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Simultaneous metal determination in artisanal cachaça by using voltammetry and multivariate calibration
Journal Pre-proofs Simultaneous metal determination in artisanal cachaça by using voltammetry and multivariate calibration Romário Junior Ferreira, Thalles Ramon Rosa, Josimar Ribeiro, Rosângela Cristina Barthus PII: DOI: Reference:
S0308-8146(19)32278-2 https://doi.org/10.1016/j.foodchem.2019.126126 FOCH 126126
To appear in:
Food Chemistry
Received Date: Revised Date: Accepted Date:
4 February 2019 13 December 2019 24 December 2019
Please cite this article as: Ferreira, R.J., Rosa, T.R., Ribeiro, J., Barthus, R.C., Simultaneous metal determination in artisanal cachaça by using voltammetry and multivariate calibration, Food Chemistry (2019), doi: https://doi.org/ 10.1016/j.foodchem.2019.126126
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1
Simultaneous metal determination in artisanal cachaça by using voltammetry and
2
multivariate calibration.
3 4 5 6
Romário Junior Ferreiraa, Thalles Ramon Rosab, Josimar Ribeiroa, Rosângela
7
Cristina Barthusa.
8
a
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Espírito Santo, Vitória-ES, Brazil.
Departamento de Química, Centro de Ciências Exatas, Universidade Federal do
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b
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Brazil.
Instituto Federal de Educação, Ciência e Tecnologia do Espírito Santo, Aracruz - ES,
12 13 14 15 16 17
Corresponding Author:
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Rosângela Cristina Barthus
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Av. Fernando Ferrari, 514, Goiabeiras, Vitória, Espírito Santo, Brazil.
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29075-910, E-mail address: [email protected]
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Phone Number: (++55 27) 4009-2846
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Abstract
27 28
In this study, square wave anodic stripping voltammetry using two different types of
29
electrodes (carbon nanotube electrode and graphite electrode) was combined with
30
chemometric methods - partial least squares (PLS) and artificial neural networks (ANN)
31
for determining copper, zinc, cadmium and lead in cachaça. The objectives were
32
comparison of methods developed and the verification of the quality of artisanal
33
cachaças in terms of metal content. For the development of the methodology,
34
inductively coupled plasma optical emission spectrometry (ICP OES) was used as
35
reference technique. The performance of multivariate models obtained was evaluated by
36
the coefficient of determination (R2) and root mean square error of prediction (RMSEP).
37
F test was utilized for comparing methods at confidence level of 95%. Better results
38
were observed by using carbon nanotube electrode regardless of the multivariate
39
method proposed. The methodology is simple, fast, and inexpensive and it can be used
40
in quality control laboratories.
41 42
Keywords: Cachaça, partial least square, artificial neural network, carbon nanotube
43
electrode, square wave anodic stripping voltammetry, metals.
44 45 46 47 48 49 50
51
1. Introduction
52
Developing faster and more sensitive analytical methods for the determination of metals
53
is extremely important due to the increasing demand for analyses of these elements in
54
order to ensure part of the quality criteria of products such as foods and beverages. In
55
this setting, this study aims to develop and compare metal determination methods in
56
cachaça and, in this context, verify the quality of artisanal cachaças based on these
57
contents. At first, copper, zinc, cadmium and lead were studied. Copper and zinc are
58
metals essential to the body, in a very specific concentration range, but they can become
59
dangerous beyond these amounts. On the other hand, lead and cadmium metals have a
60
high toxicity potential. They are non-essential and should therefore be kept within the
61
tolerable limits. Several studies characterize the performance of these metals in terms of
62
their interactions and/or toxicity. [Afridi et al., 2014; Simmons et al., 2003, Page et al.,
63
2015, Duarte et al., 2000; Oliveira et al., 2015].
64
In this aspect, the determination of these metals in cachaça – a drink obtained by
65
distilling fermented sugarcane [Brazil, 2005] – can allow the consumer to have adequate
66
levels of these elements and thus a safe consumption [Aresta et al., 2001; Miranda et al.,
67
2010; Fernandes et al., 2013]. Metal levels may vary depending on the mode of
68
production: whether artisanal or industrial, and also on the preparation and planting of
69
sugarcane. In another aspect, they have also influence in organoleptic criteria such as
70
odor and taste of the beverage [Azevedo et al., 2003].
71
Among the most commonly used techniques for determining metals in beverages are:
72
Atomic absorption spectrometry, X-ray fluorescence spectrometry and Inductively
73
coupled plasma optical emission spectrometry (ICP-OES) [Bermejo-Barrera et al.,
74
2007, Fernandes et al., 2013]. However, the disadvantages are the relatively expensive
75
cost of these techniques and generally the necessity of complex sample preparations
76
[Oliveira et al., 2015]. In this way, voltammetric techniques have been evaluated.
77
Among voltammetric techniques, anodic stripping voltammetry [Tyszczuk et al., 2007]
78
has been increasingly relevant mainly because, in many cases, it is not necessary to
79
pretreat the samples, it is a non-destructive technique, and it allows rapid analysis and
80
low detection limit. For metals, the mercury electrode has been widely employed for
81
these determinations. Nowadays, however, alternatives have been studied, due to
82
environmental concerns with the use of mercury [Lee et al., 2016, Yue et al., 2012].
83
Therefore, the use of electrodes formed by nanomaterials represents an alternative.
84
[Merkoçi, 2007, Keyvanfard et al., 2013]. These materials have a large surface area,
85
which improves mass transport and electron transfer. Specifically, nanostructured
86
carbon materials, such as those of nanotubes used here, associate these characteristics to
87
those of carbon that act as absorbing agents and pre-concentrators on the surface. The
88
electrode formed by carbon nanomaterials has already been used for metal
89
determination in cachaça, which demonstrated its application potential [Tavares et al.,
90
2012]. However, a fact to be taken into account is that the simultaneous determination
91
of several metals is complex because in most cases, there is an overlap of some peaks
92
that undergo reduction or oxidation in near potentials [Alagic et al., 2017, Gonzalez-
93
Calabui et al., 2016]. Then, despite the numerous advantages, the electrochemical
94
techniques suffer from lack of selectivity.
95
association of multivariate calibration methods and electrochemical techniques has
96
become important. This can be found in the recent literature where manuscripts report
97
this type of association while determining analytes in the most diverse types of
98
matrices, with emphasis on the pharmaceutical and environmental samples. [Melucci et
To overcome such difficulties, the
99
al., 2012; Alagic et al., 2017; Alves et al., 2011; Gonzales-Calabui et al., 2016,
100
Keyvanfard et al., 2013].
101
This study focuses on the development of a methodology for the determination of
102
metals in artisanal cachaça. The methods involve the association of chemometric
103
methods and square wave anodic stripping voltammetry for these determinations. This
104
study aims to compare the different types of electrodes (graphite and carbon nanotube
105
electrodes) used, as well as the proposed multivariate methods (partial least square –
106
PLS and artificial neural network - ANN). Ultimately, the study also aims to verify the
107
quality of the artisanal samples in terms of metal content.
108 109
2. Experimental procedures
110
2.1. Reagents and solutions
111
Nitric acid P.A was obtained from Neon® and distilled. All the other reagents used
112
were of analytical grade and used without prior purification. Diethyl alcohol, graphite
113
powder, carbon nanotube were purchased from Sigma Aldrich®. Sodium dihydrogen
114
phosphate or sodium phosphate and phosphoric acid obtained from Merck® were used
115
to prepare 1.00 mol L-1 buffer solution pH 4.0. Ultrapure water (resistivity of 18 mΩ -
116
cm 25°C), used in all experiments, was obtained from a reverse osmosis purification
117
system (Quimis®).
118
2.2. Samples
119
For the accomplishment of the experiments, we employed samples of artisanal cachaça
120
coming from several municipalities in the State of Espírito Santo: Domingos Martins,
121
São Roque de Canaã, Aracruz, Afonso Cláudio, Linhares, Cachoeiro do Itapemirim,
122
Alfredo Chaves, Anchieta, Cariacica, Santa Tereza, Vargem Alta, Serra, São Gabriel da
123
Palha and Castelo. A total of 90 samples were analyzed. These samples were acquired
124
exclusively in commercial establishments and they come from different manufacturers.
125 126
2.3. Procedure for the analysis of cachaça samples using the reference method
127
(ICP OES).
128
2.3.1. Preparation of the sample for analysis in ICP OES.
129
All the materials were washed with detergent and decontaminated in a vessel containing
130
5% nitric acid solution for 24 hours. For the determination of the metals in cachaça, the
131
volume of the cachaça samples was reduced by 50%. After that, 25.00 mL of the
132
sample obtained was acidified with 0.015 mol L-1 nitric acid solution, completing the
133
volume up to 50.00 mL solution with osmosis water.
134 135
2.3.2. ICP OES Instrumentation
136
The ICP OES experiments were performed on an optimum 7000 Perkin Elmer dual-
137
vision instrument equipped with shear gas, solid state detector (CCD) and pre-optical
138
system with purge. The parameters such as power, sample introduction flow and
139
nebulizing gas flow were optimized using response surface methodology. [Barros et al.,
140
2010]. For optimization purposes, the ratio generated by the intensities obtained from
141
the analysis of the magnesium element in lines II (280 nm) and I (285 nm) was used as
142
a response, aiming at the operation of the plasma under robust conditions. The value
143
found in the optimized condition is close to 10.0, which is considered ideal [Peixoto et
144
al., 2012]. The optimized conditions were power: 1257 W, sample introduction flow
145
1.57 mL min-1 and nebulization gas flow of 1.25 L min -1.
146
The emission lines utilized were 327.392 nm, 206.200 nm, 226.502 nm and 261.418 nm
147
for copper, zinc, cadmium and lead respectively, which demonstrated to be more
148
sensitive lines and without spectral interferences. The standard solutions of Cu2+, Zn2+,
149
Cd2+ and Pb2+ (1000 mg L
150
calibration curves.
-1)
(Qhemis High Purity ®) were used to construct the
151 152
2.3.3. Analytical quality verification of the reference method (ICP OES).
153
Factors to verify the quality of the reference method proposed were also investigated,
154
such as: the linearity of the calibration curve (in specific working range for each metal)
155
and matrix effect; determination of the limit of detection, determination of the limit of
156
quantification; accuracy and precision. [Albano et al., 2009; Brito et al., 2003; Oliveira
157
et al., 2012].
158 159
2.4. Procedure for analysis of samples by square wave anodic stripping voltammetry
160
(SWASV).
161
The electrochemical square wave anodic stripping experiments were performed using a
162
computer controlled Metrohm® 797 Computrace Voltammetric Analyzer by means of
163
the software 797 VA computrace version 1.2 for controlling the experiment and for data
164
acquisition.
165
The analysis employed a conventional 25-mL electrochemical cell consisting of an
166
auxiliary platinum wire electrode with a geometric area of 1 cm2, an Ag/AgCl reference
167
electrode (KCl 3.0 mol L-1) and, as working electrodes, two different types: commercial
168
graphite electrode (Metrohm®) and the carbon nanotube homemade electrode,
169
manufactured as described in the literature by Keyvanfard et al. Briefly, the carbon
170
nanotube paste was obtained by mixing carbon nanotubes and graphite powder in the
171
ratio of 10:1. The mixture was homogenized and dispersed in diethyl alcohol.
172
Subsequently, the organic solvent was evaporated and the paste obtained was placed in
173
a pipette tip which served as a carrier and the electric contact was made using copper
174
wire.
175
For the analysis of square wave anodic stripping voltammetry, an electrodeposition time
176
of 50s, electrodeposition potential of -0.90 V, amplitude of 0.05 V, scan rate of 0.1 V s-
177
1
178
1.0 V. Samples were inserted in the electrochemical cell of about 20.00 mL containing
179
phosphate solution of 1.00 mol L-1. All measurements were performed at room
180
temperature (25oC). After previous studies, these were the best conditions found for
181
metal analysis by using this technique.
and frequency of 100 Hz were used. The potential range showed was from -1.4 V to
182 183
2.5. Chemometric Treatment
184 185
For the simultaneous determination of metals in cachaça, the chemometric models were
186
constructed based on partial least squares - PLS [Martens and Naes, 1989] and artificial
187
neural networks - ANN [Jang et al., 1997; Zampronio et al., 2002; Barthus et al., 2005].
188
PLS is a powerful method of multivariate calibration that is applied for the treatment of
189
complex data. To construct the PLS model, the independent variables (variable x - data
190
from voltammograms) and the dependent variables (variable y - concentration data)
191
were used to establish a linear regression model for each metal. For this, the data are
192
placed in matrix form: matrix X and y and then these matrices are decomposed in
193
scores and loading matrices according to equations 1 and 2.
X TP t E t h p ht E
194
y UQ t f u h q ht f
195
(1) (2)
196
T and U are the matrices scores; P and Q are the loadings matrices and E and f are the
197
residual matrices. The superscript t indicates transposed matrix. The linear relationship
198
between the two blocks is performed by correlating scores for each component. The
199
regression vector b is determined by the following relation: uh bhth
200
(3)
201
This model is not the best one possible. The reason is that the principal components are
202
calculated for both blocks separately so that they have a weak relation to each other. It
203
would be better to give them information about each other so that slightly rotated
204
component results which lie closer to the regression line. Due to rotation, the term
205
principal component is replaced by latent variable. A mixed relation is produced and a
206
final model obtained.
207
The other multivariate method, ANN [Zampronio et al., 2002; Barthus et al., 2005. Jang
208
et al., 1997], was used for comparison. ANN is a system of several simple units
209
(artificial neurons), which are properly linked. This represents an iterative model that
210
mimics the system of biological neurons. Here, the neural architecture was composed of
211
three layers: input layer (data from voltammograms), hidden layer and output layer
212
(metal concentration data). For multilayer networks, the output of one layer becomes
213
the input to the following layer, and the output in the last layer is considered the
214
network output. In order to train a neural network, the data input for each layer
215
multiplied by weights are integrated in an artificial neuron and the output is obtained by
216
applying mathematical function to these data. The data flows with adjusted form from
217
the first and the last layer in order to achieve the desired goal. The network outputs
218
(estimated values) are compared to the expected values, resulting in the mean square
219
error . The next step is to correct the weights of all layers until the error is minimized
220
and the network is considered fully trained when an error considered to be satisfactory
221
is obtained. The algorithm utilized to correct the weights was the Marquardt-Levenberg,
222
which is robust and faster in convergence. This can be represented by equation 4. w = (JTJ +I)-1 JT e
223
(4)
224
where J is a Jacobian error matrix for each weight is a positive scalar, I is the identity
225
matrix and e is an error vector. When the artificial neural network is completely trained
226
and it is possible to evaluate the generalization properties by adopting another group of
227
data: a validation set where the metal concentration for another group of samples can
228
be predicted.
229
To evaluate the performance of the multivariate methods proposed, the root mean
230
squared error of prediction – RMSEP (equation 5), and coefficient of determination –
231
R2, (equation 6) were utilized. ^
(yi yi ) 2
RMSEP
232
(5)
n
233 ^
R
234
2
(y y ) ( y y) i
i
i
235
2
i
2
i
(6)
236 237
Where
238
estimated concentration of the analyte in the sample i; y is the average value; and n is
239
the total number of samples used in the prediction sets.
240
is the true concentration of the analyte in the sample i;
represents the
241 242
3. Results and Discussion
243
3.1. Evaluation of method quality based on ICP OES - The reference method.
244
The contents of the copper, zinc, cadmium and lead metals in the cachaça were
245
determined by ICP OES, under optimized conditions already described in the
246
methodology, and served as the reference data for the development of the chemometric
247
methods. For this purpose, the evaluation of method quality based on this technique was
248
also realized and it is presented below.
249
Linearity: The calibration curves obtained for the elements were linear throughout the
250
working range, with copper range of 0.1 to 10.0 mg L-1; for zinc the range of 0.05 to 1.5
251
mg L-1; for cadmium 0.005 to 0.5 mg L-1 and the range of 0.005 to 0.5 mg L-1 for lead.
252
The coefficients of determination (R2), used as verification criterion, found for the
253
analyzed metals were around 0.999, except for lead, which was 0.997. It was verified
254
that the residues of the calibration curves show the absence of trends where a random
255
behavior is observed (results not shown).
256
Matrix effects: In order to confirm the absence of matrix effect, standard addition curves
257
and external calibration curve were used. It was observed by means of the F test that
258
there is no significant difference in the variance of the angular coefficients of these two
259
curves. The calculated F values were lower than the theoretical values for all the metals
260
analyzed, indicating that the external calibration curve can be used for the
261
determination, because there is no matrix effect.
262
Accuracy and Precision: The evaluation of methodology accuracy was carried out by
263
means of recovery tests in three levels of fortification, where standard solutions of the
264
metals were added to the samples. The exact values found were in the range of 84.2 to
265
104.3% for copper; 80.0 to 109.3% for zinc; 82.7 to 100.5% for cadmium; and 86.4 to
266
100.7% for lead, which correspond to adequate recovery values. The methodology
267
precision evaluated by the relative standard deviation was between 1 and 4% for the
268
metals analyzed.
269
Limit of detection and limit of quantification:
270
0.010 mg L-1 for lead, 0.001 mg L-1 for cadmium, 0.014 mg L-1 for copper and 0.010
271
mg L-1 for zinc. The limits of quantification were 0.040 mg L-1 for lead, 0.002 mg L-1
272
for cadmium, 0.020 mg L-1 for copper and 0.011 mg L-1 for zinc.
The limits of detection were
273 274
3.2. Metal concentration values found in the artisanal cachaça samples.
275
After verifying the method, it was used to obtain the concentrations of the metals in the
276
samples of cachaça. Of the 90 samples analyzed, about 25% (23 samples) presented
277
levels of copper above the 5.0 mg L-1 allowed by Brazilian legislation, with total
278
variations between 0.10 mg L-1 and 9.80 mg L- 1. The presence of copper in artisanal
279
cachaça comes from distillation processes that use copper stills. During production
280
processes, there may be the formation of copper salts, which can be dissolved by the
281
alcoholic acid vapors and transmitted to the drink [Souza et al., 2010]. Some studies
282
have already shown that similar copper content was found elsewhere in the country,
283
which evidences this as the main source of contamination [Bortolleto et al., 2015;
284
Fernandes at al., 2013]. Although the presence of copper still exists in the production of
285
many cachaças, there are, however, several studies found in the literature which shows
286
ways to minimize this contamination [Fernandes at al., 2013; Lima et al., 2006]. For
287
lead content, the Brazilian legislation permits concentrations of up to 0.2 mg L-1 for this
288
metal. No lead was detected in any of the study samples produced in this area of the
289
country. Or the Pb level is below the method detection limit. Either way, this metal is
290
not a serious contaminant to this area. Lead levels were found in studies by Tavares et
291
al. The zinc contents found in the present study were between 0.03 and 1.4 mg L-1. The
292
current literature [Titilawo et al., 2018] reports that the maximum concentration of zinc
293
recommended in drinking water is 5.0 mg L-1. There is no parameter for the
294
determination of zinc specifically for cachaça, as it is not a very common source of
295
contamination.
296
contamination of zinc present in the samples is due to contamination by components
297
that are part of the distillers, as well as by the containers used in the bulk storage of the
298
cachaças. The contents of cadmium, which can come from various sources, ranged
299
from 0.01 to 0.20 mg L-1. The maximum permitted cadmium content is 0.2 mg L-1. It is
300
thus within the limits. Cadmium levels were also found to be within the limits
301
established in studies by Fernandes et al.
302
The table 1 shows representative data of metals determination for the cachaças analyzed
303
and these data were employed in prediction phase for multivariate methods
304
development and presents in next section.
According to studies [Souza et al., 2010], the probable source of
Insert table 1 here
305 306
3.3 Analysis by Voltammetry.
307 308
Insert Figure 1 here
309 310
Figure 1 show the voltammograms recorded with graphite electrode and carbon
311
nanotube electrode in the potential of –1.4 to 1.0 V for the cachaça samples. By means
312
of these voltammograms, it is possible to observe different profiles for the samples, in
313
which the difference of sensitivity in the visualization of peaks of metals ( copper, zinc
314
and cadmium) in the voltammograms is accentuated depending on the electrode utilized,
315
whereas for the carbon nanotube we have better defined voltammograms. In addition to
316
this, it is clear the overlapping of peaks in the voltammograms for metals which
317
indicates the necessity of using multivariate methods for determination. The
318
voltammograms were obtained in optimized conditions after previous studies, shown in
319
the Experimental Procedures section.
320 321
3.4. Multivariate method (partial least square - PLS)
322 323
The multivariate methods allow the use of all the voltammograms to extract relevant
324
information related to each metal. By using PLS, three independent models were
325
constructed for zinc, cadmium and copper metals. Lead was not detected in the samples,
326
as mentioned before. These models were constructed with the data obtained in the
327
square wave anodic stripping voltammetry (current at different potentials, independent
328
variable - x) and with the metal concentration values obtained by ICP OES (dependent
329
variable - y). The variables x were obtained for each type of electrode (commercial
330
graphite and homemade carbon nanotube). The data were analyzed in different models.
331
The data obtained were arranged in matrix form: matrix X and matrix y corresponding
332
calibration models. Approximately 70% of samples were applied in the calibration
333
phase.
334
Pre-treatments of data are important factors for the construction of the PLS model and
335
necessary to increase the selectivity of the parameter of interest. Unsatisfactory results
336
were observed when there is no pre-treatment. The pre-treatments used for data from
337
independent variables were: smoothing and first derivative of the voltammogram
338
current values and mean centered of these data. The first pre-treatment was obtained by
339
using the Savitzky-Golay algorithm. In this case, it was also possible to minimize
340
baseline displacement influences. The mean centered was obtained by subtracting the
341
average value of the column, for each data point in this column. This pre-treatment
342
consists of a translation of the coordinate axis to the data centre. The data from
343
dependent variables were mean centered. These pre-treatments, which proved to be the
344
most suitable, were applied to the construction of all models for the determination of
345
metals.
346
The number of latent variables used to build the models for the various types of metals
347
was independent of the electrode used. The models used for metal determination
348
consisted of five latent variables corresponding to approximately 90% of the total
349
necessary information to construct the PLS model for copper and zinc; and six latent
350
variables that are responsible for 85% of the total information used for the construction
351
of the PLS model corresponding to cadmium in samples of cachaças. The number of
352
latent variables used in these models was chosen by complete leave-one-out cross-
353
validation, which is based on the determination of the minimum prediction error
354
performing the best number of latent variables necessary in this procedure.
355
The models constructed were also evaluated in terms of leverage and residual analysis
356
for the purpose of identifying anomalies that when present were removed and a new
357
model without the presence of these samples was constructed. The relative performance
358
of the models was evaluated in terms of root-mean square error in calibration (RMSEC)
359
and the coefficient of determination (R2) was also presented. These data are present in
360
table 2. Based on results found for R2 and RMSEC, adequate models were obtained for
361
both types of electrodes and used to determine the concentration of these metals in the
362
cachaças, using other samples which constitute the prediction phase ( table 1),
363
approximately 30% of samples were used here . The data utilized in the prediction were
364
submitted to the same pre-treatment used before in calibration phase. The root-mean
365
square error in prediction (RMSEP) and coefficient of determination (R2) for the various
366
models are also shown in table 2.
367
Insert table 2 here
368 369
Figure 2 illustrates the comparison graphs of predicted vs. expected concentrations for
370
the considered metal for testing subsets using PLS model.
371 372 373
Insert Figure 2 here
374 375
By analyzing the R2 value obtained in the prediction, it is possible to notice that the
376
coefficient of determination for all metals analyzed has value around of 0.9, which
377
shows that the predicted and expected values are consistent. The RMSEP value found
378
shows that the model constructed by using carbon nanotube electrode presents a better
379
performance than the model constructed by using graphite electrode.
380
It was noticed that RMSEC and RMSEP present similar values for models constructed
381
for both electrodes, which denotes no overfitting of the models constructed. In all the
382
cases, it was possible to observe that the association of chemometric methods with
383
voltammetric data produced good results for metal prediction in cachaças.
384
385 386
3.5. Multivariate method (artificial neural networks - ANN)
387
Artificial neural networks were also used for the prediction of copper, zinc and
388
cadmium metals in the cachaça samples. Three independent models were constructed.
389
For comparative purposes, the same data from the previous model were used for the
390
construction of the model based on artificial neural networks, that is, the
391
voltammograms of the samples of cachaças (current value at different potential) and the
392
metal concentrations of previously known samples (obtained by using ICP OES
393
method). Different models were constructed for each electrode utilized. Once again, the
394
data underwent mathematical pre-treatments: smoothing and first derivative of the
395
voltammogram current values and mean centered of these data (data from independent
396
variables) and mean centered for data from dependent variables. For neural networks,
397
the data from independent variables were also reduced applying principal component
398
analysis. It is observed that six major components are responsible for more than 90% of
399
the explained variance for all metals analyzed in the different models using both
400
electrodes. All the ANN models were based on three-layered multi-layer perceptron
401
(MLP) architecture. The input data from the neural network is composed of scores of
402
the principal components, and then an input layer with six neurons was used. A hidden
403
layer with eleven neurons and an output layer with one neuron (metal concentrations)
404
make up the architecture of the neural network. In order to train the neural network, a
405
tan-sigmoidal function was utilized in the hidden layer as a transfer function. For the
406
output layer, a linear function was utilized. The neural network was trained by the
407
Marquardt–Levenberg algorithm, using a maximum number of iterations equal to one
408
thousand, and the error value employed as criterion for stopping was 1×10−2. Criteria
409
such as number of neurons in hidden layer, activation functions utilized, and iteration
410
number were chosen using a trial and error process that considered the nature of the data
411
to be modeled until their best combination was found. In order to evaluate the correct
412
fit of the neural model, the outputs were compared with the expected concentration
413
values. If there were significant differences, then the weights of the connections
414
between neurons would be modified according to the rules of the training algorithm.
415
Training is considered successfully completed when the mean square error expected is
416
found. And then, the neural network is trained.
417
Table 3 shows the results of the RMSEC and the coefficient of determination of the
418
calibration data for both electrodes after network training. It can be verified that the
419
coefficient of determination (R2) value is near 0.9, which means that there is a good
420
correlation between the data predicted by the ANN model and those expected and
421
provided by the reference method. The RMSEC values are also at appropriate levels.
422
After training, its accuracy was then evaluated towards samples of the external test
423
subset by employing the developed model to predict the concentrations of the metals of
424
those samples (external validation). The relative performance of the different models for
425
each sample was evaluated in terms of root-mean square error in the prediction
426
(RMSEP) and also by coefficient of determination (R2) for both cases. Table 3 also
427
shows the results of the RMSEP and the coefficient of determination (R2) of the
428
validation data for models constructed for both electrodes.
429 430
Insert table 3 here
431
Figure 3 illustrates the comparison graphs of predicted vs. expected concentrations for
432
the considered metal for testing subsets using ANN model.
433 434 435
Insert figure 3 here
436
In all cases, it was noticed that the association of chemometric methods with
437
voltammetric data produced good results for metal prediction in cachaças and the
438
model are not overlay adjusted.
439
In addition, Table S1 and S2 in the Supplementary Material shows the results of
440
repeated analyses of some samples using both methods, where again it is found that the
441
RMSEP values have adequate values.
442
3.6. Comparison of methods: PLS and ANN.
443
According to the results, it is possible to observe that the carbon nanotube electrode is
444
more sensitive for the determination of metals in the cachaças regardless of the method
445
used. For comparing the methods, the F test was performed considering RMSEP values.
446
By means of this test at the 95% confidence level F = 1.90 (27 samples), it is possible to
447
observe that for the metals determination, there is no statistical difference among
448
methods. The calculated F values were lower than the theoretical values for all the
449
metals analyzed. In general, both methods have good results.
450 451
4. Conclusion
452 453
According to the results, the simultaneous quantification of copper, zinc and cadmium
454
in artisanal cachaças using two types of electrodes (graphite and carbon nanotube) and
455
considering the combination of the square wave voltammetry and the chemometric
456
methods showed good results. Based on this, the proposed methodologies can be used
457
for the determination of metals in cachaças. As an advantage, these methods can
458
replace costly maintenance and performance methodologies such as ICP OES. In
459
addition, these methodologies are simple and fast and can be an effective tool for
460
routine analysis for the quality control of these products.
461 462
Conflict of interest
463 464
The authors declare that they have no conflicts of interest.
465 466
Acknowledgments
467 468
We are grateful to NCQP/UFES for supporting this study, and R. J. Ferreira would also
469
like to thank Capes for the scholarship.
470 471 472
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597 598 599 600 601 602 603 604
605 606 607
Figure Captions
608 609
Fig 1. Square wave voltammograms of cachaça samples by using graphite carbon
610
electrode and carbon nanotube electrode.
611
Fig 2. Prediction versus reference values of validation samples, commercial graphite
612
electrode (a), (b),(c); nanotube electrode (d), (e) and (f) by using PLS model.
613
Fig 3. Prediction versus reference values of validation samples, commercial graphite
614
electrode (g), (h),(i); nanotube electrode (j), (l) and (m) by using ANN model.
615
616
617
618 619
620
Table 1. Concentrations values of metals (mg L-1) obtained by ICP OES and utilized in prediction phase. Sample
Cu (mg L-1)
S.D
Zn (mg L-1)
S.D
Cd (mg L-1)
S.D
1
7.97
0.06
0.568
0.003
0.087
0.002
2
0.94
0.02
0.073
0.004
0.123
0.002
3
1.52
0.03
0.779
0.003
0.173
0.008
4
3.27
0.05
0.236
0.002
0.175
0.003
5
0.32
0.02
0.826
0.006
0.173
0.005
6
1.65
0.02
0.629
0.003
0.024
0.002
7
4.37
0.03
0.397
0.006
0.014
0.008
8
6.87
0.07
0.826
0.004
0.131
0.007
9
2.56
0.08
0.088
0.003
0.004
0.001
10
6.07
0.02
0.389
0.010
0.050
0.005
11
2.58
0.05
0.583
0.004
0.173
0.008
12
4.81
0.03
0.251
0.003
0.021
0.007
13
1.27
0.07
0.706
0.006
0.014
0.001
14
9.78
0.40
0.479
0.005
0.131
0.002
15
0.45
0.05
0.863
0.006
0.004
0.001
16
2.40
0.10
0.408
0.005
0.045
0.002
17
6.28
0.10
0.159
0.005
0.173
0.008
18
0.88
0.09
0.245
0.006
0.021
0.006
19
4.34
0.05
0.585
0.004
0.141
0.002
20
3.22
0.08
0.330
0.002
0.096
0.003
21
8.06
0.09
0.451
0.008
0.044
0.001
22
3.37
0.06
0.513
0.004
0.137
0.007
23
0.91
0.04
0.546
0.003
0.078
0.002
24
0.90
0.05
0.560
0.003
0.024
0.001
25
2.68
0.03
0.234
0.005
0.045
0.002
26
5.16
0.07
0.775
0.006
0.070
0.003
27
7.50
0.10
0.544
0.004
0.090
0.007
S.D- Standard deviation
621
Table 2. Parameters of the PLS model (calibration and validation) using two different
622
electrodes. PLS
623
(a)Cu
(a)Zn
R2calibration
0.986
0.962
0.981
0.997
0.989
0.991
RMSEC
0.186
0.108
0.0042
0.086
0.013
0.0020
R2 prediction
0.987
0.968
0.980
0.997
0.985
0.990
RMSEP
0.170
0.104
0.0039
0.078
0.010
0.0017
(a) graphite electrode.
(a)Cd
(b)
Cu
(b)Zn
(b)
Cd
(b) carbon nanotube electrode
624 625 626 627 628
Table 3. Parameters of the ANN model (calibration and validation) using two different electrodes ANN (a)Cu
629 630 631 632 633 634 635
(a)Zn
( a)Cd
R2calibration 0.993
0.995
0.989
0.994
0.991
0.981
RMSEC
0.165
0.196
0.0054
0.100
0.018
0.0018
R2prediction
0.990
0.987
0.978
0.998
0.992
0.991
RMSEP
0.157
0.194
0.0046
0.090
0.016
0.0016
(a) graphite electrode.
(b)
(b) carbon nanotube electrode
Cu
(b)Zn
(b)
Cd
636
Highlights
637 638 639 640
New methods based on association of multivariate calibration and voltammetric technique were developed.
641
Different methods for metals determination were compared.
642
Electrodes comparisons were performed.
643
Samples were analyzed without any previous pretreatment.
644
Artisanal cachaça quality was verified in terms of metal content.
645 646 647
S0308-8146(19)32278-2 https://doi.org/10.1016/j.foodchem.2019.126126 FOCH 126126
To appear in:
Food Chemistry
Received Date: Revised Date: Accepted Date:
4 February 2019 13 December 2019 24 December 2019
Please cite this article as: Ferreira, R.J., Rosa, T.R., Ribeiro, J., Barthus, R.C., Simultaneous metal determination in artisanal cachaça by using voltammetry and multivariate calibration, Food Chemistry (2019), doi: https://doi.org/ 10.1016/j.foodchem.2019.126126
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1
Simultaneous metal determination in artisanal cachaça by using voltammetry and
2
multivariate calibration.
3 4 5 6
Romário Junior Ferreiraa, Thalles Ramon Rosab, Josimar Ribeiroa, Rosângela
7
Cristina Barthusa.
8
a
9
Espírito Santo, Vitória-ES, Brazil.
Departamento de Química, Centro de Ciências Exatas, Universidade Federal do
10
b
11
Brazil.
Instituto Federal de Educação, Ciência e Tecnologia do Espírito Santo, Aracruz - ES,
12 13 14 15 16 17
Corresponding Author:
18
Rosângela Cristina Barthus
19
Av. Fernando Ferrari, 514, Goiabeiras, Vitória, Espírito Santo, Brazil.
20
29075-910, E-mail address: [email protected]
21
Phone Number: (++55 27) 4009-2846
22 23 24 25
26
Abstract
27 28
In this study, square wave anodic stripping voltammetry using two different types of
29
electrodes (carbon nanotube electrode and graphite electrode) was combined with
30
chemometric methods - partial least squares (PLS) and artificial neural networks (ANN)
31
for determining copper, zinc, cadmium and lead in cachaça. The objectives were
32
comparison of methods developed and the verification of the quality of artisanal
33
cachaças in terms of metal content. For the development of the methodology,
34
inductively coupled plasma optical emission spectrometry (ICP OES) was used as
35
reference technique. The performance of multivariate models obtained was evaluated by
36
the coefficient of determination (R2) and root mean square error of prediction (RMSEP).
37
F test was utilized for comparing methods at confidence level of 95%. Better results
38
were observed by using carbon nanotube electrode regardless of the multivariate
39
method proposed. The methodology is simple, fast, and inexpensive and it can be used
40
in quality control laboratories.
41 42
Keywords: Cachaça, partial least square, artificial neural network, carbon nanotube
43
electrode, square wave anodic stripping voltammetry, metals.
44 45 46 47 48 49 50
51
1. Introduction
52
Developing faster and more sensitive analytical methods for the determination of metals
53
is extremely important due to the increasing demand for analyses of these elements in
54
order to ensure part of the quality criteria of products such as foods and beverages. In
55
this setting, this study aims to develop and compare metal determination methods in
56
cachaça and, in this context, verify the quality of artisanal cachaças based on these
57
contents. At first, copper, zinc, cadmium and lead were studied. Copper and zinc are
58
metals essential to the body, in a very specific concentration range, but they can become
59
dangerous beyond these amounts. On the other hand, lead and cadmium metals have a
60
high toxicity potential. They are non-essential and should therefore be kept within the
61
tolerable limits. Several studies characterize the performance of these metals in terms of
62
their interactions and/or toxicity. [Afridi et al., 2014; Simmons et al., 2003, Page et al.,
63
2015, Duarte et al., 2000; Oliveira et al., 2015].
64
In this aspect, the determination of these metals in cachaça – a drink obtained by
65
distilling fermented sugarcane [Brazil, 2005] – can allow the consumer to have adequate
66
levels of these elements and thus a safe consumption [Aresta et al., 2001; Miranda et al.,
67
2010; Fernandes et al., 2013]. Metal levels may vary depending on the mode of
68
production: whether artisanal or industrial, and also on the preparation and planting of
69
sugarcane. In another aspect, they have also influence in organoleptic criteria such as
70
odor and taste of the beverage [Azevedo et al., 2003].
71
Among the most commonly used techniques for determining metals in beverages are:
72
Atomic absorption spectrometry, X-ray fluorescence spectrometry and Inductively
73
coupled plasma optical emission spectrometry (ICP-OES) [Bermejo-Barrera et al.,
74
2007, Fernandes et al., 2013]. However, the disadvantages are the relatively expensive
75
cost of these techniques and generally the necessity of complex sample preparations
76
[Oliveira et al., 2015]. In this way, voltammetric techniques have been evaluated.
77
Among voltammetric techniques, anodic stripping voltammetry [Tyszczuk et al., 2007]
78
has been increasingly relevant mainly because, in many cases, it is not necessary to
79
pretreat the samples, it is a non-destructive technique, and it allows rapid analysis and
80
low detection limit. For metals, the mercury electrode has been widely employed for
81
these determinations. Nowadays, however, alternatives have been studied, due to
82
environmental concerns with the use of mercury [Lee et al., 2016, Yue et al., 2012].
83
Therefore, the use of electrodes formed by nanomaterials represents an alternative.
84
[Merkoçi, 2007, Keyvanfard et al., 2013]. These materials have a large surface area,
85
which improves mass transport and electron transfer. Specifically, nanostructured
86
carbon materials, such as those of nanotubes used here, associate these characteristics to
87
those of carbon that act as absorbing agents and pre-concentrators on the surface. The
88
electrode formed by carbon nanomaterials has already been used for metal
89
determination in cachaça, which demonstrated its application potential [Tavares et al.,
90
2012]. However, a fact to be taken into account is that the simultaneous determination
91
of several metals is complex because in most cases, there is an overlap of some peaks
92
that undergo reduction or oxidation in near potentials [Alagic et al., 2017, Gonzalez-
93
Calabui et al., 2016]. Then, despite the numerous advantages, the electrochemical
94
techniques suffer from lack of selectivity.
95
association of multivariate calibration methods and electrochemical techniques has
96
become important. This can be found in the recent literature where manuscripts report
97
this type of association while determining analytes in the most diverse types of
98
matrices, with emphasis on the pharmaceutical and environmental samples. [Melucci et
To overcome such difficulties, the
99
al., 2012; Alagic et al., 2017; Alves et al., 2011; Gonzales-Calabui et al., 2016,
100
Keyvanfard et al., 2013].
101
This study focuses on the development of a methodology for the determination of
102
metals in artisanal cachaça. The methods involve the association of chemometric
103
methods and square wave anodic stripping voltammetry for these determinations. This
104
study aims to compare the different types of electrodes (graphite and carbon nanotube
105
electrodes) used, as well as the proposed multivariate methods (partial least square –
106
PLS and artificial neural network - ANN). Ultimately, the study also aims to verify the
107
quality of the artisanal samples in terms of metal content.
108 109
2. Experimental procedures
110
2.1. Reagents and solutions
111
Nitric acid P.A was obtained from Neon® and distilled. All the other reagents used
112
were of analytical grade and used without prior purification. Diethyl alcohol, graphite
113
powder, carbon nanotube were purchased from Sigma Aldrich®. Sodium dihydrogen
114
phosphate or sodium phosphate and phosphoric acid obtained from Merck® were used
115
to prepare 1.00 mol L-1 buffer solution pH 4.0. Ultrapure water (resistivity of 18 mΩ -
116
cm 25°C), used in all experiments, was obtained from a reverse osmosis purification
117
system (Quimis®).
118
2.2. Samples
119
For the accomplishment of the experiments, we employed samples of artisanal cachaça
120
coming from several municipalities in the State of Espírito Santo: Domingos Martins,
121
São Roque de Canaã, Aracruz, Afonso Cláudio, Linhares, Cachoeiro do Itapemirim,
122
Alfredo Chaves, Anchieta, Cariacica, Santa Tereza, Vargem Alta, Serra, São Gabriel da
123
Palha and Castelo. A total of 90 samples were analyzed. These samples were acquired
124
exclusively in commercial establishments and they come from different manufacturers.
125 126
2.3. Procedure for the analysis of cachaça samples using the reference method
127
(ICP OES).
128
2.3.1. Preparation of the sample for analysis in ICP OES.
129
All the materials were washed with detergent and decontaminated in a vessel containing
130
5% nitric acid solution for 24 hours. For the determination of the metals in cachaça, the
131
volume of the cachaça samples was reduced by 50%. After that, 25.00 mL of the
132
sample obtained was acidified with 0.015 mol L-1 nitric acid solution, completing the
133
volume up to 50.00 mL solution with osmosis water.
134 135
2.3.2. ICP OES Instrumentation
136
The ICP OES experiments were performed on an optimum 7000 Perkin Elmer dual-
137
vision instrument equipped with shear gas, solid state detector (CCD) and pre-optical
138
system with purge. The parameters such as power, sample introduction flow and
139
nebulizing gas flow were optimized using response surface methodology. [Barros et al.,
140
2010]. For optimization purposes, the ratio generated by the intensities obtained from
141
the analysis of the magnesium element in lines II (280 nm) and I (285 nm) was used as
142
a response, aiming at the operation of the plasma under robust conditions. The value
143
found in the optimized condition is close to 10.0, which is considered ideal [Peixoto et
144
al., 2012]. The optimized conditions were power: 1257 W, sample introduction flow
145
1.57 mL min-1 and nebulization gas flow of 1.25 L min -1.
146
The emission lines utilized were 327.392 nm, 206.200 nm, 226.502 nm and 261.418 nm
147
for copper, zinc, cadmium and lead respectively, which demonstrated to be more
148
sensitive lines and without spectral interferences. The standard solutions of Cu2+, Zn2+,
149
Cd2+ and Pb2+ (1000 mg L
150
calibration curves.
-1)
(Qhemis High Purity ®) were used to construct the
151 152
2.3.3. Analytical quality verification of the reference method (ICP OES).
153
Factors to verify the quality of the reference method proposed were also investigated,
154
such as: the linearity of the calibration curve (in specific working range for each metal)
155
and matrix effect; determination of the limit of detection, determination of the limit of
156
quantification; accuracy and precision. [Albano et al., 2009; Brito et al., 2003; Oliveira
157
et al., 2012].
158 159
2.4. Procedure for analysis of samples by square wave anodic stripping voltammetry
160
(SWASV).
161
The electrochemical square wave anodic stripping experiments were performed using a
162
computer controlled Metrohm® 797 Computrace Voltammetric Analyzer by means of
163
the software 797 VA computrace version 1.2 for controlling the experiment and for data
164
acquisition.
165
The analysis employed a conventional 25-mL electrochemical cell consisting of an
166
auxiliary platinum wire electrode with a geometric area of 1 cm2, an Ag/AgCl reference
167
electrode (KCl 3.0 mol L-1) and, as working electrodes, two different types: commercial
168
graphite electrode (Metrohm®) and the carbon nanotube homemade electrode,
169
manufactured as described in the literature by Keyvanfard et al. Briefly, the carbon
170
nanotube paste was obtained by mixing carbon nanotubes and graphite powder in the
171
ratio of 10:1. The mixture was homogenized and dispersed in diethyl alcohol.
172
Subsequently, the organic solvent was evaporated and the paste obtained was placed in
173
a pipette tip which served as a carrier and the electric contact was made using copper
174
wire.
175
For the analysis of square wave anodic stripping voltammetry, an electrodeposition time
176
of 50s, electrodeposition potential of -0.90 V, amplitude of 0.05 V, scan rate of 0.1 V s-
177
1
178
1.0 V. Samples were inserted in the electrochemical cell of about 20.00 mL containing
179
phosphate solution of 1.00 mol L-1. All measurements were performed at room
180
temperature (25oC). After previous studies, these were the best conditions found for
181
metal analysis by using this technique.
and frequency of 100 Hz were used. The potential range showed was from -1.4 V to
182 183
2.5. Chemometric Treatment
184 185
For the simultaneous determination of metals in cachaça, the chemometric models were
186
constructed based on partial least squares - PLS [Martens and Naes, 1989] and artificial
187
neural networks - ANN [Jang et al., 1997; Zampronio et al., 2002; Barthus et al., 2005].
188
PLS is a powerful method of multivariate calibration that is applied for the treatment of
189
complex data. To construct the PLS model, the independent variables (variable x - data
190
from voltammograms) and the dependent variables (variable y - concentration data)
191
were used to establish a linear regression model for each metal. For this, the data are
192
placed in matrix form: matrix X and y and then these matrices are decomposed in
193
scores and loading matrices according to equations 1 and 2.
X TP t E t h p ht E
194
y UQ t f u h q ht f
195
(1) (2)
196
T and U are the matrices scores; P and Q are the loadings matrices and E and f are the
197
residual matrices. The superscript t indicates transposed matrix. The linear relationship
198
between the two blocks is performed by correlating scores for each component. The
199
regression vector b is determined by the following relation: uh bhth
200
(3)
201
This model is not the best one possible. The reason is that the principal components are
202
calculated for both blocks separately so that they have a weak relation to each other. It
203
would be better to give them information about each other so that slightly rotated
204
component results which lie closer to the regression line. Due to rotation, the term
205
principal component is replaced by latent variable. A mixed relation is produced and a
206
final model obtained.
207
The other multivariate method, ANN [Zampronio et al., 2002; Barthus et al., 2005. Jang
208
et al., 1997], was used for comparison. ANN is a system of several simple units
209
(artificial neurons), which are properly linked. This represents an iterative model that
210
mimics the system of biological neurons. Here, the neural architecture was composed of
211
three layers: input layer (data from voltammograms), hidden layer and output layer
212
(metal concentration data). For multilayer networks, the output of one layer becomes
213
the input to the following layer, and the output in the last layer is considered the
214
network output. In order to train a neural network, the data input for each layer
215
multiplied by weights are integrated in an artificial neuron and the output is obtained by
216
applying mathematical function to these data. The data flows with adjusted form from
217
the first and the last layer in order to achieve the desired goal. The network outputs
218
(estimated values) are compared to the expected values, resulting in the mean square
219
error . The next step is to correct the weights of all layers until the error is minimized
220
and the network is considered fully trained when an error considered to be satisfactory
221
is obtained. The algorithm utilized to correct the weights was the Marquardt-Levenberg,
222
which is robust and faster in convergence. This can be represented by equation 4. w = (JTJ +I)-1 JT e
223
(4)
224
where J is a Jacobian error matrix for each weight is a positive scalar, I is the identity
225
matrix and e is an error vector. When the artificial neural network is completely trained
226
and it is possible to evaluate the generalization properties by adopting another group of
227
data: a validation set where the metal concentration for another group of samples can
228
be predicted.
229
To evaluate the performance of the multivariate methods proposed, the root mean
230
squared error of prediction – RMSEP (equation 5), and coefficient of determination –
231
R2, (equation 6) were utilized. ^
(yi yi ) 2
RMSEP
232
(5)
n
233 ^
R
234
2
(y y ) ( y y) i
i
i
235
2
i
2
i
(6)
236 237
Where
238
estimated concentration of the analyte in the sample i; y is the average value; and n is
239
the total number of samples used in the prediction sets.
240
is the true concentration of the analyte in the sample i;
represents the
241 242
3. Results and Discussion
243
3.1. Evaluation of method quality based on ICP OES - The reference method.
244
The contents of the copper, zinc, cadmium and lead metals in the cachaça were
245
determined by ICP OES, under optimized conditions already described in the
246
methodology, and served as the reference data for the development of the chemometric
247
methods. For this purpose, the evaluation of method quality based on this technique was
248
also realized and it is presented below.
249
Linearity: The calibration curves obtained for the elements were linear throughout the
250
working range, with copper range of 0.1 to 10.0 mg L-1; for zinc the range of 0.05 to 1.5
251
mg L-1; for cadmium 0.005 to 0.5 mg L-1 and the range of 0.005 to 0.5 mg L-1 for lead.
252
The coefficients of determination (R2), used as verification criterion, found for the
253
analyzed metals were around 0.999, except for lead, which was 0.997. It was verified
254
that the residues of the calibration curves show the absence of trends where a random
255
behavior is observed (results not shown).
256
Matrix effects: In order to confirm the absence of matrix effect, standard addition curves
257
and external calibration curve were used. It was observed by means of the F test that
258
there is no significant difference in the variance of the angular coefficients of these two
259
curves. The calculated F values were lower than the theoretical values for all the metals
260
analyzed, indicating that the external calibration curve can be used for the
261
determination, because there is no matrix effect.
262
Accuracy and Precision: The evaluation of methodology accuracy was carried out by
263
means of recovery tests in three levels of fortification, where standard solutions of the
264
metals were added to the samples. The exact values found were in the range of 84.2 to
265
104.3% for copper; 80.0 to 109.3% for zinc; 82.7 to 100.5% for cadmium; and 86.4 to
266
100.7% for lead, which correspond to adequate recovery values. The methodology
267
precision evaluated by the relative standard deviation was between 1 and 4% for the
268
metals analyzed.
269
Limit of detection and limit of quantification:
270
0.010 mg L-1 for lead, 0.001 mg L-1 for cadmium, 0.014 mg L-1 for copper and 0.010
271
mg L-1 for zinc. The limits of quantification were 0.040 mg L-1 for lead, 0.002 mg L-1
272
for cadmium, 0.020 mg L-1 for copper and 0.011 mg L-1 for zinc.
The limits of detection were
273 274
3.2. Metal concentration values found in the artisanal cachaça samples.
275
After verifying the method, it was used to obtain the concentrations of the metals in the
276
samples of cachaça. Of the 90 samples analyzed, about 25% (23 samples) presented
277
levels of copper above the 5.0 mg L-1 allowed by Brazilian legislation, with total
278
variations between 0.10 mg L-1 and 9.80 mg L- 1. The presence of copper in artisanal
279
cachaça comes from distillation processes that use copper stills. During production
280
processes, there may be the formation of copper salts, which can be dissolved by the
281
alcoholic acid vapors and transmitted to the drink [Souza et al., 2010]. Some studies
282
have already shown that similar copper content was found elsewhere in the country,
283
which evidences this as the main source of contamination [Bortolleto et al., 2015;
284
Fernandes at al., 2013]. Although the presence of copper still exists in the production of
285
many cachaças, there are, however, several studies found in the literature which shows
286
ways to minimize this contamination [Fernandes at al., 2013; Lima et al., 2006]. For
287
lead content, the Brazilian legislation permits concentrations of up to 0.2 mg L-1 for this
288
metal. No lead was detected in any of the study samples produced in this area of the
289
country. Or the Pb level is below the method detection limit. Either way, this metal is
290
not a serious contaminant to this area. Lead levels were found in studies by Tavares et
291
al. The zinc contents found in the present study were between 0.03 and 1.4 mg L-1. The
292
current literature [Titilawo et al., 2018] reports that the maximum concentration of zinc
293
recommended in drinking water is 5.0 mg L-1. There is no parameter for the
294
determination of zinc specifically for cachaça, as it is not a very common source of
295
contamination.
296
contamination of zinc present in the samples is due to contamination by components
297
that are part of the distillers, as well as by the containers used in the bulk storage of the
298
cachaças. The contents of cadmium, which can come from various sources, ranged
299
from 0.01 to 0.20 mg L-1. The maximum permitted cadmium content is 0.2 mg L-1. It is
300
thus within the limits. Cadmium levels were also found to be within the limits
301
established in studies by Fernandes et al.
302
The table 1 shows representative data of metals determination for the cachaças analyzed
303
and these data were employed in prediction phase for multivariate methods
304
development and presents in next section.
According to studies [Souza et al., 2010], the probable source of
Insert table 1 here
305 306
3.3 Analysis by Voltammetry.
307 308
Insert Figure 1 here
309 310
Figure 1 show the voltammograms recorded with graphite electrode and carbon
311
nanotube electrode in the potential of –1.4 to 1.0 V for the cachaça samples. By means
312
of these voltammograms, it is possible to observe different profiles for the samples, in
313
which the difference of sensitivity in the visualization of peaks of metals ( copper, zinc
314
and cadmium) in the voltammograms is accentuated depending on the electrode utilized,
315
whereas for the carbon nanotube we have better defined voltammograms. In addition to
316
this, it is clear the overlapping of peaks in the voltammograms for metals which
317
indicates the necessity of using multivariate methods for determination. The
318
voltammograms were obtained in optimized conditions after previous studies, shown in
319
the Experimental Procedures section.
320 321
3.4. Multivariate method (partial least square - PLS)
322 323
The multivariate methods allow the use of all the voltammograms to extract relevant
324
information related to each metal. By using PLS, three independent models were
325
constructed for zinc, cadmium and copper metals. Lead was not detected in the samples,
326
as mentioned before. These models were constructed with the data obtained in the
327
square wave anodic stripping voltammetry (current at different potentials, independent
328
variable - x) and with the metal concentration values obtained by ICP OES (dependent
329
variable - y). The variables x were obtained for each type of electrode (commercial
330
graphite and homemade carbon nanotube). The data were analyzed in different models.
331
The data obtained were arranged in matrix form: matrix X and matrix y corresponding
332
calibration models. Approximately 70% of samples were applied in the calibration
333
phase.
334
Pre-treatments of data are important factors for the construction of the PLS model and
335
necessary to increase the selectivity of the parameter of interest. Unsatisfactory results
336
were observed when there is no pre-treatment. The pre-treatments used for data from
337
independent variables were: smoothing and first derivative of the voltammogram
338
current values and mean centered of these data. The first pre-treatment was obtained by
339
using the Savitzky-Golay algorithm. In this case, it was also possible to minimize
340
baseline displacement influences. The mean centered was obtained by subtracting the
341
average value of the column, for each data point in this column. This pre-treatment
342
consists of a translation of the coordinate axis to the data centre. The data from
343
dependent variables were mean centered. These pre-treatments, which proved to be the
344
most suitable, were applied to the construction of all models for the determination of
345
metals.
346
The number of latent variables used to build the models for the various types of metals
347
was independent of the electrode used. The models used for metal determination
348
consisted of five latent variables corresponding to approximately 90% of the total
349
necessary information to construct the PLS model for copper and zinc; and six latent
350
variables that are responsible for 85% of the total information used for the construction
351
of the PLS model corresponding to cadmium in samples of cachaças. The number of
352
latent variables used in these models was chosen by complete leave-one-out cross-
353
validation, which is based on the determination of the minimum prediction error
354
performing the best number of latent variables necessary in this procedure.
355
The models constructed were also evaluated in terms of leverage and residual analysis
356
for the purpose of identifying anomalies that when present were removed and a new
357
model without the presence of these samples was constructed. The relative performance
358
of the models was evaluated in terms of root-mean square error in calibration (RMSEC)
359
and the coefficient of determination (R2) was also presented. These data are present in
360
table 2. Based on results found for R2 and RMSEC, adequate models were obtained for
361
both types of electrodes and used to determine the concentration of these metals in the
362
cachaças, using other samples which constitute the prediction phase ( table 1),
363
approximately 30% of samples were used here . The data utilized in the prediction were
364
submitted to the same pre-treatment used before in calibration phase. The root-mean
365
square error in prediction (RMSEP) and coefficient of determination (R2) for the various
366
models are also shown in table 2.
367
Insert table 2 here
368 369
Figure 2 illustrates the comparison graphs of predicted vs. expected concentrations for
370
the considered metal for testing subsets using PLS model.
371 372 373
Insert Figure 2 here
374 375
By analyzing the R2 value obtained in the prediction, it is possible to notice that the
376
coefficient of determination for all metals analyzed has value around of 0.9, which
377
shows that the predicted and expected values are consistent. The RMSEP value found
378
shows that the model constructed by using carbon nanotube electrode presents a better
379
performance than the model constructed by using graphite electrode.
380
It was noticed that RMSEC and RMSEP present similar values for models constructed
381
for both electrodes, which denotes no overfitting of the models constructed. In all the
382
cases, it was possible to observe that the association of chemometric methods with
383
voltammetric data produced good results for metal prediction in cachaças.
384
385 386
3.5. Multivariate method (artificial neural networks - ANN)
387
Artificial neural networks were also used for the prediction of copper, zinc and
388
cadmium metals in the cachaça samples. Three independent models were constructed.
389
For comparative purposes, the same data from the previous model were used for the
390
construction of the model based on artificial neural networks, that is, the
391
voltammograms of the samples of cachaças (current value at different potential) and the
392
metal concentrations of previously known samples (obtained by using ICP OES
393
method). Different models were constructed for each electrode utilized. Once again, the
394
data underwent mathematical pre-treatments: smoothing and first derivative of the
395
voltammogram current values and mean centered of these data (data from independent
396
variables) and mean centered for data from dependent variables. For neural networks,
397
the data from independent variables were also reduced applying principal component
398
analysis. It is observed that six major components are responsible for more than 90% of
399
the explained variance for all metals analyzed in the different models using both
400
electrodes. All the ANN models were based on three-layered multi-layer perceptron
401
(MLP) architecture. The input data from the neural network is composed of scores of
402
the principal components, and then an input layer with six neurons was used. A hidden
403
layer with eleven neurons and an output layer with one neuron (metal concentrations)
404
make up the architecture of the neural network. In order to train the neural network, a
405
tan-sigmoidal function was utilized in the hidden layer as a transfer function. For the
406
output layer, a linear function was utilized. The neural network was trained by the
407
Marquardt–Levenberg algorithm, using a maximum number of iterations equal to one
408
thousand, and the error value employed as criterion for stopping was 1×10−2. Criteria
409
such as number of neurons in hidden layer, activation functions utilized, and iteration
410
number were chosen using a trial and error process that considered the nature of the data
411
to be modeled until their best combination was found. In order to evaluate the correct
412
fit of the neural model, the outputs were compared with the expected concentration
413
values. If there were significant differences, then the weights of the connections
414
between neurons would be modified according to the rules of the training algorithm.
415
Training is considered successfully completed when the mean square error expected is
416
found. And then, the neural network is trained.
417
Table 3 shows the results of the RMSEC and the coefficient of determination of the
418
calibration data for both electrodes after network training. It can be verified that the
419
coefficient of determination (R2) value is near 0.9, which means that there is a good
420
correlation between the data predicted by the ANN model and those expected and
421
provided by the reference method. The RMSEC values are also at appropriate levels.
422
After training, its accuracy was then evaluated towards samples of the external test
423
subset by employing the developed model to predict the concentrations of the metals of
424
those samples (external validation). The relative performance of the different models for
425
each sample was evaluated in terms of root-mean square error in the prediction
426
(RMSEP) and also by coefficient of determination (R2) for both cases. Table 3 also
427
shows the results of the RMSEP and the coefficient of determination (R2) of the
428
validation data for models constructed for both electrodes.
429 430
Insert table 3 here
431
Figure 3 illustrates the comparison graphs of predicted vs. expected concentrations for
432
the considered metal for testing subsets using ANN model.
433 434 435
Insert figure 3 here
436
In all cases, it was noticed that the association of chemometric methods with
437
voltammetric data produced good results for metal prediction in cachaças and the
438
model are not overlay adjusted.
439
In addition, Table S1 and S2 in the Supplementary Material shows the results of
440
repeated analyses of some samples using both methods, where again it is found that the
441
RMSEP values have adequate values.
442
3.6. Comparison of methods: PLS and ANN.
443
According to the results, it is possible to observe that the carbon nanotube electrode is
444
more sensitive for the determination of metals in the cachaças regardless of the method
445
used. For comparing the methods, the F test was performed considering RMSEP values.
446
By means of this test at the 95% confidence level F = 1.90 (27 samples), it is possible to
447
observe that for the metals determination, there is no statistical difference among
448
methods. The calculated F values were lower than the theoretical values for all the
449
metals analyzed. In general, both methods have good results.
450 451
4. Conclusion
452 453
According to the results, the simultaneous quantification of copper, zinc and cadmium
454
in artisanal cachaças using two types of electrodes (graphite and carbon nanotube) and
455
considering the combination of the square wave voltammetry and the chemometric
456
methods showed good results. Based on this, the proposed methodologies can be used
457
for the determination of metals in cachaças. As an advantage, these methods can
458
replace costly maintenance and performance methodologies such as ICP OES. In
459
addition, these methodologies are simple and fast and can be an effective tool for
460
routine analysis for the quality control of these products.
461 462
Conflict of interest
463 464
The authors declare that they have no conflicts of interest.
465 466
Acknowledgments
467 468
We are grateful to NCQP/UFES for supporting this study, and R. J. Ferreira would also
469
like to thank Capes for the scholarship.
470 471 472
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605 606 607
Figure Captions
608 609
Fig 1. Square wave voltammograms of cachaça samples by using graphite carbon
610
electrode and carbon nanotube electrode.
611
Fig 2. Prediction versus reference values of validation samples, commercial graphite
612
electrode (a), (b),(c); nanotube electrode (d), (e) and (f) by using PLS model.
613
Fig 3. Prediction versus reference values of validation samples, commercial graphite
614
electrode (g), (h),(i); nanotube electrode (j), (l) and (m) by using ANN model.
615
616
617
618 619
620
Table 1. Concentrations values of metals (mg L-1) obtained by ICP OES and utilized in prediction phase. Sample
Cu (mg L-1)
S.D
Zn (mg L-1)
S.D
Cd (mg L-1)
S.D
1
7.97
0.06
0.568
0.003
0.087
0.002
2
0.94
0.02
0.073
0.004
0.123
0.002
3
1.52
0.03
0.779
0.003
0.173
0.008
4
3.27
0.05
0.236
0.002
0.175
0.003
5
0.32
0.02
0.826
0.006
0.173
0.005
6
1.65
0.02
0.629
0.003
0.024
0.002
7
4.37
0.03
0.397
0.006
0.014
0.008
8
6.87
0.07
0.826
0.004
0.131
0.007
9
2.56
0.08
0.088
0.003
0.004
0.001
10
6.07
0.02
0.389
0.010
0.050
0.005
11
2.58
0.05
0.583
0.004
0.173
0.008
12
4.81
0.03
0.251
0.003
0.021
0.007
13
1.27
0.07
0.706
0.006
0.014
0.001
14
9.78
0.40
0.479
0.005
0.131
0.002
15
0.45
0.05
0.863
0.006
0.004
0.001
16
2.40
0.10
0.408
0.005
0.045
0.002
17
6.28
0.10
0.159
0.005
0.173
0.008
18
0.88
0.09
0.245
0.006
0.021
0.006
19
4.34
0.05
0.585
0.004
0.141
0.002
20
3.22
0.08
0.330
0.002
0.096
0.003
21
8.06
0.09
0.451
0.008
0.044
0.001
22
3.37
0.06
0.513
0.004
0.137
0.007
23
0.91
0.04
0.546
0.003
0.078
0.002
24
0.90
0.05
0.560
0.003
0.024
0.001
25
2.68
0.03
0.234
0.005
0.045
0.002
26
5.16
0.07
0.775
0.006
0.070
0.003
27
7.50
0.10
0.544
0.004
0.090
0.007
S.D- Standard deviation
621
Table 2. Parameters of the PLS model (calibration and validation) using two different
622
electrodes. PLS
623
(a)Cu
(a)Zn
R2calibration
0.986
0.962
0.981
0.997
0.989
0.991
RMSEC
0.186
0.108
0.0042
0.086
0.013
0.0020
R2 prediction
0.987
0.968
0.980
0.997
0.985
0.990
RMSEP
0.170
0.104
0.0039
0.078
0.010
0.0017
(a) graphite electrode.
(a)Cd
(b)
Cu
(b)Zn
(b)
Cd
(b) carbon nanotube electrode
624 625 626 627 628
Table 3. Parameters of the ANN model (calibration and validation) using two different electrodes ANN (a)Cu
629 630 631 632 633 634 635
(a)Zn
( a)Cd
R2calibration 0.993
0.995
0.989
0.994
0.991
0.981
RMSEC
0.165
0.196
0.0054
0.100
0.018
0.0018
R2prediction
0.990
0.987
0.978
0.998
0.992
0.991
RMSEP
0.157
0.194
0.0046
0.090
0.016
0.0016
(a) graphite electrode.
(b)
(b) carbon nanotube electrode
Cu
(b)Zn
(b)
Cd
636
Highlights
637 638 639 640
New methods based on association of multivariate calibration and voltammetric technique were developed.
641
Different methods for metals determination were compared.
642
Electrodes comparisons were performed.
643
Samples were analyzed without any previous pretreatment.
644
Artisanal cachaça quality was verified in terms of metal content.
645 646 647