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Evaluation of metal content in tea samples commercialized in sachets using multivariate data analysis techniques
Microchemical Journal 151 (2019) 104248
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Evaluation of metal content in tea samples commercialized in sachets using multivariate data analysis techniques
T
Délis Alves Souza Gomesa, Juscelia Pereira dos Santos Alvesa, Erik Galvão Paranhos da Silvab, ⁎ Cleber Galvão Novaesa, , Darci Santos Silvaa, Rosane Moura Aguiara, Sulene Alves Araújoa, Ana Caroline Lessa dos Santosa, Marcos Almeida Bezerraa a
Universidade Estadual do Sudoeste da Bahia, Campus Jequié, Departamento de Ciências e Tecnologias, Rua José Moreira Sobrinho s/n, Jequié, Bahia 45208-091, Brazil Universidade Estadual de Santa Cruz, Departamento de Ciências Exatas e Tecnológicas, Campus Soane Nazaré de Andrade, Km 16 - BR-415, Ilhéus, Bahia 45662-900, Brazil
b
ARTICLE INFO
ABSTRACT
Keywords: Teas in sachets Metals Kohonen maps Neural networks Principal component analysis Hierarchical cluster analysis
Tea is a beverage consumed all over the world, and, besides the very pleasant taste, it has substances in its composition that can lead to various beneficial effects to human health. However, although teas have essential elements in their composition, they can also be contaminated with metals from the soil, air, and equipment used in their production. In this study, eight metals (Ca, Cu, Fe, Mg, Mn, Zn, Na and K) were determined in tea samples commercialized in sachets using flame atomic spectrometry (absorption and emission) after acid decomposition in a digestion block. The concentration of the analyzed metals varied as follows (in mg kg−1): Ca (1856.3–10,012), Cu (2.014–14.90), Fe (43.94–532.2), Mg (739.9–2237), Mn (26.95–946.3), Zn (12.05–41.84), Na (167.7–4322) and K (5089.1–14,334). The generated data were statistically analyzed using the following multivariate analysis tools: Principal Component Analysis, Hierarchical Cluster Analysis and Kohonen self-organizing maps. The multivariate analysis classified the different tea samples into well defined groups according to flavor, based on the mineral composition of eight quantified elements.
1. Introduction Tea is a widely appreciated beverage around the world due either to its pleasant taste or its nutritional and medicinal properties. Teas are frequently prepared from plant parts such as leaves, roots, seeds and barks in the form of infusions, that is, by immersing the material directly into hot water [1–3]. Teas are considered functional foods that, if consumed daily, can bring innumerable benefits to human health, since they have the capacity to reduce cardiovascular diseases [4], cholesterol levels, besides presenting antimicrobial, antioxidant, immunostimulatory activities [5] and antimutagenic potential [6]. In general, teas present, in their composition, substances that play a vital role in the various metabolic processes and are essential for human well-being. Thus, some of these substances, when in excess or deficiency, can cause health damage [7]. Teas may provide nutritional trace elements, but also elements that are potentially toxic to human metabolism [8–10]. Therefore, due to the great consumption of this beverage, there is also an interest in analyzing the content of metals present in its composition, mainly due to the belief that these beverages
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are healthier than the medications derived from allopathic medicine [11,12]. Modern analytical instrumentation allows sample elemental analysis over a short period of time, generating a large amount of data. Evaluating these data and extracting relevant information can be a difficult task when the analyst has only visual observation. Currently, the extensive use of multivariate pattern recognition tools has been consolidated in chemical analysis laboratories, allowing an efficient evaluation and interpretation of these data and revealing latent information [13,14]. Among the various pattern recognition methods, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) have been widely applied in analytical chemistry, once they meet the needs of researchers and due to the ease of interpretation of the results generated [15,16]. PCA has been already used to evaluate the potential of intrinsic fluorescence to characterize the antioxidant capacity of soy protein hydrolysates during sequential ultrafiltration and nanofiltration [17], differentiation of gelatin sources based on polypeptide molecular weights [18], real-time monitoring of the coffee roasting process using near infrared (NIR) spectroscopy in diffuse
Corresponding author. E-mail address: [email protected] (C.G. Novaes).
https://doi.org/10.1016/j.microc.2019.104248 Received 21 June 2019; Received in revised form 7 September 2019; Accepted 7 September 2019 Available online 10 September 2019 0026-265X/ © 2019 Elsevier B.V. All rights reserved.
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reflectance mode [19]; PCA and HCA have also been used for the characterization of marbles by capillary electrophoresis [20], among other studies. Kohonen maps (or Self-Organizing Maps, SOMs) are multivariate tools based on artificial neural network algorithms with high learning power. Teuvo Kohonen developed this algorithm, which has been heavily applied, as it is considered a simple way for organization of dimensionally complex data according to their similarities, providing pattern recognition [21,22]. In this artificial neural network application, the neurons are put in nodes of the lattices and become selectively tuned to input patterns of a competitive learning process. Kohonen maps have been applied to: data exploration of mineral content in soft drinks from different flavors and manufactures [23], evaluation of nutrient profiles of cashew nuts [24], identification of effects of Fe(III) ion and quality of humic substances on As(V) distribution in fresh water [25], visualization of contamination in packaged fresh alfalfa by Salmonella typhimurium [26], evaluation of honeybees as sentinels for lead pollution [27], screening of Passiflora L. mineral content [28], evaluation of chemical composition of waters associated with petroleum production [29], prediction of groundwater levels for management in arid areas [30], indications of airborne polychlorinated biphenyl and organochlorine pesticide dependence on spatial and meteorological parameters [31], among others. In this study, PCA, HCA and Kohonen SOMs were applied in the evaluation of data generated by analysis of tea samples using flame atomic absorption spectrometry. The three multivariate tools were compared in terms of efficiency and capacity of pattern recognition.
(Camellia sinensis (L.) Kuntze), Boldo (Peumus boldus) and Fennel (Pimpinella anisum) and 5 different brands, totaling 20 samples. They were then transported to the laboratory, where they were submitted to the preliminary treatments. The samples were milled with mortar and pestle, sieved in a 100 μm sieve and stored in decontaminated polyethylene bottles. 2.4. Acid decomposition of samples in the digestion block After grinding the tea samples, a mass of approximately 0.2 g was weighed in a glass digestion tube. Posteriorly, 5.0 mL of concentrated nitric acid and 3 mL of 30% (v v−1) hydrogen peroxide were added and the tube was coupled with a reflux system with cold finger. The samples were then taken to the digestion block and heated to 110 °C until almost dry. To prevent the sample from drying out completely, ultrapure water was added during the process until complete sample digestion. Subsequently, the samples were cooled to room temperature, transferred into a 25.0 mL volumetric flask and diluted with deionized water. The metals were determined in the final solution by flame atomic spectrometry (absorption – FAAS and emission – FAES). 2.5. Data treatment The data set was organized onto a matrix consisting of 20 tea samples and 8 variables (the determined metals). Before PCA and HCA, the matrix was self-scaled. Graphs were plotted using the Statistica 12.0 software (The StatSoft, Inc., Tulsa, OK, USA). Kohonen self-organizing maps were implemented, besides training and clustering in the Matlab software R2016a (The MathWorks, Co., Natick, MA, USA), as well as a toolbox available at: www.cis.hut.fi/ projects/somtoolbox. The implementation of codes was elaborated from algorithms suggested in the literature available at the website described above. The processed results used for training the network are based on metal concentrations in the tea samples presented in Table 3.
2. Experimental 2.1. Instrumentation A Perkin Elmer AAnalyst 200 (Norwalk, CT, USA) flame atomic absorption spectrometer equipped with a deuterium lamp for background correction was used for measuring the absorbance of solutions. Hollow cathode lamps for the analyzed elements (except for Na and K, which were determined by flame atomic emission spectrometry) were used in accordance with the recommendations of the manufacturer: wavelengths (nm): Ca (422.7), Cu (324.7), Fe (248.3), K (766.5), Mg (285.8), Na (589.0) and Zn (213.9). The burner height (13.5 mm) was also used with conventional values. The flame composition was acetylene and air (flow rate 2.0 L min−1 and 13.5 L min−1, respectively). The flow rate used for the nebulizer was 5.0 mL min−1. Mass determinations were carried out by an analytical balance (Sartorius BL D105). A digestion block (Tecnal TE 0851, Piracicaba, Brazil) fitted with cold finger was used for acid decomposition with HNO3 and H2O2.
2.6. Quality control Analytical characteristics to perform the determination of the eight metals involved in this study were verified. The limits of detection (LOD) and quantification (LOQ), precision (in form of repeatability), analytical sensitivity, and linearity are presented in Table 1. The accuracy of the method was accessed by analysis of certified reference material (Apple leaves NIST 1515). The results of this analysis and the certified values are presented in Table 2. To compare the two sets of values in Table 2, the paired t-test was applied at a 95% confidence level. The value of t (calculated in modulus) was lower than the critical value of t (2.77). Therefore, there are no significant statistical differences between the two data sets and the methodology presents adequate accuracy.
2.2. Reagents and solutions All reagents used were of analytical grade purity (PA). Ultrapure water was obtained using an Elga Purelab Classic system (High Wycombe, UK). Glassware was decontaminated with 5% (v v−1) HNO3 solution for at least 24 h. After this period, they were rinsed with deionized water and dried under dust-free environment. Concentrate nitric acid and 30% (v v−1) hydrogen peroxide were purchased from Merck (Darmstadt, Germany). Standard Ca, Cu, Fe, Mg, Mn, Zn, Na and K solutions were prepared by the dilution of the respective stock solutions (Merck, Kenilworth, NJ, USA) 1000 μg mL−1 conserved in 1% (v v−1) hydrochloric acid solution.
Table 1 Analytical characteristics of the methodology applied in the determination of metals in industrialized tea samples.
2.3. Sample collection The tea samples commercialized in sachets were purchased in the city of Jequié (Southwest of Bahia State, Brazil). The obtained samples were of 4 flavors: Chamomile (Chamomilla recutita), Green Tea
Metal
LODa
LOQa
%RSDb
Sensitivity (mg/L)
Linearity (R2)
Cu Fe Mn Zn Ca K Mg Na
0.22 0.78 0.17 0.69 4.9 1.1 0.30 0.38
0.74 2.6 0.56 2.3 16 3.7 1.0 1.3
1.8 2.2 1.2 3.1 1.5 3.1 3.2 3.8
0.0774 0.0191 0.0481 0.254 0.0177 8877 0.709 8750
0.9956 0.9954 0.9991 0.9985 0.9866 0.9964 0.9916 0.9907
a b
2
Expressed in mg kg−1 for a sample mass of 0.2 g. For a 0.5 mg/ L metal solution (N = 10).
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in fennel samples. These variabilities are frequently related to the soil where the plants are grown and to the use of fertilizers. The factors responsible for the metal content in plant samples are very complex and should consider aspects such as geographic origin, type of soil for planting, use of fertilizers, climatic factors, harvest time, use of pesticides, type of processing, among others [32]. Thus, the levels can vary widely and care must be taken when making categorical statements about a particular type of tea and its contribution to the body nutrition. Due to the scarcity of literature studies for the determination of metals in tea samples commercialized in sachets, the obtained contents were compared with those for non-industrialized tea leaves found in studies conducted by Szymczycha-Madejaet al. [33] and Castro et al. [8] (Table 4). Comparing the metal contents found in this study with the contents presented in Table 4, the variation range of Fe concentration in the analyzed samples (43.94 to 532.2 mg kg−1) was within the variation range found in the study of Szymczycha-Madeja et al. [33] and close to the values found for Mellissa officinalis and Peumusboldus in the study of Castro et al. [8]. The variation ranges of Cu (2.014 to 14.90 mg kg−1), Zn (12.05 to 41.84 mg kg−1), Ca (1856.3 to 10,012 mg kg−1) are also contemplated by the concentration ranges found in the study of Szymczycha-Madeja et al. [33]. The variation range for Na (167.70 to 4322.3 mg kg−1) also contemplates the values found by Dalipi et al. [33]. Some samples have Na contents up to about ten times higher than those found in that study. For Mg (739.9 to 2237 mg kg−1), the values are below those found by Castro et al. [8]. The variation range of K (5,089.1 to 14,334 mg kg−1) is also below the ranges found by Szymczycha-Madeja et al. [33] and well below the values found by Castro et al. [8], mainly compared to the Mellissa officinalis sample.
Table 2 Results of the analysis of certified reference material (Apple leaves NIST 1515). Metal
Certified value (mg kg−1)
Observed value (mg kg−1)
Fe Cu Zn Ca Mn Mg
82.7 ± 2.6 5.69 ± 0.13 12.4 ± 0.4 a 1.526 ± 0.015 54.1 ± 1.1 a 0.271 ± 0.012
85.2 ± 0.3 5.31 ± 0.08 11.8 ± 0.7 a 1.470 ± 0.030 55.2 ± 0.8 a 0.308 ± 0.081
N = 3. a Values in % m m−1.
3. Results and discussion 3.1. Mineral content of the tea samples After the validation of the methodology, the samples were analyzed and their metal concentrations are presented in Table 3. The average of each metal for all the samples and their standard deviations are also presented. Analysis of variance (ANOVA) was applied to the data set, aiming to evaluate the significant difference between the metal content inter and intra group. The ratio between the inter and (2.42 × 108) intro (3.48 × 106) groups mean of square is 69.48 (>critical F = 2.07), showing that this difference is significant. The analysis of the data presented in Table 3 show that boldo tea samples presented, on average, the highest concentrations of iron (328 mg kg−1). Green tea samples presented the highest concentrations of copper (9.3 mg kg−1). Samples of fennel tea showed the highest concentrations of zinc (35.3 mg kg−1), calcium (7289 mg kg−1) and potassium (13,015 mg kg−1). Finally, the samples of chamomile tea presented the highest concentrations of sodium (1918 mg kg−1) and magnesium (1847 mg kg−1). Analyzing the dispersion (in terms of % RSD) for each type of tea, it is observed that chamomile samples present the highest dispersion results for Fe (82.3%), Zn (23.6%) and Na (83.6%). Boldo tea presented the highest dispersion for Mg (23.3%), K (20.8%) and Mn (36.9%). On the other hand, the highest data dispersion for Cu (42.7%) was found in the green tea sample, and Ca (30.0%)
3.2. Multivariate analysis 3.2.1. Principal component analysis The interpretation of the results of PCA was carried out by visualizing the scores and the loadings plotted as graphs. Fig. 1(a) shows the score plot. This graph represents the linear projection of the objects (in this case, the tea samples) in the plane formed by the first two PCs. The
Table 3 Concentration of metals (mg kg−1) in tea samples commercialized in sachets after application of the digestion block decomposition procedure and determination by flame atomic spectrometry (FAAS and FAES). Samples
N°
Code
Cu
Fe
Mn
Zn
Ca
a
Boldo
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
B1 B2 B3 B4 B5 C1 C2 C3 C4 C5 F1 F2 F3 F4 F5 G1 G2 G3 G4 G5
4.1 ± 0.6 2.8 ± 0.0 2.4 ± 0.6 2.0 ± 1.0 2.4 ± 0.6 6.4 ± 0.6 6±1 6.4 ± 0.6 6.4 ± 0.6 8.5 ± 0.0 7.2 ± 0.6 4±1 8±1 10 ± 1 7±1 15 ± 2 10.5 ± 0.7 10.5 ± 0.6 5.7 ± 0.6 5.2 ± 0.0 6 3
495 ± 5 246 ± 3 112 ± 2 532 ± 46 258 ± 59 58 ± 2 55 ± 7 50 ± 4 43.9 ± 0.1 203 ± 11 472 ± 46 102 ± 7 301 ± 36 66 ± 4 231 ± 20 329 ± 18 314 ± 5 375 ± 82 143 ± 20 71 ± 7 223 159
87.1 ± 0.1 55.5 ± 0.2 34.4 ± 0.2 75.6 ± 0.1 43.8 ± 0.1 56.8 ± 0.3 34.4 ± 0.4 41.2 ± 0.4 29.4 ± 0.2 49.4 ± 0.1 43.0 ± 0.3 27.0 ± 0.2 38.1 ± 0.2 32.5 ± 0.2 41.8 ± 0.2 939 ± 2 946 ± 2 758 ± 1 789.8 ± 0.9 933 ± 2 252 370
16.5 ± 0.3 14 ± 1 17.3 ± 0.2 12 ± 1 19.2 ± 0.1 22.4 ± 0.9 28.0 ± 0.4 25 ± 2 14 ± 1 27 ± 1 37.0 ± 0.1 34 ± 2 33 ± 1 30.7 ± 0.8 41.8 ± 0.6 28.9 ± 0.3 31 ± 1 22 ± 1 17.7 ± 0.6 21.7 ± 0.5 25 8
6623 ± 380 6424 ± 56 6908 ± 139 7142 ± 298 6511 ± 116 2801 ± 98 2403 ± 98 2770 ± 145 4124 ± 141 3653 ± 145 7943 ± 79 6451 ± 220 7949 ± 194 4090 ± 190 10,012 ± 342 2362 ± 142 2446 ± 30 2564 ± 144 2067 ± 4 1856 ± 97 4855 2483
5572 ± 237 5089 ± 62 7742 ± 58 6073 ± 14 8185 ± 99 12,260 ± 126 13,179 ± 124 13,008 ± 234 11,727 ± 496 12,911 ± 53 12,652 ± 152 12,581 ± 92 14,334 ± 152 12,899 ± 46 12,610 ± 187 8620 ± 159 7919 ± 92 8733 ± 64 7756 ± 257 10,056 ± 130 10,195 2929
Chamomile
Fennel
Green
Mean Standard deviation
B = Boldo, C = Chamomile, F = Fennel, G = Green; The numbers1, 2, 3,4, and 5 refer to manufacturers. Data are presented as: mean metal concentration ± standard deviation for N = 3. a Elements determined by FAES. 3
K
Mg
a
1326 ± 46 1121 ± 18 823 ± 60 1051 ± 6 740 ± 44 1574 ± 57 2185 ± 12 1864 ± 59 1607 ± 18 2005 ± 38 1903 ± 1 1708 ± 44 1636 ± 20 2237 ± 29 1752 ± 11 1240 ± 16 1214 ± 19 1418 ± 12 1291 ± 63 1264 ± 19 1498 421
373 ± 19 328 ± 20 187 ± 4 221 ± 19 168 ± 1 1587 ± 93 541 ± 7 509 ± 51 2629 ± 54 4322 ± 85 967 ± 12 465 ± 7 1175 ± 34 1828 ± 60 881 ± 9 597 ± 2 617.3 ± 0.3 475 ± 9 356 ± 4 415 ± 3 932 1014
Na
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Table 4 Concentration ranges (mg kg−1) of some metals in tea leaf samples. Metal
a
Cu Fe Mn Zn Ca K Mg Na
0.05–602 0.70–11,700 n.d–15,000 n.d–1120 n.d–46,000 8000–27,800 – –
Black tea
a
a
0.03–270 0.03–13,000 14.4–7510 0.06–735 n.d–3700 6300–29,900 – –
0.70–448 70–10,400 390–2370 2.50–663 2260–24,000 8500–22,100 – –
Green tea
OOlong tea
b
Melissa officinalis
3.51 82.4 32.5 64.0 43,300 34,600 3870 338.0
b
Peumus boldus
2.01 67.2 55.2 42.0 35,800 22,300 2450 274.0
n.d.: not detectable. a Szymczycha-Madeja et al. b Castro et al.
Fig. 1. (a) Score and (b) loading graphs for PC1 versus PC2 obtained by the data treatment from the determination of metals in tea samples marketed in sachets.
Fig. 2. Dendrogram obtained by the treatment of the data coming from metal determination in tea samples commercialized in sachets by hierarchical cluster analysis.
first two PCs account for 64.8% of the data variance. As can be seen, there is a tendency for the samples to approach to form four groups. It is noted that the formed groups tend to represent the flavors of the analyzed teas, regardless of manufacturer or lot brand. Interestingly,
sample 14 (Fennel – F4), which is a sweet-tasting tea belonging to manufacturer 4, appeared in the group of chamomile-flavored teas. This happened due to its composition which, in terms of the concentrations of the studied metals, is more similar to this group. 4
Microchemical Journal 151 (2019) 104248
D.A.S. Gomes, et al.
Fig. 3. (a) Components of neural map showing group formation; (b) distance matrix (U-matrix) and (c) Component maps for data generated from elemental analysis of the tea samples commercialized in sachets. Each map shows the analyte concentrations in mg kg−1 (see color bars at right side of the map). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
The loading graph presented in Fig. 1(b) provides the projection of the variables on the first two PCs. From this graph, it is possible to obtain information about the correlation between the variables, their importance in modeling and their influence on the separation of sample groups. The correlation between the factors is described by the cosine of the angle formed by two loading vectors and, the smaller the angle, the greater the correlation between the variables and vice versa. Thus, poorly correlated variables are orthogonal to each other. Using the data obtained by analyzing the tea samples as an example, it can be stated that Na is much correlated with Zn and these two metals are poorly correlated with Fe. Another important characteristic is the size of the loading vector. Its measurement is related to its importance in PC modeling. Thus, vector
loadings of measures equal to or close to 1 contribute a great deal to modeling, while vectors close to the origin of the Cartesian system (close to zero) represent unimportant variables in modeling and can, therefore, be eliminated. In Fig. 1(b), the Fe loading vector is the smallest in relation to the others. However, it is still important in data modeling and cannot be excluded [13]. When scores and loading graphs are analyzed together, they can reveal the variables (the metal content) responsible for grouping the objects (the samples). Thus, the direction pointed out by the loading vector in relation to a group is related to its discriminating ability. On the other hand, the samples located on the left side have higher levels of Zn, K, Mg, Cu and Na. The samples that are in the right quadrants have higher Ca, Mn and Fe contents. Samples located in the lower quadrants tend to have higher levels of K, Mg, Zn, Na, Fe and mainly Ca. Samples 5
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D.A.S. Gomes, et al.
Fig. 4. Three-dimensional neural map for the tea samples.
and K. The F (Fennel) group is characterized by high concentrations of K, Zn and Ca; The G (Green) group, by high concentrations of Mn and Cu and, finally, the B (Boldo) group, by high concentrations of Fe and low Cu, Zn, Mg, Na, K and Mn concentrations. In general, the contents of the metals studied can vary greatly in foods of plant origin. Not only can the highest concentrations obtained for the elements determined in this study be related to the characteristics of the soil, but also the manufacturing process of the tea samples and its accumulation in leaves during the growth period [34,35]. Although the dry matter of tea leaves contains relatively high levels for some elements, most of these elements are generally not available for the solution during the preparation process for consumption [36]. In the analysis by neural networks, two errors are evaluated: (i) quantization error (QE), which represents the mean of the distances between each data vector and the corresponding weight vector of the winning neuron. The value 1.146 was obtained, according to the literature [22] and, the smaller the quantization error, the better adjusted the winning neuron will be to the input vectors; (ii) Topographic error (TE), which quantifies the map ability to represent the topology of the input data. The value zero was obtained, indicating that, for all inputs, it is the best fit neuron and also the second best fit neuron, i.e., not all the input data are correctly fitted to the respective neurons. The variables influence the location of the samples within the groups and can be observed in U-Matrix (Fig. 3(c)). Lower distances are associated with similar neuronal activation, while the greater distances are associated with the delimitation of the groups. Elements that form the same group present small distances between them and configure color uniformity (high values in red and low values in blue) [37]. The groups generated through the application of neural networks corroborate those found through PCA and HCA. Fig. 4 shows the three-dimensional neural map, evidencing the separation in groups in the distribution space of the characteristics of samples, corroborating what was presented in Fig. 3(a). Despite the proximity between distinct groups, it is observable that, in space, they are at different points.
from the upper quadrants present higher levels of Cu and Mn. Overlapping Fig. 1(a) and 1(b), it is noted that Ca and Fe are the variables responsible for grouping the boldo samples; K, Mg, Zn and Na group the samples of chamomile; Mn and Cu group the samples of green tea and K, Mg, Zn, Na and Ca group the fennel samples. 3.2.2. Hierarchical cluster analysis Hierarchical cluster analysis (HCA) was also applied in the exploration of the data originated from the analysis of the tea samples. In this exploratory analysis, the samples are grouped according to their similarities, expressed by the distances from each other. Fig. 2 presents the dendrogram obtained. It is noted that, by choosing a bond distance of 2500, the same four groups formed by the same samples seen in the PCA are obtained. HCA confirmed the behavior observed in PCA, in which the same samples with a tendency to belong to different manufacturers were grouped according to the tea flavors. 3.2.3. Artificial neural networks associated with Kohonen self-organizing maps The characteristics of artificial neural networks related to the input/ output learning form can be used for typically linear data, but also mainly non-linear data. Therefore, it is used to corroborate or not data of PCA and HCA, since the last two are used in linear data and, if the data are linear, then three tools coincide but, if the data are non-linear, then neural networks should be preferred in relation to the others. In Fig. 3(a), it is possible to observe the dimension map [6 × 4], evidencing the formation of four groups based on different types of tea. The map presents the neuron which defines sample grouping for various input data due to its ability to reduce dimensionality, eliminating redundant information. Although samples F3, F4 and F5 are part of group F (Fennel), the neural map shows only F1 and F2, once they are more relevant samples and due to the fact that each neuron tends to synthesize the information, showing only the samples that are significant for the group. The same reasoning can be extended to the other groups. This information is corroborated by observing the distance matrix (Fig. 3 (b)), where the proximity of the samples based on the color scale was found, with blue indicating greater proximity between samples. In Fig. 3(c), it is possible to observe how the concentration of metals influences the separation groups. The C (Chamomile) group is characterized by low concentrations of Fe and high concentrations of Na
4. Conclusions The metal analysis of tea samples commercialized in sachets from different brands showed that the raw material used is more influent in the concentration of the analyzed elements than their manufactures. 6
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The application of multivariate techniques (PCA and HCA) showed that the analyzed samples resemble in terms of the metal content, according to the type of herb used in their manufacture. These tools showed the formation of four groups: (1) green tea samples presenting major Mn and Cu concentrations, (2) bold tea samples presenting major Fe and Ca concentrations, (3) fennel tea samples and (4) chamomile tea samples that present major Na, Zn, Mg and K concentrations. The chamomile group presents major concentrations of Cu and the fennel group presents major amounts of Ca. However, a fennel sample (F4) is close to the chamomile group. Additionally, Artificial Neural Networks associated to Kohonen Self Organizing Maps (ANN-KSOM) corroborate the results revealed by PCA and HCA and allow evidences with more detailed information about the data set.
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Welna, Multivariate data reduction and discrimination of black and green teas due to the physical fractionation pattern of selected metals determined in their infusions, Talanta 160 (2016) 314–324. [33] A. Szymczycha-Madeja, M. Welna, P. Pohl, Elemental analysis of teas and their infusions by spectrometric methods, TrAC Trends Anal. Chem. 35 (2012) 165–181. [34] L. Zhang, J. Zhang, L. Chen, T. Liu, G. Ma, X. Liu, Influence of manufacturing process on the contents of iron, copper, chromium, nickel and manganese elements in Crush, Tear and Curl black tea, their transfer rates and health risk assessment, Food Control 89 (2018) 241–249. [35] J. Brzezicha-Cirocka, M. Grembecka, P. Szefer, Monitoring of essential and heavy metals in green tea from different geographical origins, Food Control 89 (2018) 241–249. [36] T.J.N. Ramos, O.A. Lameira, M.S.M. Silva, R.C.S. Müller, K.C.F. Faial, M.O. Lima, Evaluation of mineral composition of Embaúba (Cecropia palmata Willd. 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Declaration of competing interest The authors have no conflict of interest to declare. Acknowledgements The authors would like to acknowledge the financial support of Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Grant Number 304582/2018-2), and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) Finance Code 001. References [1] G. Schulzki, B. Nüßlein, H. Sievers, Transition rates of selected metals determined in various types of teas (Camellia sinensis L. Kuntze) and herbal/fruit infusions, Food Chem. 215 (2017) 22–30. [2] J. Brzezicha-Cirocka, M. Grembecka, P. Szefer, Monitoring of essential and heavy metals in green tea from different geographical origins, Environ. Monit. Assess. 188 (2016) 183. [3] I.R.C. Vulcano, J.N. Silveira, E.M. Alvarez-Leite, Teores de chumbo e cádmio em chás comercializados na região metropolitana de Belo Horizonte, Braz, J. Pharm. Sci. 44 (3) (2008) 425–431. [4] S.J. Duffy, J.F. Keaney Jr, M. Holbrook, N. Gokce, P.L. Swerdloff, B. Frei, J.A. Vita, Short- and long-term black tea consumption reverses endothelial dysfunction in patients with coronary artery disease, Circulation 104 (2001) 151–156. [5] F.M. Nishiyama, M.A.F. Costa, A.M. Costa, C.G.M. Souza, D.G. Bôer, C.K. Bracht, R.M. Peralta, Chá verde brasileiro (Camellia sinensis var assamica): efeitos do tempo de infusão, acondicionamento da erva e forma de preparo sobre a eficiência de extração dos bioativos e sobre a estabilidade da bebida, Cienc. Tecnol. Aliment. 30 (1) (2010) 191–196. [6] F. Catterall, E. Copeland, M.N. Clifford, C. Ioannides, Effects of black tea theaflavins on aflatoxin B1 mutagenesis in the Ames test, Mutagenesis 18 (2) (2003) 145–150. [7] A. Kumar, A.G.C. Nair, A.V.R. Reddy, A.N. Garg, Availability of essential elements in Indian and US tea brands, Food Chem. 89 (2005) 441–448. [8] J.T. Castro, M.A. Bezerra, M.A.L. Neto, M.G.A. Korn, C.G. Novaes, R.E. Santelli, Use of a constrained mixture design to optimize a procedure for closed vessel microwave-assisted digestion of vegetal leaves, Curr. Microwave Chem. 4 (1) (2017) 1–9. [9] L.M. de Oliveira, S. Das, E.B. da Silva, P. Gao, J. Gress, Y. Liu, L.Q. Ma, Metal concentrations in traditional and herbal teas and their potential risks to human health, Sci. Total Environ. 633 (2018) 649–657. [10] N. Aksuner, E. Henden, Z. Aker, E. Engin, S. Satik, Determination of essential and non-essential elements in various tea leaves and tea infusions consumed in Turkey, Food Addit. Contam., Part B 5 (2) (2012) 126–132. [11] A.F.S. Assis, F.M.O. Anastácio, M.D.B. Silva, C.B. Amarante, P.A.P.F.G. Neves, Determinação do teor de metais em chás de plantas medicinais, Enciclopédia biosfera, centro científico conhecer 11 (21) (2015) 3396–3407. [12] R.L. Santos, M.S.C. Nobre, G.P. Guimarães, T.B. Dantas, K.V.M. Vieira, D.C.F. Felismino, I.C. Dantas, Contaminação fúngica de plantas medicinais utilizadas em chás, Rev. Ciênc. Farm. Básica Apl. 34 (2) (2013) 289–293. [13] M. Otto, Chemometrics: Statistics and Computer Application in Analytical Chemistry, Third edition, Wiley VCH, Freiberg (Germany, 2017. [14] S. Amiry, M. Esmaiili, M. Alizadeh, Classification of adulterated honeys by
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Evaluation of metal content in tea samples commercialized in sachets using multivariate data analysis techniques
T
Délis Alves Souza Gomesa, Juscelia Pereira dos Santos Alvesa, Erik Galvão Paranhos da Silvab, ⁎ Cleber Galvão Novaesa, , Darci Santos Silvaa, Rosane Moura Aguiara, Sulene Alves Araújoa, Ana Caroline Lessa dos Santosa, Marcos Almeida Bezerraa a
Universidade Estadual do Sudoeste da Bahia, Campus Jequié, Departamento de Ciências e Tecnologias, Rua José Moreira Sobrinho s/n, Jequié, Bahia 45208-091, Brazil Universidade Estadual de Santa Cruz, Departamento de Ciências Exatas e Tecnológicas, Campus Soane Nazaré de Andrade, Km 16 - BR-415, Ilhéus, Bahia 45662-900, Brazil
b
ARTICLE INFO
ABSTRACT
Keywords: Teas in sachets Metals Kohonen maps Neural networks Principal component analysis Hierarchical cluster analysis
Tea is a beverage consumed all over the world, and, besides the very pleasant taste, it has substances in its composition that can lead to various beneficial effects to human health. However, although teas have essential elements in their composition, they can also be contaminated with metals from the soil, air, and equipment used in their production. In this study, eight metals (Ca, Cu, Fe, Mg, Mn, Zn, Na and K) were determined in tea samples commercialized in sachets using flame atomic spectrometry (absorption and emission) after acid decomposition in a digestion block. The concentration of the analyzed metals varied as follows (in mg kg−1): Ca (1856.3–10,012), Cu (2.014–14.90), Fe (43.94–532.2), Mg (739.9–2237), Mn (26.95–946.3), Zn (12.05–41.84), Na (167.7–4322) and K (5089.1–14,334). The generated data were statistically analyzed using the following multivariate analysis tools: Principal Component Analysis, Hierarchical Cluster Analysis and Kohonen self-organizing maps. The multivariate analysis classified the different tea samples into well defined groups according to flavor, based on the mineral composition of eight quantified elements.
1. Introduction Tea is a widely appreciated beverage around the world due either to its pleasant taste or its nutritional and medicinal properties. Teas are frequently prepared from plant parts such as leaves, roots, seeds and barks in the form of infusions, that is, by immersing the material directly into hot water [1–3]. Teas are considered functional foods that, if consumed daily, can bring innumerable benefits to human health, since they have the capacity to reduce cardiovascular diseases [4], cholesterol levels, besides presenting antimicrobial, antioxidant, immunostimulatory activities [5] and antimutagenic potential [6]. In general, teas present, in their composition, substances that play a vital role in the various metabolic processes and are essential for human well-being. Thus, some of these substances, when in excess or deficiency, can cause health damage [7]. Teas may provide nutritional trace elements, but also elements that are potentially toxic to human metabolism [8–10]. Therefore, due to the great consumption of this beverage, there is also an interest in analyzing the content of metals present in its composition, mainly due to the belief that these beverages
⁎
are healthier than the medications derived from allopathic medicine [11,12]. Modern analytical instrumentation allows sample elemental analysis over a short period of time, generating a large amount of data. Evaluating these data and extracting relevant information can be a difficult task when the analyst has only visual observation. Currently, the extensive use of multivariate pattern recognition tools has been consolidated in chemical analysis laboratories, allowing an efficient evaluation and interpretation of these data and revealing latent information [13,14]. Among the various pattern recognition methods, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) have been widely applied in analytical chemistry, once they meet the needs of researchers and due to the ease of interpretation of the results generated [15,16]. PCA has been already used to evaluate the potential of intrinsic fluorescence to characterize the antioxidant capacity of soy protein hydrolysates during sequential ultrafiltration and nanofiltration [17], differentiation of gelatin sources based on polypeptide molecular weights [18], real-time monitoring of the coffee roasting process using near infrared (NIR) spectroscopy in diffuse
Corresponding author. E-mail address: [email protected] (C.G. Novaes).
https://doi.org/10.1016/j.microc.2019.104248 Received 21 June 2019; Received in revised form 7 September 2019; Accepted 7 September 2019 Available online 10 September 2019 0026-265X/ © 2019 Elsevier B.V. All rights reserved.
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reflectance mode [19]; PCA and HCA have also been used for the characterization of marbles by capillary electrophoresis [20], among other studies. Kohonen maps (or Self-Organizing Maps, SOMs) are multivariate tools based on artificial neural network algorithms with high learning power. Teuvo Kohonen developed this algorithm, which has been heavily applied, as it is considered a simple way for organization of dimensionally complex data according to their similarities, providing pattern recognition [21,22]. In this artificial neural network application, the neurons are put in nodes of the lattices and become selectively tuned to input patterns of a competitive learning process. Kohonen maps have been applied to: data exploration of mineral content in soft drinks from different flavors and manufactures [23], evaluation of nutrient profiles of cashew nuts [24], identification of effects of Fe(III) ion and quality of humic substances on As(V) distribution in fresh water [25], visualization of contamination in packaged fresh alfalfa by Salmonella typhimurium [26], evaluation of honeybees as sentinels for lead pollution [27], screening of Passiflora L. mineral content [28], evaluation of chemical composition of waters associated with petroleum production [29], prediction of groundwater levels for management in arid areas [30], indications of airborne polychlorinated biphenyl and organochlorine pesticide dependence on spatial and meteorological parameters [31], among others. In this study, PCA, HCA and Kohonen SOMs were applied in the evaluation of data generated by analysis of tea samples using flame atomic absorption spectrometry. The three multivariate tools were compared in terms of efficiency and capacity of pattern recognition.
(Camellia sinensis (L.) Kuntze), Boldo (Peumus boldus) and Fennel (Pimpinella anisum) and 5 different brands, totaling 20 samples. They were then transported to the laboratory, where they were submitted to the preliminary treatments. The samples were milled with mortar and pestle, sieved in a 100 μm sieve and stored in decontaminated polyethylene bottles. 2.4. Acid decomposition of samples in the digestion block After grinding the tea samples, a mass of approximately 0.2 g was weighed in a glass digestion tube. Posteriorly, 5.0 mL of concentrated nitric acid and 3 mL of 30% (v v−1) hydrogen peroxide were added and the tube was coupled with a reflux system with cold finger. The samples were then taken to the digestion block and heated to 110 °C until almost dry. To prevent the sample from drying out completely, ultrapure water was added during the process until complete sample digestion. Subsequently, the samples were cooled to room temperature, transferred into a 25.0 mL volumetric flask and diluted with deionized water. The metals were determined in the final solution by flame atomic spectrometry (absorption – FAAS and emission – FAES). 2.5. Data treatment The data set was organized onto a matrix consisting of 20 tea samples and 8 variables (the determined metals). Before PCA and HCA, the matrix was self-scaled. Graphs were plotted using the Statistica 12.0 software (The StatSoft, Inc., Tulsa, OK, USA). Kohonen self-organizing maps were implemented, besides training and clustering in the Matlab software R2016a (The MathWorks, Co., Natick, MA, USA), as well as a toolbox available at: www.cis.hut.fi/ projects/somtoolbox. The implementation of codes was elaborated from algorithms suggested in the literature available at the website described above. The processed results used for training the network are based on metal concentrations in the tea samples presented in Table 3.
2. Experimental 2.1. Instrumentation A Perkin Elmer AAnalyst 200 (Norwalk, CT, USA) flame atomic absorption spectrometer equipped with a deuterium lamp for background correction was used for measuring the absorbance of solutions. Hollow cathode lamps for the analyzed elements (except for Na and K, which were determined by flame atomic emission spectrometry) were used in accordance with the recommendations of the manufacturer: wavelengths (nm): Ca (422.7), Cu (324.7), Fe (248.3), K (766.5), Mg (285.8), Na (589.0) and Zn (213.9). The burner height (13.5 mm) was also used with conventional values. The flame composition was acetylene and air (flow rate 2.0 L min−1 and 13.5 L min−1, respectively). The flow rate used for the nebulizer was 5.0 mL min−1. Mass determinations were carried out by an analytical balance (Sartorius BL D105). A digestion block (Tecnal TE 0851, Piracicaba, Brazil) fitted with cold finger was used for acid decomposition with HNO3 and H2O2.
2.6. Quality control Analytical characteristics to perform the determination of the eight metals involved in this study were verified. The limits of detection (LOD) and quantification (LOQ), precision (in form of repeatability), analytical sensitivity, and linearity are presented in Table 1. The accuracy of the method was accessed by analysis of certified reference material (Apple leaves NIST 1515). The results of this analysis and the certified values are presented in Table 2. To compare the two sets of values in Table 2, the paired t-test was applied at a 95% confidence level. The value of t (calculated in modulus) was lower than the critical value of t (2.77). Therefore, there are no significant statistical differences between the two data sets and the methodology presents adequate accuracy.
2.2. Reagents and solutions All reagents used were of analytical grade purity (PA). Ultrapure water was obtained using an Elga Purelab Classic system (High Wycombe, UK). Glassware was decontaminated with 5% (v v−1) HNO3 solution for at least 24 h. After this period, they were rinsed with deionized water and dried under dust-free environment. Concentrate nitric acid and 30% (v v−1) hydrogen peroxide were purchased from Merck (Darmstadt, Germany). Standard Ca, Cu, Fe, Mg, Mn, Zn, Na and K solutions were prepared by the dilution of the respective stock solutions (Merck, Kenilworth, NJ, USA) 1000 μg mL−1 conserved in 1% (v v−1) hydrochloric acid solution.
Table 1 Analytical characteristics of the methodology applied in the determination of metals in industrialized tea samples.
2.3. Sample collection The tea samples commercialized in sachets were purchased in the city of Jequié (Southwest of Bahia State, Brazil). The obtained samples were of 4 flavors: Chamomile (Chamomilla recutita), Green Tea
Metal
LODa
LOQa
%RSDb
Sensitivity (mg/L)
Linearity (R2)
Cu Fe Mn Zn Ca K Mg Na
0.22 0.78 0.17 0.69 4.9 1.1 0.30 0.38
0.74 2.6 0.56 2.3 16 3.7 1.0 1.3
1.8 2.2 1.2 3.1 1.5 3.1 3.2 3.8
0.0774 0.0191 0.0481 0.254 0.0177 8877 0.709 8750
0.9956 0.9954 0.9991 0.9985 0.9866 0.9964 0.9916 0.9907
a b
2
Expressed in mg kg−1 for a sample mass of 0.2 g. For a 0.5 mg/ L metal solution (N = 10).
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in fennel samples. These variabilities are frequently related to the soil where the plants are grown and to the use of fertilizers. The factors responsible for the metal content in plant samples are very complex and should consider aspects such as geographic origin, type of soil for planting, use of fertilizers, climatic factors, harvest time, use of pesticides, type of processing, among others [32]. Thus, the levels can vary widely and care must be taken when making categorical statements about a particular type of tea and its contribution to the body nutrition. Due to the scarcity of literature studies for the determination of metals in tea samples commercialized in sachets, the obtained contents were compared with those for non-industrialized tea leaves found in studies conducted by Szymczycha-Madejaet al. [33] and Castro et al. [8] (Table 4). Comparing the metal contents found in this study with the contents presented in Table 4, the variation range of Fe concentration in the analyzed samples (43.94 to 532.2 mg kg−1) was within the variation range found in the study of Szymczycha-Madeja et al. [33] and close to the values found for Mellissa officinalis and Peumusboldus in the study of Castro et al. [8]. The variation ranges of Cu (2.014 to 14.90 mg kg−1), Zn (12.05 to 41.84 mg kg−1), Ca (1856.3 to 10,012 mg kg−1) are also contemplated by the concentration ranges found in the study of Szymczycha-Madeja et al. [33]. The variation range for Na (167.70 to 4322.3 mg kg−1) also contemplates the values found by Dalipi et al. [33]. Some samples have Na contents up to about ten times higher than those found in that study. For Mg (739.9 to 2237 mg kg−1), the values are below those found by Castro et al. [8]. The variation range of K (5,089.1 to 14,334 mg kg−1) is also below the ranges found by Szymczycha-Madeja et al. [33] and well below the values found by Castro et al. [8], mainly compared to the Mellissa officinalis sample.
Table 2 Results of the analysis of certified reference material (Apple leaves NIST 1515). Metal
Certified value (mg kg−1)
Observed value (mg kg−1)
Fe Cu Zn Ca Mn Mg
82.7 ± 2.6 5.69 ± 0.13 12.4 ± 0.4 a 1.526 ± 0.015 54.1 ± 1.1 a 0.271 ± 0.012
85.2 ± 0.3 5.31 ± 0.08 11.8 ± 0.7 a 1.470 ± 0.030 55.2 ± 0.8 a 0.308 ± 0.081
N = 3. a Values in % m m−1.
3. Results and discussion 3.1. Mineral content of the tea samples After the validation of the methodology, the samples were analyzed and their metal concentrations are presented in Table 3. The average of each metal for all the samples and their standard deviations are also presented. Analysis of variance (ANOVA) was applied to the data set, aiming to evaluate the significant difference between the metal content inter and intra group. The ratio between the inter and (2.42 × 108) intro (3.48 × 106) groups mean of square is 69.48 (>critical F = 2.07), showing that this difference is significant. The analysis of the data presented in Table 3 show that boldo tea samples presented, on average, the highest concentrations of iron (328 mg kg−1). Green tea samples presented the highest concentrations of copper (9.3 mg kg−1). Samples of fennel tea showed the highest concentrations of zinc (35.3 mg kg−1), calcium (7289 mg kg−1) and potassium (13,015 mg kg−1). Finally, the samples of chamomile tea presented the highest concentrations of sodium (1918 mg kg−1) and magnesium (1847 mg kg−1). Analyzing the dispersion (in terms of % RSD) for each type of tea, it is observed that chamomile samples present the highest dispersion results for Fe (82.3%), Zn (23.6%) and Na (83.6%). Boldo tea presented the highest dispersion for Mg (23.3%), K (20.8%) and Mn (36.9%). On the other hand, the highest data dispersion for Cu (42.7%) was found in the green tea sample, and Ca (30.0%)
3.2. Multivariate analysis 3.2.1. Principal component analysis The interpretation of the results of PCA was carried out by visualizing the scores and the loadings plotted as graphs. Fig. 1(a) shows the score plot. This graph represents the linear projection of the objects (in this case, the tea samples) in the plane formed by the first two PCs. The
Table 3 Concentration of metals (mg kg−1) in tea samples commercialized in sachets after application of the digestion block decomposition procedure and determination by flame atomic spectrometry (FAAS and FAES). Samples
N°
Code
Cu
Fe
Mn
Zn
Ca
a
Boldo
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
B1 B2 B3 B4 B5 C1 C2 C3 C4 C5 F1 F2 F3 F4 F5 G1 G2 G3 G4 G5
4.1 ± 0.6 2.8 ± 0.0 2.4 ± 0.6 2.0 ± 1.0 2.4 ± 0.6 6.4 ± 0.6 6±1 6.4 ± 0.6 6.4 ± 0.6 8.5 ± 0.0 7.2 ± 0.6 4±1 8±1 10 ± 1 7±1 15 ± 2 10.5 ± 0.7 10.5 ± 0.6 5.7 ± 0.6 5.2 ± 0.0 6 3
495 ± 5 246 ± 3 112 ± 2 532 ± 46 258 ± 59 58 ± 2 55 ± 7 50 ± 4 43.9 ± 0.1 203 ± 11 472 ± 46 102 ± 7 301 ± 36 66 ± 4 231 ± 20 329 ± 18 314 ± 5 375 ± 82 143 ± 20 71 ± 7 223 159
87.1 ± 0.1 55.5 ± 0.2 34.4 ± 0.2 75.6 ± 0.1 43.8 ± 0.1 56.8 ± 0.3 34.4 ± 0.4 41.2 ± 0.4 29.4 ± 0.2 49.4 ± 0.1 43.0 ± 0.3 27.0 ± 0.2 38.1 ± 0.2 32.5 ± 0.2 41.8 ± 0.2 939 ± 2 946 ± 2 758 ± 1 789.8 ± 0.9 933 ± 2 252 370
16.5 ± 0.3 14 ± 1 17.3 ± 0.2 12 ± 1 19.2 ± 0.1 22.4 ± 0.9 28.0 ± 0.4 25 ± 2 14 ± 1 27 ± 1 37.0 ± 0.1 34 ± 2 33 ± 1 30.7 ± 0.8 41.8 ± 0.6 28.9 ± 0.3 31 ± 1 22 ± 1 17.7 ± 0.6 21.7 ± 0.5 25 8
6623 ± 380 6424 ± 56 6908 ± 139 7142 ± 298 6511 ± 116 2801 ± 98 2403 ± 98 2770 ± 145 4124 ± 141 3653 ± 145 7943 ± 79 6451 ± 220 7949 ± 194 4090 ± 190 10,012 ± 342 2362 ± 142 2446 ± 30 2564 ± 144 2067 ± 4 1856 ± 97 4855 2483
5572 ± 237 5089 ± 62 7742 ± 58 6073 ± 14 8185 ± 99 12,260 ± 126 13,179 ± 124 13,008 ± 234 11,727 ± 496 12,911 ± 53 12,652 ± 152 12,581 ± 92 14,334 ± 152 12,899 ± 46 12,610 ± 187 8620 ± 159 7919 ± 92 8733 ± 64 7756 ± 257 10,056 ± 130 10,195 2929
Chamomile
Fennel
Green
Mean Standard deviation
B = Boldo, C = Chamomile, F = Fennel, G = Green; The numbers1, 2, 3,4, and 5 refer to manufacturers. Data are presented as: mean metal concentration ± standard deviation for N = 3. a Elements determined by FAES. 3
K
Mg
a
1326 ± 46 1121 ± 18 823 ± 60 1051 ± 6 740 ± 44 1574 ± 57 2185 ± 12 1864 ± 59 1607 ± 18 2005 ± 38 1903 ± 1 1708 ± 44 1636 ± 20 2237 ± 29 1752 ± 11 1240 ± 16 1214 ± 19 1418 ± 12 1291 ± 63 1264 ± 19 1498 421
373 ± 19 328 ± 20 187 ± 4 221 ± 19 168 ± 1 1587 ± 93 541 ± 7 509 ± 51 2629 ± 54 4322 ± 85 967 ± 12 465 ± 7 1175 ± 34 1828 ± 60 881 ± 9 597 ± 2 617.3 ± 0.3 475 ± 9 356 ± 4 415 ± 3 932 1014
Na
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Table 4 Concentration ranges (mg kg−1) of some metals in tea leaf samples. Metal
a
Cu Fe Mn Zn Ca K Mg Na
0.05–602 0.70–11,700 n.d–15,000 n.d–1120 n.d–46,000 8000–27,800 – –
Black tea
a
a
0.03–270 0.03–13,000 14.4–7510 0.06–735 n.d–3700 6300–29,900 – –
0.70–448 70–10,400 390–2370 2.50–663 2260–24,000 8500–22,100 – –
Green tea
OOlong tea
b
Melissa officinalis
3.51 82.4 32.5 64.0 43,300 34,600 3870 338.0
b
Peumus boldus
2.01 67.2 55.2 42.0 35,800 22,300 2450 274.0
n.d.: not detectable. a Szymczycha-Madeja et al. b Castro et al.
Fig. 1. (a) Score and (b) loading graphs for PC1 versus PC2 obtained by the data treatment from the determination of metals in tea samples marketed in sachets.
Fig. 2. Dendrogram obtained by the treatment of the data coming from metal determination in tea samples commercialized in sachets by hierarchical cluster analysis.
first two PCs account for 64.8% of the data variance. As can be seen, there is a tendency for the samples to approach to form four groups. It is noted that the formed groups tend to represent the flavors of the analyzed teas, regardless of manufacturer or lot brand. Interestingly,
sample 14 (Fennel – F4), which is a sweet-tasting tea belonging to manufacturer 4, appeared in the group of chamomile-flavored teas. This happened due to its composition which, in terms of the concentrations of the studied metals, is more similar to this group. 4
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Fig. 3. (a) Components of neural map showing group formation; (b) distance matrix (U-matrix) and (c) Component maps for data generated from elemental analysis of the tea samples commercialized in sachets. Each map shows the analyte concentrations in mg kg−1 (see color bars at right side of the map). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
The loading graph presented in Fig. 1(b) provides the projection of the variables on the first two PCs. From this graph, it is possible to obtain information about the correlation between the variables, their importance in modeling and their influence on the separation of sample groups. The correlation between the factors is described by the cosine of the angle formed by two loading vectors and, the smaller the angle, the greater the correlation between the variables and vice versa. Thus, poorly correlated variables are orthogonal to each other. Using the data obtained by analyzing the tea samples as an example, it can be stated that Na is much correlated with Zn and these two metals are poorly correlated with Fe. Another important characteristic is the size of the loading vector. Its measurement is related to its importance in PC modeling. Thus, vector
loadings of measures equal to or close to 1 contribute a great deal to modeling, while vectors close to the origin of the Cartesian system (close to zero) represent unimportant variables in modeling and can, therefore, be eliminated. In Fig. 1(b), the Fe loading vector is the smallest in relation to the others. However, it is still important in data modeling and cannot be excluded [13]. When scores and loading graphs are analyzed together, they can reveal the variables (the metal content) responsible for grouping the objects (the samples). Thus, the direction pointed out by the loading vector in relation to a group is related to its discriminating ability. On the other hand, the samples located on the left side have higher levels of Zn, K, Mg, Cu and Na. The samples that are in the right quadrants have higher Ca, Mn and Fe contents. Samples located in the lower quadrants tend to have higher levels of K, Mg, Zn, Na, Fe and mainly Ca. Samples 5
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Fig. 4. Three-dimensional neural map for the tea samples.
and K. The F (Fennel) group is characterized by high concentrations of K, Zn and Ca; The G (Green) group, by high concentrations of Mn and Cu and, finally, the B (Boldo) group, by high concentrations of Fe and low Cu, Zn, Mg, Na, K and Mn concentrations. In general, the contents of the metals studied can vary greatly in foods of plant origin. Not only can the highest concentrations obtained for the elements determined in this study be related to the characteristics of the soil, but also the manufacturing process of the tea samples and its accumulation in leaves during the growth period [34,35]. Although the dry matter of tea leaves contains relatively high levels for some elements, most of these elements are generally not available for the solution during the preparation process for consumption [36]. In the analysis by neural networks, two errors are evaluated: (i) quantization error (QE), which represents the mean of the distances between each data vector and the corresponding weight vector of the winning neuron. The value 1.146 was obtained, according to the literature [22] and, the smaller the quantization error, the better adjusted the winning neuron will be to the input vectors; (ii) Topographic error (TE), which quantifies the map ability to represent the topology of the input data. The value zero was obtained, indicating that, for all inputs, it is the best fit neuron and also the second best fit neuron, i.e., not all the input data are correctly fitted to the respective neurons. The variables influence the location of the samples within the groups and can be observed in U-Matrix (Fig. 3(c)). Lower distances are associated with similar neuronal activation, while the greater distances are associated with the delimitation of the groups. Elements that form the same group present small distances between them and configure color uniformity (high values in red and low values in blue) [37]. The groups generated through the application of neural networks corroborate those found through PCA and HCA. Fig. 4 shows the three-dimensional neural map, evidencing the separation in groups in the distribution space of the characteristics of samples, corroborating what was presented in Fig. 3(a). Despite the proximity between distinct groups, it is observable that, in space, they are at different points.
from the upper quadrants present higher levels of Cu and Mn. Overlapping Fig. 1(a) and 1(b), it is noted that Ca and Fe are the variables responsible for grouping the boldo samples; K, Mg, Zn and Na group the samples of chamomile; Mn and Cu group the samples of green tea and K, Mg, Zn, Na and Ca group the fennel samples. 3.2.2. Hierarchical cluster analysis Hierarchical cluster analysis (HCA) was also applied in the exploration of the data originated from the analysis of the tea samples. In this exploratory analysis, the samples are grouped according to their similarities, expressed by the distances from each other. Fig. 2 presents the dendrogram obtained. It is noted that, by choosing a bond distance of 2500, the same four groups formed by the same samples seen in the PCA are obtained. HCA confirmed the behavior observed in PCA, in which the same samples with a tendency to belong to different manufacturers were grouped according to the tea flavors. 3.2.3. Artificial neural networks associated with Kohonen self-organizing maps The characteristics of artificial neural networks related to the input/ output learning form can be used for typically linear data, but also mainly non-linear data. Therefore, it is used to corroborate or not data of PCA and HCA, since the last two are used in linear data and, if the data are linear, then three tools coincide but, if the data are non-linear, then neural networks should be preferred in relation to the others. In Fig. 3(a), it is possible to observe the dimension map [6 × 4], evidencing the formation of four groups based on different types of tea. The map presents the neuron which defines sample grouping for various input data due to its ability to reduce dimensionality, eliminating redundant information. Although samples F3, F4 and F5 are part of group F (Fennel), the neural map shows only F1 and F2, once they are more relevant samples and due to the fact that each neuron tends to synthesize the information, showing only the samples that are significant for the group. The same reasoning can be extended to the other groups. This information is corroborated by observing the distance matrix (Fig. 3 (b)), where the proximity of the samples based on the color scale was found, with blue indicating greater proximity between samples. In Fig. 3(c), it is possible to observe how the concentration of metals influences the separation groups. The C (Chamomile) group is characterized by low concentrations of Fe and high concentrations of Na
4. Conclusions The metal analysis of tea samples commercialized in sachets from different brands showed that the raw material used is more influent in the concentration of the analyzed elements than their manufactures. 6
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The application of multivariate techniques (PCA and HCA) showed that the analyzed samples resemble in terms of the metal content, according to the type of herb used in their manufacture. These tools showed the formation of four groups: (1) green tea samples presenting major Mn and Cu concentrations, (2) bold tea samples presenting major Fe and Ca concentrations, (3) fennel tea samples and (4) chamomile tea samples that present major Na, Zn, Mg and K concentrations. The chamomile group presents major concentrations of Cu and the fennel group presents major amounts of Ca. However, a fennel sample (F4) is close to the chamomile group. Additionally, Artificial Neural Networks associated to Kohonen Self Organizing Maps (ANN-KSOM) corroborate the results revealed by PCA and HCA and allow evidences with more detailed information about the data set.
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