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Use of Metals and Anion Species with Chemometrics Tools for Classification of Unprocessed and Processed Coconut Waters


Coconut water is a natural isotonic, nutritive, and low-caloric drink. Preservation process is necessary to increase its shelf life outside the fruit and to improve commercialization. However, the influence of the conservation processes, antioxidant addition, maturation time, and soil where coconut is cultivated on the chemical composition of coconut water has had few arguments and studies. For these reasons, an evaluation of coconut waters (unprocessed and processed) was carried out using Ca, Cu, Fe, K, Mg, Mn, Na, Zn, chloride, sulfate, phosphate, malate, and ascorbate concentrations and chemometric tools. The quantitative determinations were performed by electrothermal atomic absorption spectrometry, inductively coupled plasma optical emission spectrometry, and capillary electrophoresis. The results showed that Ca, K, and Zn concentrations did not present significant alterations between the samples. The ranges of Cu, Fe, Mg, Mn, PO 3−4 , and SO 2−4 concentrations were as follows: Cu (3.1–120 µg L−1), Fe (60–330 µg L−1), Mg (48–123 mg L−1), Mn (0.4–4.0 mg L−1), PO 3−4 (55–212 mg L−1), and SO 2−4 (19–136 mg L−1). The principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied to differentiate unprocessed and processed samples. Multivariated analysis (PCA and HCA) were compared through one-way analysis of variance with Tukey–Kramer multiple comparisons test, and p values less than 0.05 were considered to be significant.


Coconut water is a nutritious, refreshing, isotonic, and low-caloric beverage. It has been largely consumed and has earned popularity among the soft drink world market. This beverage has some medicinal properties, such as treatment for gastric problems, inhibition of vomit caused by cholera, for oral and intravenous rehydration, treatment of dysentery, and for infant feeding (Campos et al. 1996; Coelho et al. 2002; Santoso et al. 1996). In the biotechnology, it has been considered as a potential conservative of animals’ semen. Finally, for the nutritional issue, it is a protein and amino acids complement due to its similarity with milk (Campbell-Falck et al. 2000; Pummer et al. 2001).

The complex chemical composition of green coconut water includes carbohydrates (fructose and glucose); proteins; lipids; vitamins (Jackson et al. 2004; Jayalekshmy et al. 1986; Jayalekshmy et al. 1988); minerals, such as Al, Cu, Ca, Cu, Fe, K, Mg, Mn, Na, Se, and Zn (Aleixo et al. 2000; Chumbimuni-Torres and Kubota 2006; de Sousa et al. 2005a, b, 2006; Naozuka and Oliveira 2006; Oliveira et al. 2005; Richter et al. 2005; Santoso et al. 1996); and chloride, phosphate, sulfate, malate, and ascorbate (Richter et al. 2005).

In unprocessed coconut water, chloride, malate, and potassium are the major ions. In general, the presence of high concentrations of sulfate and sodium in processed coconut waters can be related to the sodium bisulfite addition as a preserving additive, whose oxidation is a source of sulfate. Additionally, citrate, ascorbate, and benzoate are also present probably due to addition of citric and benzoic acids as preserving additives and ascorbic acid as antioxidant (Richter et al. 2005).

The element concentrations in coconut water result from the soil characteristics (pH, mineral rocks, fertilizers, insecticides, pesticides, and fungicides), plant physiology, water source for irrigation, climatic conditions, and maturation time (Fennema 1996). Alterations in the coconut water composition are usually derived from the conservation processes. However, these treatments are imperative due to the commercial demand to increase the shelf life of coconut water. Conservation processes, such as pasteurization, freezing, and ultrafiltration, have been used to increase the shelf life of coconut water, since they can promote the enzymatic inactivation and stabilization of microorganisms (Buldini et al. 2002; Ibanez and Cifuentes 2001; Matzke 1998).

In view of the current discussions and scarce literature about the coconut water pattern recognition, the determination of chemical composition is important mainly to identify its history and detecting possible adulteration or contamination during conservation process (Carvalho et al. 2003; Fernandez-Caceres et al. 2001; Iyengar and McEvily 1992; Robards and Antolovich 1995; Simpkins et al. 2000).

Inductively coupled plasma optical emission spectrometry (ICP OES) is one of the most used technique to determine elements (Al, B, Ca, Cd, Cu, Fe, K, Na, Mg, Mn, P, S, Se, and Zn) in unprocessed and processed (or industrialized) coconut waters (Aleixo et al. 2000; de Sousa et al. 2005a, b, 2006; Santoso et al. 1996). The determination of some elements by ICP OES was used to compare unprocessed and processed coconut water. This evaluation was carried out by chemometrics tools (principal component analysis (PCA) and hierarchical cluster analysis (HCA)). The results indicated that samples were statistically different when the concentrations of all the elements were considered simultaneously (de Sousa et al. 2006). However, it is interesting to point out that Al, Pb, Cd, Se, and Zn were not detected in these samples. In addition, Cu and Fe were only detected in unprocessed coconut water. These elements have been determined in low concentrations by graphite furnace atomic absorption spectrometry (GF AAS) due to the selectivity and sensitivity (Aleixo et al. 2000; Naozuka and Oliveira 2006; Oliveira et al. 2005).

Taking all the information above, the aim of this work is to evaluate coconut waters using not only the metals information but also the anions concentrations for classification of the unprocessed and processed coconut waters. According to our knowledge, this is the first attempt of using concentrations of metals and anions species to classify coconut water. For the classification chemometric tools, such as PCA and HCA were used. Additionally, one-way analysis of variance (ANOVA) + Tukey–Kramer multiple comparisons test were evaluated to compare the results.

Materials and Methods

Analytical reagent grade chemicals and high-purity deionized water, obtained by Milli-Q water purification system (Millipore, Belford, MA, USA), were used to prepare reference solutions. Stock standard solutions containing 1,000 mg L−1 of Ca, Cu, Fe, K, Mg, Mn, Na, and Zn from Tritisol (Merck, Dermstadt, Germany) and 2,000 mg L−1 Cl, SO 2−4 , PO 3−4 , malate [H4C4O 2−5 ], benzoate [C6H5O 2 ], citrate [H5C6O 3−6 ], and ascorbate [H9C6O 6 ] (Merck, Darmstadt, Germany) were used. Nitric acid and H2O2 (30% w/v) from Merck were used for sample digestion. Nitric acid was distilled in quartz sub-boiling stills (Marconi, Piracicaba, SP, Brazil). Triton X-100 (Merck) was used for sample preparation. For samples analysis by capillary electrophoresis, the running buffers were 2-[Nmorpholino]ethanosulfonic acid (MES), l-histidine (HIS), N-cetyl-N-N-N-trimethylammonium hydroxide (CTAH), and lactic acid (Merck), and the internal standards were tartaric acid and sodium nitrate (Merck).

Unprocessed and processed (pasteurized) samples were considered. Ten unprocessed coconut water (N) samples were obtained from the mixture of 90 units of coconuts. Twenty-three processed (pasteurized) coconut water samples were analyzed. Five different trade marks (C1–C5) sold in the supermarket of Sao Paulo were considered for analysis. Different quantifies of each mark were bought: three for C1, five for C2, five for C3, five for C4, and five for C5.

The determination of Ca, K, Mg, Mn, Na, and Zn was done by using a Spectro Cirosccd ICP optical emission spectrometer (Spectro Analytical Instruments, Kleve, Germany) with axial plasma viewing. Appropriate dilution of the samples was performed before analysis.

For Cu and Fe determination, the following were used: a ZEEnit® 60 model atomic absorption spectrometer (AnalytikjenaAG, Jena, Germany) equipped with hollow cathode lamp, variable Zeeman-effect background corrector, and pyrolytically coated transverse-heated graphite atomizer. For Cu and Fe determination, 1,250 μL of coconut water was mixed with 250 μL of diluent solution (0.6% m/v Triton X-100 + 0.6% v/v HNO3), and 100 μL of the sample was mixed with 1,400 μL of 0.1% v/v HNO3, respectively. In the case of Cu determination, a volume of 10 μL of matrix modifier (1% v/v HNO3 + 15% v/v H2O2) was co-injected into the graphite atomizer with 15 μL of the analytical solutions or sample. The detailed procedure was described in the literature (Naozuka and Oliveira 2006).

The instrumental conditions for Ca, K, Mg, Mn, Na, and Zn determination by ICP OES and Cu and Fe determination by GF AAS are shown in Tables 1, 2, and 3.

Table 1 ICP OES parameters for the determination of Ca, K, Mg, Mn, Na, and Zn
Table 2 GF AAS parameters for the determination of Cu and Fe
Table 3 Heating program for Cu and Fe determination

Homemade capillary electrophoresis equipment with contactless conductivity detector was used for chloride, sulfate, phosphate, malate, benzoate, citrate, and ascorbate determination. This detector was inserted at 9 cm from the end of the capillary. The injection of the samples was accomplished by gravity from a height of 90 mm for 30 s into a 75-µm inner diameter and 50-cm-long fused-silica capillary (Agilent Technologies, São Paulo, Brazil). The applied voltage was 28 kV (Silva and Lago 1998).

Appropriate dilution of the samples was performed before injection: chloride and malate (50 times), sulfate (10 times), and phosphate and ascorbate (five times). The running buffer used for chloride, sulfate, and malate determination was a mixture of 20 mmol L−1 of MES/HIS and 0.16 mmol L−1 of CTAH (pH = 6.0), and for phosphate and ascorbate determination, 30 mmol L−1 of lactic acid, 7.5 mmol L−1 of HIS, and 0.3 mmol L−1 of CTAH solution (pH = 3.7) were used. Different running buffers were necessary due to elution time of the interest species. Nitrate was used as internal standard for chloride, sulfate, and malate and tartaric acid for phosphate and ascorbate determinations (Richter et al. 2005).

Additions of 20 mg L−1 Ca, 10 µg L−1 Cu, 20 µg L−1 Fe, 200 mg L−1 K, 20 mg L−1 Mg, 1.0 mg L−1 Mn, 20 mg L−1 Na, 0.10 mg L−1 Zn, 1,000 mg L−1 chloride and malate, 10 mg L−1 sulfate, 100 mg L−1 phosphate, and 50 mg L−1 ascorbate and recovery tests were used to the check accuracy of the analytical procedures.

In the chemometrics methods, a matrix of 33 × 13 (23 × 13 for the processed samples classification) was constructed using concentration results of cations and anions in triplicate. Principal component analyses and HCA calculations were performed using Statistica 8.0 (StatSoft Inc. Tulsa, OK, USA) with all data preprocessing (mean centering and autoscaling). In PCA, the multivariated data were decomposed in scores and loadings matrices. Thus, graphics bi- and tridimensional of score and loading represent the distribution of samples and intervariable relationship. The cross-validation method for PCA was based on the predicted error sum of squares criteria (Valle et al. 1999). Finally, similarities between unprocessed and processed coconut water were verified using HCA, considering the concentrations (variables) of all cations and anions. The measurement of the similarity was the squared Euclidean distance. To compare the multivariate results with univariated analysis, the one-way ANOVA + Tukey–Kramer multiple comparisons test were performed using Origin 8.0 (OriginLab Corporation, Northampton, MA, USA). One-way ANOVA was initially used to determine significant differences among the samples considering metal and anion contents. However, when more than two means were compared, only the ANOVA test could not explain if the means are significantly different from each other. For this reason, a Tukey–Kramer multiple comparisons test was used. For the one-way ANOVA and Tukey–Kramer, the raw data were used.

Results and Discussions

The results of the addition and recovery tests for the elements and anions determined by ICP OES, GF AAS, and capillary electrophoresis are shown in Table 4. The good results attest to the possibility to determine these elements after only a dilution of the coconut water.

Table 4 Results of recovery in the unprocessed (cations and anions) and processed (anions) samples

The calculated detection limits based on the standard deviation of 10 measurements of the blank solution, according to 3 S blk/m, where S corresponds to the blank measurement standard deviation and m is the calibration curve slope, were as follows: Ca (0.002 mg L−1), K (0.014 mg L−1), Mg (0.001 mg L−1), Mn (0.0004 mg L−1), Na (0.017 mg L−1), Zn (0.006 mg L−1), Cu (0.7 µg L−1), Fe (2.0 µg L−1), chloride (0.09 mg L−1), sulfate (0.12 mg L−1), phosphate (0.50 mg L−1), malate (0.97 mg L−1), and ascorbate (1.2 mg L−1).

The concentrations of metals and anions in the unprocessed and processed coconut waters are shown in Table 5. The found concentrations for Ca, K, Zn, and chloride did not show significant differences at 95% of confidence level between unprocessed and processed coconut waters, except for the sample C2, whose concentrations of K and chloride were higher than the others. On the other hand, concentrations of Fe, Mg, Mn, sulfate, and phosphate presented significant bias between the samples. These variations can be related to pasteurization process and addition of antioxidant conservatives. Additionally, as elements concentrations in coconuts water are also dependent on the geographic origins, the variations observed could be related to the soil composition (Fernandez-Caceres et al. 2001). Besides the effect of the soil, the use of fertilizers can alter the metal content in plants (Fernandez-Caceres et al. 2001), as it was observed for four agricultural crops (tomato seeds, lettuce seeds, radish seeds, and strawberry plants) in which the soil was treated with selenium compounds (Carvalho et al. 2003).

Table 5 Cations and anions concentrations in unprocessed and processed coconut water

The maturation time of coconut water can influence the malate concentration. In coconut water, malate can be also found in the form of malic acid, and alterations in its concentration can be associated to the changes in the pH (Jackson et al. 2004).

In processed coconut waters, Na and sulfate concentration was higher than in unprocessed samples. This can result from the addition of sodium metabisulfite as antioxidant. In foods, the oxidation reaction from sulfite to sulfate occurs in the presence of the oxygen gas, water, and enzymes, such as sulfite oxidase. Sulfites compounds are widely used as an additive in many foods, for examples, fruits, vegetables, and beverages, to prevent oxidation and bacterial growth and to control enzymatic reaction during the production and storage. The sulfiting agents are generally used due to the nucleophilicity of the sulfite ion that may react with quinines, which are the precursors of enzymatic browning formed when mono- or diphenols react with polyphenol oxidase, in the presence of oxygen gas. Therefore, the oxidation reaction of phenols to quinines, followed by spontaneous polymerization of quinines to melanin, is prevented (Jayalekshmy et al. 1986).

It is important to point out the necessity of controlling sodium metabisulfite concentration in foods, mainly due to the harmful effects toward hypersensitive people. Besides the addition of sodium metabisulfite, other additives can be added in the commercial samples, for example ascorbate. The ascorbate was found in only one commercial coconut water (C1).

The application of chemometrics analysis using the metals and anions concentrations obtained by ICP OES, GF AAS, and capillary eletrophoresis allow an evaluation of similarities and differences between the unprocessed and processed coconut water samples.

The dendrogram (Fig. 1) obtained by HCA showed two different clusters distinguishing the unprocessed samples (N) of the processed coconut waters (C1–C5). For PCA, a model was obtained using five principal components (PCs), which explained up to 92.43% of variance between the unprocessed and processed samples. The PC1, PC2, and PC3 are responsible by 43.53%, 22.43%, and 11.88%, respectively.

Fig. 1

Dendrogram obtained for unprocessed (N) and processed (C 1 –C 5 ) coconut waters by HCA

The differentiation between the samples or the variables can be seen in the scores and the loadings graphics (Fig. 2a, b, respectively). In Fig. 2a, two clusters were obtained: one at negative scores of PC1, including the unprocessed samples, and other formed by the group of processed samples. This result was also verified by HCA (Fig. 1). According to some variables (Ca, Mn, phosphate, and malate), it is possible to separate unprocessed and processed samples (Fig. 2b). The macrocomponents (Fe, Mg, Na, K, chloride, and sulfate) and microcompoments (Cu, Zn, and ascorbate) are found, mainly, in the processed coconut waters. The presence of the ascorbate is related to the addition of antioxidants to increase the lifetime of the coconut water.

Fig. 2

PC1 versus PC2 scores (a) and loadings (b) for unprocessed (N) and processed (C 1 –C 5 ) coconut waters

The processed samples were compared using a PCA model with five PCs explaining a variance of 96.81% between these coconut waters. A score plot is shown in Fig. 3a and in Fig. 3b a loading plot. The PC1 and PC2 could separate the samples C1, C2, and C3. In Fig. 3b, Na, ascorbate, malate, and K are the responsible variables identifying the C1, while C2 is characterized by Ca, phosphate, sulfate, and Mg. In the case of C3 (Fig. 3b), Zn, Mn, and Cl are variables that separate these samples from other commercial coconut waters.

Fig. 3

PC1 versus PC2 scores (a) and loadings (b) for processed coconut waters

To compare the multivariated analysis, the one-way ANOVA + Tukey–Kramer multiple comparisons test were evaluated. Tables 6 and 7 show the one-way ANOVA result and Tukey–Kramer multiple comparisons test. From this comparison, it is possible to see that all quantified species are important to discriminate the samples (F value calculated is higher than F critic value; Table 6). However, some of metals determined, such as K, Ca, and Zn, have an F value near to the F critic. Taking these into account, these metals could not be useful in discriminating all the samples. This supposition is confirmed using the Tukey–Kramer test. Only C2 and N samples could be discriminated by the potassium. Zinc and Ca could be used to discriminate only five or six samples. From this analysis, the species responsible for the classification of C1 from the other commercial samples are Fe, sulfate, Na, malate, phosphate, and ascorbate (ascorbate could be refused, because it was only detected in C1 sample); for C2, samples are chlorine, phosphate, malate, Mg, Mn, and sulfate; and for classification of C3 from C4 and C5, samples are Cu, malate, Mn, Na, and Zn. Sulfate, malate, and phosphate were detected in all samples and consequently are responsible for the discrimination of the same samples more than the others. The detected species that only discriminated C1, C2, and C3 samples are as follows: C1, Na and Fe; C2, Cl and Mg; C3, Zn, Cu, and Mn, corroborating with the multivariated analysis.

Table 6 One-way ANOVA test performed to the ascorbate, Ca, chlorine, Cu, phosphate, K, maleate, Mg, Mn, Na, sulfate, Zn, and Fe content for the six samples analyzed (N, C1, C2, C3, C4, and C5)
Table 7 Application of Tukey–Kramer multiple comparisons test


The metals and anions determinations and chemometrics tools (PCA and HCA) allowed a comparison between unprocessed and processed coconut water and an evaluation of possible adulterations when product is submitted to conservation processes. The presence of sulfate, phosphate, and malate was essential to separate unprocessed and processed samples. Among the processed samples, Na, malate, and K are the variables responsible in identifying sample C1; Ca, Cl, phosphate, sulfate, and Mg are the variables responsible in identifying sample C2; and Zn, Cu, and Mn are the variables responsible in identifying sample C3 from the other commercial samples (C4 and C5). One-way ANOVA test + Tukey–Kramer multiple comparisons test could be useful to corroborate the multivariated analysis.


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The authors are grateful to the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for financial support and PVO for sponsorship.

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Correspondence to Pedro V. Oliveira.

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Naozuka, J., da Veiga, M.A.M.S., Richter, E.M. et al. Use of Metals and Anion Species with Chemometrics Tools for Classification of Unprocessed and Processed Coconut Waters. Food Anal. Methods 4, 49–56 (2011).

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  • Coconut Water
  • GF AAS
  • Capillary Electrophoresis
  • Chemometric