fast Analysis Of Some Phenolics In Olive Oil

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To the Editorial Office of

Analytical and Bioanalytical Chemistry

Dear Editors,

please

find

attached

a copy of

our

manuscri

pt “Using

Chemom

etric Resolution Methods For Fast Analysis Of

Some Phenolics In Olive Oil” to be considered

for publication on the special issue of Analytical and Bioanalytical

Chemistry titled “Analytical Sciences in Italy” (Editor: prof. Aldo

Roda).

This manuscript presents an application of chemometric curve

resolution methods for the a posteriori deconvolution of coeluted

chromatographic peaks resulting from the HPLC-DAD analysis of the

phenolic fraction in virgin olive oil samples. In particular, the

possibility of dealing with incomplete chromatographic resolution

through the use of mathematical processing of the signal allowed the

use of fast gradients and the choice of a rapid extracting procedure. In

terms of the achieved resolution, it was possible to separate up to 7

components in the chromatographic profile, even in the presence of

spectral fingerprints which were rather similar among one another.

The reduction of the analysis time and, correspondingly, in the amount

of solvents used are outcomes that move towards the direction of

greener procedures in analytical chemistry, thus representing a very

promising perspective. Moreover, these results are easily generalizable

to similar systems were a perfect chromatographic separation of the

peaks can’t be achieved and were coelution stems from the complexity

of the matrix or from the quest for reduced analytical times.

In submitting the manuscript, I confirm on behalf of all the authors that

the research proposed is original and that it hasn’t been presented

elsewhere.

Best regards,

Federico Marini

--

51525354555657585960

For Peer ReviewFor Peer Review


Dr. Federico Marini Dept. of Chemistry University of Rome “La Sapienza” P.le Aldo Moro 5 00185 Rome Italy Tel +39 06 4991 3680 Fax +39 06

4457050 e-mail: [email protected]


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USING CHEMOMETRIC RESOLUTION METHODS FOR FAST ANALYSIS

OF SOME PHENOLICS IN OLIVE OIL

Riccardo Nescatelli, Remo Bucci, Antonio L. Magrì, Andrea D. Magrì, Federico

8 4 Marini* 910 5 Dept. of Chemistry, University of Rome “La Sapienza”, P.le Aldo Moro 5, I-00185

11 6 Rome, Italy. 1213 7 1415 8 *Corresponding author:

16 9 Dr. Federico Marini 1718 10 Dept. of Chemistry 19

20 11

21 12 2223 13 00185 Rome 24

25 14 Italy

26 15 Tel +39 06 4991 3680 2728 16 Fax +39 06 4457050

515253545556575859

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P.le Aldo Moro 5

University of Rome “La Sapienza” For Peer Review


1

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30 17 e-mail: [email protected]

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33 USING CHEMOMETRIC RESOLUTION METHODS FOR FAST ANALYSIS

OF SOME PHENOLICS IN OLIVE OIL

Riccardo Nescatelli, Remo Bucci, Antonio L. Magrì, Andrea D. Magrì, Federico

Marini*

Dept. of Chemistry, University of Rome “La Sapienza”, P.le Aldo Moro 5, I-00185 Rome, Italy.

*e-mail: [email protected]

Abstract

Phenolic compounds are related to the stability of the oil, but also to its biological properties. The

phenolic compounds of virgin olive oils are currently a subject of great interest, due to their

antioxidant action, their health effects and how they affect the organoleptic characteristics of food . In

this study, the possibility of using a chemometrics, and in particular multivariate curve resolution, for

the a posteriori separation of the pure component signals from coeluting chromatographic peaks in the

analysis of the neutral polyphenolic fraction of olive oil was shown.

In particular, two groups of coeluting peaks were identified in the chromatogram and MCR-ALS in the

multi-set configuration was used to recover from the signals the elution and spectral profiles of the

pure components. Results were validated by comparison with a complete chromatographic separation

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1

2

3 of the substances

giving rise to the two

cluster of peaks:

comparison of the

spectral profiles

showed a good

consistency,

Additionally, when

considering the

relative amount of the

different components in the analyzed samples, results were quite promising as in almost all the cases

errors lower than 5% were obtained.

Keywords: Multivariate Curve Resolution (MCR-ALS); HPLC-DAD; chemometrics; coelution;

polyphenols; olive oil

Introduction

The phenolic fraction of olive oil is a complex mixture of compounds with different chemical

structures. The literature on these compounds has increased exponentially

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over the last ten years for various reasons: the phenolic compounds are related to the

stability of the oil, but also to its biological properties. To date, there is greater

interest in this feature: indeed, many compounds have been studied thoroughly with

the aim to

8 4 establish a relationship between their dietary intake and the risk of various 910 5 degenerative diseases. In this respect, the studies agree in identifying a beneficial role

11 6 to human health associated with the consumption of these compounds. The 1213 7 composition of the polyphenolic fraction is very heterogeneous, with at least 36 1415 8 phenolic compounds identified, which can be grouped according to their structural

16 9 characteristics in the following classes: alcohols, phenols, secoiridoids, phenolic acids, 515253545556575859

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5 2 6 3 71718 10 hydroxy-isocromans, flavonoids and lignans [1]. The quality of oil differs in terms of 19

20 11 the qualitative and quantitative composition of the phenolic fraction. The average

21 12 concentration of phenolic compounds is 100-300 mg/kg [1]. There are differences in 2223 13 composition and concentration due to many factors. First, the olive cultivar and the 24

25 14 region in which fruits grow, as it was shown that olive oils of different quality but

26 15 from the same geographical area have similar phenolic profiles [2]. Secondly, 2728 16 agricultural techniques also influence the concentration of some compounds which, 29

30 17 for instance, is affected by the level of irrigation [3]. The phenolic compounds of

31 18 virgin olive oils are currently a subject of great interest, due to their antioxidant action, 3233 19 their health effects and how they affect the organoleptic characteristics of food [4]. 34

35 20 Besides their antioxidant properties, polyphenols have other features that make them

36 21 important to our health. Indeed, they lower blood cholesterol levels; slow down tumor 3738 22 growth; strengthen the immune system; inhibit certain cancer-causing chemicals; 3940 23 inhibit cyclooxygenase and lipoxygenase enzymes; inhibit platelet aggregation;

41 24 inhibit the peroxidation of LDL; carry out anti-allergic and anti-inflammatory 42

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3 43 25 activities [5-11]. 4445 26 In this context, it is clear that the analytical determination of the composition of the

46 27 polyphenolic fraction of olive oil is becoming more and more important, to the point 4748 28 that in recent years an increasing number of methods have been proposed in the 4950 29 literature [12-15]. However, most of these methods require rather long analysis time,

often using more than two solvents and anyway do not allow a complete resolution

of all compounds under consideration. In this respect, also taking the lead from an

earlier work by our group on the separation and quantification of phenolic acids

[16], the aim of this study was to investigate the possibility of using chemometric

resolution methods to improve the analysis of some compounds present in the

neutral

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46 27 4748 28 4950 29 polyphenolic fraction of olive oils. Indeed, the introduction of appropriate

chemometric techniques to resolve coeluting peaks has resulted in the possibility of

using faster gradients, simpler extractions and also, in the case of hyphenated

chromatographic techniques, the combination with a less selective (but also less

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1

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3 expensive and

more widespread)

spectroscopy as

the UV-Visible. In

these cases, the

coupling with

chemometric

techniques allows

to resolve signals

of analytes that

coelute (even in

the presence of

significant

baseline

contribution)

through a

mathematical

analysis a

posteriori that

does not require a

perfect separation

of the peaks

already at the level

of instrumental

analysis. This feature translates into considerable savings in time and cost of

analysis. Therefore, this research has investigated the possibility of applying this

concept, i.e. the mathematical resolution of coeluted hyphenated chromatographic

peaks to the analysis of the content of some polyphenols in samples of extra virgin

olive oil. In particular, we assessed the possibility of using a rather fast gradient for

the HPLC-DAD analysis of some relevant polyphenolic fractions, making up for the

co-elution of different groups of peaks with the application of chemometric methods

for resolution of the signal.

Materials and methods

Samples

Thirteen extra virgin olive oils from Sabina (Italy) were analyzed in this study. Each

sample was collected directly from the oil mill just after pressing and then stored in

dark brown glass bottles at 4C until used. Each sample was analyzed in replicate.

Extraction of phenolic compounds from virgin olive oils

A great number of procedures for the isolation of the phenolic fraction of VOO

utilizing two basic extraction techniques, LLE or SPE, have been published in the

literature. In this study, LLE was chosen to isolate the phenolic fraction of oil, as

being less selective, allows to extract more analytes in less time. In particular, the

protocol was optimized to use less solvents to extract most of the polar phenolic

compounds excluding phenolic acids.

Five grams of oil were dissolved in 6 mL of n-hexane. The presence of n-hexane

allowed to avoid the formation of emulsions and to eliminate the glycerides. 51 30 5253 31 5455 32

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5 2 6 3 7Extraction of

phenolic

compounds was performed by adding 5 mL of CH3OH:H2O 80:20 (v/v), and mixture

was shaken for 3 min. After that, the two phases were

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separated by centrifugation at 3000 rpm for 3min and the

hydroalcoholic phase was transferred to a balloon. This step

was repeated three times and the hydroalcoholic extracts were

collected and evaporated to dryness by a rotary evaporator;

the

8 4 temperature was always controlled (<40 °C) to avoid the deterioration of phenols. The 910 5 residue was dissolved with 3 mL of CH3OH. The extracts were then filtered

through a

11 6 13-mm PTFE 0.45 µm membrane filter from Waters (Waters, Milford, MA) and 20 1213 7 µL were injected into the liquid chromatograph for HPLC-DAD analysis. 1415 8

16 9 HPLC-DAD analysis 1718 10 Methanol extracts prepared according to what described in Section 2.2 were then 19

20 11 analyzed by HPLC-DAD on a Thermo Quest Spectrasystem LC (Thermo Fisher

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21 12 Scientific, Waltham, MA) equipped with a P4000 pump, a UV6000 UV–Vis diode 2223 13 array detector, and a SN4000 interface to be operated via a personal computer. 24

25 14 Instrument software ChromQuest 5.0 (Thermo Fisher Scientific, Waltham, MA) was

26 15 used for data acquisition. Water/methanol mixture was used as the mobile phase, flow

2728 16 rate was 1 mL/min, and the column was kept at 25ºC. The column was an Eclipse xdb 29

30 17 C18 (5 µm particle, 0.46 mm i.d., 15 cm length; Agilent Technologies, Santa Clara,

31 18 CA). As the scope of the study was to take advantage of the potential of chemometric

3233 19 a posteriori resolution methods, the gradient chosen was faster than those proposed in 34

35 20 the literature, allowing the analysis of the polyphenolic fraction in less than 45 mins.

36 21 Moreover, MeOH was preferred to acetonitrile, being cheaper and less toxic. In detail,

3738 22 the initial composition of the mobile phase was 80% water/20% methanol and this 3940 23 ratio was maintained for 5 min; after that, the amount of methanol was linearly

41 24 increased to 100% in 30 min, and this percentage was maintained for additional 10 4243 25 min. The chromatograms were registered from 258 to 360 nm at 2 nm intervals using 44

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5 2 6 3 745 26 diode array detector.

46 27 Use of this gradient resulted in coeluting peaks that were resolved by chemometric 4748 28 curve resolution methods. In a further stage of the study, to validate the results of 4950 29 chemometric analysis, the same groups of peaks that appeared as coeluted using the

gradient reported above, were separated chromatographically.

In order to do so, the corresponding eluting fractions were

collected when coming out from the detector and stored. After

preconcentration, they were then injected in the HPLC

apparatus and separated using a different gradient:

water:acetonitrile 90:10 was chosen as the initial mobile

phase and this ratio was kept constant for 5 minutes;

successively, the amount

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46 27 4748 28 4950 29 of acetonitrile was

linearly increased to 100% in 25 mins and kept at 100% for further 10 mins.

Chemometric analysis

Whenever an hyphenated technique is used for the analysis, the experimental

outcome of each measurement is a data matrix, having one elution and one spectral

dimensions; accordingly, when more than one sample is analyzed, the resulting data

structure is a three dimensional (three-way) array. In principle, this kind of data

could be someway reduced to lower dimensional data arrays and analyzed by

standard chemometric techniques; however, there are some relevant theoretical

advantages in the use of the full landscape when performing the calibration stage:

this is the socalled “second-order advantage” [17], that stems from the fact that

second-order tensors (data matrices) are used to describe experimentally each

sample. Simply stated, second-order advantage means that calibration can be done in

the presence of unknown interferences, calibration samples may be pure, and

identification/confirmation of compound identity through the pure response

detection profile is possible.

In these framework, multi-set Multivariate Curve Resoultion (MCR) was used in this

study.

Multivariate Curve Resolution [18-20]

The hypothesis of MCR is that the overall chromatographic landscape for a sample

can be decomposed into the contribution of the elution profile and the spectral

fingerprint of individual components. This means that, ideally, if two analytes are

coeluting and their spectral profile is sufficiently different, the corresponding 51 30 5253 31 5455 32

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5 2 6 3 7chromatographic

landscape

resulting in

overlapping

signals can be

separated into the

individual

contribution for

the two chemical species. In mathematical terms, if X is the 2D signal measured on a

sample, it is decomposed in the invidual component elution and spectral matrices, C

and S, respectively, according to:

X=CST (1)

What is relevant is that the search for the pure contributions is normally done on a

data-driven basis, meaning that it is not necessary (even if when possible it can help)

to know in advance the number of species giving rise to overlapping bands and their

individual spectra. As a consequence, in non-ideal cases more components than the

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expected chemical rank can be added to the model to account for

baseline effect or the presence of unknown interferents.

When more than one sample is measured, since the method works on matrix

8 4 decomposition, an unfolding step is necessary: operationally this means that the 910 5 individual landscapes corresponding to the different samples are aligned one after

the

11 6 other, providing that they share the same spectral dimension: this corresponds to the

1213 7 hypothesis that the same component are present in all the samples but allows for the 1415 8 elution profiles to be different not only in terms of analyte concentration but also of

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3 16 9 peak positions (therefore allowing to deal with the presence of shifts) [20]. With

the 1718 10 same nomenclature as before, if Xi is the chromatographic landscape

measured on the 19 th

20 11 i sample, then the decomposition is performed according to:

21 12 Xi=CiST (2) 2223 13 S being the matrix

composed of the spectral loadings that are common to all samples

24 th

25 14 and Ci being the elution profiles estimated for the i sample.

26 15 Algebraically, chemical meaningfulness of the solution is achieved by imposing

2728 16 mathematical constraints on the algorithm used to compute the components: non-29

30 17 negativity of concentration and spectral profiles and unimodality of chromatographic

31 18 peaks are just the most common that can be implemented. 3233 19 34

35 20 Software

36 21 All chemometric computations were run under Matlab® R2011a (The Mathworks, 3751 30 5253 31 5455 32

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5 2 6 3 738 22 Natick, MA) environment. In particular, MCR-ALS computations were performed 3940 23 using the MCR toolbox developed by the chemometric group of the University of

41 24 Barcelona (freely downloadable at http://www.mcrals.info) [21]. 4243 25 4445 26 3. Results and discussion

46 27 As anticipated, the aim of this study was to show the potential of chemometric 4748 28 resolution methods to improve the quality of the HPLC-DAD analysis of some 4950 29 phenolics in extra virgin olive oil samples. To this purpose, HPLC analysis using the

conditions reported in the methodological section was carried

out on each sample and the corresponding chromatograms

were recorded in the wavelength range 258-360 nm. In Figure

1, the profiles recorded at three wavelengths chosen as

representative of the different absorption of phenolic

substances (258, 280 and 320 nm) are reported. It is evident

from the Figure that the choice of a fast gradient using

methanol as modifier

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46 27 4748 28 4950 29 result in some peaks being coeluted and not fully resolved. Chemometric curve

resolution methods were then used to a posteriori separate the coeluted signals into

their single component profiles. In particular, as an example of the potential of curve

resolution methods for the identification and separation of the contributions from

individual coeluting components, the two clusters of peaks between around 20 and

25 minutes were considered.

It can be seen in Figure 1 that there is a not negligible contribution of the baseline to

the chromatographic signals and that this contribution is wavelength dependent,

being more pronounced at the lower and less significant at the higher wavelengths.

Therefore, prior to operate multivariate curve resolution on the two selected

retention time windows, the 2D HPLD-DAD landscapes from each sample were

baseline corrected using the penalized asymmetric least squares approach proposed

by Eilers [22]. Figure 2 shows the effect of baseline correction on the

chromatographic signals reported in Figure 1. It is apparent from the Figure that the 51 30 5253 31 5455 32

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5 2 6 3 7contribution of

baseline was

completely

removed at all

wavelengths.

Starting from these

corrected data,

MCR was

operated

separately on the

two

chromatographic

windows 19.99-

22.65 min and

23.00-25.21 min.

MCR on the first cluster of peaks

The first chromatographic window considered in this study comprised the retention

time interval between 19.99 and 22.65 min. In this region, samples present highly

overlapping peaks as shown in Figure 3a, where the signals recorded at 280 nm for

the analyzed oils are reported. On the other hand, as multiple wavelengths were

recorded during the chromatographic runs, for each sample the signal takes the form

of a 2D landscape as the one reported in Figure 3b. Accordingly, the experimental

data corresponding to the chromatographic landscapes in this retention time window

recorded for all samples were organized into an array of dimension 26 (number of

runs) x 161 (number of retention time points) x 52 (number of wavelengths). This

array was the basis of the following chemometric analysis.

In a first stage, Multivariate Curve Resolution analysis was carried out on the single

data matrices corresponding to the chromatographic landscapes measured on

individual samples. For each sub-matrix, initialization of the ALS algorithm was

performed using the purest spectra extracted by the SIMPLISMA algorithm [23],

while Principal Component Analysis and Evolving Factor Analysis [24] were used to

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estimate the overall number of components and the local

rank , i.e. how many species are present in the different

portions of the retention time window. Then, MCR-ALS

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1

2

3 optimization was carried out using the non-negativity

constraint on both spectra and

8 4 concentration profiles and the unimodality constraint on concentration profiles only. 910 5 Comparison of the optimal resolved profiles for the different sub-matrices showed

a

11 6 good consistency and suggested that 7 could be the optimal number of components. 1213 7 Therefore, based on this considerations in a second stage the analysis was repeated on 1415 8 the complete data set.

16 9 In order to be analyzed by MCR in a multi-set arrangement, the 3-way 1718 10 chromatographic data were unfolded in a column-wise augmented fashion, by putting 19

20 11 sub-matrices on one another keeping the spectral dimension constant. Accordingly,

21 12 the resulting augmented matrix had dimensions 4186 (number of retention time points

2223 13 x number of runs) x 52 (number of wavelengths). In this case, to have an initial 24

25 14 estimate of the spectral profiles to be used in the ALS optimization, the average of the

26 15 spectral profiles obtained on the individual sub-matrices in the previous stage of the

2728 16 analysis was used. Then multi-set MCR was run on the augmented matrix using non-29

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5 2 6 3 7

30 17 negativity constraint for spectra and concentration and uni-modality constraint for

31 18 concentration only, and incorporating the information on local rank obtained by 3233 19 Evolving Factor Analysis. 34

35 20 Eventually, the cluster of coeluted peaks was resolved into the contribution of 7

36 21 components, whose spectra are reported in Figure 4b. It can be seen in the Figure that

3738 22 there is a very close similarity among the spectra, as expected, considering the 3940 23 structural similarity of the phenolic compounds and the low selectivity of UV

41 24 spectroscopy. Anyway, even with a so close similarity, it was possible to obtain a 4243 25 very good resolution of the chromatographic peaks in this retention time window, as 4445 26 evidenced in Figure 4a, where the concentration

profiles of the 7 resolved components

46 27 for one of the analyzed samples is reported. 4748 28 As all the components were unknown to us (we are at present trying to identify at 4950 29 least some using HPLC-MS), chemical validation of the results was done by

comparing the outcomes of curve resolution with those

obtained by chromatographic resolution of the coeluted

cluster, achieved by changing the experimental conditions

(see Materials and Methods). In Figure 5, the results of 51 30 5253 31 5455 32

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1

2

3 chromatographic resolution of the overlapping peaks is

shown for one of the samples chosen as example. It can be

seen that, as estimated by MCR-ALS, the cluster was made of

the contribution of 7

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3 species. Moreover,

comparison of the

spectral profiles

obtained by

chemometric

resolution and

those after

chromatographic

separation

confirmed the

consistency of the

results. As far as

the quantitative

analysis is

concerned, as no

standard was

available only

relative

concentrations

could be

estimated, i.e. only

the relative

amount of each

analyte from

sample to sample

and not its absolute quantity. Also under this respect, very good results were obtained

as the integrated area of the peaks obtained by MCR were in very good agreement

with the areas of the corresponding peaks after chromatographic resolution (relative

errors being in almost all cases less than 5%).

MCR for the second cluster of peaks

The data coming from the HPLC-DAD analysis of the second cluster of peaks were

treated analogously. In this case, a data cube of dimension 26 (runs)x133(retention

times)x52(wavelengths) was processed. Also in this case, analysis was at first

performed on the individual sub-matrices to have hints about the number of

components in each chromatographic run and the local rank, using EFA.

Successively, the 3-way data cube was unfolded in a column-wise fashion and the

resulting augmented data matrix was processed using multi-set MCR-ALS.

Investigation of the results obtained on the individual sub-matrices suggested that 8

components could be optimal for the chemometric resolution and, as in the previous

case, the algorithm was initialized using the averages of the optimal spectral profiles

obtained in the analysis of the single 2D landscapes. Non-negativity (spectra and

concentrations), unimodality (concentration) and local rank were used as constraints

for the ALS algorithm. The final results are reported in Figure 6.

As it was for the other cluster of coeluting peaks, it can be observed that even if the

spectral profiles were very similar (Figure 6b), it was possible to achieve a good

resolution of the peaks (an example is reported in Figure 6a, for one of the samples).

Also in this case, in the absence of standards and lacking the knowledge about the

identity of the coeluted compounds, validation of the obtained results was achieved

by comparison with the outcomes of complete chromatographic separation under

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5 2 6 3 7different

experimental

conditions. A very

good consistency

between the chemometrically resolved spectral profiles and those recorded on the

chromatographically separated analytes was found. Moreover, as observed for the

other group of peaks, the error in the quantification of the relative amount of the

Page 12 of 21

analytes in the different samples was almost always less than 5 %, thus

confirming the goodness of the multivariate resolution approach.

8 4 Conclusions 910 5 In this study, the possibility of using a chemometrics, and in particular multivariate

11 6 curve resolution, for the a posteriori separation of the pure component signals from 1213 7 coeluting chromatographic peaks in the analysis of the neutral polyphenolic fraction 1415 8 of olive oil was shown.

16 9 In particular, two groups of coeluting peaks were identified in the chromatogram and

1718 10 MCR-ALS in the multi-set configuration was used to recover from the signals the 19

20 11 elution and spectral profiles of the pure components. A good resolution was obtained

21 12 in both cases, even if the spectroscopic fingerprints of the analytes were very

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1

2

3 similar

2223 13 with one another. 24

25 14 Results were validated by comparison with a complete chromatographic separation of

26 15 the substances giving rise to the two cluster of peaks: comparison of the spectral 2728 16 profiles showed a good consistency, Additionally, when considering the relative 29

30 17 amount of the different components in the analyzed samples, results were quite

31 18 promising as in almost all the cases errors lower than 5% were obtained. 3233 19 Based on this considerations, it is possible to conclude that the use of chemometric 34

35 20 curve resolution methods can help and improve chromatographic analysis, by

36 21 allowing the a posteriori separation of the pure signals from coeluting compounds. 3738 22 This can result in the possibility of using fast gradients and cheaper and/or more less 3940 23 harmful solvents (towards a “greener” analytical chemistry) without losing in

41 24 accuracy, and with a corresponding saving of time and money. 4243 25 4445 26 References

46 27 [1] Servili M, Montedoro GF (2002) Contribution of phenolic compounds to virgin oil

4748 28 quality. Eur J Lipid Sci Technol 104: 602-613.

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5 2 6 3 74950 29 [2] Esti M, Cinquanta L, La Notte E (1998) Phenolic Compounds in Different

Olive 30 Varieties. J Agric Food Chem 46: 32-35.

31 [3] Servili M, Selvaggini R, Esposito S, Taticchi A, Montedoro GF,

Morozzi G 32 (2004) Health and sensory properties of virgin olive oil

hydrophylic phenols:

33 agronomic and technological aspects of production that affect the occurrence in the oil.

34 J Chromatogr A1054: 113-127. Page 13 of 21

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33 [4] Skevin D,

Rade D,

Strucelj D,

Mokrovcak Z,

Nederal S,

Bencic D

(2003) The

influence of

variety and

harvest time on the bitterness and phenolic compounds of olive oil. Eur J Lipid

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11 6 [18] Tauler R (1995) Multivariate curve resolution applied to second order data. 1213 7 Chemometr Intell Lab Syst 30: 133-146. 1415 8

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chromatographic peaks. Anal Chem 59: 527-530.

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363738394041424344454647484950Figure Captions

Figure 1 - Raw chromatographic profiles of the analyzed oil samples at three selected

wavelengths: 258 nm, 280 nm and 320 nm.

Figure 2 – Effect of baseline correction using penalized asymmetric least squares on

the chromatographic signals reported in Figure 1.

Figure 3 – (a) Chromatographic profiles of the analyzed samples in the retention time

window corresponding to the first cluster of coeluted peaks (signal recorded at 280

nm); (b) 2D elution-spectral landscape of one sample, chosen as example, in the

same chromatographic window.

Figure 4 – ààResults of MCR-ALS analysis on the first cluster of coeluted peaks. (a)

Elution profiles of the 7 components identified as significant for one of the analyzed

samples chosen as example; (b) spectral profiles of the 7 resolved components.

Figure 5 – Chromatographic separation of the components coeluting in the first

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1

2

3 cluster of peaks

using an additional

chromatographic

step (Materials &

Methods Section)

evidenced the

same number of

constituents as

estimated by

MCR.

Figure 6 – Results

of MCR-ALS

analysis on the

second cluster of

coeluted peaks. (a)

Elution profiles of

the 8 components

identified as

significant for one

of the analyzed

samples chosen as

example; (b)

spectral profiles of

the 8 resolved

components.

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mm (300 x 300 DPI) 299x179and 320 nm.

Raw chromatographic profiles of the analyzed oil samples at three selected wavelengths: 258 nm, 280 nm

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mm (300 x 300 DPI) 296x178reported in Figure 1.

Effect of baseline correction using penalized asymmetric least squares on the chromatographic signals

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mm (300 x 300 DPI) 303x182chosen as example, in the same chromatographic window.

cluster of coeluted peaks (signal recorded at 280 nm); (b) 2D elution-spectral landscape of one sample, a) Chromatographic profiles of the analyzed samples in the retention time window corresponding to the first (

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mm (300 x 300 DPI) 296x180resolved components.

identified as significant for one of the analyzed samples chosen as example; (b) spectral profiles of the 7 Results of MCR-ALS analysis on the first cluster of coeluted peaks. (a) Elution profiles of the 7 components

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mm (300 x 300 DPI) 298x179estimated by MCR.

chromatographic step (Materials & Methods Section) evidenced the same number of constituents as Chromatographic separation of the components coeluting in the first cluster of peaks using an additional

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mm (300 x 300 DPI) 297x177profiles of the 8 resolved components.

components identified as significant for one of the analyzed samples chosen as example; (b) spectral Results of MCR-ALS analysis on the second cluster of coeluted peaks. (a) Elution profiles of the 8

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