IS REGULATORY CAPITAL A LEGITIMATE, COMPARABLE AND OBJECTIVE GLOBAL STANDARD? EVIDENCE FROM 51 INSTITUTIONS ACROSS 17 COUNTRIES

Johann Jacobs
School of Economics
North-West University

Gary Van Vuuren
School of Economics
North-West University

IS REGULATORY CAPITAL A LEGITIMATE, COMPARABLE AND OBJECTIVE GLOBAL STANDARD? EVIDENCE FROM 51 INSTITUTIONS ACROSS 17 COUNTRIES
07CEA8580A9C2D5AA470C0D2E1D651C9Accepted: September 2013capital requirementscost of capitalbanksBaselSolvency II JEL: F3768G212228O16
Capital as an instrument for financial regulation has come under scrutiny since the financial crisis of 2007 to 2010 highlighted some deficiencies in the ability of capital to absorb unexpected losses and the procyclical nature of capital.This scrutiny arises mainly from the perspective that one of the principal objectives of capital requirements is to promote and contribute to financial stability.However, the literature on the topic is scarce almost to the point of non-existence regarding capital's validity as tool to level the playing fields between financial institutions.The objective of this article is therefore to investigate financial regulations based on capital requirements from the perspective of its goal of providing equal competitive conditions for financial institutions, the attainment of which is based on the assumption that the cost of capital between institutions (and countries) is the same, which might not necessarily be the case.The cost of capital for 51 financial institutions across 17 countries (three institutions per country) is accordingly calculated in this article using original weighted average cost of capital and capital asset pricing models, as well as modified versions of these to include more country-specific factors.The objective of the article is sought firstly by determining whether the cost of capital is the same among countries and secondly, based on the results, ascertaining whether financial regulations based on capital requirements can therefore realistically achieve this objective of providing equal competitive conditions for financial institutions.

Introduction

The objective of the article is to assess whether capital, as a regulatory instrument, can level playing fields between countries based on the cost of capital (COC) between different countries.The COC for 51 banking institutions and financial institutions across 17 countries was calculated using three variants of the capital asset pricing model (CAPM) and the weighted average cost of capital (WACC) models.

The article attempts to investigate the ability of providing level playing fields of financial regulations as a whole, i.e. the Basel Accords (Basel) and Solvency II, based on major similarities between the two sets of regulations.

The article is structured as follows: section 2 introduces the objective of the research.

Section 3 provides a literature review covering the major similarities between banking and insurance regulations, the objectives of financial regulations, and a brief discussion on the history and theory of the cost of capital.The calculation and analysis methods employed in the article are discussed in section 4, while the data and assumptions employed are elucidated in section 5. Section 6 discusses the results and findings of the article before section 7 concludes the research.

Objective

The objective of the article is to investigate financial regulations based on capital requirements from the perspective of its goal of providing equal competitive conditions for Abstract financial institutions, the achievement of which is based on the assumption that the COC between financial institutions (and countries) is the same, which might not necessarily be the case.

In essence, the introduction of capital adequacy standards alone may not be able to achieve this goal, as a certain percentage of capital required might, for example, cost one institution in Country A more than it costs another institution in Country B.

The objective of the article is sought firstly by determining whether the COC is the same among countries, and secondly, based on the results, ascertaining whether financial regulations based on capital requirements can therefore realistically achieve this objective.

It may be argued that there may be an offset in the COC (which is based on systematic risks) in some countries in financial institutions' actual capital requirements (which are based on idiosyncratic risks), resulting in some financial institutions being required to hold less capital although this capital might cost them more than it would other financial institutions.However, this article does not attempt to relate the cost of capital back to capital requirements of individual financial institutions, but focuses instead on the cost of capital between financial institutions based in different countries.The relationship between idiosyncratic risks (capital requirements) and systematic risks (the cost of capital) falls outside the scope of the article.It can reasonably be expected that capital requirements based on idiosyncratic risks for financial institutions operating in higher cost of capital environments (based on systematic factors) will not substantially offset higher capital costs because such financial institutions operate in more volatile environments.

It is also important to note that the comparison of the cost of capital is done from an outsider's perspective and not from the perspective of a company wishing to raise capital domestically.It would have been inappropriate had a country risk premium been added if this study were for different companies within the same country.For this reason it is deemed acceptable to add country risk premia to the calculations of the cost of capital to all companies in this study.

Literature review

This section provides a brief overview of the relevant literature.First, it explains similarities between banking and insurance regulations; second, it provides a description of the objectives of financial regulations, and finally it provides a brief theoretical background to COC models.

Similarities between banking and insurance regulations

The  Clutterbuck (2011:8), to name but a few.From these sources, high-level similarities between Basel and Solvency II can be drawn, the major ones being that:

• both are based on a similar three-pillar approach, with Pillar 1 being minimum capital requirements and the basis on which both sets of financial regulations are based; and • both set out to achieve the same broad objectives, including levelling the playing fields between financial institutions.The remainder of this article is based on these two major similarities between Basel and Solvency II.

Levelling of playing fields objective

The three major objectives of financial regulations are contributing to financial stability, levelling playing fields between financial institutions, and being based on more risksensitive measures and tools (BIS, 1999:9;SFRC, 1999:2;De Carvalho, 2005:7-8;Horcher, 2005:257;Van Roy, 2005:7;CEA, 2006:5;Koch & MacDonald, 2006:312;CEC, 2007:3;Sandström, 2007:12;EU, 2009:3;Lloyd's, 2010:4).This article focuses on the achievement of the second objective, namely to level playing fields among financial institutions.

The cost of capital: brief background and theory

For the sake of brevity and because they are widely available in literature, the formulas for calculating the components of COC and the actual COC itself are not included in this article.

The concept of COC has evolved over the past 60 to 65 years and its origins can be traced back to the development of portfolio theory in the 1950s, when Markowitz (1952;1959) and Roy (1952) started to attempt to relate expected returns to risk.The work done by Modigliani and Miller (1958) is considered as the starting point of the literature on COC (Exley & Smith, 2006:230), while from the original work done by Markowitz, the CAPM was introduced independently by Sharpe (1964), Lintner (1965), andMossin (1966), although their work was predated by unpublished work from Treynor in 1961 and 1962 (French, 2003:60).

Since companies fund themselves through a combination of debt and equity (Ernst & Young, 2011:4), their overall COC is calculated by adding their weighted cost of debt and weighted cost of equity together according to the weights that each component contributes to total capital -this determines the WACC.The objective of companies is to minimise their WACC by determining their optimal capital structure, because in doing so a company will maximise its shareholder value (Brealey, Myers & Marcus, 2001:572).Much research has been undertaken on the topic of companies' optimal capital structures, including work by, among others, Baxter (1967), Opler, Saron andTitman (1997), Exley and Smith (2006) and Ratshikuni (2009).

The cost of debt is based on the risk-free rate of the country in which the debt is issued plus a credit risk premium according to the riskiness of the country, which is typically easily observable (Brealey et al., 2001:452;Ross, Westerfield and Jaffe, 2003:508;Jenkinson, 2006:3;Madura, 2009:477).

The CAPM is used to calculate the cost of equity and expresses the trade-off between risk and expected return (Madura, 2009:475).Since its introduction, many variations to the original CAPM have been introduced, most notably the Ibbotson Associates Modified CAPM (Annin & Falaschetti, 1998) and the Fama-French Three-Factor Model (Fama & French, 2004:25-46).

In contrast, Ross, et al. (2003:502) define the WACC as the sum of the weighted average of the cost of equity and the after-tax cost of debt.

The CAPM and WACC have some inherent weaknesses in their basic forms and these are widely discussed in the literature, most notably in research by Fama and French (2004:13), Perold (2004:16), Jenkinson (2006:7), Sercu (2008: 720), Greenen, Kirisits, Chadwick and Hoeveler (2009:3), Sánchez, Preve and Allende (2010:7) and Villarreal and Córdoba (2010: 11).

As a result of these weaknesses, certain adjustments need to be made to the original CAPM (Villarreal & Córdoba, 2010:8) and much research has been done on the topic, including research by McCauley and Zimmer (1989), Shoven and Topper (1992), Godfrey and Espinosa (1996), Estrada (2001), Lally (2004) Lambert, Leuz and Verrecchia (2006), Lambert andVerrecchia (2010), andMcMorran (2010).The major research that was considered for this article includes the studies of Erb, Harvey andViskanta (1996), Koedijk, Kool, Schotman, andvan Dijk (2002), Harvey (2004;2005), Sercu (2008), and Villarreal and Córdoba (2010).

The theme of the challenges experienced in calculating the cost of capital for emerging markets surfaces time and again, and it is clear that current models do not yet consider all factors that could impact the cost of equity capital, especially for emerging markets.The relative segregation of emerging markets from global markets along with their increased riskiness remain challenges to obtaining more accurate approximations of the cost of capital and, although many different theories and approaches to determine more accurate results exist, it seems that there is no agreement or uniformity in terms of calculating the cost of equity capital among countries (Estrada, 2001: 10).

Research on the calculation of the cost of capital across countries includes that of Godfrey and Espinosa (1996) where they adjust beta values to incorporate low correlations among countries (segmentation) and credit spreads into the original CAPM to reflect market segmentation and country risk; Erb et al. (1996) explores alternative measures for calculating expected returns and volatility in developing markets; Lambert, Leuz and Verecchia (2006) and Lambert and Verecchia (2010) where the effect of accounting and general information asymmetries on the cost of capital is explored because information asymmetries would mean that investors would not have access to the same information and therefore not homogenous expectations; McMorran (2010) modifies the original CAPM to take into account a company specific risk premium and an industry premium, while Lally (2004:20-32) provides a description of CAPM models, including international CAPMs.Building on this, Harvey (2004) explores the components of country risk; Sercu (2008:705-748) attempts to add an international diversification dimension to the original CAPM model; and Harvey (2005) critically assesses 12 ways in which the international cost of capital can be calculated, and suggests that the CAPM is used for integrated markets and that a combination of models is used for emerging markets.

Despite these weaknesses and challenges in their application, the CAPM and WACC remain popular methods to calculate COC (Bruner, et al., 1998:15;Brealey, et al., 2001:572;Ross et al., 2003:543).

Villarreal and Córdoba (2010) explore a consistent approach to calculating the COC in emerging markets and reinforce the need to adjust traditional methods of calculating the COC.The need to adjust it for emerging markets is specifically based on the fact that these markets are typically not fully integrated into global markets and therefore are rendered more inefficient, and also for the country risk component of a developing country.

The following section explains the approach that was adopted for this study, after which the results are presented and discussed.

Calculation methods and analysis

A comparative analysis of the COC is conducted for various institutions across different regions in an attempt achieve the objective of this article.

Three methods were used to calculate the COC among different institutions across different countries:

• the original CAPM and WACC models;

• the original CAPM and WACC models where an equity risk premium is added to the cost of equity; and • a modified CAPM and WACC model as explained by Villarreal and Córdoba (2010).In pursuit of its objectives, this article employs the Villarreal and Córdoba (2010) model because it has been specifically developed with the calculation of the cost of capital for developing countries in mind, and the analyses in this paper include a large sample of developing countries.In applying this model to developed countries, it can be reasonably expected that the adjustments made for developing country-specific factors would not distort results for developed countries.

Before providing a brief description of the calculation methods and formulas, it is again necessary to highlight that the calculation of the COC using the CAPM and WACC have some known weaknesses.Two challenges that are often highlighted include the term structure of credit ratings and the time variation of risk premiums (Erb et al., 1996;Harvey, 2004;2005).These time-dependencies might be as a result of external events and/or shocks and may impact the calculations of the COC.In pursuing its objective, this paper does not explicitly take into account these time effects through advanced regressions analyses as often prescribed.The paper rather focuses on the results of a model that is applied consistently across a sample set of countries instead of focussing on the effect of time on the calculations of the COC.

Without detailing the deduction of this model, the WACC formula used reflects a hypothetical world where taxes, transaction cost and an additional country risk (CR) component are present, and is shown as Equation 1:
!"## = ! !!! !(! − !)!′ ! + ( ! !!! )!! ! (1)
where D is the total debt capital of a particular company, E is its total equity capital, D + E its total capital, K' D the pre-tax cost of debt, and K E the cost of equity.K' D is presented by the cost of debt (K D ) (risk-free rate (R F ) plus intermediation spread) plus a CR premium:
!′ ! !! = !! (! ! + !"#$%&$'!(#!)"!!"#$%&) + !" (2)
Similarly, the original calculation of the K E for this model is modified to incorporate a country risk premium that is adjusted to incorporate taxes (CR(1-t)) and a non-diversified country risk premium (CR(β E )) so that:
! ! !! = !! ! ! (! − !)] + [! ! ! ! -! ! + { !" ! − ! + !" ! ! ]} (3)
where β U is the beta of a specific equity, R M the expected market return and therefore (R M -R F ) the market risk premium, and equity beta (β E ), as explained in Equation 5.

From the original CAPM, it is important to point out that beta is estimated by the quotient of the covariance between the returns of a company's equity returns (R E ) and R M , and the variance of the market returns (Sercu, 2008:720): Villarreal and Córdoba (2010:19) refer to the beta in Equation 4as 'unlevered beta', or β U , because it is based on equity data only, i.e. it is assumed that the company's capital structure consists of equity only and does not take into account financial leverage.On the other hand, when referring to β E , Villarreal and Córdoba (2010) refer to a beta which is adjusted by the debt-to-equity ratio ( ! ! ) of a company, and reflects systematic risk given a company's !!(5)
! ! !! = !! !"#$%(! ! ,! ! ) !"#(! ! ) (4)
In turn, Villarreal and Córdoba's (2010:22-23) explanation of levered beta, or β L , is the beta of a company where financial leverage is taken into account, or where debt is incorporated into a company's capital structure and is calculated as:
! ! = ! !!! ! ! + ! ! !!! ! ! (6)
where β D is the beta of debt.For the purpose of this paper it is assumed β D = 0 as per Villarreal (2004), where it is explained that it can be assumed that the risk of the bondholders is void, to the extent that an institution should always fulfil its obligations toward them irrespective of the ability to pay.

Sometimes the terms 'levered beta', 'unlevered beta', and 'equity beta' are used interchangeably and can lead to much confusion, but for the purpose of this article, the Villarreal and Córdoba (2010) definitions of beta are used throughout.Now, following from Equation 3, for notational simplicity it is assumed that:
!" ! − ! + !" ! ! = ! !" * (7)
so that Equation 3 can be re-written as:
! ! !! = !! ! ! (! − !)] + [! ! ! ! -! ! + !" * (8)
Alternatively, the cost of equity can be calculated using K' D as a starting point:
! ! !! = !! !′ ! (! − !) + ! ! [ ! ! -! ! + !"] (9)
From this, a principle of coherence is applied such that the opportunity cost calculation does not depend on the method used, and that there must be consistency between the CAPM and WACC, so that their CAPM formula is modified as follows (note that β L is used here):
!"#$ = !′ ! (! − !) + ! ! [ ! ! -! ! + !"] (10)
With these modifications and the principle of coherence, the results of the WACC and CAPM should be equal and the one can be used to verify the other.

Although this model was developed with the specific aim of addressing challenges experienced in calculating the COC for developing countries, it will be applied to the entire data set used in this article, including developed countries.

Data

Although the data used in this research were for banks only, therefore explicitly showing the costs of capital for banks across different countries, the results could easily apply to insurance companies also because of the similarities between Basel and Solvency II that were highlighted.

Data selected for this article were based on two principles, firstly the need to be representtative of developed and developing markets; and secondly, in order to keep with one of the major assumptions of the Villarreal and Córdoba (2010) model, these markets had to be efficient with at least some degree of sophistication and integration into global markets.

Sample countries and institutions

The Group of eight countries (G8) (Canada, France, Germany, Italy, Japan, Russia, USA and UK) and a group of countries considered the most important emerging market economies, the so-called 'Outreach 5' (O5) (Brazil, China, India, Mexico and South Africa) were used.

Other developing countries including Indonesia, the Philippines, Poland and Turkey were used in addition to the O5.From each of these, the largest three banks were selected based on balance-sheet size 1 , all of which can be seen in Appendix A. Financial groups were included in some cases and not purely banks, as the aim of the exercise is not necessarily to calculate the COC among banks only; rather it is to calculate the COC among countries based on banking data.

Time series

The time period for which data were obtained was for the seven years 2005 to 2011, based on the rationale that these were recent: they would be sufficient to provide meaningful results, and that they included benign and challenging economic conditions.

Risk-free rate (R F )

The average daily United States (US) ten-year Treasury bond rate was used as a proxy for R F across all countries in this study over the period analysed, i.e. 3.80 per cent (FRED, 2012).This assumption is considered as being rather conservative, considering that most countries that were used in this study will have a higher R F than the US.

Risk-free rate (R M )

R M is the average returns for the period analysed and were calculated using the returns for each country's equity market using each country's Morgan Stanley Capital International (MSCI) index as a proxy for equity returns.

Equity returns (R E )

The actual daily equity price movements of each of the institutions analysed in the sample set was obtained from Bloomberg (2012) and the equity returns were calculated for each for the periods analysed.

Intermediation spreads

The intermediation spreads in this article are determined from data obtained from the Federal Reserve Bank of St Louis Economic Data (FRED, 2012).The categories of the data that were used are reflected in Table 1 and the average spread is the average daily basis points (bps) spread per category above R F .Credit rating categories were used in this research, which are illustrated in Table 1.These were applied to each bank as per its credit rating obtained from Fitch Ratings (Fitch, 2012).The US daily average rates per credit rating were applied to all banks that operate in developed countries.

Country risk (CR) spread

To estimate the CR premium, data were used from Damodaran (2012), who uses credit ratings as a starting point.In this information, local currency credit ratings were used to obtain a default spread in bps above the US Treasury bond rate using historical credit data of US corporates and country bonds.This default spread is illustrated in Table 2:  2012) Damodaran (2012) then adds this default spread to a local market risk premium of 5.5 per cent for each country multiplied by an equity-to-bond market volatility factor of 1.5.This represents the total equity market premium for that country.The country risk premium is obtained by subtracting the original market risk premium from this number.In this article, the same methodology was employed in estimating the country risk premium but for some modifications and additions.Instead of assuming a flat 5.5 per cent local market risk premium for all countries, data were obtained from a survey conducted by Fernández, Aguirreamalloa and Corres (2011), which obtained the market risk premia used by different stakeholders across 56 countries.From this, the average market risk premia for the countries used in this article are indicated in Table 3. Bps default spreads were also used in this article (Table 2), but each country's credit rating as measured by Fitch was obtained so that the default spread used for each country was as shown in Table 4.For equity-to-bond market volatility spreads, individual country spreads were calculated using the returns for each country's equity market using each country's MSCI index as a proxy for equity returns and the Emerging Markets Bond Index (EMBI) for bond market returns in the developing countries in this article.The Royal Bank of Canada (RBC) Global Corporate Bond Fund 2 was used as a benchmark for developed markets' bond market returns.The relative volatilities for each of the countries' stock market returns against these bond market return proxies were calculated and used for this input into the model, and the results are presented in Table 5.The results obtained did not differ much from the original 1.5 value as per Damodaran (2012) and the overall average was 1.66.For the purpose of the present study the average for each of the groups is used, i.e. 1.75 for emerging markets and 1.50 for developed markets.

The country risk rating was obtained as explained earlier, i.e. by subtracting the local market risk premium from the total equity risk premium, the results of which are shown in Table 6 2012) and Fernández et al. (2011), data from Fitch (2012) and Bloomberg (2012) Betas (β U , β E , β L ) β U was calculated using the standard variancecovariance approach (Equation 4) in which each bank's monthly equity returns over the period were used to calculate the beta relative to each country's MSCI index described above.β E and β L were derived from these using the formulas highlighted in Equations 5 and 6 respectively.

Debt-to-equity ratios ( ! ! ), total debt (D) and total equity (E)

For banks' ( ! ! ), D and E data were obtained from Bloomberg (2012), which is where these ratios are calculated.

These data set out above were used as the inputs into calculating the COC according to the three chosen methods explained in section 4. The results and findings are presented in section 6.

Results and findings

The results are presented in the following sections, with each section highlighting the method employed and the results obtained.The first calculation of the COC was done by employing the original WACC, while the second calculation was done on a similar basis for an equity market risk premium that was added to the original WACC calculations.These results are denoted by 'WACC 1 ' and 'WACC 2 ' respectively, both in Table 7 and in the results in Appendix A. Some inputs and the full set of results are attached in Appendix A. The averages of the countries' COC obtained are illustrated in Table 7.

Original WACC (WACC 1 )

Using this calculation method, the cost of debt is calculated by adding a risk premium to the risk-free rate and is denoted by K' D1 in Appendix A. In this case, the intermediation spread was added to the cost of debt while the country risk spread was ignored.The original CAPM was employed to calculate the cost of equity (K E1 ) and the WACC was calculated by aggregating the weighted averages of these.

In using these CAPM and WACC models, the average COC was found to be 2.7 per cent across all the countries, and the COC between emerging-market countries and developed markets was closely aligned, with an average COC of 2.7 per cent and 2.8 per cent respectively.

These results highlight some of the weaknesses of the original CAPM and WACC where it is assumed that all countries' financial markets are integrated while ignoring countryspecific risk and taxation.These assumptions drive the relative alignment and low costs of capital between countries, specifically between emerging markets and developed countries.

WACC plus equity market premium (WACC 2 )

The second set of results (WACC 2 ) was obtained using the same methodology as was used with WACC 1 , but for an equity market risk premium, as per Table 3, that was incorporated into the CAPM calculation of the cost of equity (K E2 ).This addition not only increased the COC (as would be expected), but this increase for emerging markets was incrementally more than that of developed markets.

The WACC in emerging markets increased from the previous average of 2.7 per cent to 5.6 per cent while the WACC for developed markets increased from 2.8 per cent to 3.7 per cent.This incremental increase in emerging markets' COC is due to the perceived higher risk in their equity markets, also indicating the effect on the COC where markets are not fully integrated globally.The results of WACC 2 indicate that, if country-specific factors are considered, the COC between countries differs and that there is a larger difference between the COC of developing countries and that of developed countries.

Villarreal and Córdoba models

(WACC and CAPM) Villarreal and Córdoba (2010) argue that the results of the WACC and the CAPM should be equal if their principles of intermediation spreads, country risk, taxation, and different betas used are applied correctly, which was the case in this article (WACC and CAPM were verified as per Appendix A).

The cost of debt used in these models is denoted as K' D and the cost of equity used as K E in Appendix A. CR and CR * are used as the country risk spreads as explained in Equations 7, 8, and 9.

The results obtained with these models show that an even larger gulf in the COC between developed countries and emerging markets develops as more factors are considered for COC calculations as illustrated in Figure 1.The average COC of emerging markets more than doubled from 5.6 per cent to 12.3 per cent, while the COC for developed countries increased from 3.7 per cent to 5.9 per cent.Again, it may be deduced from the results that emerging markets are still not fully integrated into global financial markets and that they are more risky than developed markets.The COC among developing countries remained relatively closely aligned when using this model.

The conclusions that can be drawn from these results are discussed in the following section.

Conclusions

From the results obtained, a clear pattern emerged, namely that the COC increased significantly more in emerging markets than in developed countries when additional countryspecific factors were included in the calculations.This provides further results from which certain conclusions can be drawn.

Different COC between emerging markets and developed markets

The results obtained from the COC calculations indicate that the COC increases at an increasing rate between developed countries and emerging markets as more countryspecific factors are used as inputs to the models.This illustrates the well-known fact that emerging markets are not fully integrated into global markets and that there is a difference between the COC in emerging markets and in developed markets.The results in this article may even understate the extent of this, because there may be a potentially infinite list of factors that could be included as additional country-specific factors which may accentuate this gap in the COC.

Unequal benefits

Based on the results, an argument can be made that financial regulations based on capital requirements are biased towards developedmarket economies.Financial regulations are designed in developed countries mainly for the developed world where financial markets are closely integrated (and, from the results in this article, their COC) while nuances in developing countries are not taken into account.Developing countries are often socially and politically relatively worse off than developed countries already.Accordingly, in complying with financial regulations that do not apply to them or that do not consider nuances in their domestic markets, developing countries may find themselves being disadvantaged compared to their counterparts in developed countries.

Therefore, financial regulations based on capital requirements may be a useful tool to ensure equal footing among financial institutions in developed countries, but not across all markets.Essentially, as long as certain markets pay more for their capital than others, financial regulations, which aim to ensure no competitive advantage among financial institutions through using capital requirements, cannot fully realise this objective.

Capital as regulatory instrument

Following the financial crisis, the ability of capital as regulatory tool to contribute to financial stability through acting as a buffer against unexpected losses has been questioned because of its procyclical nature (e.g.Atik, 2011;Dowd, Hutchinson, Ashby & Hinchliffe, 2011).

Adding impetus to the previous finding, the results of this article may further strengthen arguments against capital as an instrument for financial regulation based on its inability to fulfil the objective of levelling playing fields between countries and institutions, in addition to the argument from a financial stability perspective.

Development of financial regulations

The development of financial regulations should take into account more country-specific factors to ensure that countries are not forced into competitive disadvantages in complying with financial regulations.It could therefore be useful to have more emerging-market representation in the design and conceptualisation of financial regulations to take into account certain country-specific factors.Financial regulations should also be flexible enough to allow for emerging markets to simply not comply with certain requirements that may significantly penalise them for factors over which they have no control.In addition, policymakers in emerging markets should engage with regulatory bodies on nuances where complying with certain regulations might disadvantage them.

The alignment of financial regulations between developed and emerging markets is further complicated in view of the fact that, from a regulatory arbitrage perspective, banks and insurers operate on equal grounds (European Central Bank (ECB), 2007;Al-Darwish, Hafeman, Impavido, Kemp & O'Malley, 2011).It is therefore imperative that in attempting to ensure consistency between Basel and Solvency II, regulators should pay heed to the challenges faced by emergingmarket countries when the COC and countryspecific factors are considered.

Relevance to Solvency II

Although the data used for this research related specifically to banks and banking groups, it is reasonable to infer that the results will apply to all financial institutions and not only to banks, as the majority of the inputs used in the models were not bank-specific.In addition, these results, findings and conclusions apply to Solvency II based on the similarities between Basel and Solvency II that were highlighted in section 0.

With this in mind and with the implementation of Solvency II being an ongoing task, the opportunity exists for emergingmarket countries and their regulators to highlight some of the more specific factors and influences in their markets that might place them at a disadvantage compared to developed countries.

Usefulness of the Villarreal and Córdoba model

A further finding is not related to the objective of this article and relates to the methodology employed to calculate the cost of capital as suggested by Villarreal and Córdoba (2010).

The results affirm earlier work and confirm the theoretical foundations of their work and the usability of their models for a study of this nature.

Endnotes 1 In some cases banks not representing the three largest balance sheets in a specific country were selected based on data availability.In this regard, according to banks' size, for Mexico numbers 1, 3 and 4 were used; for Russia 1, 2 and 6; for Turkey 1, 2 and 4; and for Germany 1, 4 and 6. 2 RBC Global Corporate Bond Fund (the Fund) is an open-end fund incorporated in Canada.The Fund seeks to provide a high level of interest income with the potential for modest capital growth by investing primarily in global corporate bonds.The Fund will invest in investment grade corporate debt securities from anywhere around the world.

Appendix A

Calculations and results

!

Figure 1
1
Figure 1Increasing COC for emerging markets

Table 1 :
1
Intermediation spreads
CategoryAverage spread (bps)US AAA Daily58US AA Daily89US A Daily148US BBB Daily216Emerging markets AAA-A134Emerging markets BBB-B257Emerging markets BB490Source: FRED (2012)

Table 2
2
Estimated default spreads by credit rating
Moody's ratingFitch RatingDefault spread (bps)AaaAAA0Aa1AA+25Aa2AA50Aa3AA-70A1A+85A2A100A3A-115Baa1BBB+150Baa2BBB175Baa3BBB-200Ba1BB+240Ba2BB275Ba3BB-325B1B+400B2B500B3B-600CaaCCC700CaCC850CD1,000

Table 3
3
Average market risk premia per country
CountryAverage local market risk premium usedNumber ofrespondentsBrazil7.7%35Canada5.9%36China9.4%31France6.0%45Germany5.4%71India8.5%28Indonesia7.3%14Italy5.5%76Japan5.0%14Mexico7.3%56Philippines5.6%6continued/

Table 4
4
Country default spreads
CountryCountry ratingDefault spread (bps)BrazilBBB175CanadaAAA0ChinaA+115FranceAAA0GermanyAAA0IndiaBBB-200IndonesiaBBB-200ItalyA-115JapanA+115MexicoBBB175PhilippinesBB+325PolandA-115RussiaBBB175South AfricaBBB+200TurkeyBB+325UKAAA0USAAAA0Source: Fitch (2012), Damodaran (2012)

. Equity-to-bond market volatility spreads
Emerging marketsDeveloped marketsBrazil1.33Canada1.82China1.80France1.21India1.91Germany1.55Indonesia1.23Italy1.28Mexico1.53Japan1.31Philippines1.78UK1.44Poland2.01USA1.90Russia1.74South Africa2.00Turkey1.21Average:Average:Emerging markets1.75Developed Markets1.50Average:1.66Source: Bloomberg (2012)

Table 6
6
Country risk premia
CountryLocal market risk premiumDefault spread (bps)Equity-to-bond-market volatilityTotal equity risk premiumCountry risk premiumBrazil7.70%1751.7516.54%8.84%Canada5.90%01.508.85%2.95%China9.40%1151.7518.46%9.06%France6.00%01.509.00%3.00%Germany5.40%01.508.10%2.70%India8.50%2001.7518.38%9.88%Indonesia7.30%2001.7516.28%8.98%Italy5.50%1151.7511.64%6.14%Japan5.00%1151.509.23%4.23%Mexico7.30%1751.7515.84%8.54%Philippines5.60%3251.7515.49%9.89%Poland6.20%1151.7512.86%6.66%Russia7.50%1151.7515.14%7.64%South Africa6.30%2001.7514.53%8.23%Turkey8.10%3251.7519.86%11.76%UK5.30%01.507.95%2.65%US5.50%01.508.25%2.75%Source: Adapted from Damodaran (

Table 7 :
7

Source: Compiled by the author

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Screening South African potato, tomato and wheat cultivars for five carotenoids Affiliation
Published: 05 Sept. 2011

Charlotte S Mashaba
Council for Scientific and Industrial Research
PO Box 3950001Biosciences, Pretoria, PretoriaSouth Africa, South Africa

Eugenia Barros firstname.lastname@example.com
Council for Scientific and Industrial Research
PO Box 3950001Biosciences, Pretoria, PretoriaSouth Africa, South Africa

Eugenia Barros
Council for Scientific and Industrial Research
PO Box 3950001Biosciences, Pretoria, PretoriaSouth Africa, South Africa

Screening South African potato, tomato and wheat cultivars for five carotenoids Affiliation
Published: 05 Sept. 201110.4102/sajsDates: Received: 03 Nov. 2010 Accepted: 18 Apr. 2011S Afr J Sci 2011; 107(9/10) Research Article Correspondence to: Postal address: How to cite this article: Mashaba CS, Barros E. Screening South African potato, tomato and wheat cultivars for five carotenoids. S Afr J Sci. 2011;107(9/10), Art. #507, 6 pages. Licensee: AOSIS OpenJournals. This work is licensed under the Creative Commons Attribution License.
In South Africa malnutrition is of great concern. Vitamin A deficiency is one of the leading causes of infections as a result of micronutrient malnutrition. Although supplementation and food fortification programmes exist, these either are not available or are unaffordable to communities in remote rural areas. The selection of crops that are naturally rich in provitamin A (β-carotene) and other carotenoids that can be recommended to small-scale farmers for breeding and for food production, could be an effective way to address vitamin A deficiencies and associated diseases. The aim of this study was to profile two cultivars each of potato, tomato, bread wheat and durum wheat, which are highly consumed crops in South Africa, for their carotenoid content using high-performance liquid chromatography. To this effect, reliable extraction and quantification of five carotenoids -lutein, zeaxanthin, canthaxanthin, β-carotene and lycopene -were performed for these crops. Lutein and zeaxanthin were found to be the major carotenoids in potato, whilst lycopene was the major carotenoid in tomato. In durum wheat, only lutein and zeaxanthin were identified whilst bread wheat contained lutein, zeaxanthin and β-carotene. The methodology used proved to be robust and suitable to screen a large number of potato, tomato and wheat cultivars for their carotenoid content.

Introduction

Malnutrition, in general terms, is the medical condition caused by improper or inadequate nutrition. In developing countries, malnutrition contributes to 53% of deaths associated with infectious diseases among children under the age of five. 1,2 In South Africa, malnutrition is responsible for up to 50% of the deaths of children that are admitted to hospital. 3 Although there are many reasons for malnutrition, and many possible interventions to prevent it, the promotion of a diverse diet using micronutrient-rich and locally produced foods from locally grown crops could be a more long-term solution.

Carotenoids are lipid-soluble organic pigments that can only be synthesised by plants and microorganisms and exhibit several biological activities that include protection against certain cancers and other diseases in humans. 4 The most well-understood role of carotenoids is their provitamin A activity, whereby provitamin A carotenoids such as β-carotene can be converted to vitamin A. 5,6 Vitamin A plays a role in maintaining the surface linings of the eye and digestive, urinary and respiratory tracts; it also plays a role in bone growth and cell division and it helps regulate the immune system, which assists in preventing and fighting off infections. 7,8,9,10 Common provitamin A carotenoids found in food crops are α-carotene, β-cryptoxanthin and β-carotene. β-carotene is more efficiently converted to vitamin A than any other carotenoid. 6 β-carotene, β-cryptoxanthin, lycopene and lutein have been linked to a 50% reduction in the risk of women developing breast cancer, whilst lycopene is associated with a reduced risk of coronary heart disease. 11,12,13 Lutein and zeaxanthin have been suggested to play a protective role against cataracts and age-related macular degeneration, the leading cause of irreversible vision loss and blindness, especially in the elderly. 14 The mechanisms by which carotenoids act on the body to prevent or slow down diseases have not yet been fully elucidated but have been strongly linked to their antioxidant properties, their ability to scavenge free radicals that can cause a great deal of damage to human cells. 12,15 Because humans rely on agricultural food crops as their primary source of nutrients and because the only way they can accumulate carotenoids in certain tissues is through ingestion, it is essential that food crops be the focus of analysis for nutritional content to generate a wider understanding of what each crop can provide to ensure an adequate and healthy diet. For this study, potato, tomato and wheat were the crops of choice because they are consumed in high amounts in South Africa, are grown in rural communities and are alternative foods to control dietary micronutrient deficiencies.

In potato, carotenoids, especially β-carotene, are minor constituents. Although potatoes are not a good source of provitamin A, they have high lutein and zeaxanthin contents, which have been correlated with a reduced risk of age-related macular degeneration in humans. 16,17 Tomatoes and tomato products are almost the only available source of lycopene to humans and are a good source of provitamin A (β-carotene). 5,18 The principal carotenoids in wheat are lutein and its stereoisomer zeaxanthin, which are the major carotenoids found in human macula. 19 β-carotene and α-carotene can also be found in some wheat species. 20,21 In this study, the methods for extraction and quantification of carotenoids from potato, tomato and wheat cultivars were reproducible and robust. The data generated will constitute the baseline data of the carotenoid levels and of the carotenoid profiles for the three crops. This data can be used in the screening and evaluation of additional South African grown cultivars for their antioxidant properties. It is important to screen different cultivars to know which to recommend for growing by small-scale farmers and to support the development of new varieties that are high in carotenoids.

Methods

Chemicals

All chemicals used were high-performance liquid chromatography (HPLC) grade and were purchased from Sigma (St Louis, MO, USA). Lutein, zeaxanthin, canthaxanthin, lycopene and β-carotene standards were up to 99% pure and acquired from Industrial Analytical (Johannesburg, South Africa). Water was purified using the Millipore Milli-Q water purification system (Millipore Corp, Bedford, MA, USA).

Potato, tomato and wheat samples

Potato Mondial and BP1 commercial varieties were purchased from a local vegetable market in Pretoria, South Africa. The potato tubers were washed thoroughly, dried and placed into small plastic bags with holes in them. The bags were immediately immersed into liquid nitrogen and left there until the bubbling stopped. The bags were then immediately stored at -80 °C before being freeze dried in the dark. The dried potatoes were transferred into vacuum bags, vacuum packed and stored at -20 °C.

Tomato Salad and Roma cultivars were also purchased from a local vegetable market in Pretoria. The tomatoes were thoroughly washed, dried and freeze dried whole immediately after purchase. Freeze-dried tomatoes were transferred into vacuum bags, vacuum packed and stored at -20 °C.

Bread wheat Elands and Mathabas cultivars and durum wheat Moni and Orania cultivars were made available to us by the Agricultural Research Council-Small Grain Institute in Bethlehem, South Africa. Because the grains were already dry, they were immediately vacuum packed and stored at -20 °C.

Extraction of carotenoids

Carotenoids were extracted from potato, tomato and wheat using the protocol supplied by the Cereal Research Centre (CRA) in Foggia, Italy, with minor volume changes, depending on the crop. Powdered samples (2 g) of potato and wheat were resuspended in 8 mL of extraction solvent (hexane/acetone, 8:2 v/v) containing 0.1% butylated hydroxyl toluene. For tomatoes, 2 g of powder was resuspended in 10 mL of the above extraction solvent. The mixtures were streamed under nitrogen for 10 s to prevent oxidation, vortexed for 30 s and left in the dark at room temperature overnight. Following this period, samples were vortexed and centrifuged at 4000 g for 15 min. The pellet was washed with 4 mL of hexane and centrifuged as above. Supernatants collected after both centrifugations were pooled together and filtered using 0.45-µm, 25-mm GHP membrane syringe filters (Separations, Johannesburg, South Africa). The filtrate was dried under a gentle stream of oxygen-free nitrogen gas and then reconstituted in 400 µL methanol/ dichloromethane (45:54 v/v). Final extracts were transferred into amber HPLC vials (Separations) and a sample volume of 20 μL was subjected to quantitative HPLC analysis.

High-performance liquid chromatography analysis

HPLC analysis was carried out using a Hewlett-Packard 1100 system with a diode array detector set for monitoring the wavelength at 450 nm. The system was also equipped with a binary pump, autosampler, column thermostat for temperature control (all G1312A) and G1656 ChemoStation software (Agilent Technologies, Santa Clara, CA, USA). Carotenoids were separated using an YMC C 30 5-µm (250 mm x 2 mm) column coupled with a 20 mm x 2 mm, 5-µm guard column (YMC Inc., Waters, Milford, MA, USA). The column was kept at a constant temperature of 25 °C and the flow rate was 0.4 mL/min. There were two mobile phases (A and B) made up with the same solvents at different concentrations: Phase A consisted of 83% methanol containing 15% tert-butyl methyl ether (TBME) and Phase B consisted of 8% methanol in 90% TBME. The following linear gradient of elution programme was used: starting with 100% mobile phase A, 8 min of 70% mobile phase A and 30% mobile phase B, 22 min of 45% mobile phase A and 55% mobile phase B, 24 min of 5% mobile phase A and 95% mobile phase B, and 27 min of 100% mobile phase B. The total separation time was 50 min.

Identification and quantification

All five individual carotenoids -lutein, zeaxanthin, β-carotene, lycopene and canthaxanthin -were identified on the basis of retention times and absorption spectra between 450 nm and 470 nm, compared with those of pure standards. Approximately 5 mg of each standard was prepared in ethanol, except for lycopene which was prepared in petroleum ether. With each run, an aliquot was taken from each standard to verify its purity by HPLC and for calibrating the instrument (i.e. obtaining a chromatogram showing a single peak corresponding to the carotenoid and giving the same characteristic spectrum at all times). Instrument calibration was performed prior to each analysis cycle using 6-9 levels of different concentrations of the five carotenoid standards with a single or double injection. Pure standards were also used to construct standard curves used to determine the concentrations of carotenoids in the crops. Concentrations of the pure standards were determined spectrophotometrically using A 1cm values and linear standard curves between 0.01 µg/mL and 10 µg/mL were created with r 2 values of 0.999. The limit of detection for each carotenoid was: lutein -0.01 μg/mL, zeaxanthin -0.02 μg/mL, lycopene -0.02 μg/mL, β-carotene -0.01 μg/mL and canthaxanthin -0.02 μg/mL. Concentrations below these levels were denoted as not detected as they could not be accurately quantified.

Recovery rates

For recovery rate experiments, lutein and β-carotene standards used for spiking were separately prepared in ethanol. Aliquots (200 µL) of each standard with known concentrations were immediately added to three replicates of the dry, powdered samples of BP1 potato, Salad tomato and Durum wheat cultivars and carotenoids were extracted and analysed using the methods above. Percentage recoveries for each carotenoid were calculated using the following equation:
[(C A -C U )/C B ] x 100,
where C A is the concentration of the specific carotenoid in spiked samples, C U is the average amount of carotenoid measured in unspiked samples and C B is the concentration of the carotenoid used to spike the sample.

Statistical analysis

Carotenoid content per cultivar is expressed as mean ± standard deviation of three replicate experiments. To measure the significance of the differences observed for each carotenoid within the two cultivars of the same crop, a Student's t-test was used where p < 0.05 was considered statistically significant.

Results

HPLC was used to identify and quantify individual carotenoid levels of lutein, zeaxanthin, β-carotene, lycopene and canthaxanthin from potato, tomato and wheat cultivars grown and consumed in South Africa. Our objective was to establish a reliable and optimal method that can be used for the future screening of other cultivars of these three crops.

Individual carotenoids were identified on the basis of retention times and spectra compared with those produced from a mixture of pure standards. Figure 1 shows chromatograms representative of the carotenoids extracted from potato, tomato and wheat cultivars. Lutein was the first carotenoid to be eluted through the column just after 7 min, followed by zeaxanthin (± 8.384 min), canthaxanthin (± 9.150 min), β-carotene (± 15.952 min) and the last carotenoid of interest detected in this study was lycopene at approximately 34.435 min. The chromatograms indicated that a greater number of carotenoids have to be included for quantification when other cultivars are screened.

Lutein was used as a measure for recovery of polar carotenoids whilst β-carotene was used as a measure of recovery of non-polar carotenoids. As expected, recovery rates varied between the three different crop matrices. For potato, average recovery rates of 70% for lutein and 60.79% for β-carotene were obtained. The recovery rates for wheat were 89.3% for lutein and 87.6% for β-carotene. For tomato, average recovery rates of 93.2% and 91% were obtained for lutein and β-carotene, respectively. Although the recovery rates for potato could be considered to be at a bare minimum, these values were considered sufficient for the purpose of this analysis, which was to optimise a single extraction method that could be used for screening crops of different matrices as efficiently and accurately as possible. Table 1 shows the carotenoid content of the eight cultivars. The potato cultivar Mondial had a higher total carotenoid content (0.730 µg/g) than the potato cultivar BP1 (0.464 µg/g). Figure 2 shows the variation in the levels of individual carotenoids identified in both potato cultivars. Lutein, zeaxanthin and β-carotene were the only carotenoids identified out of the five investigated in this study. An interesting observation between the two cultivars was that zeaxanthin was the major carotenoid found in Mondial potato, whereas lutein, the isomer of zeaxanthin, was the major carotenoid in BP1 potato. The concentration of zeaxanthin in Mondial potato was measured at 0.441 µg/g, which accounted for 61% of its total carotenoid content, and the concentration in BP1 potato was 0.120 µg/g, which accounted for 26% of its total carotenoid content. The concentration of lutein was slightly To quantify zeaxanthin and canthaxanthin, a more concentrated tomato sample was used.  Canthaxanthin higher in BP1 potato than in Mondial potato. Although the difference in concentration was small, lutein still accounted for 71% of the total carotenoid content in BP1 potato and only 36% in Mondial potato. β-carotene was also detected in both potato cultivars, and accounted for 3% of the total carotenoid content in both Mondial and BP1 potato cultivars. Mondial potato contained a marginally higher content of the provitamin A carotenoid, β-carotene (0.0201 µg/g), than did BP1 potato (0.0142 µg/g).

All five carotenoids of interest were detected in Roma and Salad tomato cultivars grown in South Africa. The total carotenoid content in Roma tomato was higher than that of Salad tomato, with average concentrations of 479.45 µg/g and 427.12 µg/g, respectively. Figure 3 shows the variation in the levels of individual carotenoids identified in both tomato cultivars. Lycopene, the major carotenoid in tomatoes, represented 92% of the total carotenoid content in Roma tomato, with a concentration of 443.09 µg/g, and 91% of the total carotenoid content in Salad tomato, with a concentration of 387 µg/g. β-carotene was the second highest component of the total carotenoid content in both tomato cultivars ( Figure 3). In contrast to lycopene, Salad tomato contained more β-carotene than did Roma tomato. β-carotene contributed 5% to the total carotenoid content in Roma tomato (25.19 µg/g) and 7% in Salad tomato (28.94 µg/g) ( Table 1). With lycopene and β-carotene being higher in the chain of carotenoid biosynthesis and available in high amounts, the remaining identified carotenoids were available in small or even trace amounts. There were no significant differences in the levels of lutein and zeaxanthin between the two cultivars. Roma tomato contained 10.867 µg/g of lutein whilst Salad tomato contained 10.810 µg/g. Only trace amounts of zeaxanthin were detected in the two tomato cultivars whilst canthaxanthin was marginally different between the two cultivars.

Of the five carotenoids selected for analysis, lutein was the major carotenoid detected in both bread wheat and durum wheat varieties. This observation is in agreement with previous studies on bread wheat, durum wheat and speciality wheats (e.g. Kamut and einkorn) where it was found that lutein accounted for 80% -90% of the total wheat carotenoid content followed by zeaxanthin and small amounts of β-carotene. 20,22,23 Figure 4 shows the variation in the levels of individual carotenoids identified in the wheat cultivars. Lutein and zeaxanthin were the only two carotenoids detected in both durum wheat cultivars and in the bread wheat cultivar Elands, whereas β-carotene was also detected in the bread wheat cultivar Mathabas. The absence of lycopene in the wheat varieties was no surprise because, to date, there have been no data reported on the presence of lycopene in wheat.

Total carotenoid contents of bread wheat cultivars selected for this study were within the reported range for bread wheat varieties, that is, ranging from 0.1 µg/g to 2.4 µg/g. 4,23,24 The Mathabas cultivar had a 40% higher average total carotenoid content at 0.716 µg/g compared to the Elands cultivar, which had an average total of 0.422 µg/g. Lutein was the major carotenoid detected in the bread wheat cultivars, followed by zeaxanthin (Figure 4). The concentration of lutein was significantly different between the two bread wheat cultivars, accounting for 75.5% of the total carotenoid content of the Mathabas cultivar (with an average of 0.544 µg/g) and 63% of the total carotenoid content of the Elands cultivar (with an average of 0.268 µg/g) ( Table 1). A variation in the zeaxanthin concentration was observed between bread wheat cultivars, with zeaxanthin accounting for 40% of the total carotenoid content in the Elands cultivar and 20% in the Mathabas cultivar. β-carotene was only detected in the Mathabas extracts, at an average concentration of 0.034 µg/g, which may possibly account for the lower levels of zeaxanthin in the Mathabas cultivar compared to the Elands cultivar.

In durum wheat, lutein and zeaxanthin were the only carotenoids detected (Figure 4). There was a marginal variation in average total carotenoid content between the durum wheat cultivar Moni (0.384 µg/g) and the Orania cultivar (0.343 µg/g) ( Table 1). Lutein was also the major carotenoid detected in the durum wheat cultivars, accounting for 79% of the total carotenoid content in the Moni cultivar (0.302 µg/g) and a higher 90% in the Orania cultivar (0.307 µg/g). The minor variation in total carotenoid content was as a result of the difference in zeaxanthin concentrations, with a slightly higher average content in the Moni cultivar (0.082 µg/g) than in the Orania cultivar (0.036 µg/g). Although both durum wheat varieties showed an expected trend in the kind of carotenoids detected, their concentrations were exceedingly below the average total

Discussion and conclusions

Micronutrient deficiencies are very common worldwide, especially in developing countries. 25 Supplementation and food fortification programmes are the traditional methods currently used to improve nutrient intake in vulnerable population groups. 26 However, both methods have shown to be insufficient in eliminating deficiencies as they do not reach all the people who are affected and have also not proven to be sustainable because of the costs involved. 27 Plant breeding for crops high in micronutrients has been proposed to offer the best and more sustainable solution to address food insecurity and nutrient deficiency, especially for remote rural areas. 28 To ensure such strategies work, it is necessary to quantify the nutrient levels of highly consumed food crops, to screen for the best cultivars from the current breeding population and to make them available to small-scale farmers and subsistence farmers.

In order to screen a large number of potato, tomato and wheat cultivars for five carotenoids, a simple and efficient extraction method, together with a robust HPLC protocol that could be used for all three crops, was required. The methods used in this study proved to be appropriate and robust because separate carotenoid peaks were observed on chromatograms produced from potato, tomato and wheat matrices. In addition to the identification and quantification of these five carotenoids, unidentified compounds, possibly other carotenoids, were also extracted and separated, as can be seen in Figure 1. However, the characterisation of these compounds was beyond the scope of this paper.

The results obtained for potato cultivars were in agreement with those reported in the available literature. Total carotenoid content of Mondial and BP1 potato cultivars fell within the average range reported by Breithaupt and Bamedi 29 for white fleshed potatoes (that is, between 0.38 µg/g and 0.62 µg/g fresh weight). This range was also comparable to that reported by Diretto et al. 30 (0.5 µg/g -2.5 µg/g fresh weight). Both of these studies used phase-separation methods, whilst in this study comparative data was obtained without separating the polar and non-polar phases. Furthermore, petroleum ether was used by both Breithaupt and Bamedi 29 and Diretto et al. 30 , whilst we used hexane as the non-polar solvent. Lycopene, the major carotenoid in tomato, was a strong indicator of how efficient the extraction method was. Lycopene contents, in both Salad and Roma tomato cultivars, were exceedingly higher (± 3-fold) than those reported by Agarwal et al. 31 and Khachik et al. 32 (125.4 µg/g and 92.7 µg/g, respectively). The quantification of zeaxanthin from fresh tomatoes was an additional benefit because zeaxanthin levels in tomatoes have not been reported, mainly because zeaxanthin is present in very low quantities in tomatoes. The presence of a carotenoid in very low quantities can pose a challenge in such screening studies because levels could at times be too low to achieve a reliable measurement. 24 We believe that the optimised methods of extraction and analysis developed in this study will minimise such challenges and will produce reliable data for South African crops, because levels as low as 0.045 µg/g were detected for canthaxanthin in durum wheat.

Although it would be impractical at this stage to make a significant decision on which cultivar could be recommended to subsistence and small-scale farmers, the carotenoids data

Wheat cultivars

Values shown are the mean of three replicates.

FIGURE 4:

A graphical comparison of the concentrations of carotenoids quantified from bread wheat (Mathabas and Elands) and durum wheat (Moni and Orania) varieties.

β-carotene

Zeaxanthin Lutein produced in this study is vital for crop-breeding purposes, where selective crop breeding may be used to overcome micronutrient deficiencies, especially those that exist in lowincome South African households. In this study, it became apparent that there are significant differences in the content of individual and total carotenoids within South Africa's breeding populations of potato, tomato and wheat. Data produced from durum wheat cultivars also provide evidence that there is a need to identify and select wheat varieties that are rich in carotenoids, as both cultivars were more than 1000-fold below the reported average level of 1.5 µg/g -5.0 µg/g. 4,23,24 This finding validates the need to continue evaluating more cultivars to assist in the endeavour to combat vitamin A and other carotenoid deficiencies in South African rural communities.

FIGURE 1 :
1Representative chromatograms to indicate the retention times of carotenoids extracted and separated using high-performance liquid chromatography, from (a) potato samples and (b) from a diluted carotenoid sample extracted from tomato.

are the mean of three replicates.

FIGURE 2 :
2The concentration of three carotenoids -β-carotene, zeaxanthin and lutein -quantified from Mondial and BP1 potato cultivars. are the mean of three replicates.

FIGURE 3 :
3The concentration of five selected carotenoids quantified from Salad and Roma tomato cultivars.

TABLE 1 :
1Concentrations (µg/g dry weight) of carotenoids identified from potato, tomato and wheat cultivars using high-performance liquid chromatography.Crop
Cultivar
Lutein
Zeaxanthin
Canthaxanthin
β-carotene
Lycopene

Mean
s.d.
Mean
s.d.
Mean
s.d.
Mean
s.d.
Mean
s.d.

Potato
Mondial
0.265
0.008
0.441
0.010
ND
-
0.0201
0.004
ND
-

BP1
0.333
0.003
0.120
0.003
ND
-
0.0142
0.005
ND
-

Tomato
Salad
10.801
0.028
0.303
0.008
0.065
0.006
28.9400
0.550
387.000
5.000

Roma
10.867
0.056
0.212
0.001
0.080
0.010
25.1970
1.370
443.090
8.908

Wheat
Mathabas
0.544
0.017
0.138
0.004
ND
-
0.0340
0.003
ND
-

Elands
0.268
0.017
0.154
0.011
ND
-
ND
-
ND
-

Moni
0.302
0.034
0.082
0.006
ND
-
ND
-
ND
-

Orania
0.307
0.008
0.036
0.001
ND
-
ND
-
ND
-

ND, not detected in all replicate experiments as per limits of detection.
Values shown are mean ± standard deviation of three replicates.

AcknowledgementsThis work was supported by the South African Department of Science and Technology and by the European Union Project DEVELONUTRI, Framework 6 Programme. We acknowledge Mrs Judy Reddy and Mrs Lauraine Mlotshwa for technical assistance with HPLC.
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