Study of the DPPH-scavenging activity: Development of a free software for the correct interpretation of data

Food Chemistry 114 (2009) 889–897

Contents lists available at ScienceDirect

Food Chemistry journal homepage: www.elsevier.com/locate/foodchem

Study of the DPPH-scavenging activity: Development of a free software for the correct interpretation of data Monica Locatelli *, Roberto Gindro, Fabiano Travaglia, Jean-Daniel Coïsson, Maurizio Rinaldi, Marco Arlorio Dipartimento di Scienze Chimiche Alimentari Farmaceutiche e Farmacologiche and DFB Center, Via Bovio 6, 28100 Novara, Italy

a r t i c l e

i n f o

Article history: Received 21 April 2008 Received in revised form 10 October 2008 Accepted 15 October 2008

Keywords: DPPH Antioxidant activity EC50 Probit regression Outliers

a b s t r a c t Antioxidants are important because they prevent lipid oxidation in food, and decrease the adverse effects of reactive species on normal physiological functions in humans. A wide variety of in vitro chemical models have been developed to assess the ability to prevent oxidative damages; amongst the chemical tests that measure radical scavenging capacity, the DPPH assay is one of the most widely employed method. EC50 (concentration required to obtain a 50% antioxidant capacity) is typically employed to express the antioxidant activity and to compare the antioxidant capacity of various samples; however its measurement requires some care, because of the non-linear relation between antioxidant concentration and antiradical activity. In this work a statistical software was developed in order to apply different linearising transformations in the study of DPPH-scavenging properties of antioxidants. The software was also implemented to perform the determination of outliers, the calculation of the EC50 values and the comparison of the curves, and of their corresponding straight lines, obtained from the regression analysis of the data. First, the analysis of the DPPH-scavenging activity was performed on standard molecules and then applied to different food extracts. The regression models employed in this work (probit, logit and angular regressions) appeared to be equivalent and to fit well the antiradical activity curves obtained for both standard molecules and food extracts; probit regression was finally chosen to discuss the results and to introduce a new parameter characterising these curves. Ó 2008 Elsevier Ltd. All rights reserved.

1. Introduction Free radicals, particularly reactive oxygen species (ROS) and reactive nitrogen species (RNS), are involved in the pathogenesis of several chronical and degenerative diseases such as inflammation, cardiovascular diseases, neurodegenerative diseases, cancer and aging-related disorders. Although ROS and RNS are secondary messengers in normal physiological functions of the organism and participate in various regulatory redox-mechanism, an overproduction of these species can overwhelm protective enzymes and cause destructive and lethal cellular effects (Darley-Usmar, Wiseman, & Halliwell, 1995; Halliwell & Cross, 1994; Valko et al., 2007). Furthermore, oxidation in food can induce rancidity and/ or deterioration of nutritional quality, organoleptic properties (colour, flavour, texture) and, consequently, become a safety concern (Antolovich, Prenzler, Patsalides, McDonald, & Robards, 2002). In order to prevent oxidative reactions in foods and biological tissues against molecular targets such as proteins, lipids, carbohydrates and DNA, various synthetic or natural antioxidants can be

* Corresponding author. Tel.: +39 0321375774; fax: +39 0321375621. E-mail address: [email protected] (M. Locatelli). 0308-8146/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodchem.2008.10.035

used. In recent years the use of natural antioxidants has been promoted because of concerns regarding the safety of synthetic ones (Shahidi, 2000); so, numerous studies have been conduced in order to investigate the antioxidant activity of plant-extracts (Arlorio et al., 2008; Madhujith & Shahidi, 2006). A wide variety of in vitro chemical models have been developed to assess the ability to prevent oxidative damage. The most widely employed chemical tests measure the radical scavenging capacity, the uptake of oxygen, the inhibition of induced lipid autoxidation, the reducing power, and the chelation of the transition metals (Decker, Warner, Richards, & Shahidi, 2005; Prior, Wu, & Schaich, 2005). Regarding the in vitro methods to assess radical scavenging activity, different radical species are usually used, such as ABTS+ (Re et al., 1999), DMPD+ (Fogliano, Verde, Randazzo, & Ritieni, 1999) and DPPH (Brand-Williams, Cuvelier, & Berset, 1995). They are artificial radicals and do not reproduce in vivo situation, however, they are useful to evaluate the antioxidant activity in a rapid, easy and inexpensive way. DPPH (1,1-diphenyl-2-picrylhydrazyl) is a stable radical of organic nitrogen, characterised by a typical deep purple colour and a maximum absorbance in the range of 515–520 nm. The DPPHbased method was first reported by Blois (1958), who observed

890

M. Locatelli et al. / Food Chemistry 114 (2009) 889–897

that the DPPH radical was reduced by the thiol-containing amino acid cysteine and other active compounds. Afterward, BrandWilliams et al. (1995) revised the original method and the DPPH radical scavenging test became a reference point to evaluate the in vitro antioxidant capacity (Gil, Tomás-Barberán, Hess-Pierce, Holcroft, & Kader, 2000). The DPPH method is technically simple and needs only a UV– vis spectrophotometer to perform: in the presence of a hydrogen/electron donor (free radical scavenging antioxidant) the absorption intensity is decreased, and the radical solution is discoloured according to the number of electrons captured (Markowicz Bastos et al., 2007). However, in the case of antioxidant compounds, such as carotenoids, having spectra that overlap DPPH at its maximum absorbance, the use of electron paramagnetic resonance (EPR) spectroscopy is the preferred way to assess the DPPH radical, since it directly measures the free radical concentration (Wettasinghe & Shahidi, 2000). Several factors may influence the method and the interpretation of the experimental data, such as solvent and pH (Litwinienko & Ingold, 2003), reagent and sample concentrations, reaction time, and reporting of the results (Molyneux, 2004). For example, the DPPH radical can only be dissolved in organic solvents (especially methanol and ethanol), not in aqueous ones. Although the opinions about the influence of the hydro-lipophilic nature of the antioxidant in the reaction with DPPH are contradictory (Castro, Rogero, Junqueira, & Carrapeiro, 2006; Yamaguchi, Takamura, Matoba, & Terao, 1998), this fact could be an important limitation in the interpretation of hydrophilic antioxidants properties (Arnao, 2000). This problem is not deeply considered or discussed in some papers (Chen, Lin, & Hsieh, 2007; Summa et al., 2006). To express the results, the terms ‘‘radical scavenging activity” or ‘‘percent inhibition of free radical” are usually employed; the absorbance of the reaction mixture containing the DPPH radical and the antioxidant sample is related to the absorbance of the reaction mixture without any antioxidant. In some cases the results are presented as ‘‘concentration of DPPH remaining”, however, this practice can be considered unnecessary, in that absorbances are accurately representative of concentrations (Molyneux, 2004). Nevertheless, both of these ways in expressing the results cannot be used easily and, in the case of compounds and foods exhibiting different antioxidant properties, it is difficult to compare their activity by employing the same concentration. So, the results are often expressed using the more appropriate EC50 parameter, defined as the concentration of substrate that brings about 50% loss of the DPPH. Even though the computation of the EC50 parameter requires more work, this parameter is independent of the sample concentration and it is widely employed to compare samples exhibiting different antioxidant properties. Several experimental evidences have indicated a non-linear relationship between the antioxidant concentration and the DPPH radical scavenging activity (Eklund et al., 2005; Prior et al., 2005; Villaño, Fernández-Pachón, Troncoso, & García-Parrilla, 2005). As a consequence, the measurement of the EC50 is quite problematic, however, many scientific works reported in the literature do not consider this problem. The aim of the present work was to develop a method to accurately calculate the EC50 of antioxidants in the DPPH radical scavenging assay. A mathematical software was developed to process the data and then obtain a relation between antioxidant activity and sample concentration. Three different regression models (probit, logit and angular regressions) were considered to calculate the EC50 values. The probit analysis was finally used to evaluate the antiradical properties of several standard compounds and natural food extracts.

2. Materials and methods 2.1. Chemicals 1,1-Diphenyl-2-picrylhydrazyl (DPPH) radical and some pure standard compounds (trolox, ()-epicatechin, myricetin, quercetin dihydrate, kaempferol, caffeic acid, gallic acid monohydrate, rosmarinic acid, octyl gallate, butylated hydroxyanisole – BHA) were obtained from Sigma–Aldrich (Milano, Italy). Clovamide (Ncaffeoyl-L-3,4-dihydroxyphenylalanine) and vanillyl nonanoate (4-hydroxy-3-methoxybenzyl-nonanoate, synthetic analogue of natural capsiates) were obtained by chemical synthesis in our laboratories. Clovamide was synthesized from L-3,4-dihydroxyphenylalanine, protected as methyl ester and subsequently condensed with caffeic acid, as described in Arlorio et al. (2008). The organic synthesis of vanillyl nonanoate was carried out as described in Torregiani, Seu, Minassi, and Appendino (2005). All reagents and solvents used for syntheses were obtained from Sigma–Aldrich (Milano, Italy). Methanol used to perform the DPPH assay and to obtain all natural extracts from plant foods was provided by Sigma–Aldrich (Milano, Italy). All reagents used in this study were of analytical grade. 2.2. Antioxidant standard compounds Antioxidant standard compounds (trolox, ()-epicatechin, myricetin, quercetin dihydrate, kaempferol, caffeic acid, gallic acid monohydrate, octyl gallate, BHA, clovamide, rosmarinic acid and vanillyl nonanoate) were dissolved in methanol (1 mg/mL) and the stock solution appropriately diluted. A fresh stock solution of standard compounds was prepared before each analysis. 2.3. Food samples Samples of Forastero cocoa beans obtained from different geographical areas (Ghana, Ivory Coast and Ecuador), their roasted nibs (roasted cocoa beans separated from hull and broken in small bits) and a mixture of hulls obtained from pre-roasted beans were provided by Ferrero SpA (Alba, Italy) and Elah–Dufur SpA (Novi Ligure, Italy). Samples of hazelnut skins were obtained by roasting ‘‘Nocciola Piemonte IGP” hazelnut kernels (two different roasting conditions: 180 °C for 10 min – medium roasting; 180 °C for 20 min – high roasting), kindly provided by Dr. Giuseppe Zeppa (University of Turin, Italy). Yellow and red good ripened fruits of Italian sweet peppers ‘‘Tumaticot di Carmagnola” were obtained from ‘‘Consorzio di Tutela e Valorizzazione del Peperone di Carmagnola”, Carmagnola, Turin (Italy). 2.4. Sample preparation and polyphenols extraction 2.4.1. Cocoa samples Ten grams of cocoa beans, roasted nibs and hulls were finely ground in a laboratory mill, sieved (particles size <1 mm) and then extracted using a semi-automatic Soxhlet Büchi Extraction System B-811 (Büchi Labortechnik AG, Flawil, Switzerland) (12 h), employing dichloromethane to remove the lipid fraction. The phenolic fraction was then extracted from all the defatted cocoa powders in a conventional Soxhlet apparatus, using methanol as the solvent for 4 h (exhaustive extraction). The solvent was then evaporated to dryness (vacuum, 40 °C) and dry extract was stored at 20 °C until use (maximum period of storage 1 month). 2.4.2. Hazelnut skin samples First, hazelnut skins were ground into a fine powder in a laboratory mill, sieved (particles size <1 mm) and then extracted with

891

M. Locatelli et al. / Food Chemistry 114 (2009) 889–897

dichloromethane to remove the lipid fraction, as described above. Approximately 10 g of the resultant defatted hazelnut skin powders were extracted with methanol in the Soxhlet apparatus for 7 h (exhaustive extraction). The solvent was then evaporated to dryness (vacuum, 40 °C) and dry extract was stored at 20 °C until use (maximum period of storage 1 month). 2.4.3. Sweet pepper samples Washed samples, deprived of seeds and placenta, were homogenised. Approximately 10 g of the plain homogenate were immediately freeze-dried and extracted twice with 15 mL of methanol for 1 h (exhaustive extraction); the suspensions obtained were collected and centrifuged for 5 min at 4610 g. The solvent was evaporated to dryness (vacuum, 40 °C) and dry extracts were stored at 20 °C until use (maximum period of storage 1 month). Natural antioxidant extracts were dissolved in methanol (1 mg/mL for both cocoa and hazelnut skin samples; 5 mg/mL for sweet pepper samples) and the stock solution was appropriately diluted. 2.5. DPPH method The DPPH radical scavenging assay was performed according to the method reported by Brand-Williams et al. (1995) with some modification. Briefly, 700 lL of sample or MeOH (control) were added to the same volume of methanolic solution of a 100 lM DPPH. Mixtures were shaken vigorously and left to stand in the dark at room temperature for 20 min, then absorbance was read at 515 nm, using a Kontron UVIKON 930 Spectrophotometer (Kontron Instruments, Milano, Italy). The absorbance measured for the control solution was in the range 0.500 ± 0.040. Antiradical activity was expressed as inhibition percentage (I%) and calculated using the following equation:

Inhibition percentage ðI%Þ ¼

Abscontrol  Abssample  100 Abscontrol

In the case of red peppers extract, having UV absorptions that overlap DPPH at 515 nm, blank solutions (without DPPH) were prepared to correct any influence due to colour. So, in the previous equation the term Abssample was substituted with (Abssample  Absblank). Different sample concentrations were used in order to obtain calibration curves and to calculate the EC50 values (EC50: concentration required to obtain a 50% radical scavenging activity).

between antioxidant concentration and antiradical activity. For example Villaño et al. (2005) confirmed a linear relation between percentage of inhibition and antioxidant concentration only for a limited range of concentrations. Buenger et al. (2006) indicated that, in order to obtain satisfactory results, the highest concentration tested should give a maximum value of radical scavenging activity 670%. In fact, it would be erroneous to employ the linear regression for the calculation of the EC50 over this value. However, even working in the limited range of concentrations for which linearity is approximately true, the obtained EC50 value could be affected by a significant error. Heimler, Vignolini, Dini, and Romani (2005) calculated the EC50 values plotting the ratio between the DPPH concentration after (t = 20 min) and before (t = 0 min) the reaction with the radical against the antioxidant concentration, obtaining a decreasing curve for several antioxidant standard compounds and food samples. The authors employed two different mathematical models to linearise the curves and consequently calculate the EC50 values, namely exponential and power models, as previously indicated by Sánchez-Moreno, Larrauri, and Saura-Calixto (1998). Then, we applied the method to some standard compounds, however, both these two mathematical models did not work in our case, except if we were ready to restrict ourselves to a small range of concentrations. So, we decided to express the antiradical activity in terms of inhibition percentage (I%) and to use different models correlating these data with the antioxidant concentration. The curves obtained were like the dose/response curves going to saturation. 3.2. Linearisation of antiradical activity curves In order to determine correctly the EC50 values, the data have to be interpolated by an appropriate curve. Various models can be used to fit a saturation curve (i.e. with response in the interval [0, 1), corresponding to [0%, 100%) inhibition). Particularly, we considered three different saturation models: logit, probit and angular. We started considering a set of points (X: antioxidant concentration; Y: inhibition on a [0, 1] scale) and three different models Y1, Y2 and Y3 (respectively logit, probit and angular transformation):

Y 1 ðxÞ ¼

ebþa logðxÞ þ1

½logit Z Y 2 ðxÞ ¼ probitðb þ a log½xÞ ¼ ebþa logðxÞ

bþa log½x

N½tdt

½probit

1 2

2.6. Data analysis

Y 3 ðxÞ ¼ sin ðb þ a log½xÞ

Data analysis was performed using an algorithm implemented in Microsoft Visual BasicÒ 6.0 (Microsoft Corporation, Redmond, WA, USA). This software was used to obtain i) the computation of EC50 (on the basis of both probit, logit and angular regressions); ii) the determination of outliers from the antiradical calibration curves; iii) the comparison of antiradical activity amongst different samples. The software can be freely downloaded at http:// www.pharm.unipmn.it/rinaldi/software/blesq/BLeSq.html. All other statistical analyses were performed using Microsoft ExcelÒ 2007 for Windows XP (Microsoft Corporation, Redmond, WA, USA). The statistical significance level was set to 0.05.

where N is the standard normal distribution and log is the natural logarithm, and the corresponding change of variables needed to linearise the data:

3. Results and discussion 3.1. The DPPH-based method EC50 is a useful parameter to evaluate the antioxidant activity and compare the antioxidant capacity of various samples. However, as previously highlighted, the measurement of the EC50 values requires some care, because of the non-linear relation

½angular

Y Þ 1Y 1 0 Y ¼ probit ðYÞ pffiffiffiffi 1 Y 0 ¼ sin ð Y Þ Y 0 ¼ logð

The X transformation is always X’ = log (X). After data linearisation we computed the EC50 and obtained intercept and slope values. The application of each of these models allowed to obtain new data sets well fitting the linear equation y = ax + b. However, we did not observe any significant difference amongst the methods employed (two-way ANOVA: p > 0.05); statistical parameters calculated to compare the different models are reported in Table 1. So, for the final discussion we have chosen the probit transformation, in that i) is well adapting to the data obtained from the DPPH assay, ii) generally gives the intermediate EC50 amongst the three regression models considered, and iii) the parameters

892

M. Locatelli et al. / Food Chemistry 114 (2009) 889–897

Table 1 Statistical parameters of the comparison between the regression models. Correlations

Observations

Dfa

Slope

Intercept

Probit–logit Probit– angular Logit– angular

22–22 22–22

21 21

0.984151 1.024082

22–22

21

1.040230

a b

Pearson’s correlation (R)

p valueb

0.125299 0.187801

0.999934 0.999851

0.161011 0.162225

0.310555

0.999587

0.161699

Linear regression

Degree of freedom. Calculated using the paired Student t-test (two-tails).

obtained from the probit data analysis allow to introduce a new parameter characterising the DPPH antiradical activity, as described later. Moreover, other applications of probit analysis in the evaluation of antioxidant activity are reported in the literature (Domínguez et al., 2005). In order to obtain more representative curves, for each sample we have performed at least three different experiments (conduced in different days), processing all the data obtained in a single analysis. 3.3. Detection of outlier values Even if the applied regression models appeared appropriate to analyse the data sets, we decided to detect outlier values, particularly in the case of natural food extracts, that are very complex matrices. As usual, we worked with the linearised data. First, we have computed the leverage coefficients (hi) by the formula

hi ¼

R

ðxi  xÞ2 N 2 i¼1 ðxi  xÞ

þ

1 N

The values of hi, which range from near 0 to 1, give an indication of the leverage of a given value of xi (that is the influence of the corresponding response value yi on the estimated value). Then we have calculated the residual (Sr,i) with respect to the regression line given by

Sr;i ¼

^ yi  y pffiffiffiffiffiffiffiffiffiffiffiffiffi 1  hi

Syy^

where Syy^ is the standard deviation with respect to the regression line. Whilst the values of hi depend only on the independent variable and so are already known from the start of the experiment, and in principle every possible value can be obtained, the values of Sr,i depend on the experimental results and values of |Sr,i| greater than ta ,n2 are not likely within the given confidence level 1  a (here t denotes the quantiles of the Student t-distribution at the given confidence level and degrees of freedom). Still low values of h coupled to high values of |Sr| do not spoil the regression equation and so there is no need to discard them. Therefore, in order to detect the outliers (U) we proceeded defining hmax = 4/n and Sr,max = ta,n2 (Sokal & Rohlf, 1994), and for each point we considered the linear combination:

U¼

bh ð1  bÞjSr j þ hmax Sr;max

Finally, we chose the weight (b) and the outlier threshold (Umax) values. By checking in a wide number of experiments, performed on both standard compounds and natural food extracts, the best values in our working situation appeared to be:

b ¼ 0:22 U max ¼ 1:3

Using the linear combination of these parameters and this b value, the influence of the leverage coefficients, responsible of experimentally wrong data eliminations, strongly decreased. The original software has been integrated to calculate the outliers: in the presence of outliers the regression curves were recomputed omitting these data and new EC50 values were obtained. 3.4. Comparison of the samples We used the regression curves without outliers to compare the antioxidant activity of standard compounds and natural food extracts. For each analysis we have taken into account two different dataset (namely, D1 and D2), corresponding to two different samples (e.g. epicatechin and gallic acid) and we resorted to the covariance analysis on the linearised models. More precisely, we considered the linear model

y¼a xþbþD cþD d x where on the first dataset (D1) the dummy variable D assumes the value 0 and on the second (D2) the value 1. This model allows to compare the regression lines corresponding to D1 and D2 (Snedecor & Cochran, 1991). The hypothesis that some or all the coefficients are zero has to be tested. This provides all the required answers. For instance, if one is able to draw the conclusion (within the chosen confidence level) that c = d = 0, then the variable D is irrelevant and the two datasets are described by the same model (this is the criterion used for the comparison of the curves); if c = 0, then the two models differ in slope whilst if d = 0, the two models differ in the intercept and so are described by parallel lines. In order to test the various hypotheses the ANOVA has been used. 3.5. Antiradical activity of standard compounds A wide range of antioxidant standard compounds (Fig. 1) were analysed to obtain various examples of antiradical activity vs. antioxidant concentrations curves. Amongst the numerous antioxidant molecules we decided to test Trolox (a useful commercial standard compound in the evaluation of antioxidant properties), some natural flavonoids (well-known strong antioxidants: epicatechin, myricetin, quercetin, kaempferol), some phenolic acids (gallic acid, caffeic acid) and common synthetic antioxidants used in food industry like BHA and octyl gallate. We have also evaluated the antiradical activity of clovamide (minor component of cocoa seeds and cocoa hulls), rosmarinic acid (ester isoster of clovamide) and of a synthetic capsiate (vanillyl nonanoate), analogue of natural capsinoids occurring in sweet peppers (Kobata, Todo, Yazawa, Iwai, & Watanabe, 1998). Standard compounds were assayed at least at nine different concentrations; the concentrations were chosen according to relative antiradical capacity of molecule under study. The curves obtained for antioxidant standard compounds on the basis of probit regression are reported in Fig. 2. Outliers are identified by light grey dots; confidence limits at 95% and prediction limits at 95% are represented respectively by light and dark grey hatched curves; the EC50 values are also indicated. All standard compounds tested fitted very well the probit regression; for each sample the curves relative to confidence interval and confidence predictive interval appeared to be almost identical. Table 2 shows the probit regression parameters (slope (a), intercept (b) and correlation coefficient (R)), the EC50 values (expressed as lg of standard compounds per mL of solvent) and their relative limits of confidence (lg/mL) at 95% of probability. R values calculated for linearised data sets confirmed the goodness of fit with probit regression, showing values ranging from 0.9238 for epicatechin to 0.9752 for gallic acid. The DPPH

893

M. Locatelli et al. / Food Chemistry 114 (2009) 889–897

OH

O

CH3 HO

OH

H3C

O

OH

O

A

C

OH A HO

CH3

OH

C O

HO

B

CH3

O

R B

OH

R'

OH Trolox

R'' R = R' = R'' = OH Myricetin R = H, R' = R'' = OH Quercetin R = R'' = H, R' = OH Kaempferol

Epicatechin

O

O

R

CH3 O

HO

O CH3

OH OH

OH

Vanillyl nonanoate

R = OH Gallic acid R= O

CH3 Octyl gallate OO

O HO

HO OH

HO

Caffeic acid

OH

HO R

HO

OH

O

O

CH3 3 CH

R = O Rosmarinic acid R = NH Clovamide

OH OH

CH3 CH CH 33 CHCH 3 3 CH3

BHA

Fig. 1. Chemical structures of the antioxidant standard compounds.

antiradical activity obtained for standard compounds was in the gallate (1.65 lg/ order gallic acid (1.03 lg/mL) > octyl mL) > myricetin (1.95 lg/mL)  quercetin (1.99 lg/mL) > rosmarinic acid (2.49 lg/mL)  clovamide (2.65 lg/mL)  caffeic acid (3.11 lg/mL)  trolox (3.32 lg/ (2.93 lg/mL) > epicatechin mL) > kaempferol (4.26 lg/mL) > BHA (8.18 lg/mL)  vanillyl nonanoate (110.91 lg/mL). Significant differences were obtained amongst all the molecules, except in the cases of myricetin vs. quercetin, clovamide vs. rosmarinic acid, clovamide vs. caffeic acid and trolox vs. epicatechin. It is interesting to note that, even though some pairs of molecules appear to be statistically different (namely, rosmarinic acid vs. caffeic acid; caffeic acid vs. trolox; caffeic acid vs. epicatechin; epicatechin vs. clovamide), their relative EC50 values are similar and the corresponding confidence intervals are partly overlapped. For example, caffeic acid and epicatechin are different for p < 0.001, but their EC50 confidence intervals are 2.73– 3.14 lg/mL and 2.88–3.34 lg/mL, respectively. Furthermore, the EC50 values (mean ± standard deviation) calculated for every single experiment (caffeic acid: 2.65 ± 0.32 lg/mL, n = 4; epicatechin: 3.02 ± 0.39 lg/mL, n = 7) are not statistically different (p > 0.05). The method employed in this paper to compare the antiradical activity of different samples takes into account not only a single value (i.e. the EC50 value), but the whole curve. The relative DPPH antiradical activities of standard compounds tested are in agreement with data reported in literature (Nenadis, Lazaridou, & Tsimidou, 2007; Villaño, Fernández-Pachón, Moyá, Troncoso, & García-Parrilla, 2007). For example, it can be observed that molecules showing the higher antioxidant activity are characterised by the hydroxylation pattern of gallic acid (in the order gallic acid, octyl gallate and myricetin), probably in that the hydrogen bonds formed inside the phenol molecule between hydroxyl

groups next to each other are stronger than those with solvent and stabilize the phenolic structure. The flavonol quercetin is a more effective antioxidant than the corresponding flavan-3-ol epicatechin (quercetin has an identical number of hydroxyl groups in the same positions as epicatechin, but also contains the 2,3-double bond in the C ring of flavonoid structure and the 4-oxo function), whilst kaempferol, differing from quercetin in the absence of the 3’-OH group in the B ring, is less active than quercetin, suggesting a significant contribution of the catechol structure towards the antioxidant/antiradical activity (Silva et al., 2002). However, the presence of a third OH group (myricetin) in the B ring does not enhance significantly the antiradical activity (Rice-Evans, Miller, & Paganga, 1996; Villaño et al., 2005). Furthermore, the dihydroxylation in the 3,4 position can contribute to the antioxidant activity of the caffeic acid and, particularly of clovamide and rosmarinic acid, that show in their structure a caffeoyl portion. The steric hindrance of tested compounds and, consequently, the accessibility of the radical could also influence the order of the antiradical power (Prior et al., 2005). 3.6. Antiradical activity of natural food extracts Natural food extracts were analysed in order to test the DPPH antiradical activity of more complex samples, such as methanolic extracts obtained from plant foods. In fact, it is known that food extracts are characterised by a very heterogeneous chemical composition. In this paper different food samples were analysed, namely phenolic-reach cocoa beans from different geographical origin (Ghana, Ecuador and Ivory Coast), waste products from food industry (cocoa hulls and hazelnut skins), and fresh vegetables (sweet peppers). Concerning cocoa, roasting is an usual manufacturing

894

M. Locatelli et al. / Food Chemistry 114 (2009) 889–897

Fig. 2. Antiradical activity curves obtained for standard compounds on the basis of probit regression.

process applied to obtain chocolate and cocoa products; for this reason we also analysed roasted nibs obtained from the same unroasted beans, and a mixture of their cocoa hulls, that are an indus-

trial by-product obtained after the pre-roasting process of cocoa beans. Similarly to cocoa hulls, hazelnut skins are by-products of hazelnut kernels roasting.

895

M. Locatelli et al. / Food Chemistry 114 (2009) 889–897 Table 2 Probit regression parameters and EC50 values (lg/mL) obtained for antioxidant standard compounds. Standards

Gallic acid(a) Octyl gallate(b) Myricetin(c) Quercetin(c) Rosmarinic acid(d) Clovamide(d,e) Caffeic acid(e) Epicatechin(f) Trolox(f) Kaempferol(g) BHA(h) Vanillyl nonanoate(i)

Probit regression a

b

1.0283 1.1695 0.8690 0.8362 0.8822 1.0594 1.0057 0.8616 0.9196 1.2030 0.7304 0.5823

2.7990 0.5883 0.5792 0.5757 0.8042 1.0340 1.0807 0.9763 1.1040 1.7430 1.5349 2.7417

R

EC50 (lg/mL)

0.9752 0.9558 0.9489 0.9269 0.9563 0.9351 0.9703 0.9238 0.9420 0.9341 0.9558 0.9722

1.03 (0.96–1.10) 1.65 (1.52–1.80) 1.95 (1.81–2.10) 1.99 (1.77–2.23) 2.49 (2.24–2.76) 2.65 (2.29–3.08) 2.93 (2.73–3.14) 3.11 (2.88–3.34) 3.32 (3.05–3.62) 4.26 (3.89–4.66) 8.18 (7.44–8.99) 110.91 (102.51–119.98)

Probit regression parameters a and b correspond to slope and intercept, respectively. R is the correlation coefficient. Ranges between brackets following EC50 values represent the 95% confidence interval. Standard compounds followed by the same letter are not significantly different (p > 0.05).

Table 3 shows the EC50 values (expressed as lg/mL of solvent), their relative limits of confidence (lg/mL) at 95% of probability, and the parameters of probit regression, obtained for natural food extracts. Also in the case of complex food extracts probit regression showed a good fit to experimental data. High R values were observed for both cocoa extracts (from 0.9911 to 0.9385 for Ghana raw beans and for Ecuador roasted beans, respectively) and sweet peppers extracts (0.9338 and 0.9215 for yellow and red peppers, respectively). Hazelnut skin extracts showed lower R values (0.8548 and 0.7648 for high and medium roasting, respectively): this fact is probably due to the large variability of the results obtained during the different experiments. The different food samples showed significant differences about scavenging activity against the DPPH radical (one-way ANOVA: p < 0.05): hazelnut skin appeared the most active extracts, followed by cocoa beans, cocoa hulls and sweet peppers. The high antioxidant activity observed for hazelnut skins is in agreement with results previously reported by Shahidi, Alasalvar, and Liyana-Pathirana (2007). High and medium roasted hazelnut skins appeared to be statistically different (p < 0.001), indicating the influence of roasting conditions on antioxidant properties.

For cocoa samples, EC50 values are in accordance with the results previously reported by Arlorio et al. (2008). Roasted samples showed an antiradical activity significantly higher than unroasted ones; cocoa from Ivory Coast and from Ecuador were not statistically different. Furthermore, cocoa hulls showed lower antiradical properties than cocoa beans, according to their lower polyphenols content. EC50 value obtained for cocoa hulls extract (EC50: 120.56 lg/mL) appeared similar to that obtained for sweet pepper extracts, particularly to the red coloured ones (EC50: 121.65 lg/mL). As previously observed for some standard compounds, also in this case samples showing comparable EC50 values and relative confidence intervals resulted significantly different for the comprehensive DPPH-scavenging activity (p value calculated between cocoa hulls and both red and yellow sweet peppers was <0.0001). Fig. 3 shows the comparison between cocoa hulls and red sweet peppers curves; 0.5 and 0 values on the y-axes correspond to the 50% inhibition of DPPH radical, before (Fig. 3a–c) and after (Fig. 3d) the linearisation, respectively. It can be observed that, even if these two extracts show similar EC50 values, their antiradical activity curves are totally different. The antioxidant activity appears strictly related to the concentration considered: for the lower concentrations cocoa hulls were more effective in scavenging DPPH radicals than red peppers, but after the EC50 this trend is inverted. This different trend may be due to the different composition of the two extracts considered: for example sweet peppers methanolic extracts are rich in both carotenoids and ascorbic acid, whilst the composition of cocoa hulls extract is mainly phenolic. So, EC50 parameter is a useful and necessary, but not exhaustive measure of the antioxidant power and cannot adequately discriminate the antioxidant activity. These differences between the curves can be expressed by the parameters describing the corresponding straight lines, namely the slope a and the intercept with the y-axis b or, equivalently, both the intercepts, namely EC50 (intercept on x-axis) and b. Data based on probit regression are strictly related to the normal distribution: the probit transformation can also be expressed through a simple change of integration variables in terms of the generic normal distribution N

YðxÞ ¼

Z

log½x

N½t; l; rdt 1

where l = b/a = log EC50 is the mean, and r = 1/a is the standard deviation. The variation coefficient, a measure of dispersion of a

Table 3 Probit regression parameters and EC50 values (lg/mL) obtained for natural food extracts. Food extract

R

EC50 (lg/mL)

2.4505 2.3688 2.1328 2.2585 1.7042 2.1243 2.5654

0.9911 0.9763 0.9593 0.9854 0.9560 0.9385 0.9459

20.85 (19.53–22.25) 27.99 (25.13–31.20) 14.13 (12.50–15.95) 17.86 (16.56–19.26) 12.61 (11.27–14.11) 19.06 (16.39–22.19) 120.56 (104. 95–138.71)

0.9505 0.6723

1.3209 1.1410

0.8548 0.7648

4.01 (3.33–4.83) 5.46 (4.30–6.94)

1.2266 1.4062

5.8224 6.7512

0.9338 0.9215

115.22 (101.38–130.84) 121.65 (105.74–139.84)

Probit regression a

b

Cocoa Ghana (raw)(a) Ghana (roasted)(b) Ivory Coast (raw)(c) Ivory Coast (roasted)(d) Ecuador (raw)(c) Ecuador (roasted)(d,a) Hulls(e)

0.8068 0.7110 0.8055 0.7835 0.6724 0.7207 0.5353

Hazelnut skin High roasting(f) Medium roasting(g) Sweet pepper Yellow(h) Red(h)

Probit regression parameters a and b correspond to slope and intercept, respectively. R is the correlation coefficient. Ranges between brackets following EC50 values represent the 95% confidence interval. Food extracts followed by the same letter are not significantly different (p > 0.05).

896

M. Locatelli et al. / Food Chemistry 114 (2009) 889–897

Fig. 3. Antiradical activity of cocoa hulls (3a) and red peppers (3b) extracts: comparison between the curves before (3c) and after (3d) the linearisation. DPPH antiradical activity calculated as inhibition percentage and expressed on a [0, 1] scale (Fig. 3a–c) or according to the probit transformation (Fig. 3d).

probability distribution, defined as the ratio of the standard deviation to the mean CV = r/l, can be written as CV = 1/b. So, the y-intercept value b is strictly related to the coefficient of variation; moreover, unlike the EC50, b is a dimensionless parameter, independent of the chosen units, and so extremely useful and easy to interpret. For all these reasons we argue that b is a crucial parameter, that could be indicated together with the EC50 value in order to express the antiradical activity of the antioxidant standard compounds and/or natural extracts. Moreover, the rate of reaction is an another important parameter to be considered for the evaluation of the antioxidant activity, particularly in the case of complex media, such as natural food extracts, in which synergistic effects of known and unknown substances can occur. At this purpose, another method employed to express the antioxidant activity in DPPH assay is the calculation of the ‘‘antiradical efficiency (AE)”. This last parameter, defined as AE ¼ 1=ðEC50 T EC50 Þ, takes into account both the stoichiometry (in term of EC50) and the reaction time, including in its definition the time needed to reach the steady state ðT EC 50 Þ (Sánchez-Moreno et al., 1998). Even though the determination of AE is time expensive and the determination of the steady state can be difficult for extracts showing a slow kinetic profile (Eklund et al., 2005), this index is largely employed to express the results of DPPH-scavenging assay. Consequently, the method proposed in this paper can be also considered an optimisation of the AE determination (in order to correctly calculate the EC50 value), also providing a further parameter (b) characterising the antiradical activity of standard compounds and complex food extracts. 4. Conclusions In this work the relation between antioxidants concentration and DPPH radical scavenging activity was studied. A mathematical software was developed in order to facilitate the calculation of the correct EC50 values: the software allowed to apply different regression models to the data (probit, logit and angular regressions), to detect outlier values and to perform the comparison of the curves of the samples analysed.

All the models applied appeared to fit well the antiradical activity curves obtained for both standard compounds and natural food extracts; probit regression was chosen to discuss the results obtained. The probit regression was effective to calculate the EC50 values, showing high correlation coefficients, and to identify an additional significant parameter to express the DPPH antiradical activity. Concluding, we consider that all these observations are of fundamental importance in the evaluation of antiradical activity in the in vitro DPPH-scavenging assay, and that could also be applied to other chemical or biological ex vivo antiradical/antioxidant assays. Acknowledgement This work was funded by grants from the Regione Piemonte (Assessorato Agricoltura and Ricerca Sanitaria finalizzata 2006) and Università degli Studi del Piemonte Orientale. References Antolovich, M., Prenzler, P. D., Patsalides, E., McDonald, S., & Robards, K. (2002). Methods for testing antioxidant activity. Analyst, 127, 183–198. Arlorio, M., Locatelli, M., Travaglia, F., Coïsson, J. D., Del Grosso, E., Minassi, A., et al. (2008). Roasting impact on the contents of clovamide (N -caffeoyl-L-DOPA) and the antioxidant activity of cocoa beans (Theobroma cacao L.). Food Chemistry, 106, 967–975. Arnao, M. B. (2000). Some methodological problems in the determination of antioxidant activity using chromogen radicals: A practical case. Trends in Food Science and Technology, 11, 419–421. Blois, M. S. (1958). Antioxidant determinations by the use of a stable free radical. Nature, 181, 1199–1200. Brand-Williams, W., Cuvelier, M. E., & Berset, C. (1995). Use of a free radical method to evaluate antioxidant activity. LWT-Food Science and Technology, 28, 25–30. Buenger, J., Ackermann, H., Jentzsch, A., Mehling, A., Pfitzner, I., Reiffen, K. A., et al. (2006). An interlaboratory comparison of methods used to assess antioxidant potentials. International Journal of Cosmetic Science, 28, 135–146. Castro, I. A., Rogero, M. M., Junqueira, R. M., & Carrapeiro, M. M. (2006). Free radical scavenger and antioxidant capacity correlation of alpha-tocopherol and trolox measured by three in vitro methodologies. International Journal of Food Science and Nutrition, 57, 75–82. Chen, H. Y., Lin, Y. C., & Hsieh, C. L. (2007). Evaluation of antioxidant activity of aqueous extract of some selected nutraceutical herbs. Food Chemistry, 104, 1418–1424.

M. Locatelli et al. / Food Chemistry 114 (2009) 889–897 Darley-Usmar, V., Wiseman, H., & Halliwell, B. (1995). Nitric oxide and oxygen radicals: a question of balance. FEBS Letters, 369, 131–135. Decker, E. A., Warner, K., Richards, M. P., & Shahidi, F. (2005). Measuring antioxidant effectiveness in food. Journal of Agricultural and Food Chemistry, 53, 4303–4310. Domínguez, M., Nieto, A., Marin, J. C., Keck, A. S., Jeffery, E., & Céspedes, C. L. (2005). Antioxidant activities of extracts from Barkleyanthus salicifolius (Asteraceae) and Penstemon gentianoides (Scrophulariaceae). Journal of Agricultural and Food Chemistry, 53, 5889–5895. Eklund, P. C., Långvik, O. K., Wärnå, J. P., Salmi, T. O., Willför, S. M., & Sjöholm, R. E. (2005). Chemical studies on antioxidant mechanisms and free radical scavenging of lignans. Organic and Biomolecular Chemistry, 3, 3336–3347. Fogliano, V., Verde, V., Randazzo, G., & Ritieni, A. (1999). Method for measuring antioxidant activity and its application to monitoring the antioxidant capacity of wines. Journal of Agricultural and Food Chemistry, 47, 1035–1040. Gil, M. I., Tomás-Barberán, F. A., Hess-Pierce, B., Holcroft, D. M., & Kader, A. A. (2000). Antioxidant activity of pomegranate juice and its relationship with phenolic composition and processing. Journal of Agricultural and Food Chemistry, 48, 4581–4589. Halliwell, B., & Cross, C. E. (1994). Oxygen-derived species: their relation to human disease and environmental stress. Environmental Health Perspectives, 102(Suppl. 10), 5–12. Heimler, D., Vignolini, P., Dini, M. G., & Romani, A. (2005). Rapid tests to evaluate the antioxidant activity of Phaseolus vulgaris L. dry beans. Journal of Agricultural and Food Chemistry, 53, 3053–3056. Kobata, K., Todo, T., Yazawa, S., Iwai, K., & Watanabe, T. (1998). Novel capsaicinoidlike substances, capsiate and dihydrocapsiate, from the fruits of a nonpungent cultivar, CH-19, of pepper (Capsicum annuum L.). Journal of Agricultural and Food Chemistry, 46, 1695–1697. Litwinienko, G., & Ingold, K. U. (2003). Abnormal solvent effects on hydrogen atom abstraction. 1. The reactions of phenols with 2,2-diphenyl-1-picrylhydrazyl (DPPH) in alcohols. Journal of Organic Chemistry, 68, 3433–3438. Madhujith, T., & Shahidi, F. (2006). Optimization of the extraction of antioxidative constituents of six barley cultivars and their antioxidant properties. Journal of Agricultural and Food Chemistry, 54, 8048–8057. Markowicz Bastos, D. H., Saldanha, L. A., Catharino, R. R., Sawaya, A. C. H. F., Cunha, I. B. S., Carvalho, P. O., et al. (2007). Phenolic antioxidants identified by ESI-MS from Yerba Maté (Ilex paraguariensis) and green tea (Camelia sinensis) extracts. Molecules, 12, 423–432. Molyneux, P. (2004). The use of the stable free radical diphenylpicrylhydrazyl (DPPH) for estimating antioxidant activity. Songklanakarin Journal of Science and Technology, 26(2), 211–219. Nenadis, N., Lazaridou, O., & Tsimidou, M. Z. (2007). Use of reference compounds in antioxidant activity assessment. Journal of Agricultural and Food Chemistry, 55, 5452–5460.

897

Prior, R. L., Wu, X., & Schaich, K. (2005). Standardized methods for the determination of antioxidant capacity and phenolics in foods and dietary supplements. Journal of Agricultural and Food Chemistry, 53, 4290–4302. Re, R., Pellegrini, N., Proteggente, A., Pannala, A., Yang, M., & Rice-Evans, R. (1999). Antioxidant activity applying an improved Abts+ radical cation decolorization assay. Free Radical Biology and Medicine, 26, 1231–1237. Rice-Evans, C. A., Miller, N. J., & Paganga, G. (1996). Structure-antioxidant activity relationships of flavonoids and phenolic acids. Free Radical Biology and Medicine, 20, 933–956. Sánchez-Moreno, C., Larrauri, J. A., & Saura-Calixto, F. (1998). A procedure to maesure the antiradical efficiency of polyphenols. Journal of the Science of Food and Agriculture, 76, 270–276. Shahidi, F. (2000). Antioxidants in food and food antioxidants. Nahrung, 44, 158–163. Shahidi, F., Alasalvar, C., & Liyana-Pathirana, C. M. (2007). Antioxidant phytochemicals in hazelnut kernel (Corylus avellana L.) and hazelnut byproducts. Journal of Agricultural and Food Chemistry, 55, 1212–1220. Silva, M. M., Santos, M. R., Caroço, G., Rocha, R., Justino, G., & Mira, L. (2002). Structure-antioxidant activity relationships of flavonoids: a re-examination. Free Radical Research, 36, 1219–1227. Snedecor, G. W., & Cochran, W. G. (1991). Statistical methods. Ames (IA): Iowa State University Press. Sokal, R. R., & Rohlf, F. J. (1994). Biometry – the principles and practice of statistics in biological research. New York (NY): W.H. Freeman and Company. Summa, C., Raposo, F. C., McCourt, J., Lo Scalzo, R., Wagner, K. H., Elmadfa, I., et al. (2006). Effect of roasting on the radical scavenging activity of cocoa beans. European Food Research and Technology, 222, 368–375. Torregiani, E., Seu, G., Minassi, A., & Appendino, G. (2005). Cerium(III) chloridepromoted chemoselective esterification of phenolic alcohols. Tetrahedron Letters, 46, 2193–2196. Valko, M., Leibfritz, D., Moncol, J., Cronin, M. T. D., Mazur, M., & Telser, J. (2007). Free radicals and antioxidants in normal physiological functions and human disease. The International Journal of Biochemistry and Cell Biology, 39, 44–84. Villaño, D., Fernández-Pachón, M. S., Troncoso, A. M., & García-Parrilla, M. C. (2005). Comparison of antioxidant activity of wine phenolic compounds and metabolites in vitro. Analitica Chimica Acta, 538, 391–398. Villaño, D., Fernández-Pachón, M. S., Moyá, M. L., Troncoso, A. M., & García-Parrilla, M. C. (2007). Radical scavenging ability of polyphenolic compounds towards DPPH free radical. Talanta, 71, 230–235. Wettasinghe, M., & Shahidi, F. (2000). Scavenging of reactive-oxygen species and DPPH free radicals by extracts of borage and evening primrose meals. Food Chemistry, 70, 17–26. Yamaguchi, T., Takamura, H., Matoba, T., & Terao, J. (1998). HPLC method for evaluation of the free radical-scavenging activity of foods by using 1,1-diphenyl2-picrylhydrazyl. Bioscience, Biotechnology, and Biochemistry, 62, 1201–1204.