Detection of glucose and triglycerides using ...

Sensors and Actuators B 166–167 (2012) 231–238

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Detection of glucose and triglycerides using information visualization methods to process impedance spectroscopy data Marli L. Moraes a,∗ , Laís Petri a , Victor Oliveira b , Clarissa A. Olivati c , Maria Cristina F. de Oliveira b , Fernando V. Paulovich b , Osvaldo N. Oliveira Jr. d , Marystela Ferreira a a

Universidade Federal de São Carlos, campus de Sorocaba, 18052-780 Sorocaba, SP, Brazil Instituto de Ciências Matemáticas e de Computac¸ão, Universidade de São Paulo, CP 668, 13560-970 São Carlos, SP, Brazil c Depto de Física, Química e Biologia, Universidade Estadual Paulista, CP 467, 19060-900, Presidente Prudente, SP, Brazil d Instituto de Física de São Carlos, USP CP 369, 13560-970 São Carlos, SP, Brazil b

a r t i c l e

i n f o

Article history: Received 5 December 2011 Received in revised form 14 February 2012 Accepted 18 February 2012 Available online 25 February 2012 Keywords: Nanostructured film Biosensor Information visualization Multidimensional projection Lipase Glucose oxidase

a b s t r a c t In this paper we discuss the detection of glucose and triglycerides using information visualization methods to process impedance spectroscopy data. The sensing units contained either lipase or glucose oxidase immobilized in layer-by-layer (LbL) films deposited onto interdigitated electrodes. The optimization consisted in identifying which part of the electrical response and combination of sensing units yielded the best distinguishing ability. It is shown that complete separation can be obtained for a range of concentrations of glucose and triglyceride when the interactive document map (IDMAP) technique is used to project the data into a two-dimensional plot. Most importantly, the optimization procedure can be extended to other types of biosensors, thus increasing the versatility of analysis provided by tailored molecular architectures exploited with various detection principles. © 2012 Elsevier B.V. All rights reserved.

1. Introduction The determination of triglycerides and glucose is important because high concentrations can cause coronary diseases and disorders including diabetes mellitus nephrosis, liver obstruction and endocrine pathologies [1–3]. The conventional methods for detecting triglycerides include colorimetric and fluorometric techniques, based on the enzymatic hydrolysis of triglycerides to glycerol and free fatty acids. For detecting glucose one normally detects hydrogen peroxide (H2 O2 ) generated in an enzymatic reaction, which can be done with several analytical techniques, such as titrimetry [4,5], spectrophotometry [6,7], chemiluminescence [8–10] and electrochemistry [11–14]. Biosensors for glucose are actually found in large numbers, including cases in which the sensing units were made with nanostructured films containing immobilized glucose oxidase (GOx). Examples of such biosensors are those involving layer-by-layer (LbL) films [15] where layers of GOx were adsorbed on solid substrates in conjunction with other materials that may enhance sensitivity [16–18]. The major advantage of the

∗ Corresponding author. Tel.: +55 15 32295980. E-mail address: [email protected] (M.L. Moraes). 0925-4005/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2012.02.046

LbL method lies in the possibility of minimizing protein denaturing because the adsorption process is carried out in aqueous solutions, under mild conditions [19]. Furthermore, the possible control of molecular architectures allows for the synergistic combination of interesting properties from different materials [20]. For example, carbon nanotubes have been used to increase sensitivity [21,22] as have gold nanoparticles [17,23]. Redox mediators such as Prussian Blue have also been incorporated in the LbL films to help avoiding effects from interferents [18,24]. In most of these cases, the principle of detection in glucose biosensors is based on electrochemical methods. In contrast to the large number of biosensors for glucose, we were unable to find biosensors made with LbL films to detect triglycerides. The methods for simultaneous detection of glucose and triglycerides, on the other hand, normally require expensive reagents, in addition to being time consuming and requiring a skilled person to operate the equipment. Therefore, there is demand for low cost, easy-to-use biosensors for the task. In this paper, we address this problem with fabrication of sensing units based on LbL films containing one of the two enzymes, namely GOx and lipase. The rationale behind this choice of enzymes is to combine the expected molecular recognition ability of GOx and lipase for glucose and triglycerides, respectively. As we shall show, albeit

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electrochemical methods, especially cyclic voltammetry, did not lead to good distinction among the different samples, this could be achieved with impedance spectroscopy. For the latter, however, good distinction was only possible by employing appropriate information visualization methods [25]. More specifically, we built on our previous experience in handling impedance spectroscopy data with multidimensional projection and parallel coordinates techniques [26–28]. The paper is organized as follows. Section 2 brings a description of the methods to produce the sensing units, detect the analytes and treat the data with multidimensional projections and parallel coordinates. The optimization of the performance for sensing is described in Sections 3 and 4.

2. Materials and methods 2.1. Fabrication of the sensing units Two sensing units were prepared separately, one of which contained glucose oxidase from Aspergillus niger (EC 1.1.3.4) and another contained lipase from A. niger (EC 3.1.1.3). The enzyme layers were alternated with layers of poly(allylamine) hydrochloride (PAH) using the layer-by-layer (LbL) method [15]. Glucose oxidase and lipase solutions for LbL film fabrication had a concentration of 2 and 1 mg mL−1 , respectively, in a phosphate buffer, pH 6.3. LbL films were prepared by immersing the substrate alternately into the polycationic (PAH) and anionic (glucose oxidase or lipase) solutions. During deposition the enzyme solutions were maintained at room temperature. The PAH/enzyme LbL films were assembled onto an indium tin oxide (ITO) glass for the electrochemical measurements and on gold interdigitated electrodes containing 50 pairs of 10 ␮m wide electrodes, 10 ␮m apart from each other. The adsorption time and immobilization of glucose oxidase in LbL film were described by Ferreira et al. [18]. The adsorption kinetics of lipase onto a PAH layer and the growth of PAH/lipase LbL film were monitored using UV–vis spectroscopy with a Thermo Scientific Spectrophotometer. The saturation time observed at ca. 600 s indicates that a complete layer of lipase is achieved within 10 min (results not shown). The adsorption time for the growth of the PAH/lipase LbL film was 10 min for the lipase and 3 min for PAH. The linear increase at the maximum absorption with the number of layers (results not shown) indicates that the same amount of material was adsorbed in each deposition step. For the sensing measurements we chose LbL films with 7 bilayers, for it is known that a small number of layers generally leads to a better performance owing to the small thickness [29]. Though we did not perform specific experiments to investigate the stability of the LbL films, reproducible measurements could be obtained within at least 4 weeks, which is consistent with the literature as LbL films made from enzymes are normally stable for 4–6 weeks [30].

2.2. Detection measurements The electrical response was obtained by impedance spectroscopy using a Solartron 1260A impedance/gain phase analyzer in a frequency range from 1 to 105 Hz. The 7-bilayer PAH/lipase and the 7-bilayer PAH/GOx LbL films were deposited onto interdigitated electrodes. Each sensing unit was immersed in 10 mmol/L phosphate buffer (pH 6.3) solutions containing different molar concentrations of triglycerides and glucose (10−6 –10−2 mol/L). The measurements were obtained after 10 min of the electrode immersion, which is the period of time for the system to stabilize. Three impedance spectra were collected for each sample. The real and

imaginary parts of these impedance data were used to calculate the capacitance (C) and loss (G/ω) values. 2.3. Visualization methods The data generated with the sensing units were analyzed using Multidimensional Projection Techniques, or simply Projection Techniques [31]. Projection techniques are information visualization approaches which enable creating a visual representation that supports tasks involving analysis of similarity information among data instances. In this visual representation, each sample, in our case a spectrum of impedance values for a specific liquid solution containing either glucose or triglyceride, is represented as a graphical element, normally a circle, and their positioning seeks to mimic the similarity relationships among the samples. Formally, consider X = {x1 , x2 , . . ., xn } a set of impedance (or capacitance and loss) vs. frequency curves produced by one or more sensing units, Y = {y1 , y2 , . . ., yn } are the corresponding visual elements, ı(xi ,xj ) is a distance function between two different curves, and d(yi ,yj ) is a distance function between two graphical elements – here d and ı are Euclidean distances. A projection technique can be considered as a function f: X → Y which seeks to make |ı(xi ,xj ) − d(f(xi ),f(xj ))| ≈ 0 ∀xi , xj ∈ X. According to this formulation similar curves will render close graphical elements while dissimilar ones will lead to far apart elements. Thereby the human visual ability can be employed to analyze the responses of different sensors and verify how they behave as the film or analyte concentration changes. Several formulations are possible for the function f, the choice of which affects the computational performance and/or the precision of the optimization process [32]. Here we employed the Interactive Document Map (IDMAP) technique [33]. Prior to projection the data were normalized to zero mean and unity variance. IDMAP works by generating an initial two-dimensional layout of the data samples employing the well-known FASTMAP [34] dimension reduction algorithm. FASTMAP is known to be very fast, but it does not necessarily achieve good preservation of the original dissimilarity relations. Thus, as a second step IDMAP applies the Force algorithm [31] to improve the initial layout. Force attempts to recover some of the lost precision by iteratively perturbing the layout of data samples, trying to reach a stable layout that reduces the global error. It attracts or repels data point pairs based on a pairwise error function that considers differences between the pair’s original and projected distances. The error incurred in multidimensional projections is usually measured by a stress function given by a normalized sum of the squared differences between the original and projected distances, for all data pairs. IDMAP was originally developed to create visual representations of document collections; however, it has been successfully applied in the analysis of data from different domains and shown to achieve good tradeoff between precision (i.e. neighborhood preservation) and performance. We also tried other projection methods on the data samples, including Sammon’s Mapping [35] and Principal Component Analysis (PCA) [36], but the results were either inferior or of similar quality. These findings are in agreement with empirical studies comparing the stress of 16 projection methods applied to 7 distinct data sets, which indicate the superiority of Force over the technique known as Classical Scaling and also over Sammon’s [32]. Note that when taking Euclidean distances as approximation for dissimilarity, as it is the case here, PCA is known to be equivalent to Classical Scaling. In the case of Sammon’s mapping, obtaining the layout is also computationally more expensive as compared to IDMAP or PCA. Because we have been hitherto conducting analysis on relatively small data sets, computational cost is not a critical issue and our users are willing to trade-off time for precision, if required.

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However, albeit there is a wide range of projection methods available targeted at different data domains and situations (e.g., massive data sets) [32] we have achieved good distinguishing capability on impedance spectroscopy data with IDMAP and Sammon’s mapping [25–28]. Although projections are valuable tools to assist the analysis of the similarity relationships among sample measurements obtained with a single or multiple sensing units, they do not convey information on the role of specific frequencies or frequency ranges in producing such relationships. To support this kind of analysis we used a technique referred to as Parallel Coordinates [37], a multidimensional data visualization that is very effective to display multiple data attributes simultaneously. Parallel coordinate plots represent the set of measurement frequencies as equally spaced parallel axes that are scaled to depict the range of measured values. The responses from the sensing units are mapped as polylines that intersect the axes on the points representing the value measured, each axis depicting a specific frequency. The resulting plots allow simultaneously observing the distributions of measured values at each and all frequencies. It also evidences global correlations among frequency measurements and favors perception of highdimensional clusters [38]. The combination of projections and parallel coordinates into an analytical framework allows analysts to verify the global behavior of different samples and sensing units regarding their similarity, while simultaneously assessing the contribution from specific frequencies or frequency ranges to this similarity. The effectiveness of this combination for the analysis of sensing data was recently demonstrated elsewhere [26]. All visual representations and analysis were produced using the Projection Explorer Sensors (Pex-Sensors), an information visualization tool developed for the analysis of sensing and biosensing data [26].

3. Results 3.1. Detection measurements The immobilization of enzymes in nanostructured films has been proven excellent for biosensing [39], where the molecular recognition capability of the enzymes is combined with tailored molecular architectures afforded by the methods to produce the films [19,27]. The choice of sensing units for the simultaneous detection of triglyceride and glucose, therefore, seemed at first obvious. We produced LbL films with lipase and glucose oxidase (GOx) in separate units for electrochemical sensing, the results of which could yield the determination of triglyceride and glucose. In contrast to GOx that forms good electrochemical sensors [40,41], the PAH/lipase film did not show significant electrochemical response in the presence of various concentrations of triglycerides. This result may be explained by the lack of electroactive species in the reaction. In subsidiary experiments, we tried other approaches within electrochemistry, but no reasonable detection could be obtained using lipase and GOx only. It is known that triglycerides can be determined with an electrochemical biosensor containing a set of enzymes, e.g. lipase, glycerol kinase, glycerol-3-phosphate oxidase and horseradish peroxidase [42–44]. However, multienzymatic biosensors increase the cost and reduce life-time. With the negative results from electrochemical methods, we resorted to impedance spectroscopy for exploiting the concept of an extended electronic tongue [45]. In this concept, electrical impedance measurements are carried out with a sensor array that contains sensing units capable of molecular recognition for an analyte of interest. Accordingly, a fully-fledged search was made for

Fig. 1. Capacitance (Cp) and loss (G/ω) curves for the sensing units made with PAH/GOx (A) and PAH/lipase (B) LbL film.

sensors that would be suitable to distinguish different concentrations of triglyceride and glucose. One unit had 7-bilayer PAH/lipase and the other had 7-bilayer PAH/GOx LbL film and three nominally identical sets were used. We have chosen 7-bilayer LbL films because it is known that enhanced performance in sensing units based on impedance spectroscopy is obtained with 5–7 bilayers [46,47]. It seems that films with such thickness are sufficiently thick to have complete coverage while still thin for having enhanced sensitivity. Some impedance vs. frequency curves are shown in Fig. 1A and B for the electrodes coated with the PAH/GOX and PAH/lipase LbL film, respectively. As is usual for sensing units made with nanostructured films, the capacitance decreases monotonically with the frequency, while the loss curves display peaks corresponding to the relaxation mechanisms. A visual inspection or manual analysis of the whole dataset showed that the distinction between the triglyceride and glucose was very good, however the distinction among different concentrations of triglyceride or glucose was poor even in the low-frequency region where the capacitance curves, in particular, did not coincide with each other. 3.2. Interactive document map When the data from the impedance curves produced by the sensing units with PAH/GOx and PAH/Lipase films are visualized using the IDMAP technique, it was again clear that distinction was

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Fig. 2. Plots obtained with IDMAP for the capacitance and loss data with the sensing units containing PAH/GOx (a) and PAH/lipase (b) LbL films used to detect glucose and triclycerides in various concentrations (mol/L). The distinction between different concentrations of each analyte is very poor. Triglyceride and glucose buffer is 0 mM. Note that because the plots are based on relative distances, the axes are not labeled.

difficult (see Fig. 2). This distinguishability problem can be numerically assessed using the silhouette coefficient [48] on the layouts produced. The silhouette coefficient is a measure that indicates how cohesive is a group of similar samples, in our case sensing units with analytes with the same concentration, and how separate these groups are among themselves. This coefficient may be obtained as follows. Let si be a sample belonging to a group of sensing units with analytes with the same concentration. Its cohesion ai is calculated as the average of the distances between i and all other samples belonging to the same group. Its separation bi is computed as the minimum distance from si to each other group (i.e., to all groups that do not include si ). The distance from si to a group is

taken as the average distance from si to all elements in this group. In computing the silhouette we consider distances as defined in the original multidimensional space. The silhouette of a projection is given as the average of the silhouette of all samples, where n is the total number of samples. It is defined as follows: 1  (bi − ai ) n max(ai , bi ) n

S=

i=1

The coefficient varies between −1 and 1, with larger values representing better results. The silhouette coefficients are 0.8138 and

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Fig. 3. Plot using IDMAP for the capacitance and loss data obtained with the sensing units made with PAH/GOx and PAH/lipase LbL films.

0.7830 for the PAH/GOx and PAH/Lipase films, respectively, indicating that the sensing units containing GOx group and separate the samples better than the units containing lipase (the concentrations which are not distinguishable are circled on the layout) in Fig. 2(b). However, sensing units with PAH/GOx films were not capable of separating glucose from triglyceride samples. We have then proceeded to an optimization process, searching for an ideal combination of frequency range and sensors. For this we simply combined the responses of both films for taking advantage of their properties, namely good distinguishability of the concentrations (PAH/GOx) and good separation between glucose and triglyceride (PAH/Lipase) samples. The result is shown in Fig. 3. The silhouette coefficient for this layout is 0.9228, which is a significant improvement, with glucose and triglyceride samples now clearly split into two different groups. Note that the response of nominally identical sensing units is not the same on the entire frequency curve, therefore the circles representing the samples of the same concentration do not coincide on the layout produced. This is a well-known experimental fact, for the high sensitivity afforded by the impedance spectroscopy method is due to the strong

dependence of the interface electrical properties on any change on the film surface. Such high sensitivity, which is obviously a positive feature, is unfortunately accompanied by the difficulty in reproducibility. For it is virtually impossible to obtain exactly the same electrical response with distinct organic films, even though they may be prepared under identical conditions with nominally identical materials. Although the sensing units do not present identical responses on the entire frequency curve, some parts are more similar than others. Therefore a further development in optimizing the biosensing performance is taken by automatically selecting the measures which present the more similar responses. This is performed using a genetic algorithm, taking the silhouette coefficient as the fitness function and choosing only the 10 measures [28] (real and imaginary parts of impedance data) that lead to the most similar responses for the curves obtained with PAH/GOx and PAH/lipase sensing units. Fig. 4 shows the resulting layout, and now the silhouette coefficient is 0.9594, with samples having the same concentrations exhibiting a much more similar response, while the good separation between glucose and triglyceride is kept.

Fig. 4. Plot using IDMAP for the capacitance and loss data obtained with the sensing units containing PAH/GOx and PAH/lipase LbL films, in each sensor unit, after a selection of suitable frequencies, used to detect glucose and triclycerides, respectively, in various concentrations (mol/L). Measurements were made using two separate sensing units, but the results were combined to be placed using IDMAP.

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Fig. 5. Parallel coordinates plot for the capacitance and loss data obtained with the sensing units made with PAH/GOx and PAH/lipase LbL films, after a selection of suitable frequencies. Measurements were made using two separate sensing units, but the results were combined to be placed using IDMAP.

3.3. Parallel coordinates Fig. 5 shows the parallel coordinates plot of these selected measures. Sensing units with the same analyte concentration define polygonal lines very close on the visual representation, and the groups of lines are well separated between themselves. This indicates that the selected measurements represent almost identical responses. The boxes on the top of each axis represent the silhouette coefficient computed for each measurement, with more filled boxes indicating higher silhouette values. In most measurements, the silhouette is close to 0.8, but upon combining the measurements the silhouette value reaches 0.95945, close to the ideal limit of 1.0.

4. Discussion Using two sensing units containing LbL films of lipase and GOx, we have been able to detect glucose and triglycerides simultaneously, including distinction of various concentrations. This was made possible by treating the impedance spectroscopy data with multidimensional projection techniques, more specifically using the IDMAP projection. The main advantages inherent in the use of such techniques are: (i) huge amounts of data may be handled quickly employing several projection techniques (including the widely used principal component analysis), which then allows the user to select the methods leading to the best distinguishing ability. This is especially the case if the various methods are implemented on a single suite of tools as the PEx-Sensors developed recently [26]. (ii) The performance of the sensing units (or sensor arrays) may be optimized by selecting parts of the data to be considered in distinguishing the various samples. Here this optimization process involved the use of the parallel coordinates technique and feature selection basically consisted of choosing the 10 frequencies for which distinction was best. A genetic algorithm was used to scan the data space in the search for the best frequencies. One current limitation in the use of information visualization

methods to treat data from sensing and biosensing is in the difficulty to determine the detection limit. Because a linear dependence is not established between the analyte concentration and the electrical response, determining the detection limit will require combining regression methods with the multidimensional projections. This has not been done so far, to the best of our knowledge. With regard to the choice of impedance spectroscopy as the principle of detection, it proved a good alternative to electrochemical methods. In the latter, with amperometry, for instance, the product of the enzymatic reaction must be electroactive. This applies to the reaction catalyzed by GOX, where H2 O2 is generated, but not to triglycerides, and may be the reason why our attempts to detect the analytes using electrochemical methods failed. With impedance spectroscopy, on the other hand, any change in the electrical response induced by changes in the analyte can be captured, but then there is the disadvantage that distinction of different samples may be made difficult. With the optimization process for treating the data, we have overcome this difficulty. In this process we found out that using data from both the capacitance and the loss curves was beneficial for the distinction between similar samples. This is not a general rule, however, as in a previous study we observed that employing only the capacitance data yielded a superior performance [26].

5. Conclusions In this paper we have shown that the molecular recognition capability and flexibility in film architectures available in sensing units made with LbL films can be combined with computational methods to enhance the sensitivity and selectivity of biosensors. Significantly, the methods used in this paper can be extended to other types of sensors and biosensors, which would be in line with the proposals by Saurina [49] who advocates that different sources of information should be used to characterize complex systems. Furthermore, as discussed in Paulovich et al. [26,28], the use of such information visualization methods may revolutionize the way

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Biographies Marli Leite de Moraes obtained her bachelor degree in chemistry in 2001, master degree in biophysics in 2003, and Ph.D. in physical chemistry in 2008 at Universidade de São Paulo, São Carlos – SP (Brazil). Her fields of interest are nanostructured thin films for biosensing fabricated from enzymes, peptides, polymers, and liposomes, and spectroscopic, electrochemical and electrical (impedance) characterization of these films. She is a researcher at Universidade Federal de São Carlos, Sorocaba – SP since 2009.

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Laís Petri is an undergraduate student of bachelor in biology since 2008 at Universidade Federal of São Carlos, Sorocaba – SP (Brazil). She conducted research of nanostrucutured films with biological components and liposomes for immuno and biosensing fabrication from 2009 to 2011 through a Scientific Initiation program. Her main fields are spectroscopy characterization of nanostructured thin films with peptides, enzymes and liposomes for biosensing. Victor Ribeiro Alberto de Oliveira is an undergraduate student in computer engineering since 2008 at Universidade de São Paulo, São Carlos – SP (Brazil). He is currently in the Scientific Initiation program, working in the field of information visualization. Clarissa A. Olivati obtained her bachelor degree in physics in 1997 at Universidade Estadual Paulista, Rio Claro – SP (Brazil) and her master degree and Ph.D. in applied physics in 2000 and 2004, respectively, at Universidade de São Paulo, São Carlos – SP (Brazil). Her main fields of interest are organic electronics: charge transport and physics device. She is teaching and researching at Universidade Estadual Paulista, Presidente Prudente – SP since 2010. Maria Cristina Ferreira de Oliveira received the BSc in computer science from the University of São Paulo, Brazil, in 1985, and the Ph.D. degree in electronic engineering from the University of Wales, Bangor, in 1990. She is currently a Professor in the Computer Science Department of the Instituto de Ciências Matemáticas e de Computac¸ão, at the University of São Paulo, Brazil, and has been a visiting scholar at the Institute for Visualization and Perception Research at University of Massachusetts, Lowell, in

2000/2001. Her research interests are in visual analytics, visual data mining, information visualization and scientific visualization. She is a member of the ACM, IEEE and of the Brazilian Computer Society. Also she is currently the chief editor of the Journal of the Brazilian Computer Society, published by Springer. Fernando Vieira Paulovich obtained his bachelor and master degree in computer science in 2000 and 2003, respectively, at Universidade Federal de São Carlos, São Carlos – SP (Brazil), and he received his Ph.D. in computer science in 2008 at Universidade de São Paulo, São Carlos – SP (Brazil). His main fields of interest are information visualization, visual data mining and visual analytics. Currently, he is a lecturer and researcher at Universidade de São Paulo, São Carlos – SP. Osvaldo N. Oliveira Jr. obtained his Ph.D. at the University of Wales, Bangor (UK), and is now a professor at the Institute of Physics of São Carlos, University of São Paulo (Brazil). His fields of interest are in molecular control of properties in nanostructured films, including for sensing and biosensing. Marystela Ferreira obtained her bachelor degree in chemistry in 1993, master degree and Ph.D. in physical chemistry in 1996 and 2000, respectively, at Universidade de São Paulo, São Carlos – SP (Brazil). Her main fields of interest are preparation and characterization of nanostructured thin films for sensing, and layer-by-layer and Langmuir–Blodgett films of polymers for sensing different analytes in environmental and biological samples. She is teaching and researching at Universidade Federal de São Carlos, Sorocaba – SP since 2007.