african journal of pure and applied chemistry

African Journal of Pure and Applied Chemistryvolume 9 Number 2 February 2015ISSN 1996-0840

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African Journal of Pure and Applied Chemistry

Table of Contents: Volume 9 Number 2 February, 2015

ARTICLES

Research Articles Extraction of silica gel from Sorghum bicolour (L.) moench bagasse ash Mupa M., Hungwe C. B., Witzleben S., Mahamadi C. and Muchanyereyi N. 12 Multifactorial discriminant analysis of leaf oil of C. odorata L. King and Robinson from Côte d'Ivoire Esse Leon Wognin, Affia Florence Brou, Thierry Acafou Yapi, Kidjegbo Augustin Touré, Tomi Félix and Zanahi Felix Tonzibo 18 A physico-chemical analysis of soil and selected fruits in one rehabilitated mined out site in the Sierra Rutile environs for the presence of heavy metals: Lead, Copper, Zinc, Chromium and Arsenic P. O. Egbenda, F. Thullah and I. Kamara 27

Vol. 9(2), pp. 12-17, February, 2015 DOI: 10.5897/AJPAC2015.0603 Article Number: 06A7E2550929 ISSN 1996 - 0840 Copyright © 2015 Author(s) retain the copyright of this article http://www.academicjournals.org/AJPAC


Full Length Research Paper

Extraction of silica gel from Sorghum bicolour (L.) moench bagasse ash

Mupa M.1*, Hungwe C. B. 1, Witzleben S. 2, Mahamadi C.1 and Muchanyereyi N.1

1Department of Chemistry, Bindura University of Science Education, P. Bag 1020, Bindura, Zimbabwe. 2Bonn-Rhein-Sieg University of Applied Sciences, Inorganic Chemistry and Materials Analysis, von Liebig Str. 20, 53359

Rheinbach, Germany.

Received 3 January, 2015; Accepted 12 February, 2015

Sweet sorghum (Sorghum bicolor (L.) moench), a crop that is grown by subsistence farmers in Zimbabwe was used to extract silica gel in order to assess its possible use as a raw material for the production of silica-based products. The gel was prepared from sodium silicate extracted from sweet sorghum bagasse ash by sodium hydroxide leaching. Results show that maximum yield can be obtained at pH 5 and with 3 M sodium concentration. The silica gel prepared at optimum pH 5 had a bulk density of 0.5626 g/cm3 and anestimated porosity of 71.87%. Silica gel aged over 10 h had improved moisture adsorption properties. X-ray fluorescence (XRF) determinations show that the silica content in the ash is 40.1%. Characterization of sweet sorghum ash and silica gels produced at pH 5, 7 and 8.5 by Fourier Transform Infrared spectroscopy gave absorption bands similar to those reported by other researchers.Transmission electron micrographs show that silica prepared under optimum conditions is amorphous and consisted of irregular particles. Sweet sorghum proved to be a potential low cost raw material for the production of silica gel. Key words: Silica gel, sweet sorghum, extraction, ash, bagasse, X-ray powder diffraction (XRD).

INTRODUCTION Silica gel is a non-toxic and inert inorganic polymer composed of amorphous silicon dioxide. It is characterized by a large surface area of up to 800 m2/g. This property and its ability to adsorb a number of compounds has been exploited in a number of ways especially in catalysis and sorbent technology. Silica has also been used as molecular sieves in petroleum refining, air separation and nuclear waste management, as stationary phase material in liquid chromatography and as reinforcement for rubber and plastic (Devi and Dhanalakshmi, 2012; Tongjian et al., 2009).

Silica gel is prepared commercially through acid precipitation from sodium silicate solution. The sodium silicate is prepared through high temperature reaction of quartz with soda ash. The main disadvantage of this approach is that it is energy intensive requiring temperatures of up to 1300°C and the silica product may contain heavy metal impurities making it unsuitable for certain applications where purity is crucial. Spherical silica gel particles can also be prepared through hydrolysis of Stoiber reagents such as tetraethoxysilane and tetramethoxysilane (Sing et al., 2011). These

*Corresponding author. E-mail: [email protected] Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution License 4.0 International License

reagents have been reported to be hazardous and are generally very expensive making them unsuitable for large scale production (Dorcheh and Abbasi, 2008).

A number of researches have shown that silica gel can be extracted from plant waste (Worathanakul et al., 2009; Alayande et al., 2012). Extraction from rice husk ashes has been widely reported (Prasad and Pandey, 2012). Research has so far focused on the preparation of silica gels of various morphology e.g. nano particles, meso, macro and microporous particles (Noushad et al., 2012; Thuadaji and Nuntiya, 2008). Another potential silica source is the sugarcane bagasse ash. Ugheoke and Mamat (2012) reported a new approach of preparing ordered and disordered silica gel particles from bagasse ash. A cation exchange method was used to extract silica from sugarcane.

Sorghum bicolor (L.) moench, better known as sweet sorghum is widely grown in sub-Saharan Africa as a subsistence crop under various climatic conditions. A number of researchers have investigated potential applications of sweet sorghum and these include bio-fuels and bio-ethanol production(Almodares and Hadi, 2009; Srinivasa et al., 2009; Nadir et al., 2009). Sweet sorghum can also be a potential raw material for the production of syrups and jaggery (Nimbkar et al., 2006; Gnansounou et al., 2005). The by-product bagasse can be used for firing plants such as in ethanol production. Just like sugarcane and other plants in the grass family, sorghum bicolor (L.) moenchis a silicon accumulator.Venkata et. al., 2012 found that sweet sorghum has a silica content of 5.48%. When produced in large quantities, sweet sorghum can therefore be a potential source of silica.

This research paper reports a new biobased approach of extracting silica from sweet sorghum bagasse ash. Sweet sorghum is a drought resistant crop that grows under various climatic conditions and growing it does not require special skills. Sweet sorghum bagasse ash is renewable since is derived from plant source. METHODS Materials and reagents Sweet sorghum was harvested from Hippo Valley High School Garden near Chiredzi, in south eastern Zimbabwe. Chemical reagents used in this research were of analytical grade and these were sodium hydroxide (NaOH), hydrochloric acid (HCl) and ammonia (NH3) obtained from Associated Chemical Enterprises (PTY) Ltd, Republic of South Africa. Sampling and sample preparation Mature sweet sorghum (SS) stalks were cut into smaller pieces and dried overnight at 110°C. The dried samples were ignited in a muffle furnace for three hours. The ash content of the whole plant was determined atignition temperatures of 200, 400, 600 and 800°C.

Mupa et al. 13 Preparation of silica gel Silica gel extraction was adapted from methods reported in the literature with minor changes (Gnansounou et al., 2005; Venkata et al., 2012). Five grams of sweet sorghum bagasse ash was leached for 30 min in 100 ml of 1 M HCl solution to remove heavy metals. The leached ash was washed with copious amounts of distilled water. This was followed by refluxing the ash suspended in 100 ml of 3 M NaOH for three hours. The resultant solution which predominantly contained sodium silicate was filtered off to remove any suspended solids. The clear solution was titrated with 3 M HCl until an optimum pH 5 whereby then silica gel would have started precipitating. The gel was allowed to age for 10 h. Silica gel was separated by centrifugation and washed until the aqueous part reacted negative to a chloride test. The experiments were repeated for pH 7 and 9 and aging times of 10, 18 and 48. Characterization of silica gels The bulk density and porosity were determined using reported methods by Kalapathy et al. (2002) and Adam et al. (2011) and calculated as follows:

(1)

Where is the bulk density, M and V the mass and volume of silica gel respectively.

The porosity of the gel was estimated using the following equation:

(2)

Where Si is the bulk density of silica gel and s the specific density of amorphous silica assumed to be 2.0 g/cm3 (Adam et al., 2011).

FTIR spectra of samples were recorded on a Thermo-Fisher Nicolet 600 FTIR instrument.The samples were prepared as KBr pellets. The composition of the ash and purity of extracted silica was determined using an XRF-spectrometer, Oxford X-Supreme 8000 with a tungsten X-ray tube and powder sample holder.The results are shown in Table 1. A BrukerD2 phase XRD instruments equipped with Cu-Kα, powder sample holder, powder sample holder and LynxEye detector in the range 2 theta 10° to 65° was used to record diffractograms of the silica samples and the results are shown in Figure 5. SEM images were recorded on a Jeol JSM 6510 instrument. Samples were prepared on a powder sample holder. RESULTS AND DISCUSSION The effect of temperature on ash content was investigated for the temperature range 200 to 800°C. At temperatures above 600°C, no significant changes in ash content were observed and 600°C was hence selected as the optimum ignition temperature. The effect of ignition temperature on ash content is shown on Figure 1. The average ash content for the whole plant was found to be 5.6%. A similar value 5.48% was reported by other researchers (Alayande et al., 2012).

Results of XRF analysis on the ash and silica prepared under optimum conditions are shown in Table 1. The ash

14 Afr. J. Pure Appl. Chem.

Table 1. Percentagecomposition of sweet sorghum bagasse ash and extracted silica. Component Ash Extracted silica gel SiO2 40.16 96.36 K2O 26.46 0.922 Na2O - 2.146 Al2O3 15.23 0.467 CaO 6.91 - P2O5 5.92 - SO3 0.55 - Fe2O3 0.41 - Cl- 4.25 - MnO 0.03 - TiO2 0.03 -

Figure 1. Effect of ignition temperature on ash content.

was found to have an average silica content of 40.16%. This shows that silica can commercially be extracted from sweet sorghum bagasse ash in processes where it is produced in large quantities as a waste product and adds value to the crop that is a potential raw material for bio-ethanol and syrups.

An XRF spectrum of sweet sorghum ash is shown in Figure 2. A small peak at 1.74 keV is typical of Si-kα and hence confirms the presence of Si in the ash. The spectrum shows that the ash contains fewer heavy metals or very low concentrations of heavy metals that can be leached out prior to extraction.

Silica gel yield was dependent upon the pH and decreased from pH 5 to 8.5. At pH outside this range no meaningful precipitation was observed. The silica gel prepared at optimum pH 5 had a bulk density of 0.5626 g cm3and an estimated porosity of 71.87%.

Water adsorption properties of silica gel prepared at pH 5 but at different aging times were determined. Silica gel that was aged for 10 hours had better moisture adsorption properties than any gel aged over longer periods of time.The results of selected aging times are shown in Table 2. It can be assumed that the decrease in adsorption capacities is due to a decrease in surface area for gels aged over longer periods.

FTIR spectra of sweet sorghum bagasse ash and dried silica gel samples prepared at different pH are shown in Figure 3. The strong absorption bands at 1044 cm-1 was observed in all the samples and was expectedly much stronger in silica gel samples than the ash. The absorption band is associated with asymmetric vibrations of the siloxane, Si-O-Si. The spectra also show absorption bands at about 800 cm-1 which corresponds to a stretching vibration of Si-O-Si. The weak absorption

Mupa et al. 15

Figure 2. XRF spectrum of sweet sorghum ash.

Table 2. Effect of aging time on moisture absorption for silica gel prepared at pH 5.

Aging time (hours) Maximum moisture absorption (%) 10 167.0 18 126.7 48 122.3

Figure 3. FTIR Spectra of sweet sorghum ash, silica gels prepared at pH 5 (HB-A1), pH 7 (HB-A2) and pH 9 (HB-A3).


Figure 4. XRD diffractogram of sweet sorghum silica.

(A) (B)

(C) Figure 5. Micrographs of sweet sorghum silica at (A)100x, (B) 1000x and (C) 4500x magnification.

band at about 480 cm-1 is due to bending vibration of Si-O-Si. These Si-O-Si absorption bands are typical of silica gels and have also been observed by various researchers (Kalapathy et al., 2002; Essien et al., 2012; Nayak and Bera, 2009; Uzma et al., 2008).

An XRD diffractogram of sweet sorghum silica is shown in Figure 4. This shows that silica gel particles werehighly amorphous and the other phase was to sodium salts trapped in the gel. Figure. 5 shows micrographs of sweet sorghum silica at magnification of 100x, 1000x and

4500x. The silica gel consist of irregular particles of non-uniform size ranging from 5 m to more than 100 m. Conclusion Sweet sorghum is an important agricultural crop from which a number of products can be derived. The main advantage of this crop is its ability to grow under different climatic conditions. This research was able to demonstrate that sweet sorghum bagasse ash, a waste product of heat energy generation in the production of bio-ethanol can be used for extraction of silica gel for various applications. Conflict of Interest The authors have not declared any conflict of interest. ACKNOWLEDGEMENT The researchers would like to thank the Research Borad of the Bindura University of Science Education for providing funds under the Research Grant No 123/2013 and the International Office, Bonn-Rhein-Sieg University of Applied Sciences for having facilitated my research visit to the institution. REFERENCES Adam F, Chew TS, Andas J (2011). A simple template-free sol-gel

synthesis of spherical nanosilica from agricultural biomass. J. Sol-Gel Sci. Technol. 59:580-583.

Alayande OS, Dare OE, Ayinde WB, Bamigbose J, Ayedun PA, Osinkolu GA (2012). Development of Ordered and Disordered Macroporous Silica from Bagasse Ash. Afr. J. Pure Appl. Chem. 6(1):10–14.

Almodares A, Hadi MR (2009). Production of Bio-ethanol from Sweet Sorghum: A review. Afr. J. Agric. Res. 4(9):772-780.

Devi PR, Dhanalakshmi KG (2012). Application of Mesoporous silica nanomaterial: An Overview. Int. J. Adv. Life Sci. 4:1-9.

Dorcheh AS, Abbasi MH (2008). Silica aerogel: Synthesis, properties and characterization. J. Mater. Process. Technol.199:10-26.

Essien ER, Olaniyi OA, Adams LA, Shaibu RO (2012). Sol-gel derived porous silica: Economic Synthesis and Characterization. J. Minerals Mater. Charact. Eng.11:976–981.

Gnansounou E, Dauriat A, Wyman CE (2005). Refining sweet sorghum to ethanol and sugar: Economic Trade-offs in the context of North China. Bioresource Technol. 96(6):985–1002.

Kalapathy U, Proctor A, Schultz J (2002). An improved method for the production of silica from rice hull ash. Bioresource Technol. 85:285-289.

Nadir N, Miel M, Karim MI, Yunus RM (2009). Comparison of sweet sorghum and cassava for ethanol production by using Saccharomyces cerevisiae. J. Appl. Sci. 9(17):3068-3073.

Nayak JP, Bera J (2009). Preparation of Silica aerogel by ambient pressure drying process using rice husk ash as raw material. Trns. Ind. Ceram. Soc. 68(2):1-4.

Nimbkar N, Akande NMJM, Rajavanshi AK (2006). Syrup production from sweet Sorghum. National Agricultural Research Institute (NARI).

Mupa et al. 17 Noushad M, Rahman IA, Huein A, Mohamad D, Ismail AR (2012). A

Simple Method for obtaining Spherical Nanosilica from Rice Husk, Int. J. Adv. Sci. Eng. Infor. Technol. 2(2):28–30.

Prasad R, Pandey M (2012). Rice Husk Ash as a renewable source for the production of value added silica gel and its application: An Overview. Bull. Chem. Reaction Eng. Catalysis. 7(1):1-25.

Sing LP, Agrawal SK, Bhattacharyya SK, Sharma U, Ahalawat S (2011). Preparation of silica nanoparticles and its beneficiation role in cementitious materials. Nanomater. Nanotechnol.1:44-51.

Srinivasa RP, Rao SS, Seetharama N, Umakath AV, Reddy S, Reddy BVS, Gowda CLL (2009). Sweet Sorghum for biofuels and strategies for its improvement. Information Bulletin No. 77, Patancheru 502 324, Andra Pradeshi, India: International Crops Research Institute for the Semi-Arid Tropics, 80 pp, ISBN: 978-92-9066-518-2.

Thuadaij N, Nuntiya A (2008). Synthesis and characterization of Nanosilica from Rice Husk Ash Prepared by Precipitation Method, CMU. J. Nat. Sci. Nanotechnol. 7(1):59–65.

Tongjian Z, Yun X, Jiang W, Xin F (2009). Effect of application Rates of Assistant on the Properties of Nano SiO2/NR Composites. J. Wuhan Univ. Technol. Mater. Scie. Ed. 24(3):387–392.

Ugheoke IB, Mamat O (2012). A critical assessment and new research directions of rice husk silica processing methods and properties. Maejo Int. J. Sci. Technol. 6(03):430–448.

Uzma KH, Bangi A, Venkateswara R, Parvathy RA (2008). A new method of sodium silicate-based hydrophobic silica aerogels via ambient-pressure drying. Sci. Technol. Adv. Mater. 9:035006(10 pp).

Venkata SCh, Ramana YR, Nagalashmi D, Jagadeeswara RS (2012). Evaluation of Sweet Sorghum (Sorghum bicolour (L.) moench) bagasse by Chemical, In sacco and In vivo Techniques in Graded Murrah Buffallo. Bulls. J. Veterinary Adv. 2(8):418–423.

Worathanakul P, Payubnop W, Muangpet A (2009). Characterization for post-treatment effect of Bagasse Ash for Silica extraction. World Acad. Sci. Eng. Technol. 32:360–262.

Vol. 9(2), pp. 18-26, February, 2015 DOI: 10.5897/AJPAC2015.0605 Article Number: 560794750935 ISSN 1996 - 0840 Copyright © 2015 Author(s) retain the copyright of this article http://www.academicjournals.org/AJPAC



Multifactorial discriminant analysis of leaf oil of C. odorata L. King and Robinson from Côte d'Ivoire

Esse Leon Wognin1, Affia Florence Brou1, Thierry Acafou Yapi1, Kidjegbo Augustin Touré3,

Tomi Félix2 and Zanahi Felix Tonzibo1*

1Laboratoire de Chimie Organique Biologique, UFR-SSMT, Université ´ Félix Houphouët-Boigny, BPV 34 Abidjan, Côte d’Ivoire.

2Université de Corse-CNRS, UMR 6134 SPE, Equipe Chimie ET Biomasse Route des Sanguinaires, 20000 Ajaccio, France.

3Laboratoire des Mathématiques et des Nouvelles Technologies de l’Information, Institut National Polytechnique Félix Houphouët Boigny BP 1093 Yamoussoukro, Côte d’Ivoire.

Received 5 January, 2015; Accepted 27 January, 2015

The chemical composition of 71 essential-oil samples isolated from the leaves of Chromolaena odorata harvested in eight Ivoirian sites (Toumodi, Dougba, Yamoussoukro, Zambakro, Toumbokro, Attiegbakro, Tiebissou and Tie-N’Diekro) was investigated by GC-FID, including the determination of retention indices (RIs), and by 13C-NMR analyses. In total, 31 components accounting for 55.5 - 90.2% of the oil composition were identified. The content of the main components varied from sample to sample namely geijerene (3.2 - 26.4%), germacrene D (1 - 28.5%), (E)-β-caryophyllene (8.1 - 18.3%), α-pinene (0.2 - 8.5%) and δ-cadinene (1.4 - 9.5%). Other constituents present at appreciable contents were β-pinene (0 - 8.3%), α-copaene (2.2 - 8.0%), α-humulene (2.1 - 4.7%). The 71 samples were submitted to factorial discriminant analysis using 40 variables (4 physicochemical constants yield 31 chemical constituents and 4 geographical coordinates), which allowed the distinction of eight groups within the oil samples labeled according to the eight sites of harvest in respect of chemical components. Key words: Chromolaena odorata, Asteraceae, essential oil composition, factorial discriminant analysis, physico-chemical constants, geijerene, germacrene D, (E)-β-caryophyllene).

INTRODUCTION The family Asteraceae is a very large cosmopolitan family. It is represented by 13 tribes, 84 genera and over 240 species (Adedeji and Jewoola, 2008; Walter, 1979). The family is highly advanced, easily recognized and with worldwide distribution. The members of the family are largely woody herbs or shrubs, a few are trees and

climbing herbs (Adedeji and Jewoola, 2008: Olorode, 1984). Many plants in the family Asteraceae are economically important as weeds, ornamentals, medicinals and green vegetables (Adedeji and Jewoola, 2008). Important weed species in this family include Chromolaena (C.) odorata Linn.


Traditionally this plant is used in coughs and colds, treatment of skin diseases (Joshi, 2013: Morton, 1981), wound healing and as a local antiseptic agent (Joshi, 2013: Phan et al., 2001). C. odorata is a medicinal plant having diverse pharmacological properties such as antihelmintic (Joshi, 2013; Patel et al., 2010), antigonorrhoeal (Joshi, 2013; Caceres et al., 1995), diuretic (Joshi, 2013; Gopinath et al., 2009), analgesic (Joshi, 2013; Jena and Chakraborty, 2010), anti-inflammatory (Joshi, 2013; Owoyele et al., 2005), antipyretic, antispasmodic (Joshi, 2013; Taiwo et al., 2000), wound healing (Joshi, 2013; Phan et al., 2001) activities and antimicrobial properties (Joshi, 2013; Chomnawang et al., 2005).

The biological activity of extracts of C. odorata has been shown by several studies in the world. Indeed, the extracts of fresh leaves of C. odorata have been used in the treatment of malaria in Ghana and Benin (Inya-Agha et al., 1987; Bedi et al., 2010). Aqueous extracts of this plant presented an anti-microbial activity against gonococcus in Guatemala (Caceres et al., 1995).

Extracts of C. odorata are used in folk medicine of Côte d’Ivoire as cataplasms to stop hemorrhages or as anti-inflammatory drugs against pains (Bedi et al., 2001).

From the point of chemical view, several works concerning the essential oils of leaves of C. odorata have reported a diversity of chemotype, the essential oils from Nigeria are constituted by α-pinene, β-pinene, germacrene D, β-copaen-4α-ol, β-caryophyllene, geijerene and pregeijerene (Owolabi et al., 2010). In the oils from Benin, the main constituents were α-pinene, pregeijerene, geijerene, β-pinene, germacrene-D (Avlessi et al., 2012). The chemotype of oils from India are constituted by pregeijerene, epi-cubebol, cubebol, cis-sabinene hydrate, 10-epi-γ-eudesmol, germacrene-D-4-ol and δ-cadinene (Joshi, 2013).

Our previous works permitted to describe leaves oil of C. odorata from different locations of Côte d’Ivoire (Tonzibo et al., 2007). The oils from Man (west) and Bonoua (south-east) showed the same constituents predominantly by germacrene D, pregeijerene, β-caryophyllene, caryophyllene oxide and geijerene. While, the sample from Guiglo (west) contained germacrene, β-caryophyllene and trans-longipinocarveol as major components. In Divo (centre-west), we had the predominance of β-caryophyllene, germacrene D, pregeijerene and γ-cadinene. The oil from Yamoussokro (centre), contained the combination of β-caryophyllene, germacrene D, pregeijerene and geijerene. The oil from Agboville (south-west) was characterized by pregeijerene, germacrene D and α-pinene. The oil from Abengorou (east) was constituted by germacrene D, β-caryophyllene and pregeijerene (Tonzibo et al., 2007).

In continuation of our work on the characterization of C. odorata leaf oil growing wild in Côte d’Ivoire, due to the chemical polymorphism of this oil, four factors, notably, chemical composition, physicochemical constants, yields

Wognin et al. 19 and geographical coordinates have been selected to improve the description of the leaf oil. Indeed, the aim of this study was to use, for the first time, the factorial discriminant analysis to observe homogeneity or to evidence an eventual chemical variability among the samples due to location effect of C. odorata from “Region du Belier” of Côte d’Ivoire. MATERIALS AND METHODS Plant material Leaves from eight harvest localities were collected from May to November during three years (2007, 2008 and 2009) in Toumodi (9 Samples), Dougba (10 Samples), Yamoussoukro (17 Samples), Zambakro (9 Samples), Toumbokro (9 Samples), Attiegbakro (2 Samples), Tiebissou (6 Samples) and Tie-N’Diekro (9 Samples) from ‘Region du Belier’ in the center of Côte d’Ivoire (Figure 1). Essential-oil isolation Clevenger-type apparatus was used for 3 h. Essential-oil physicochemical constants and yields were estimated according to AFNOR (Association Française de NORmalisation, 1982). GC-FID analysis The oil samples were analyzed with a Perkin Elmer Clarus 500 apparatus equipped with FID and two fused-silica cap. columns (50 m × 0.22 mm i.d. film thickness 0.25 mm), an apolar BP-1 (polydimethylsiloxane) and a polar BP-20 (polyethylene glycol) column.

The oven temperature was programmed rising from 60 to 220° at 2°/min and then held isothermal at 220° for 20 min; injector temp., 250°; detector temp., 250°; carrier gas, He (0.8 ml/min); split ratio, 1/60. retention indices (RIs) were determined relative to the tR of a series of n-alkanes with linear interpolation using the software Target Compounds from Perkin Elmer. 13C-NMR analysis The 13C-NMR spectra were recorded with a Bruker AVANCE 400 Fourier Transform spectrometer operating at 100.63 MHz and equipped with a 5-mm probe, in CDCl3, with all shifts referred to internal Me4Si. The 13C-NMR spectra of the oil samples were recorded with the following parameters: Pulse width, 4 ms (flip angle 45°); acquisition time, 2.7 s for 128 K data table with a spectral width of 25000 Hz (250 ppm); CPD (Composite Pulse Decoupling) mode decoupling; digital resolution, 0.183 Hz/pt. The number of accumulated scans was 2000 to 3000 for each sample, depending on the available amount of oil (when available, 45 - 50 mg of essential oil in 0.5 ml of CDCl3). Identification of components The identification of the individual components was based on the comparison of the GC retention indices (RIs) for the polar and apolar columns, determined rel. to the Retention Time (tR) of a series of n-alkanes with linear interpolation, with those of reference compounds. And for investigated samples, on the comparison of chemical shift values in the 13C-NMR spectra of the essential oils


Figure 1. Sampling locations of Chromolaena odorata leaves from “Region du Belier” (or “Region des Lacs”) in Côte d’Ivoire.

with those of reference spectra compiled in a laboratory-built library, following a computerized method developed in our laboratories, using home-made software. In the investigated samples, individual components were identified by 13C-NMR at contents as low as 0.3 to 0.4%. Quantification of components The relative contents of the oil constituents were expressed as percentage obtained by peak-area normalization without using correction factors. Statistical analysis The factorial discriminant analysis was performed with Xlstat-Pro7.5.2 (Adinsoft, France). RESULTS Essential oil physicochemical constants The leaves of C. odorata growing wild in “Region du Belier” (center) of Côte d’Ivoire were collected during three years in the rainy season, and the essential oils were isolated by hydrodistillation.

Observing Table 1, the oil yields calculated on the fresh weight basis were in the range of 0.05 to 0.35% (w/w). The oil optical rotation were in the range of -1.6°-(-0.6°). The oil refractive index was in the range of 1.499 to 1.509. The oil density was in the range of 0.725 to 0.995. The oil acid index were in the range of 0.24 to 1.8 (Table 1).

These essential oils are levorotatory, lighter than water and less acidic (AFNOR, 1982). These mean values are compared with those found in the literature on C. odorata, those of Benin, recorded in the Table 2, for all designated variables (Noudogbessi et al., 2006). The observation of this table shows similarities but also differences between the values from Côte d’Ivoire and those from Benin. The mean values of the yield, the refractive index and density are similar, and the two medium oils are levorotatory (Table 2). Essential oil composition All the 71 samples were submitted to GC-FID analysis to determine retention indices (RIs) on two columns of different polarity.

Among them, some samples, selected on the basis of their chromatographic profile, were also analyzed by 13C-NMR. Therefore, the oil components were identified by

Wognin et al. 21

Table 1. Physicochemical constants and yield variability of the leaf essential oils from C. odorata. Variables Min Max Average Std-Dev Yield (%) 0.050 0.350 0.205 0.062 Optical rotation (°) -1.800 -0.600 -1.452 0.337 Refractive index 1.499 1.509 1.504 0.002 Density 0.725 0.995 0.906 0.052 Acid index 0.240 1.800 0.682 0.315

Table 2. Physicochemical constants and yield variability of the leaf essential oils from C. odorata of Côte d’Ivoire and Benin. Variable Côte d'Ivoire Benin (*) Yield (%) 0.205±0.062 0.12±0.01 Optical rotation (°) -1.452±0.337 -33.5 Refractive index 1.5040±0.0020 1.5046±0.1100 Density 0.906±0.052 0.910±0.001 Acid index 0.682±0.315 15.62±0.10

*Data provided from literature. comparison of their RIs and 13C-NMR chemical shift values with those of authentic samples compiled in our laboratory-made libraries. In total, 31 components, accounting for 55.5 to 90.2% of the whole oil composition, were identified in the C. odorata leaf oils; among them 11 monoterpenes, 20 sesquiterpenes (Table 3). Although the occurrence of various components was observed in all the investigated samples, their content varied drastically from sample to sample: geijerene (3.2 - 26.4%), germacrene D (1 - 28.5%), (E)-β-caryophyllene (8.1 - 18.3%), α-pinene (0.2 - 18.5%) and δ-cadinene (1.4 - 9.5%). Other constituents present at appreciable contents were β-pinene (0.0 - 8.3%), α-copaene (2.2 - 8.0%), α-humulene (2.1 - 4.7%). The whole essential oil had two fractions: the monoterpene fraction and sesquiterpene fraction.

The monoterpene fraction is characterized by the predominance of α-pinene (0.2 - 8.5%). It is assisted by the β-pinene (0 - 8.3%) and (E)-β-ocimene (0 - 2.4%). The monoterpene fraction is dominated by the hydrogenated compounds (ten out of eleven molecules) and a single oxygenated monoterpene compound (linalool, a tertiary alcohol unsaturated in which the maximum proportion is 0.6%). It is not present in all samples as the α-thujene, p-cymene, (Z)-β-ocimene and γ-terpinene. And when these molecules are present, they are in small proportion. The sesquiterpene fraction is dominated by hydrogenated sesquiterpenes including germacrene D (1.0–28.5%), followed geijerene (3.2 - 26.4%), (E)-β-caryophyllene (8.1 - 18.3%), δ-cadinene (1.4 - 9.5%), α-copaene (2.2 - 8%) and α-humulene (2.1 - 4.7%). There is also caryophyllene oxide (0.1 - 11.6%) the most abundant of all the oxygenated sesquiterpenes.

Statistical analysis The 71 oil compositions were submitted to statistical analysis, that is, factorial discriminant analysis (FDA; Figure 2). The essential oil samples were labeled according to the eight sites of harvest leaves: Toumodi, Dougba, Yamoussoukro, Zambakro, Toumbokro, Attiegbakro, Tiebissou and Tie-N’diekro (Figure 1). FDA of concentration data from 40 variables (4 physico-chemical constants, yield, 31 chemical compounds and 4 geographic coordinates) showed differences of the constituent proportions according to the geographic origin.

Therefore, the mean content and the standard deviation of the major components were calculated for groups Toumodi, Dougba, Yamoussoukro, Zambakro, Toumbokro, Attiegbakro, Tiebissou and Tie-N’diekro. Highlighting the discriminating power of the effect of harvest site leaves The statistical treatment of data on Figure 2, has highlighted the discriminating power of the site effect; discriminating capacity, which existed statistically when samples were described by the chemical constituents (31 variables). When the chemical constituents have been associated with physicochemical constants and yield (36 variables), the discriminant power was visually reinforced. It has been total with the addition of the geographical coordinates (Figure 2). The use of geographic coordinates associated with chemical descriptors created the contraction of each group, to the point of reducing to

22

Afr.

J. P

ure

App

l. C

hem

. Ta

ble

3. C

hem

ical

var

iabi

lity

of th

e le

af e

ssen

tial o

ils is

olat

ed fr

om C

. odo

rata

of R

egio

n du

Bel

ier c

ente

r of C

ôte

d’Iv

oire

.

Com

poun

d na

me

and

cl

assa

RI ap

olar

b ) R

I pola

rc ) C

onte

nt (%

)d ) To

umod

i D

ougb

a Ya

mou

ssou

kro

Zam

bakr

o To

umbo

kro

Atti

egba

kro

Tieb

isso

u Ti

e-N

'Die

kro

(9 s

ampl

es)

(10

sam

ples

) (1

7 sa

mpl

es)

(9 s

ampl

es)

(9 s

ampl

es)

(2 s

ampl

es)

(6 s

ampl

es)

(9 s

ampl

es)

α-th

ujen

e 91

9 10

23

0.04

±0.0

4 0.

04±0

.04

0.2±

0.2

0.04

±0.0

4 0.

07±0

.03

0.04

±0

0.06

±0.0

3 0.

04±0

.04

α-pi

nene

92

7 10

23

7.8±

2.8

7.8±

2.8

5.2±

4.9

6.7±

3.8

9.1±

3.4

7.3±

0.5

9.2±

3.2

8.1±

4.7

Sabi

nene

96

1 11

22

0.9±

0.3

0.9±

0.3

0.7±

0.5

0.9±

0.4

0.99

±0.3

0.

84±0

.04

1.1±

0.3

0.8±

0.4

β-pi

nene

96

7 11

11

3.9±

1.1

3.9±

1.1

3.0±

2.4

4.1±

1.7

4.8±

1.5

3.9±

0.2

5.0±

1.3

4.2±

2.0

Myr

cene

97

6 11

59

0.6±

0.4

0.6±

0.4

0.5±

0.5

0.8±

0.6

0.7±

0.5

0±0

0.8±

0.6

0.6±

0.4

p-cy

men

e 10

08

1271

0.

09±0

.07

0.09

±0.0

7 0.

1±0.

1 0.

1±0.

1 0.

1±0.

2 0.

08±0

.01

0.1±

0.2

0.1±

0.1

Lim

onen

e 10

17

1200

0.

9±0.

1 0.

9±0.

1 0.

7±0.

4 1.

1±0.

2 1.

0±0.

2 0.

82±0

1.

1±0.

2 1.

0±0.

3 (Z

)-β-o

cim

ene

1022

12

32

0.2±

0.1

0.15

±0.1

0.

1±0.

1 0.

2±0.

2 0.

2±0.

1 0.

19±0

.02

0.2±

0.1

0.11

±0.0

8 (E

)-β-o

cim

ene

1032

12

49

1.1±

0.5

1.1±

0.5

1.1±

0.7

1.3±

0.9

1.5±

0.9

1.1±

0.1

1.2±

0.7

0.8±

0.5

γ-te

rpin

ene

1045

12

45

0.02

±0.0

3 0.

02±0

.03

0.02

±0.0

3 0.

04±0

.04

0.05

±0.0

4 0.

05±0

.01

0.04

±0.0

4 0.

03±0

.07

Lina

lol

1079

15

41

0.21

±0.0

9 0.

20±0

.04

0.1±

0.1

0.17

±0.0

6 0.

3±0.

3 0.

30±0

.01

0.2±

0.2

0.32

±0.0

8 G

eije

rene

11

34

1327

16

.9±3

.2

13.6

±3.1

17

.6±6

.2

19.7

±4.3

16

.3±6

.8

10.9

±1.2

15

.5±6

.7

9.9±

4.2

Preg

eije

rene

12

76

1569

1.

9±0.

3 1.

5±0.

5 1.

1±1.

0 2.

0±0.

6 2.

0±1.

4 2.

1±0.

2 1.

7±0.

8 0.

9±0.

5 δ-

elem

ene

1331

14

68

0.38

±0.0

7 0.

43±0

.04

0.37

±0.0

8 0.

45±0

.07

0.36

±0.0

3 0.

35±0

.01

0.4±

0.1

0.34

±0.0

5 α-

copa

ene

1372

14

89

3.9±

1.0

4.9±

0.8

3.9±

1.2

3.8±

1.2

4.4±

1.6

5.0±

0.1

4.3±

1.2

6.0±

1.3

β-el

emen

e 13

84

1588

1.

6±0.

3 1.

9±0.

3 2.

0±0.

5 1.

8±0.

5 1.

6±0.

4 1.

78±0

.01

1.6±

0.4

1.9±

0.4

(E)-β

-car

yoph

ylle

ne

1415

15

96

10.7

±2.1

13

.5±1

.9

12.6

±3.3

11

.1±2

.7

11.2

±2.0

13

.5±0

.4

10.0

±1.5

13

.6±1

.9

selin

a-4(

15),

6-di

ene

1442

16

30

0.45

±0.0

5 0.

52±0

.09

0.4±

0.2

0.5±

0.1

0.43

±0.0

8 0.

57±0

.02

0.51

±0.0

9 0.

39±0

.1

α-hu

mul

ene

1447

16

67

2.8±

0.6

3.3±

0.5

3.1±

0.7

3.1±

0.8

2.8±

0.6

3.4±

0.1

2.9±

0.5

3.4±

0.6

γ-m

uuro

lene

14

67

1686

1.

0±0.

3 1.

3±0.

3 0.

8±0.

4 1.

2±0.

5 1.

1±0.

4 0.

33±0

.01

1.1±

0.4

1.3±

0.3

germ

acre

ne D

14

72

1705

12

.6±1

.8

15±2

16

.9±6

.9

13.5

±3.1

12

.3±4

.8

17.5

±0.4

10

.4±4

.8

11.7

±2.2

Bi

cycl

oger

mac

rene

14

85

1730

1.

5±0.

2 1.

8±0.

3 1.

0±0.

7 1.

7±0.

3 1.

4±0.

5 1.

68±0

.03

1.4±

0.6

1.4±

0.2

α-se

linen

e 14

87

1720

0.

8±0.

3 1.

0±0.

2 0.

5±0.

6 0.

9±0.

3 0.

8±0.

4 0.

930±

0.00

4 1.

0±0.

3 1.

0±0.

3 γ-

cadi

nene

15

00

1753

0.

6±0.

2 0.

8±0.

2 0.

6±0.

3 0.

7±0.

2 0.

67±0

.3

0.74

±0.0

4 0.

7±0.

4 0.

8±0.

2 δ-

cadi

nene

15

09

1753

5.

5±1.

4 7.

12±1

.03

5.5±

1.4

6.3±

1.8

5.1±

1.7

6.7±

0.2

5.1±

1.7

6.3±

0.9

β-el

emol

15

29

2069

0.

6±0.

1 0.

7±0.

3 0.

7±0.

3 1.

1±0.

4 0.

7±0.

2 0.

53±0

.09

1.1±

0.4

0.34

±0.0

9 ca

ryop

hylle

ne o

xyde

15

65

1973

1.

5±0.

8 1.

0±0.

5 1.

4±1.

8 0.

6±0.

4 2.

0±3.

7 2.

3±0.

2 2.

4±3.

6 2.

8±1.

5 γ-

eude

smol

16

12

2158

0.

18±0

.07

0.3±

0.1

0.3±

0.3

0.3±

0.1

0.2±

0.1

0.2±

0.0

0.4±

0.1

0.08

±0.0

5 β-

eude

smol

16

29

2218

0.

23±0

.09

0.3±

0.2

0.2±

0.3

0.3±

0.1

0.3±

0.3

0.1±

0.2

0.6±

0.4

0.18

±0.0

9 α-

cadi

nol

1632

22

21

0.4±

0.2

0.5±

0.1

0.4±

0.3

0.4±

0.2

0.3±

0.1

0.4±

0.1

0.4±

0.2

0.4±

0.2

α-eu

desm

ol

1634

22

10

0.22

±0.0

7 0.

3±0.

2 0.

3±0.

2 0.

4±0.

1 0.

4±0.

3 0.

18±0

.03

0.5±

0.2

0.14

±0.0

4 M

onot

erpe

ne h

ydro

carb

ons

15.4

15

.4

11.5

15

.3

18.5

14

.3

18.6

15

.8

Oxy

gena

ted

mon

oter

pene

s

0.

2 0.

2 0.

1 0.

2 0.

3 0.

3 0.

2 0.

3 Se

squi

terp

ene

hydr

ocar

bons

60

.6

66.6

66

.2

66.5

4 60

.4

65.4

56

.4

58.8

Wog

nin

et a

l.

23

Tabl

e 3.

Con

td.

O

xyge

nate

d se

squi

terp

enes

3.

1 3.

03

3.3

3.08

3.

8 3.

8

5.2

4.0

Tota

l ide

ntifi

ed (m

ean

valu

e)

79.3

85

.2

81.1

85

.08

83.0

83

.8

80.4

78

.9

a ) Ord

er o

f elu

tion

and

cont

ents

det

erm

ined

on

the

apol

ar a

nd p

olar

col

umn

(BP

-1 a

nd B

P-2

0); b )R

I lar:

Ret

entio

n in

dex

dete

rmin

ed o

n th

e ap

olar

col

umn

(BP

-1);

c )RI la

r: R

eten

tion

inde

x de

term

ined

on

the

pola

r col

umn

(BP

-20)

; d )Con

tent

s ar

e gi

ven

as m

ean±

stan

dard

dev

iatio

n.

Fi

gure

2. G

raph

ical

rep

rese

ntat

ion

of s

ampl

es o

f C

. od

orat

a oi

l. Pr

ojec

tion

onto

FD

A di

scrim

inan

t ax

es F

1 an

d F2

for

the

eig

ht a

reas

usi

ng 5

, 31

, 36

or

40 v

aria

bles

. G

raph

ical

hi

ghlig

htin

g of

the

disc

rimin

atin

g po

wer

of t

he s

ite e

ffect

by

incr

easi

ng m

ultid

imen

sion

al d

escr

ipto

rs o

f ess

entia

l oil

sam

ples

.

24 Afr. J. Pure Appl. Chem. a point. This helped raise the apparent confusion that existed between these groups. The eight defined groups were so reconstituted. They are also different and each define a chemical variant: the chemical variability due to site effect. Chemical composition of the different classes Table 3 shows the mean chemical composition of each of the eight chemical variants. Their contents are given as mean±standard deviation.

In the samples of Toumodi (13% of the samples), the chemical composition was dominated by geijerene (16.9±3.2%). The other important components were germacrene D (12.6±1.8%), (E)-β-caryophyllene (10.7±2.1%), α-pinene (7.8±2.8%) and δ-cadinene (5.5±1.4%). α-copaene (3.9±1.0%), β-pinene (3.9±1.1%), α-humulene (2.8±0.6%) and caryophyllene oxide (1.5±0.8%) were present at appreciable contents.

The major components of Dougba (14% of the samples) were germacrene D (15±2%), geijerene (13.6±3.1%), (E)-β-caryophyllene (13.5±1.9%), α-pinene (7.8±2.8%) and δ-cadinene (7.12±1.03%). We have the same compounds as Toumodi in contrast to the majority molecule. α-copaene (4.9±0.8%), β-pinene (3.9±1.1%), α-humulene (3.3±0.5%) and caryophyllene oxide (1.0±0.8%) were present at appreciable contents.

The samples belonging to Yamoussoukro (24% of the samples) were characterized by the predominance of geijerene (17.6±6.2%), germacrene D (16.9±6.9%), (E)-β-caryophyllene (12.6±3,3%), δ-cadinene (5.5±1.4%) and α-pinene (5.2±4.9%). α-copaene (3.9±1.2%), β-pinene (3.0±2.4%), α-humulene (3.1±0.7%) and caryophyllene oxide (1.4±1.8%) were present at appreciable contents. We have the same compounds as Toumodi in contrast to the last molecule. In the samples of Zambakro (13% of the samples), the chemical composition was dominated by geijerene (19.7±4.3%), germacrene D (13.5±3.1%), (E)-β-caryophyllene (11.1±2.7%), α-pinene (6.7±3.8%) and δ-cadinene (6.3±1.8%). β-pinene (4.1±1.7%), α-copaene (3.8±1.2%) and α-humulene (3.1±0.8%) were present at appreciable contents.

The major component of Toumbokro was geijerene (16.3±6.8%), germacrene D (12.3±4.8%), (E)-β-caryophyllene (11.2±2.0%), α-pinene (9.1±3.4%) and δ-cadinene (5.1±1.7%). β-pinene (4.8±1.5%), α-copaene (4.4±1.6%) and α-humulene (2.8±0.6%) were present at appreciable contents.

The samples belonging to Attiegbakro (3% of the samples) were characterized by the predominance of germacrene D (17.5±0,4%), (E)-β-caryophyllene (13.5±0.4%), geijerene (10.9±1.2%), α-pinene (7.3±0.5%) and δ-cadinene (6.7±0.2%). α-copaene (5.0±0.1%) and β-pinene (3.9±0.2%) were present at appreciable contents.

In the samples of Tiebissou (8% of the samples), the chemical composition was dominated by geijerene (15.5±6.7%), germacrene D (10.4±4.8%), (E)-β-caryophyllene (10.0±1.5%), α-pinene (9.2±3.2%) and δ-cadinene (5.1±1.7%). β-pinene (5.0±1.3%), α-copaene (4.3±1.2%) and α-humulene (2.9±0.5%) were present at appreciable contents.

The samples belonging to Tie-N’Diekro (13% of the samples) were characterized by the predominance of (E)-β-caryophyllene (13.6±1.9%), germacrene D (11.7±2.2%), geijerene (9.9±4.2%), α-pinene (8.1±4.7%) and δ-cadinene (6.3±0.9%). α-copaene (6.0±1.3%), β-pinene (4.2±2.0%), α-humulene (3.4±0.6%) and caryophyllene oxide (2.8±1.5%) were present at appreciable contents

Therefore, the mean content and the standard deviation of the major components were calculated for the eight harvest localities and reported in the Table 3 and in Figure 3. The important percentage of geijerene (19.7±4.3%), germacrene D (17.5±0.4%) or (E)-β-caryophyllene (13.6±1.9%) is observed respectively in Zambakro, Attiegbakro and Tie-N’Diekro. DISCUSSION C. odorata essential oil samples analyzed by gas chromatography (GC) and gas chromatography - mass spectrometry (GC/MS), published in the literature have shown the existence of a chemical variability.

This first study using the factorial discriminant analysis, dealing with the combination data of 40 variables (4 physicochemical constants, yield, 31 chemical compounds and 4 geographic coordinates), led to observe the homogeneity or to evidence an eventual chemical variability among the samples due to location effect of C. odorata from “Region du Belier” of Côte d’Ivoire.

The additional use of geographical coordinates, as a descriptor of the essential oil, with the ability to reduce each of the eight groups at a single point is proof of the importance and supremacies knowledge of the origin of the oil sample. However, the mean values of physicochemical constants and yields comparing with those of Benin (Noudogbessi et al., 2006), according to optical rotation and acid index, essential oils from Benin seemed richer in volatile substances and more acidic than those from Côte d'Ivoire (AFNOR, 1982). The differences in the values of the physicochemical constants could be explained by the presence of isomers α- and β- pinenes as major constituents in the essential oil of Benin.

These results are broadly in agreement with those obtained by Smadja (1990) in a similar study on vetyver bourbon (Smadja et al., 1990). The leaf oil of C. odorata from Côte d’Ivoire exhibited a chemical variability with three composition patterns dominated either by geijerene,

Wognin et al. 25

Leave harvest site Figure 3. Mean content of major component of the C. odorata leaf oil samples of the eight harvest sites. Error bar: standard-deviation

germacrene D or (E)-β-caryophyllene respectively. The composition of the oils of the major group (Toumodi, Yamoussoukro, Zambakro, Toumbokro, Tiebissou) (50 samples) was dominated by geijerene. The oils of the second group {Dougba, Attiegbakro} (12 samples) contained germacrene D as the principal compound, while the oils of Tie-N’Diekro (9 samples) were dominated by (E)-β-caryophyllene.

Data from the literature show that the essential oil from the leaves of C. odorata has different chemical composition from one country to another; chemotypes revealed in Region du Belier seem different.

The chemical variability of C. odorata in Côte d’Ivoire is observed, however the oil of Region du Belier is similar to that of Divo geographically close to it. Conclusion Taking account the combination of chemical constituents, physicochemical constants, yield essential oils and geographical coordinates of the harvest sites of C. odorata leaves, the treatment of data by AFD showed clearly the discrimination from different sites in “Region du Belier” of Côte d'Ivoire. Each group became more homogeneous and better separated from other despite of certain similarity of major components. This has led to a chemical polymorphism according to eight leaves harvest

sites: Toumodi, Dougba, Yamoussoukro, Zambakro, Toumbokro, Attiegbakro, Tiebissou and Tie-N'diekro.

The chemical variability found in our samples seemed to be linked to exogenous factors; however, the investigations taking account the geographic factors in the selected localities could confirm clearly the relationship between the samples and their correlation with the main constituents. Conflict of Interest The authors have not declared any conflict of interest. REFERENCES Adedeji O, Jewoola OA (2008). Importance of Leaf Epidermal

Characters in the Asteraceae Family. Not. Bot. Hort. Agrobot. 36(2):7-16.

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Vol. 9(2), pp. 27-32, February, 2015 DOI: 10.5897/AJPAC2015.0606 Article Number: 035BC3050947 ISSN 1996 - 0840 Copyright © 2015 Author(s) retain the copyright of this article http://www.academicjournals.org/AJPAC



A physico-chemical analysis of soil and selected fruits in one rehabilitated mined out site in the Sierra Rutile

environs for the presence of heavy metals: Lead, Copper, Zinc, Chromium and Arsenic

P. O. Egbenda*, F. Thullah and I. Kamara

Department of Chemistry, Fourah Bay College, University of Sierra Leone, Freetown, Sierra Leone.

Received 7 January 2015; Accepted 27 January, 2015

The accumulation of heavy metals in soils especially in mining environments is of increasing concern to researchers in the Agricultural Industry. This is because the metals are biomagnified by plants. Accumulation of heavy and trace metals in plants occur by various sources but soil is considered the major one. Consumption of vegetables and fruits containing heavy metals is one of the main ways in which these elements enter the human body. Once in the body, heavy metals are deposited in bone and fat tissues, overlapping noble minerals and cause an array of diseases. The present study investigated the concentration of heavy metals that is, Cu, Zn, Cr, As and Pb in soil as well as mango (Mangifera indica L.) and cashew (Anacardium occidentale) fruit samples collected from the Mokaba rehabilitated site in the Sierra Rutile environs, to evaluate the possible health risks to human body through food chain transfer. Atomic absorption spectrophotometry was used to estimate the levels of these metals in the fruits and soil. Results showed that the concentrations of Pb and Cu in both soil and fruits are higher than the World health average values. However, Zn and Cr were found to be below the World health average values, whereas As was not detected. Translocation factors (TF) from soil to fruits were calculated from the data on levels of metals in both soil and fruits. The sampled plants showed high translocation factor values (TF > 1in almost all cases) implying that the plants could be labeled as accumulators of pollution. Pearson’s product moment correlation showed a very strong relationship between soil and fruits. It can be concluded that the crops/plants grown in the rehabilitated lands in the Sierra Rutile environs absorb significant levels of some heavy metals from the polluted soil. Key words: Rehabilitated, heavy metals, bioaccumulation, translocation, bioavailability, biomagnified.

INTRODUCTION Heavy metals are significant environmental pollutants, and their toxicity is a problem of increasing concern for

ecological, evolutionary, nutritional and environmental reasons. In addition to being non biodegradable, heavy


28 Afr. J. Pure Appl. Chem. metals have long biological half-lives as well as the potential to accumulate in different body organs, leading to unwanted side effects. One consequence of mineral exploitations is the exposure of metals to the earth’s surface. After several years of operation, the Sierra Rutile mining Company has left behind lakes, mine spoil heaps and sand tailings in many areas of land that were once viable for agricultural activities. The mine spoil heaps and sand tailings are believed to contain various heavy metals in different forms. In order to restore the mined out areas into productive agricultural resources, the Company established land and water rehabilitation programmes. The Mokaba rehabilitated land is one of the mined out sites that was rehabilitated several years before the civil war in Sierra Leone. The site is about half a mile from Mokaba town, a fairly large settlement in the Impere Chiefdom, in Bonthe District, in the Sierra Rutile environs. A variety of economic trees such as mango, guava, cashew, coconut and oil palm have been grown at the Mokaba site. Fruits from these plants are harvested and sold to communities in the Sierra Rutile environs. Notably fruits and vegetables are rich sources of vitamins, minerals and fibers. They also have beneficial anti-oxidative properties. Sadly however, plants can take up heavy metals from contaminated soils through root systems. Consumption of fruits and vegetables contaminated with heavy metals may pose a risk to human health (Sal Jasir et al., 2005). The aim of this study is to carry out a physicochemical analysis of soil and fruits (cashew, Anacardium occidentale and mango, Mangifera indica L.) from the Mokaba site to ascertain soil pollution and to provide guidance for pollution assessment and control in the rehabilitated lands in the Sierra Rutile environs. Heavy metals in soil environment Heavy metal contamination of soil results from anthropogenic processes such as mining, smelting procedures and agriculture as well as natural activities (Aziz et al., 2004). Heavy metals are generally present in agricultural soils at low levels. Due to their cumulative behaviour and toxicity, however, they have a potentially hazardous effect not only on crops but also on human health (Slagle et al., 2004). High concentration of heavy metals in soils is toxic for soil organisms such as bacteria, fungi and higher organisms (Elvingson and Agren, 2004). In soil Lead tightly binds itself to organic soil particles which may decrease its mobility and reduce uptake by plants (Cooper et al., 1999). It has been suggested that the mobility of lead and copper is greater in sandy soils, with apparently very little organic matter, than in organic soils. Chromium exists in two possible oxidation states in soils namely Cr(III) and Cr(VI). The Cr(VI) ions are more toxic than Cr(III) ions. Because of the anionic nature of Cr(VI), its association with soil

surfaces is limited to positively charged exchange sites, the number of which decreases with increasing soil pH. Cr(VI) was found to be highly mobile in alkaline soils (Griffin and Shimp, 1978) and can be reduced to Cr(III) under normal soil pH and redox conditions. Soil organic matter has been identified as the electron donor in this reaction (Bartlett and Kimble, 1976). The presence of sulfate can enhance Cr(VI) adsorption to kaolinite (Zarchara et al., 1988). The parameters that correlated with Cr(VI) immobilization in the soils were free iron oxides, total manganese, and soil pH. On the other hand, soil properties, cation exchange capacity, surface area, and percent clay had no significant influence on Cr(VI) mobility (Rai et al., 1987). Zinc is readily adsorbed by clay minerals, carbonates, or hydrous oxides. The greatest percent of the total Zn in polluted soils and sediments is associated with Fe and Mn oxides. Zinc hydrolyses at pH>7.7 and the hydrolyzed species are strongly adsorbed to soil surfaces. It also forms complexes with inorganic and organic ligands that will affect its adsorption reactions with the soil surface (Hickey and Kittrick, 1984). Acidic and sandy soils with low organic content have a reduced capacity for zinc absorption. Copper may exist in soils in the following forms: water soluble, exchangeable, organically bound, associated with carbonates and hydrous oxides of Fe, Mn and Al, and residual. Copper is adsorbed on the soil, forming an association with organic matter, Fe and Mn oxides, soil minerals, etc., thus making it one of the least mobile of the trace metals (Ioannou et al., 2003). The metal is retained in soils through exchange and specific adsorption mechanisms. Clay mineral exchange phase may serve as a sink for Cu in noncalcareous soils. In calcareous soils, specific adsorption of Cu onto CaCO3 surfaces may control Cu concentration in solution (Cavallaro and McBride, 1978). Arsenic exists as either arsenate, (AsO4

3-), or arsenite, (AsO33-) in the soil

environment. Arsenite is the more toxic form of arsenic. Arsenite compounds are reported to be 4 to 10 times more soluble than arsenate compounds. In the adsorption by kaolinite and montmorillonite, maximum adsorption of As(V) occurs at pH 5. Adsorption of arsenate by aluminum and iron oxides is a maximum at pH 3 to 4 and gradually decrease with increasing pH (Anderson et al., 1976). As(III), is also strongly pH- dependent. It was observed that an increase in sorption of As(III) by kaolinite and montmorillonite occur over a pH range of 3 to 9 (Griffin and Shimp, 1978). The maximum adsorption of As(III) by iron oxide occurred at pH 7. Adsorption of As(III) is rapid and irreversible on some soils. Formation of As(III) also may lead to the volatilization of arsine (AsH3) and methyl- arsines from soils (Woolson, 1977). The loss of organic arsenic compounds from the soil was far greater than for the inorganic source of arsenic. As(III), can be oxidized to As(V) in which manganese oxides act as primary electron acceptor (Oscarson et al., 1983).

Uptake and translocation of heavy metals in fruits Migration of metals in the soil is influenced by physical and chemical characteristics of each specific metal and by several environmental factors; the most significant appear to be: soil type, total organic content, redox potential, and pH (Murray et al., 1999). The fate of heavy metals in polluted soils is a subject of study because of the direct potential toxicity to biota and the indirect threat to human health via the contamination of groundwater and accumulation in food crops (Martinez and Motto, 2000). Heavy metal pollution of soil enhances plant uptake causing accumulation in plant tissues and eventual phytotoxicity and change of plant community (Gimmler et al., 2002). Compounds accumulate in living organisms any time they are taken up faster than they are broken down (metabolized) or excreted (O’Brien, 2008).The soil to plant transfer factor known as Translocation Factor (TF) is one of the important parameters used to estimate the possible accumulation of toxic elements, especially radionuclides through food ingestion (El-Ghawi et al., 2005). Translocation Factor (TF) is the transfer capability of heavy metals from soil to various parts of the plant. TF of heavy metals depends upon bioavailability of metals, which in turn depends upon its concentration in the soil, their chemical forms, difference in uptake capability and growth rate of different plant species (FAO/WHO, 2011). TF > 1, indicates that the plant translocate metals effectively from root to the shoot (Baker and Brooks, 1989). Most plants translocate inorganic and nutrient constituents from roots to leaves (Roselli et. al., 2003). Higher values of TF also suggest poor retention of metals in soil and/or more translocation in plants (indicating stronger accumulation of the respective metal by that fruit). The higher uptake of heavy metals in fruits may be due to higher transpiration rate to maintain the growth and moisture content of plants (Gildon and Tinker, 1981). A related study reported highest translocation factor for heavy metals through leafy vegetables. The TF does not present the risk associated with the metal in any form. The degree of toxicity of heavy metal to human beings depends upon their daily intake (Sridhara et al., 2008). Several studies have indicated that crops grown on soils contaminated with heavy metals have higher concentrations of heavy metals than those grown on uncontaminated soil (Nabulo, 2006). The translocation factor (TF) of heavy metals can be calculated as follows:

Metal concentration in fruit (shoot)

Metal concentration in soil in which fruit was grown (root) TF =

MATERIALS AND METHODS Sampling Soil samples were collected from the Mokaba site at different

Egbenda et al. 29 locations (A, B, C, D and E) and at variable depths ranging from 0 to 10 cm from the surface. Fresh ripe cashew fruits (A. occidentale) and mango fruits (M. indica L.) were collected from the trees at locations indicated in Figure 1 (not to scale). Sample preparation The soil samples were air‒dried in an oven at 50°C temperature until constant weight. The cashew (A. occidentale) and mango (M. indica L.) fruits were washed thoroughly with distilled water, peeled, sliced and then dried in oven at 60°C. The dried fruit and soil samples were powdered in an agate mortar, homogenized and sieved in a 60 micron sieve. The powdered samples were stored in clean stoppered sterile bottles (Djingova et al., 1993; Keane et al., 2001). Heavy metal analysis Five grams of soil sample was placed in a 250 ml beaker followed by addition of 50 ml distilled water and 60 ml aqua regia (HNO3: HCl;3:1). Boiling chips were added and the mixture digested on a hot plate at 100°C for one hour and at125°C for further 15 min to concentrate to 5 ml volume. The 5 ml volume concentrate wascooled, 1 ml 30% H2O2 added and then heated for further 10 min. The hot solution was again cooled and then treated with 3 ml 30% H2O2 before heating for another 10 min. 50 ml distilled water and 25 ml HCl was added to the solution and then heated to boiling. The resulting hot solution was cooled, filtered and then diluted to 250 ml with distilled water.

10 ml HNO3 was added to 2 g of the powdered fruit sample in a 100 ml beaker and the mixture heated at 40°C for 15 min. To the cooled digest 5 ml concentrated HNO3 was added and then heated for another 30 min at 40°C to concentrate to 5 ml volume. 5 ml H2O was added to the solution followed by 3 ml 30% H2O2. The beaker was covered and the solution gently heated until vigorous effervescence occurred. 1 ml 30% H2O2 was added to the solution followed by gentle heating until effervescence subsided. 5 ml concentrated HCl was added followed by 10 ml distilled water and the resulting solution heated for 15 min, cooled, filtered using ash less filter paper and then diluted to 60 ml with distilled water.

The concentrations of Pb, Cr, Cu, Zn and As were determined in soil, cashew and mango fruit samples using the PG-800 Atomic Absorption Spectrophotometer. The working standards were prepared by diluting concentrated stock solution of 1000 mg/L for Cu, Cr and Zn and 1000 μg/L for As, and Pb in deionized water. The matrix modifiers NH3H2PO4 and Mg (NO3)2 were used. RESULTS AND DISCUSSION The results for pH and conductivity measurements of the soil samples in five different locations are given in Table 1. The WHO and E.U recommended limits for pH and conductivity in soil are given in Table 2. The pH of the soil at the Mokaba site has a range of 4.90 to 5.24 and mean value of 5.11 (Table 1).This implies that the soil is acidic compared to the pH values of WHO (6-5) and EU (6-8). The pH has a great impact on bioavailability in the soil. At high pH metals tend to form insoluble metal mineral phosphates and carbonates, whereas at low pH they tend to be found as free ionic species or as soluble organometals and are more bio available (Sandrin and Hoffman, 2007; Twiss et al., 2001; Rensing and Maier,


Mokaba town

Main Road

Road

70ft

A15 cm depth

10ft from road

predominantly cashew nut pickeddried

B 20cm depth

20ft

20ft60ft

D 20cm depth

acacia tree80ft

70ft20cmC

swampycashew

E10cm

mango tree

cashew

cashew

N

Figure 1. Sketched map of the samples collection sites.

Table 1. pH and conductivity in the soil. Location pH Conductivity (S/cm) A 4.90 0.02 B 5.24 0.01 C 5.12 0.01 D 5.14 0.01 E 5.15 0.01 Mean value 5.11 0.01

Table 2. Recommended values for pH and Conductivity by WHO and EU in soil.

Parameter WHO EU pH 6-5 6-8 Conductivity (μS/cm) 250 250

2003; Naidu et al., 1997). The measured conductivity values of the soil samples (0.01-0.02 S/cm) testify the presence of trace metal ions or ionizable materials in the soil.

Table 3 gives the concentrations of the metals in the soil and fruit samples (in mg/L). The WHO recommended limits of the investigated metals Pb, Zn, Cr, Cu and Asin mg/L are given in Table 4. Both mango and cashew fruit samples showed higher average metal content for the

elements Cu, Pb and Zn (Table 3) and relatively lower values for Cr. This is because Cr accumulates mainly in roots followed by stems and leaves and that only small amount of the metal is translocated to leaves (Tiwar et al., 2009; Huffmann and All away, 1973). Also the mango fruit tend to bio accumulate Pb, Zn and Cu more whereas cashew fruit tend to bio accumulate Zn and Cu more. The mango fruit bio accumulates Pb metal more than the cashew fruit whereas the cashew fruit bio accumulates

Egbenda et al. 31

Table 3. Average concentrations of metals in soil and fruits (in mg/L). Element Soil (mg/L) Mango (mg/L) Cashew (mg/L) Pb 0.053 0.355 0.055 Zn 0.222 0.396 0.685 Cr 0.027 0.019 0.025 Cu 0.817 0.847 1.140 As ND ND ND

Table 4. WHO recommended permissible limits of the metals (Pb, Zn, Cr, Cu and As in mg/L in fruits. Metal Permissible limit Pb 0.050 Zn <5.000 Cr <0.050 Cu <1.000 As 0.010

Zn, Cu and Cr more than the mango fruit. Cu has the highest concentration in the soil (0.817 mg/L) and fruits (0.847 mg/L in mango and 1.140 mg/L in cashew). This could be due to the presence of different forms of copper in the soil e.g. water soluble, exchangeable, organically bound, associated with carbonates and hydrous oxides of Fe, Mn, Al, and residual (Ioannou et al., 2003). Moreover the levels of Zn and Cu were found to be higher in the cashew fruit (younger trees) than in the mango fruit (older trees). This suggests that younger plants bio accumulate heavy metals more than older ones. In both soil and fruits the average concentrations of heavy metals follow the order: Cu > Zn >Pb> Cr. Arsenic was not detected in any of the fruit samples.

Table 3 also shows concentration of metals to be higher in fruits than in the soil. This could be due to loss in soil by leaching and uptake by the plant growing there including the plants under investigation. The sampled plants showed high translocation factor values (TF > 1in almost all cases) implying that the plants could be labeled as accumulators of pollution (Table 5). Trends in translocation factor are: for Mango (M. indica L.), Pb>Zn>Cu>Cr, and for Cashew (A. occidentale), Zn > Cu > Pb > Cr. The high TF values could be attributed to low retention rate of the metals in the soil. Pearson’s product moment correlation reveal a very strong correlation between soil and mango (Rm

2 = 0.925) and between soil and cashew (Rc2 = 0.933)

fruits. This implies that the source of the extra metal concentrated by the fruits is most likely the soil. Conclusion This study revealed that the soil in the rehabilitated

Table 5. Translocation factors ( TF) in mango and cashew fruit samples.

Metals TF in mango fruit TF in cashew fruit Pb 6.698 1.038 Zn 1.784 3.086 Cr 0.704 0.926 Cu 1.037 1.395

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