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Homogeneity study of a corn flour laboratory reference material candidate for inorganic analysis
Food Chemistry 178 (2015) 287–291
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Analytical Methods
Homogeneity study of a corn flour laboratory reference material candidate for inorganic analysis Ana Maria Pinto dos Santos a,⇑, Liz Oliveira dos Santos a, Geovani Cardoso Brandao a, Danilo Junqueira Leao a, Alfredo Victor Bellido Bernedo b, Ricardo Tadeu Lopes c, Valfredo Azevedo Lemos d a
Universidade Federal da Bahia, Instituto de Química, Grupo de Pesquisa em Química e Quimiometria, Campus Ondina, 41170-115 Salvador, Bahia, Brazil Universidade Federal Fluminense, Centro de Estudos Gerais, Instituto de Química, Niterói, Rio de Janeiro, Brazil Universidade Federal do Rio de Janeiro, Instituto Alberto Luiz Coimbra de Pós Graduação e Pesquisa de Engenharia, Programa de Engenharia Nuclear, Rio de Janeiro, Brazil d Universidade Estadual do Sudoeste da Bahia, Laboratório de Química Analítica (LQA), Campus de Jequié, 45206-190 Jequié, Bahia, Brazil b c
a r t i c l e
i n f o
Article history: Received 14 June 2014 Received in revised form 30 August 2014 Accepted 3 January 2015 Available online 19 January 2015 Keywords: Corn flour Reference material candidate Homogeneity study Analysis of variance Principal component analysis
a b s t r a c t In this work, a homogeneity study of a corn flour reference material candidate for inorganic analysis is presented. Seven kilograms of corn flour were used to prepare the material, which was distributed among 100 bottles. The elements Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo were quantified by inductively coupled plasma optical emission spectrometry (ICP OES) after acid digestion procedure. The method accuracy was confirmed by analyzing the rice flour certified reference material, NIST 1568a. All results were evaluated by analysis of variance (ANOVA) and principal component analysis (PCA). In the study, a sample mass of 400 mg was established as the minimum mass required for analysis, according to the PCA. The between-bottle test was performed by analyzing 9 bottles of the material. Subsamples of a single bottle were analyzed for the within-bottle test. No significant differences were observed for the results obtained through the application of both statistical methods. This fact demonstrates that the material is homogeneous for use as a laboratory reference material. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Corn is one of the most consumed cereals in the world and is the second most grown cereal in Brazil. It is a very nutritive food and is utilized as a food for both humans and animals. In the form of flour, it is greatly utilized in Brazilian cuisine in several ways, including in the preparation of cakes, breads, cookies, and other baked goods (Schwanz et al., 2012). Therefore, the development of analytical methods for assessing the content of essential and toxic elements in corn flour is relevant. The usual process for accuracy evaluation of an analytical method is the analysis of a certified reference material (CRM) that is recommended by the International Union of Pure and Applied Chemistry (IUPAC) (dos Santos, Lima, & de Jesus, 2011; IUPAC, 2002; Lima et al., 2010; Valente, Sanches-Silva, Albuquerque, & Costa, 2014). However, CRMs of many types of matrices are not available, and they have high costs. Thus, there has been a growing requirement for the development of new reference materials (Kato et al., 2013; Spisso et al., 2013; Ulrich & Sarkis, 2013; Valente et al., 2014). The homogeneity test is one of the most important steps in ⇑ Corresponding author. Fax: +55 71 32374117. E-mail address: [email protected] (A.M.P. dos Santos). http://dx.doi.org/10.1016/j.foodchem.2015.01.024 0308-8146/Ó 2015 Elsevier Ltd. All rights reserved.
the development and production of a CRM (dos Santos et al., 2011; Lima et al., 2010). According to the International Organization for Standardization (ISO) Guide 35 (ISO, 2006), homogeneity is related to various factors, such as the type of material, the size of the sample, and the types of analytes in addition to the accuracy and precision of the determination. Under these guidelines, a material is considered homogeneous with respect to a given property if the trials of subsamples of a material batch are in accordance with the assigned value and the uncertainty (ISO, 2006). In homogeneity studies, the various units of a material batch are important for establishing the properties of the batch. To evaluate the units, the choice of the unit should be performed randomly for that assessment to be representative of the total quantity. In many studies of homogeneity, this test is performed in 2–5% of the total number of units (bottles) during the storage procedure, in which the variation should be included in the certified reference material (CRM)/reference material (RM) uncertainty. Additionally, the homogeneity of a unit (bottle) is used to determine the recommended minimum amount of representative sample that should be used in an analysis. In addition to these studies, a review of the different quantities of the reference material candidate is recommended prior to conducting laboratory tests (ISO, 2006).
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According to ISO Guide 35, the certification process for a reference material, similar to a homogeneity study, is performed using univariate statistical method, such as a one-way analysis of variance (ANOVA) (ISO, 2006). In recent years, multivariate data analysis has been applied to chemical data with the goal of evaluating different types of results (Rocha, Nogueira, da Silva, Queiroz, & Sarmanho, 2013). Multivariate statistical methods allow the extraction of information that is not clearly shown by the data set (do Nascimento et al., 2010). Among these methods, principal component analysis (PCA) has been most widely used in analytical chemistry (Anunciação, Leao, de Jesus, & Ferreira, 2011; do Nascimento et al., 2010; dos Santos et al., 2013a,b; Lima et al., 2010; Liu et al., 2014; Oliveira et al., 2014; Rocha et al., 2013; Šelih, Šala, & Drgan, 2014). PCA can be applied in data analysis with the following goals: data reduction, structural simplification, object grouping, modeling, outlier detection, variable selection, and prediction. Furthermore, PCA using a correlation matrix is not influenced by factors with high variance because PCA displays the data in the same measurement scale (Wold, Esbensen, & Geladi, 1987). This feature can be very useful for homogeneity studies of reference material candidates due to the high variability of the data obtained in the determination of macro- and microelements. However, only a small number of works reported in the literature have employed PCA to evaluate the homogeneity of reference material candidates, such as wheat flour (Lima et al., 2010), active pharmaceutical ingredients (Rocha & Nogueira, 2011) and sodium diclofenac (Rocha et al., 2013). This work aims to present a homogeneity study for a corn flour reference material candidate by considering the quantitative results of nine elements in the samples, whose main function is to ensure the measurement reliability. Both univariate and multivariate statistical methods (ANOVA and PCA) were used to evaluate the homogeneity of the reference material candidate. 2. Experimental 2.1. Instrumentation The multi-element determination of Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo was performed using the following equipment: an inductively coupled plasma optical emission spectrometer (ICP OES) model Vista PRO from Varian (Mulgrave, Australia) with axial viewing and a charge-coupled device detector. The instrumental parameters used for the multi-element determination were as follows: RF generator of 40 MHz, power of 1.3 kW, plasma gas flow rate of 15 L min1, auxiliary gas flow rate of 1.5 L min1 and nebulizer gas flow rate of 0.7 L min1. The elements and the analytical spectral lines (nm) used were: Ca II (422.673), K I (766.491), Mg II (279.553), P I (213.618), Zn II (202.548), Cu II (327.395), Fe II (238.204), Mn II (257.610), and Mo II (204.598), where ‘‘I’’ is the atomic emission line and ‘‘II’’ is the ionic emission line. A digester block model TE-040/25 from Tecnal (São Paulo, Brazil) was used for the acid digestion of the samples of corn flour and the rice flour CRM. 2.2. Reagents and solutions Ultrapure water produced from a Milli-Q purification system from Millipore (Bedford, MA, USA), with resistivity of 18 MX cm1, was used throughout the experiments. The reagents nitric acid and hydrogen peroxide were of analytical grade and obtained from Merck (Darmstadt, Germany). A working standard solution was prepared fresh daily by serial dilution from stock solutions containing 1000 mg L1 (Merck) of the elements Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo.
2.3. Preparation of the reference material candidate The samples of corn flour used in this work were obtained from the same manufacturer but from different batches and were acquired in a supermarket in Salvador City, Bahia, Brazil. Seven kilograms of corn flour were used to produce the reference material candidate. After irradiation with 15 kGy gamma radiation to prevent the development of fungi and bacteria, the material was placed in a polyethylene bucket for homogenization. Six subsamples were taken from the bulk material for the preliminary study of homogeneity. After a satisfactory level of homogeneity was achieved, the corn flour was then transferred to 100 polyethylene flasks. Approximately 80 g of the material was transferred to each flask. A total of 100 bottles were distributed into batches containing 10 bottles each, resulting in a total of 10 batches. The bottles used allowed airtight storage, and they were labeled with the name of the material and numbered from 1 to 100. 2.4. Digestion of the samples and determination of the elements Triplicates of each sample were run for the determination of the total contents of the elements. Approximately 2.0 g of the sample were placed in a digester tube, and 2.0 mL of 65% (w/w) nitric acid and 1.0 mL of 30% (w/w) hydrogen peroxide were added. Cold finger was used as reflux system (Ferreira et al., 2013). Then, the mixture was heated on a digester block until a limpid solution was obtained. The digested solution was then quantitatively transferred to centrifuge tubes and diluted with ultrapure water up to a final volume of 12.0 mL. A blank digest was carried out in the same way as the samples of interest. The multi-element determination of Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo was performed using the inductively coupled plasma optical emission spectrometry (ICP OES) technique. 2.5. Homogeneity study The homogeneity study evaluated representative samples of the entire lot. After remixing the sample inside the bottle, three subsamples from each unit were taken for analysis, and twenty bottles of the corn flour were randomly selected for this study. The steps of this test were as follows: (1) The influence of the sample mass on the homogeneity of the reference material candidate was examined by analyzing mass quantities of 100, 200, 300, 400 and 500 mg. (2) The between-bottle homogeneity was studied using the measurement results obtained from 9 units of the reference material candidate. (3) The within-bottle homogeneity was analyzed using subsamples of one selected unit. All experiments were performed to determine the concentrations of the elements Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo. These elements were chosen because they are important macro- and micronutrients for the human diet and their contents are often determined in cereals for nutritional and toxicological studies. All homogeneity study was performed during a time interval of two weeks for ensure the stability of the samples. 2.6. Evaluation of the homogeneity The homogeneity study was performed with 9 flasks of the reference material candidate. The flasks were selected in a random way and analyzed as described in the previous section. Then, the variance analysis (ANOVA) was applied to the measurement results to evaluate the homogeneity. According to the
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recommendations of ISO Guide 35:2006 (ISO, 2006), the uncertainty due to between-bottle inhomogeneity (ubb) can be calculated using Eqs. (1) or (2).
method is statistically satisfactory for the determination of all elements involved in the certification process.
ubb ¼ ððMSb MSw Þ=nÞ1=2
ð1Þ
3.2. Homogeneity study
ubb ¼ ðMSw =nÞ1=2 ð2=mMSw Þ1=4
ð2Þ
where MSb is the mean square between batch units of the CRM, MSw is the mean square within batch units of the CRM, and n is the number of repetitions. When MSb is greater than MSw, both equations can be applied, and the higher value of ubb is chosen. Conversely, if MSb is less than MSw, it indicates that the measurement method has a low repeatability, and only Eq. (2) can be used, where mMSw is the number of degrees of freedom for the within batch test. PCA was also applied to the data matrix as an alternative statistical method to ANOVA. The data were processed after auto-scaling the individual values of the samples using the Statistica 6.0 program.
3. Results and discussion 3.1. Validation of the analytical method used for the quantification of the elements Some analytical parameters were evaluated for the validation process of the method used for the quantification of the elements. The limits of detection (LOD) and quantification (LOQ) were obtained according to IUPAC recommendations (IUPAC, 2002). LOD was calculated as (3s)/S and LOQ as (10s)/S, where s is the standard deviation of ten measures of the blank solution and S is the slope of the analytical curve used for the quantification. This way, the LOD and the LOQ calculated (mg L1) for the calibration curve were: 0.19 and 0.65 for Ca; 0.04 and 0.12 for K; 0.11 and 0.38 for Mg; 0.78 and 2.62 for P; 0.04 and 0.14 for Zn; 0.001 and 0.004 for Cu; 0.06 and 0.20 for Fe; 0.003 and 0.009 for Mn; and 0.03 and 0.10 for Mo, respectively. These limits were also expressed as milligrams of analyte per kilogram of sample for a sample mass of 400 mg, and they were: 5.8 and 19.4 for Ca; 1.1 and 3.7 for K; 3.4 and 11.3 for Mg; 23.5 and 78.4 for P; 1.21 and 4.04 for Zn; 0.04 and 0.12 for Cu; 1.79 and 5.97 for Fe; 0.08 and 0.27 for Mn; and 0.94 and 3.12 for Mo, respectively. The precision of the method was evaluated as relative standard deviation (RSD) for the nine elements, considering a sample mass of 400 mg (n = 10). This way, the RSD values were of: 1.8%, 2.9%, 1.4%, 0.5%, 4.1%, 3.7%, 4.0%, 7.4% and 2.8% for Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo, respectively. The accuracy of the method used for the quantification of Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo in corn flour samples by ICP OES was confirmed by analyzing the rice flour certified reference material (NIST 1568a) furnished by the National Institute of Standards and Technology (Gaithersburg, MD, USA). The analysis of this CRM was performed using the same procedure as applied for the samples of corn flour. The results from applying the paired t-test at a 95% confidence level demonstrated that this
To evaluate the minimum mass that allows the quantification of all analytes investigated in this work (Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo), samples of various masses in the range of 100–500 mg from the same bottle were evaluated. All measurements were performed in triplicate. Analysis of variance (ANOVA) was applied to the real data matrix at a 5% significance level. This statistical test is recommended by ISO Guide 35 and is conventionally utilized in most works that involve the preparation of a CRM (Lima et al., 2010). PCA was also applied to evaluate the homogeneity of the material using the individual values of the samples. The results of this test are presented in Table 1. From the ANOVA test, it was observed that the F-calculated values were less than the F-critical values for all of the sample mass values in the range of 100–500 mg (Table 2). This result demonstrates that there is no significant difference between the results, which also indicates that the material is homogeneous for all analytes quantified, considering the mass interval studied (Cardoso, da Nóbrega, Vital, & Abrantes, 2010). However, higher standard deviations were observed for sample masses of 100 and 200 mg. Based on the evaluation of the minimum sample mass by PCA, it was possible to determine that the first two principal components represent 70.4% of the explained variance. In score plot (Fig. 1A), dispersion is observed for the mass values of 100 and 200 mg. Therefore, it is not possible to ensure the homogeneity of the material for these mass values. By analyzing the loadings plot (Fig. 1B), it was verified that iron is the variable with higher discrimination power of data because it has highest negative loading in the PC1, following by potassium and copper. In relation to PC2, manganese and calcium were the variables that presented highest loadings, negative and positive, respectively. These elements were most responsible by dispersion observed for the samples masses of 100 and 200 mg. For the mass values of 300, 400 and 500 mg, the formation of a group was observed in score plot, which
Table 2 ANOVA results for the study of the influence of the minimum sample mass on the homogeneity of the reference material candidate for the sample masses of 100, 200, 300, 400 and 500 mg. Element
MSBetween
MSWithin
Fcal*
p-Value
Ca K Mg P Zn Cu Fe Mn
3.57 559.9 18.9 57.2 0.0270 0.0016 0.148 0.0038
32.1 1774 41.0 654.9 0.0715 0.0131 1.31 0.0057
0.11 0.32 0.46 0.09 0.38 0.12 0.11 0.66
0.98 0.86 0.76 0.98 0.82 0.97 0.98 0.63
*
Fcrit = 3.48.
Table 1 Concentrations of Ca, K, Mg, P, Zn, Cu, Fe and Mn in the corn flour reference material candidate samples of various masses, as analyzed by ICP OES after sample decomposition procedure (mean ± standard deviation, n = 3). Mass (mg)
100 200 300 400 500
Concentration of the elements (mg kg1) Ca
K
Mg
P
Zn
Cu
Fe
Mn
32.4 ± 12.4 35.7 ± 0.5 35.0 ± 0.8 34.6 ± 0.5 34.2 ± 2.2
1225 ± 6 1259 ± 6 1227 ± 4 1233 ± 4 1235 ± 2
188.6 ± 10.7 189.7 ± 7.7 194.2 ± 2.9 190.5 ± 3.8 193.7 ± 2.6
527.7 ± 25.4 532.3 ± 22.1 524.9 ± 12.7 521.1 ± 7.8 530.0 ± 4.3
5.4 ± 0.3 5.2 ± 0.3 5.4 ± 0.4 5.5 ± 0.1 5.5 ± 0.1
0.50 ± 0.20 0.50 ± 0.10 0.50 ± 0.01 0.54 ± 0.01 0.55 ± 0.03
7.80 ± 2.40 8.40 ± 0.70 8.02 ± 0.52 8.13 ± 0.20 8.06 ± 0.30
0.90 ± 0.10 0.80 ± 0.10 0.83 ± 0.01 0.83 ± 0.02 0.85 ± 0.07
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Fig. 1. Score (A) and loadings (B) plots of the first two principal components for the study of the influence of the minimum sample mass on the homogeneity of the corn flour reference material candidate.
confirms the homogeneity of the material for the analytes quantified. Therefore, a sample mass of 400 mg was chosen for further studies to ensure the robustness of the method. The homogeneity of the candidate reference material was also evaluated. Between- and within-bottle homogeneity tests were carried out according to the recommendations of ISO Guide 35:2006 (ISO, 2006). The homogeneity of the elements Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo was evaluated by comparing the variances obtained in the between-bottle test with the F-critical values. The F-calculated values for all elements were less than the F-critical value of 2.51 at a 5.0% significance level, demonstrating that there are no significant differences between the variances obtained using a sample mass of 400 mg (Table 3). The coefficients of variance (CV) achieved in the homogeneity tests varied from 1.7% to 6.5%.
Table 3 ANOVA results for the between-bottle homogeneity test using a sample mass of 400 mg.
*
Element
MSWithin
MSBetween
Fcal*
p-Value
Ca K Mg P Zn Cu Fe Mn Mo
3.38 2382 142.5 728.2 0.0676 0.0031 0.155 0.00010 0.00035
2.29 2998 98.5 458.4 0.0836 0.0051 0.212 0.0012 0.00026
0.68 1.26 0.69 0.63 1.24 1.64 1.37 1.25 0.73
0.70 0.32 0.69 0.74 0.33 0.18 0.27 0.33 0.66
Fcrit = 2.51.
Table 4 Standard uncertainty due to inhomogeneity arising from between- and within-bottle tests for all elements quantified. Element
Mass fraction (mg kg1)
Standard uncertainty (mg kg1)
Ca Cu Fe K Mg Mn P Zn Cr Mo
27.51 0.55 8.07 1234 189.1 0.830 520.3 5.53 0.53 0.078
0.24 0.01 0.05 5 1.5 0.003 3.5 0.02 0.02 0.002
In this study, PCA was also applied to the data matrix to evaluate the between- and within-bottle homogeneity tests using the individual values of the samples. From this analysis, it was possible to obtain score plots for both tests (Fig. 2). By analyzing the two plots, it was possible verify a random distribution of the samples in terms of scores, indicating that no sample groupings were formed. This result confirms that the corn flour reference material candidate is homogeneous for the analytes determined. The material homogeneity was also evaluated by subjecting the data to a hierarchical cluster analysis (HCA). The HCA results were obtained using Ward’s clustering algorithm and Euclidean distances were used to calculate sample inter-point distances and similarities. HCA was also applied to individual values of the samples after auto-scaling. The HCA results were in agreement with those obtained by PCA, since no clustering was formed.
Fig. 2. Score plots of PC1 versus PC2 for the between-bottle homogeneity test (A) and the within-bottle homogeneity test (B) using a sample mass of 400 mg.
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The test of Cochran was also performed to evaluate the uncertainty level of the homogeneity study (Yafa et al., 2010). The test was applied to detect outliers due to fluctuations in the analytical method and to verify the existence of significant differences between bottles. The uncertainty due to inhomogeneity (ubb) was calculated using the expression given by ISO Guide 35:2006 (ISO, 2006). Table 4 shows the uncertainty due to inhomogeneity arising from between- and within-bottle tests for all quantified elements. 4. Conclusion The homogeneity of a corn flour reference material candidate for the elements Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo was confirmed by an ANOVA test, through which it was possible to prove that there were no significant differences between the flasks investigated at a 95% confidence level. Furthermore, the reference material candidate was considered homogeneous by principal component analysis, which has been shown to be an excellent tool for evaluating the homogeneity of a material. This fact demonstrates that PCA can be applied as an alternative statistical method to the ANOVA test in the preparation of reference materials. Acknowledgements The authors are grateful to the Brazilian agencies PRONEX/Fundacão de Amparo à Pesquisa do Estado da Bahia (FAPESB), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Coordenacão de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for grants and fellowships. References Anunciação, D. S., Leao, D. J., de Jesus, R. M., & Ferreira, S. L. C. (2011). Use of multivariate analysis techniques for evaluation of analytical data – Determination of the mineral composition of cabbage (Brassica oleracea). Food Analytical Methods, 4, 286–292. Cardoso, M. H. W. M., da Nóbrega, A. W., Vital, H. C., & Abrantes, S. (2010). Preparation of a certified reference material for pesticide control in the cultivation of fruits and vegetables: A homogeneity study. Ciência e Tecnologia de Alimentos, 30(2), 429–438. do Nascimento, I. R., de Jesus, R. M., dos Santos, W. N. L., Souza, A. S., Fragoso, W. D., & dos Reis, P. S. (2010). Determination of the mineral composition of fresh bovine milk from the milk-producing areas located in the State of Sergipe in Brazil and evaluation employing exploratory analysis. Microchemical Journal, 96, 37–41. dos Santos, I. F., dos Santos, A. M. P., Barbosa, U. A., Lima, J. S., dos Santos, D. C., & Matos, G. D. (2013a). Multivariate analysis of the mineral content of raw and cooked okra (Abelmoschus esculentus L.). Microchemical Journal, 110, 439–443. dos Santos, A. M. P., Lima, J. S., Anunciação, D. S., Souza, A. S., dos Santos, D. C. M. B., & Matos, G. D. (2013b). Determination and evaluation employing multivariate analysis of the mineral composition of broccoli (Brassica oleracea L. var. Italica). Food Analytical Methods, 6, 745–752.
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Food Chemistry journal homepage: www.elsevier.com/locate/foodchem
Analytical Methods
Homogeneity study of a corn flour laboratory reference material candidate for inorganic analysis Ana Maria Pinto dos Santos a,⇑, Liz Oliveira dos Santos a, Geovani Cardoso Brandao a, Danilo Junqueira Leao a, Alfredo Victor Bellido Bernedo b, Ricardo Tadeu Lopes c, Valfredo Azevedo Lemos d a
Universidade Federal da Bahia, Instituto de Química, Grupo de Pesquisa em Química e Quimiometria, Campus Ondina, 41170-115 Salvador, Bahia, Brazil Universidade Federal Fluminense, Centro de Estudos Gerais, Instituto de Química, Niterói, Rio de Janeiro, Brazil Universidade Federal do Rio de Janeiro, Instituto Alberto Luiz Coimbra de Pós Graduação e Pesquisa de Engenharia, Programa de Engenharia Nuclear, Rio de Janeiro, Brazil d Universidade Estadual do Sudoeste da Bahia, Laboratório de Química Analítica (LQA), Campus de Jequié, 45206-190 Jequié, Bahia, Brazil b c
a r t i c l e
i n f o
Article history: Received 14 June 2014 Received in revised form 30 August 2014 Accepted 3 January 2015 Available online 19 January 2015 Keywords: Corn flour Reference material candidate Homogeneity study Analysis of variance Principal component analysis
a b s t r a c t In this work, a homogeneity study of a corn flour reference material candidate for inorganic analysis is presented. Seven kilograms of corn flour were used to prepare the material, which was distributed among 100 bottles. The elements Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo were quantified by inductively coupled plasma optical emission spectrometry (ICP OES) after acid digestion procedure. The method accuracy was confirmed by analyzing the rice flour certified reference material, NIST 1568a. All results were evaluated by analysis of variance (ANOVA) and principal component analysis (PCA). In the study, a sample mass of 400 mg was established as the minimum mass required for analysis, according to the PCA. The between-bottle test was performed by analyzing 9 bottles of the material. Subsamples of a single bottle were analyzed for the within-bottle test. No significant differences were observed for the results obtained through the application of both statistical methods. This fact demonstrates that the material is homogeneous for use as a laboratory reference material. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Corn is one of the most consumed cereals in the world and is the second most grown cereal in Brazil. It is a very nutritive food and is utilized as a food for both humans and animals. In the form of flour, it is greatly utilized in Brazilian cuisine in several ways, including in the preparation of cakes, breads, cookies, and other baked goods (Schwanz et al., 2012). Therefore, the development of analytical methods for assessing the content of essential and toxic elements in corn flour is relevant. The usual process for accuracy evaluation of an analytical method is the analysis of a certified reference material (CRM) that is recommended by the International Union of Pure and Applied Chemistry (IUPAC) (dos Santos, Lima, & de Jesus, 2011; IUPAC, 2002; Lima et al., 2010; Valente, Sanches-Silva, Albuquerque, & Costa, 2014). However, CRMs of many types of matrices are not available, and they have high costs. Thus, there has been a growing requirement for the development of new reference materials (Kato et al., 2013; Spisso et al., 2013; Ulrich & Sarkis, 2013; Valente et al., 2014). The homogeneity test is one of the most important steps in ⇑ Corresponding author. Fax: +55 71 32374117. E-mail address: [email protected] (A.M.P. dos Santos). http://dx.doi.org/10.1016/j.foodchem.2015.01.024 0308-8146/Ó 2015 Elsevier Ltd. All rights reserved.
the development and production of a CRM (dos Santos et al., 2011; Lima et al., 2010). According to the International Organization for Standardization (ISO) Guide 35 (ISO, 2006), homogeneity is related to various factors, such as the type of material, the size of the sample, and the types of analytes in addition to the accuracy and precision of the determination. Under these guidelines, a material is considered homogeneous with respect to a given property if the trials of subsamples of a material batch are in accordance with the assigned value and the uncertainty (ISO, 2006). In homogeneity studies, the various units of a material batch are important for establishing the properties of the batch. To evaluate the units, the choice of the unit should be performed randomly for that assessment to be representative of the total quantity. In many studies of homogeneity, this test is performed in 2–5% of the total number of units (bottles) during the storage procedure, in which the variation should be included in the certified reference material (CRM)/reference material (RM) uncertainty. Additionally, the homogeneity of a unit (bottle) is used to determine the recommended minimum amount of representative sample that should be used in an analysis. In addition to these studies, a review of the different quantities of the reference material candidate is recommended prior to conducting laboratory tests (ISO, 2006).
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According to ISO Guide 35, the certification process for a reference material, similar to a homogeneity study, is performed using univariate statistical method, such as a one-way analysis of variance (ANOVA) (ISO, 2006). In recent years, multivariate data analysis has been applied to chemical data with the goal of evaluating different types of results (Rocha, Nogueira, da Silva, Queiroz, & Sarmanho, 2013). Multivariate statistical methods allow the extraction of information that is not clearly shown by the data set (do Nascimento et al., 2010). Among these methods, principal component analysis (PCA) has been most widely used in analytical chemistry (Anunciação, Leao, de Jesus, & Ferreira, 2011; do Nascimento et al., 2010; dos Santos et al., 2013a,b; Lima et al., 2010; Liu et al., 2014; Oliveira et al., 2014; Rocha et al., 2013; Šelih, Šala, & Drgan, 2014). PCA can be applied in data analysis with the following goals: data reduction, structural simplification, object grouping, modeling, outlier detection, variable selection, and prediction. Furthermore, PCA using a correlation matrix is not influenced by factors with high variance because PCA displays the data in the same measurement scale (Wold, Esbensen, & Geladi, 1987). This feature can be very useful for homogeneity studies of reference material candidates due to the high variability of the data obtained in the determination of macro- and microelements. However, only a small number of works reported in the literature have employed PCA to evaluate the homogeneity of reference material candidates, such as wheat flour (Lima et al., 2010), active pharmaceutical ingredients (Rocha & Nogueira, 2011) and sodium diclofenac (Rocha et al., 2013). This work aims to present a homogeneity study for a corn flour reference material candidate by considering the quantitative results of nine elements in the samples, whose main function is to ensure the measurement reliability. Both univariate and multivariate statistical methods (ANOVA and PCA) were used to evaluate the homogeneity of the reference material candidate. 2. Experimental 2.1. Instrumentation The multi-element determination of Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo was performed using the following equipment: an inductively coupled plasma optical emission spectrometer (ICP OES) model Vista PRO from Varian (Mulgrave, Australia) with axial viewing and a charge-coupled device detector. The instrumental parameters used for the multi-element determination were as follows: RF generator of 40 MHz, power of 1.3 kW, plasma gas flow rate of 15 L min1, auxiliary gas flow rate of 1.5 L min1 and nebulizer gas flow rate of 0.7 L min1. The elements and the analytical spectral lines (nm) used were: Ca II (422.673), K I (766.491), Mg II (279.553), P I (213.618), Zn II (202.548), Cu II (327.395), Fe II (238.204), Mn II (257.610), and Mo II (204.598), where ‘‘I’’ is the atomic emission line and ‘‘II’’ is the ionic emission line. A digester block model TE-040/25 from Tecnal (São Paulo, Brazil) was used for the acid digestion of the samples of corn flour and the rice flour CRM. 2.2. Reagents and solutions Ultrapure water produced from a Milli-Q purification system from Millipore (Bedford, MA, USA), with resistivity of 18 MX cm1, was used throughout the experiments. The reagents nitric acid and hydrogen peroxide were of analytical grade and obtained from Merck (Darmstadt, Germany). A working standard solution was prepared fresh daily by serial dilution from stock solutions containing 1000 mg L1 (Merck) of the elements Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo.
2.3. Preparation of the reference material candidate The samples of corn flour used in this work were obtained from the same manufacturer but from different batches and were acquired in a supermarket in Salvador City, Bahia, Brazil. Seven kilograms of corn flour were used to produce the reference material candidate. After irradiation with 15 kGy gamma radiation to prevent the development of fungi and bacteria, the material was placed in a polyethylene bucket for homogenization. Six subsamples were taken from the bulk material for the preliminary study of homogeneity. After a satisfactory level of homogeneity was achieved, the corn flour was then transferred to 100 polyethylene flasks. Approximately 80 g of the material was transferred to each flask. A total of 100 bottles were distributed into batches containing 10 bottles each, resulting in a total of 10 batches. The bottles used allowed airtight storage, and they were labeled with the name of the material and numbered from 1 to 100. 2.4. Digestion of the samples and determination of the elements Triplicates of each sample were run for the determination of the total contents of the elements. Approximately 2.0 g of the sample were placed in a digester tube, and 2.0 mL of 65% (w/w) nitric acid and 1.0 mL of 30% (w/w) hydrogen peroxide were added. Cold finger was used as reflux system (Ferreira et al., 2013). Then, the mixture was heated on a digester block until a limpid solution was obtained. The digested solution was then quantitatively transferred to centrifuge tubes and diluted with ultrapure water up to a final volume of 12.0 mL. A blank digest was carried out in the same way as the samples of interest. The multi-element determination of Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo was performed using the inductively coupled plasma optical emission spectrometry (ICP OES) technique. 2.5. Homogeneity study The homogeneity study evaluated representative samples of the entire lot. After remixing the sample inside the bottle, three subsamples from each unit were taken for analysis, and twenty bottles of the corn flour were randomly selected for this study. The steps of this test were as follows: (1) The influence of the sample mass on the homogeneity of the reference material candidate was examined by analyzing mass quantities of 100, 200, 300, 400 and 500 mg. (2) The between-bottle homogeneity was studied using the measurement results obtained from 9 units of the reference material candidate. (3) The within-bottle homogeneity was analyzed using subsamples of one selected unit. All experiments were performed to determine the concentrations of the elements Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo. These elements were chosen because they are important macro- and micronutrients for the human diet and their contents are often determined in cereals for nutritional and toxicological studies. All homogeneity study was performed during a time interval of two weeks for ensure the stability of the samples. 2.6. Evaluation of the homogeneity The homogeneity study was performed with 9 flasks of the reference material candidate. The flasks were selected in a random way and analyzed as described in the previous section. Then, the variance analysis (ANOVA) was applied to the measurement results to evaluate the homogeneity. According to the
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recommendations of ISO Guide 35:2006 (ISO, 2006), the uncertainty due to between-bottle inhomogeneity (ubb) can be calculated using Eqs. (1) or (2).
method is statistically satisfactory for the determination of all elements involved in the certification process.
ubb ¼ ððMSb MSw Þ=nÞ1=2
ð1Þ
3.2. Homogeneity study
ubb ¼ ðMSw =nÞ1=2 ð2=mMSw Þ1=4
ð2Þ
where MSb is the mean square between batch units of the CRM, MSw is the mean square within batch units of the CRM, and n is the number of repetitions. When MSb is greater than MSw, both equations can be applied, and the higher value of ubb is chosen. Conversely, if MSb is less than MSw, it indicates that the measurement method has a low repeatability, and only Eq. (2) can be used, where mMSw is the number of degrees of freedom for the within batch test. PCA was also applied to the data matrix as an alternative statistical method to ANOVA. The data were processed after auto-scaling the individual values of the samples using the Statistica 6.0 program.
3. Results and discussion 3.1. Validation of the analytical method used for the quantification of the elements Some analytical parameters were evaluated for the validation process of the method used for the quantification of the elements. The limits of detection (LOD) and quantification (LOQ) were obtained according to IUPAC recommendations (IUPAC, 2002). LOD was calculated as (3s)/S and LOQ as (10s)/S, where s is the standard deviation of ten measures of the blank solution and S is the slope of the analytical curve used for the quantification. This way, the LOD and the LOQ calculated (mg L1) for the calibration curve were: 0.19 and 0.65 for Ca; 0.04 and 0.12 for K; 0.11 and 0.38 for Mg; 0.78 and 2.62 for P; 0.04 and 0.14 for Zn; 0.001 and 0.004 for Cu; 0.06 and 0.20 for Fe; 0.003 and 0.009 for Mn; and 0.03 and 0.10 for Mo, respectively. These limits were also expressed as milligrams of analyte per kilogram of sample for a sample mass of 400 mg, and they were: 5.8 and 19.4 for Ca; 1.1 and 3.7 for K; 3.4 and 11.3 for Mg; 23.5 and 78.4 for P; 1.21 and 4.04 for Zn; 0.04 and 0.12 for Cu; 1.79 and 5.97 for Fe; 0.08 and 0.27 for Mn; and 0.94 and 3.12 for Mo, respectively. The precision of the method was evaluated as relative standard deviation (RSD) for the nine elements, considering a sample mass of 400 mg (n = 10). This way, the RSD values were of: 1.8%, 2.9%, 1.4%, 0.5%, 4.1%, 3.7%, 4.0%, 7.4% and 2.8% for Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo, respectively. The accuracy of the method used for the quantification of Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo in corn flour samples by ICP OES was confirmed by analyzing the rice flour certified reference material (NIST 1568a) furnished by the National Institute of Standards and Technology (Gaithersburg, MD, USA). The analysis of this CRM was performed using the same procedure as applied for the samples of corn flour. The results from applying the paired t-test at a 95% confidence level demonstrated that this
To evaluate the minimum mass that allows the quantification of all analytes investigated in this work (Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo), samples of various masses in the range of 100–500 mg from the same bottle were evaluated. All measurements were performed in triplicate. Analysis of variance (ANOVA) was applied to the real data matrix at a 5% significance level. This statistical test is recommended by ISO Guide 35 and is conventionally utilized in most works that involve the preparation of a CRM (Lima et al., 2010). PCA was also applied to evaluate the homogeneity of the material using the individual values of the samples. The results of this test are presented in Table 1. From the ANOVA test, it was observed that the F-calculated values were less than the F-critical values for all of the sample mass values in the range of 100–500 mg (Table 2). This result demonstrates that there is no significant difference between the results, which also indicates that the material is homogeneous for all analytes quantified, considering the mass interval studied (Cardoso, da Nóbrega, Vital, & Abrantes, 2010). However, higher standard deviations were observed for sample masses of 100 and 200 mg. Based on the evaluation of the minimum sample mass by PCA, it was possible to determine that the first two principal components represent 70.4% of the explained variance. In score plot (Fig. 1A), dispersion is observed for the mass values of 100 and 200 mg. Therefore, it is not possible to ensure the homogeneity of the material for these mass values. By analyzing the loadings plot (Fig. 1B), it was verified that iron is the variable with higher discrimination power of data because it has highest negative loading in the PC1, following by potassium and copper. In relation to PC2, manganese and calcium were the variables that presented highest loadings, negative and positive, respectively. These elements were most responsible by dispersion observed for the samples masses of 100 and 200 mg. For the mass values of 300, 400 and 500 mg, the formation of a group was observed in score plot, which
Table 2 ANOVA results for the study of the influence of the minimum sample mass on the homogeneity of the reference material candidate for the sample masses of 100, 200, 300, 400 and 500 mg. Element
MSBetween
MSWithin
Fcal*
p-Value
Ca K Mg P Zn Cu Fe Mn
3.57 559.9 18.9 57.2 0.0270 0.0016 0.148 0.0038
32.1 1774 41.0 654.9 0.0715 0.0131 1.31 0.0057
0.11 0.32 0.46 0.09 0.38 0.12 0.11 0.66
0.98 0.86 0.76 0.98 0.82 0.97 0.98 0.63
*
Fcrit = 3.48.
Table 1 Concentrations of Ca, K, Mg, P, Zn, Cu, Fe and Mn in the corn flour reference material candidate samples of various masses, as analyzed by ICP OES after sample decomposition procedure (mean ± standard deviation, n = 3). Mass (mg)
100 200 300 400 500
Concentration of the elements (mg kg1) Ca
K
Mg
P
Zn
Cu
Fe
Mn
32.4 ± 12.4 35.7 ± 0.5 35.0 ± 0.8 34.6 ± 0.5 34.2 ± 2.2
1225 ± 6 1259 ± 6 1227 ± 4 1233 ± 4 1235 ± 2
188.6 ± 10.7 189.7 ± 7.7 194.2 ± 2.9 190.5 ± 3.8 193.7 ± 2.6
527.7 ± 25.4 532.3 ± 22.1 524.9 ± 12.7 521.1 ± 7.8 530.0 ± 4.3
5.4 ± 0.3 5.2 ± 0.3 5.4 ± 0.4 5.5 ± 0.1 5.5 ± 0.1
0.50 ± 0.20 0.50 ± 0.10 0.50 ± 0.01 0.54 ± 0.01 0.55 ± 0.03
7.80 ± 2.40 8.40 ± 0.70 8.02 ± 0.52 8.13 ± 0.20 8.06 ± 0.30
0.90 ± 0.10 0.80 ± 0.10 0.83 ± 0.01 0.83 ± 0.02 0.85 ± 0.07
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Fig. 1. Score (A) and loadings (B) plots of the first two principal components for the study of the influence of the minimum sample mass on the homogeneity of the corn flour reference material candidate.
confirms the homogeneity of the material for the analytes quantified. Therefore, a sample mass of 400 mg was chosen for further studies to ensure the robustness of the method. The homogeneity of the candidate reference material was also evaluated. Between- and within-bottle homogeneity tests were carried out according to the recommendations of ISO Guide 35:2006 (ISO, 2006). The homogeneity of the elements Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo was evaluated by comparing the variances obtained in the between-bottle test with the F-critical values. The F-calculated values for all elements were less than the F-critical value of 2.51 at a 5.0% significance level, demonstrating that there are no significant differences between the variances obtained using a sample mass of 400 mg (Table 3). The coefficients of variance (CV) achieved in the homogeneity tests varied from 1.7% to 6.5%.
Table 3 ANOVA results for the between-bottle homogeneity test using a sample mass of 400 mg.
*
Element
MSWithin
MSBetween
Fcal*
p-Value
Ca K Mg P Zn Cu Fe Mn Mo
3.38 2382 142.5 728.2 0.0676 0.0031 0.155 0.00010 0.00035
2.29 2998 98.5 458.4 0.0836 0.0051 0.212 0.0012 0.00026
0.68 1.26 0.69 0.63 1.24 1.64 1.37 1.25 0.73
0.70 0.32 0.69 0.74 0.33 0.18 0.27 0.33 0.66
Fcrit = 2.51.
Table 4 Standard uncertainty due to inhomogeneity arising from between- and within-bottle tests for all elements quantified. Element
Mass fraction (mg kg1)
Standard uncertainty (mg kg1)
Ca Cu Fe K Mg Mn P Zn Cr Mo
27.51 0.55 8.07 1234 189.1 0.830 520.3 5.53 0.53 0.078
0.24 0.01 0.05 5 1.5 0.003 3.5 0.02 0.02 0.002
In this study, PCA was also applied to the data matrix to evaluate the between- and within-bottle homogeneity tests using the individual values of the samples. From this analysis, it was possible to obtain score plots for both tests (Fig. 2). By analyzing the two plots, it was possible verify a random distribution of the samples in terms of scores, indicating that no sample groupings were formed. This result confirms that the corn flour reference material candidate is homogeneous for the analytes determined. The material homogeneity was also evaluated by subjecting the data to a hierarchical cluster analysis (HCA). The HCA results were obtained using Ward’s clustering algorithm and Euclidean distances were used to calculate sample inter-point distances and similarities. HCA was also applied to individual values of the samples after auto-scaling. The HCA results were in agreement with those obtained by PCA, since no clustering was formed.
Fig. 2. Score plots of PC1 versus PC2 for the between-bottle homogeneity test (A) and the within-bottle homogeneity test (B) using a sample mass of 400 mg.
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The test of Cochran was also performed to evaluate the uncertainty level of the homogeneity study (Yafa et al., 2010). The test was applied to detect outliers due to fluctuations in the analytical method and to verify the existence of significant differences between bottles. The uncertainty due to inhomogeneity (ubb) was calculated using the expression given by ISO Guide 35:2006 (ISO, 2006). Table 4 shows the uncertainty due to inhomogeneity arising from between- and within-bottle tests for all quantified elements. 4. Conclusion The homogeneity of a corn flour reference material candidate for the elements Ca, K, Mg, P, Zn, Cu, Fe, Mn and Mo was confirmed by an ANOVA test, through which it was possible to prove that there were no significant differences between the flasks investigated at a 95% confidence level. Furthermore, the reference material candidate was considered homogeneous by principal component analysis, which has been shown to be an excellent tool for evaluating the homogeneity of a material. This fact demonstrates that PCA can be applied as an alternative statistical method to the ANOVA test in the preparation of reference materials. Acknowledgements The authors are grateful to the Brazilian agencies PRONEX/Fundacão de Amparo à Pesquisa do Estado da Bahia (FAPESB), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Coordenacão de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for grants and fellowships. References Anunciação, D. S., Leao, D. J., de Jesus, R. M., & Ferreira, S. L. C. (2011). Use of multivariate analysis techniques for evaluation of analytical data – Determination of the mineral composition of cabbage (Brassica oleracea). Food Analytical Methods, 4, 286–292. Cardoso, M. H. W. M., da Nóbrega, A. W., Vital, H. C., & Abrantes, S. (2010). Preparation of a certified reference material for pesticide control in the cultivation of fruits and vegetables: A homogeneity study. Ciência e Tecnologia de Alimentos, 30(2), 429–438. do Nascimento, I. R., de Jesus, R. M., dos Santos, W. N. L., Souza, A. S., Fragoso, W. D., & dos Reis, P. S. (2010). Determination of the mineral composition of fresh bovine milk from the milk-producing areas located in the State of Sergipe in Brazil and evaluation employing exploratory analysis. Microchemical Journal, 96, 37–41. dos Santos, I. F., dos Santos, A. M. P., Barbosa, U. A., Lima, J. S., dos Santos, D. C., & Matos, G. D. (2013a). Multivariate analysis of the mineral content of raw and cooked okra (Abelmoschus esculentus L.). Microchemical Journal, 110, 439–443. dos Santos, A. M. P., Lima, J. S., Anunciação, D. S., Souza, A. S., dos Santos, D. C. M. B., & Matos, G. D. (2013b). Determination and evaluation employing multivariate analysis of the mineral composition of broccoli (Brassica oleracea L. var. Italica). Food Analytical Methods, 6, 745–752.
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