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Sensors and Actuators B 198 (2014) 173–179

Contents lists available at ScienceDirect

Sensors and Actuators B: Chemical

jo ur nal home page: www.elsev ier .com/ locate /snb

rtificial neural networks applied to fluorescence studies for accurateetermination of N-butylpyridinium chloride concentration inqueous solution

ohn C. Cancillaa, Pablo Díaz-Rodrígueza, Jesús G. Izquierdob,uis Banaresb, José S. Torrecillaa,∗

Departamento de Ingeniería Química, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, SpainCentro de Láseres Ultrarrápidos y Departamento de Química Física I, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid,pain

r t i c l e i n f o

rticle history:eceived 31 December 2013eceived in revised form 26 February 2014ccepted 28 February 2014vailable online 19 March 2014

eywords:onic liquidluorescenceonlinear relationrtificial neural network

a b s t r a c t

N-butylpyridinium chloride ([bpy][Cl]) is an ionic liquid (IL) extensively employed as an effective catalystfor many chemical reactions. Therefore, precise monitoring of its concentration can ensure the desiredhigh yields in these catalyzed chemical processes. In this work, a fluorescence study has been carried outto determine the concentration of [bpy][Cl] in aqueous solution. A light emitting diode (LED), a continuouswave laser diode (CWLD), and a femtosecond pulsed laser (FPL) have been employed as light sources toelectronically excite IL samples at a central wavelength of 400 nm. The measured fluorescence spectraobtained at different concentrations have been used to design three mathematical models for each oneof the light sources. These models rely on artificial neural networks (ANNs) to assess the concentrationof IL aqueous solutions in a wide range of concentrations. ANNs have been selected thanks to theirability to discover and adequately interpret nonlinear relationships among datasets. ANNs have beensuccessful due to the existence of a nonlinear dependence between the IL fluorescence signal and itsconcentration, most likely due to the inner filter effect. The three light sources employed were suitable

to fulfill the goal (mean prediction errors were 4.9%, 2.5%, and 1.7% for the LED, CWLD, and FPL models,respectively). These results suggest the existence of a potential source of reliable sensors based on thecombination of fluorescence and ANNs. Furthermore, the accurate concentration estimation of manyfluorescent compounds in aqueous solution appears achievable and, therefore, applicable to multiplechemical processes.

. Introduction

Ionic liquids (ILs) are chemical compounds formed solely by ions1]. These are organic cations, like pyridinium-, pyrrolidinium-, andmidazolium-based ones, and organic or inorganic anions, such ascetates or halides, respectively. ILs are salts that greatly differ fromonventional ones in many senses. For example, ILs are found, in thereat majority of cases, in liquid phase around and below 100 ◦C [2].n addition, it is possible to select among the millions of differentossible ion combinations for the design of custom-made ILs, well

uited for highly specific applications [3,4]. ILs have many outstand-ng properties such as being nonflammable [3] and very stable in a

∗ Corresponding author. Tel.: +34 91 394 42 44; fax: +34 91 394 42 43.E-mail addresses: jstorre@ucm.es, jstorre@quim.ucm.es (J.S. Torrecilla).

ttp://dx.doi.org/10.1016/j.snb.2014.02.097925-4005/© 2014 Elsevier B.V. All rights reserved.

© 2014 Elsevier B.V. All rights reserved.

wide range of temperatures [5], as well as presenting an extremelylow vapor pressure or volatility [3,6].

These unconventional compounds are becoming more and morepresent in a wide variety of fields and applications, due to theirunique, useful, and manageable properties [7,8]. For instance, theyare currently being utilized as electrolytes in lithium batteries [9] oras components for separation processes like liquid–liquid extrac-tions [10] and azeotrope separations [11]. In addition, ILs are alsobeing heavily exploited to attain safe and highly effective catalytic[12,13] and biocatalytic [14] reactions.

Some of the most important ILs used as catalysts for variousreactions are pyridinium-based ones [15]. These ILs have beenemployed to favor reactions such as benzimidazol synthesis [16],

ethylene oligomerization and polymerization [17], and dihydro-pyridine derivative synthesis [18]. For these catalyzed reactionsto progress properly and efficiently, the pyridinium-based IL cata-lyst should be evaluated beforehand or monitored during its use to

174 J.C. Cancilla et al. / Sensors and Actu

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to be estimated [2].

Fig. 1. Ionic liquid N-butylpyridinium chloride.

ssure that its purity or concentration are suitable, which should, inhe end, lead to the desired high synthesis yields. To do so, it is usu-lly necessary to carry out appropriate measurements to determineoncentrations accurately.

A spectroscopic approach that allows the determination anduantification of a diversity of molecules in aqueous solution is flu-rescence emission after one-photon electronic excitation [19,20].his technique is based on the fact that many chemical compoundsan be electronically excited when irradiated with light at a fixednd particular wavelength, and, afterwards, when they deactivateo the ground electronic state, they emit a specific fluorescencepectrum. So as to measure this kind of data, an adequate excita-ion light source must be used. Interesting and inexpensive optionsre light emitting diodes (LEDs) or continuous wave laser diodesCWLDs). LEDs and CWLDs provide incoherent and coherent lightources, respectively. The use of femtosecond pulsed lasers (FPLs)rovides an intermediate case of coherent and broad band lightources. In the present work, the three approaches for molecularlectronic excitation have been compared by studying the fluo-escence emission spectra of a series of aqueous solutions witharying concentrations of N-butylpyridinium chloride ([bpy][Cl])see Fig. 1), a typical pyridinium-based IL.

The analysis of the measured fluorescence spectra has shown nonlinear trend between the emission fluorescence signal andhe concentration of the [bpy][Cl] aqueous solutions along the con-entration range studied, probably due to an inner filter effect,hich leads to a downturn in the fluorescence at higher concen-

rations [21]. Because of this, linear models are not able to estimateccurately the concentration in a wide range using the data fromuorescence measurements without greatly over-fitting. There-

ore, in order to correctly interpret these results, an interestingption is to use a versatile mathematical tool known as artificialeural networks (ANNs) [22]. These algorithms are very well suitedo discover, process, and interpret nonlinear relationships presentn databases to create simple and manageable mathematical mod-ls [23]. In the present work, ANNs have been essential to processhe data gathered from the fluorescence studies, and have played

crucial role in the accurate estimation of the concentration ofbpy][Cl] in aqueous solution.

To sum up, in this study a tool based on the combination of flu-rescence measurements and ANN models has been designed tostimate the concentration of [bpy][Cl] in aqueous solution. Inter-retation of the statistical results of the nonlinear mathematicalodels has further aided in the comparison of the different light

ources employed (LED, CWLD, and FPL) to excite the [bpy][Cl]amples. Consequently, the fluorescence-ANN combination can beiewed as a source of many potential sensors to detect and quantify

any different fluorescent molecules relevant in numerous chem-

cal processes. Besides the mentioned monitoring application foratalytic reactions, these sensors could be used to control industrial

ators B 198 (2014) 173–179

and other water contamination, and to identify possible leakagesor unknown polluting sources.

2. Materials and methods

2.1. N-butylpyridinium chloride aqueous solutions preparation

The IL was purchased from Iolitec with purity greater than 99%.It was mixed with milli-Q water to prepare 39 different solutionswith concentrations varying from 0 to 1370 mM.

2.2. Absorption and fluorescence studies

The absorption spectrum of an aqueous solution of [bpy][Cl] wasattained employing an UV-VIS spectrophotometer (JASCO ModelV-530). On the other hand, the prepared [bpy][Cl] aqueous solu-tions were irradiated using three different light sources in orderto carry out the fluorescence studies. The first source was an UV(400 nm wavelength), 5 mm diameter, 20 mW power light emit-ting diode (LED). The second one was a 90 mW, 400 nm continuouswave laser diode (CWLD). Finally, the last source was a fem-tosecond pulsed laser (FPL) at 400 nm obtained by doubling thefundamental frequency of an arm (400 mW) from a Spectra PhysicsTi:sapphire amplified laser system which provides 50 fs per pulse,3.4 W, centered at 800 nm with a 1 kHz repetition rate. The emittedfluorescence was detected at right angles with respect to the illumi-nation direction using a fiber spectrometer (Ocean Optics HR2000)with a 1 nm resolution, and the fluorescence emission spectra werecollected and analyzed in a computer. All samples were introducedin a UV/VIS quartz cuvette with four transparent and uncoloredsides, as well as a 1 cm length path, which is adequate for absorptionand fluorescence studies.

2.3. Artificial neural networks

The estimation of the concentration of [bpy][Cl] in aqueous solu-tions has been attempted using ANNs to create a model with thedata obtained from the fluorescence measurements. The goal ofANNs is to discover and interpret nonlinear relationships presentin databases [24], which, in this case, are fluorescence spectra and ILconcentrations in aqueous solutions (vide infra). A supervised mul-tilayer perceptron (MLP), which is the most employed ANN [25],has been selected to perform the desired estimation [26]. The MLPused, which is trained with data from the fluorescence spectra (videinfra), is supervised because it requires target data (known IL con-centrations, in this case) for its correct training and optimization[27]. It should be noted that these mathematical tools are effec-tive when interpolating within the range of data utilized duringits training, and otherwise provide excessive errors when they areforced to extrapolate [28].

MLPs are formed by three types of layers, which are input, hid-den, and output. The input layer is formed by nodes, and these areused to select the number of independent variables that are usedin the estimative tool [7]. Hidden and output layers are both com-posed of neurons, which are the actual calculation centers of theMLP. The hidden neuron number (HNN) should be adequately opti-mized to find the network topology that provides the best statisticalresults. The correct definition of the HNN is fundamental becausea low HNN may negatively affect the learning ability of the MLP. Incontrast, when the HNN is too high, the resulting ANN may be over-fit toward the employed dataset [29]. Finally, the output neuronsare selected depending on the dependent variables that are trying

Every unit (node or neuron) in each layer of a MLP is connectedwith all of the units in surrounding layers. Individual weightedcoefficients, or weights, control each one of these connections in

d Actuators B 198 (2014) 173–179 175

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Fig. 2. Fluorescence spectra (fluorescence intensity (arbitrary units (a.u.)) versusemission wavelength (�; nm)) attained from a 1010 mM aqueous solution of

J.C. Cancilla et al. / Sensors an

rder to adequately define their relative importance [28]. Their val-es are modified during the optimization of the ANN so the accuracyf the estimations increases [26]. This accuracy is greatly attachedo the database in terms of its size, range, and quality. This lastspect, the data quality, is firmly linked to the precision of thenstruments employed during experimentation and, therefore, tohe measurements carried out.

Multiple ANN-related parameters have been selected, whilethers have been optimized to find the best possible model forhe estimation of the concentration of [bpy][Cl] in aqueous solu-ions, which is evaluated through its mean prediction error (MPE;q. (1)) and R2 correlation coefficient. The selected parametersere the input nodes, output neurons, and training function. On

he other hand, the HNN was optimized along with the Marquardtdjustment parameter (Lc), the decrease factor for Lc (Lcd), and thencrease factor for Lc (Lci) [30], all three of which were optimizedsing a thorough experimental design based on the “Box-Wilsonentral Composite Design 23 + star points”. The Lc parameter cane seen as the learning coefficient in the classic back-propagationlgorithms [31]. Its value is respectively decreased or increased bycd and Lci parameters until these modifications result in a reducederformance value, which is assessed with MPE and R2 values [30].

PE = 1N

N∑

k=1

∣∣rk − yk

∣∣rk

× 100 (1)

In this equation, N represents the number of data points consid-red, rk is the real already known target value, and yk stands for theesult of a given output neuron. All ANN-related calculations haveeen carried out utilizing Matlab version 7.0.1.24704 (R14) [30].

. Results and discussion

.1. LED, CWLD, and FPL fluorescence spectra

To be able to adequately compare the three 400 nm light sourcesLED, CWLD, and FPL) employed to obtain the fluorescence emissionpectra, measurements of different [bpy][Cl] aqueous solutionsomprised within the concentration range 0–1370 mM have beenarried out. As an example, in Fig. 2, the fluorescence emission spec-ra measured using the three light sources for a 1010 mM [bpy][Cl]queous solution can be seen. Each resulting spectrum is the aver-ge of three independent measurements, which allowed to suitablynalyze the quality of the data and to relate it to the specific lightource employed (LED, CWLD, or FPL).

As can be seen in Fig. 2, the three light sources yield analogouspectra. This also happened for every other IL aqueous solution,hus validating the interchange of light sources depending on theser’s requirements. Analyses of the spectra indicate the pres-nce of two main overlapping emission bands centered at around97 nm (Peak 1) and 524 nm (Peak 2).

Any of the three light sources can be employed to define theelation between the fluorescence signal and the [bpy][Cl] con-entration. It is worth mentioning that the fluorescence spectrabtained with lasers (coherent light sources) offer higher signal-o-noise ratios than those obtained using LED (incoherent source),

ainly due to the fact that the former light sources are more stablend intense.

.2. Fluorescence studies to evaluate the concentration ofbpy][Cl] aqueous solutions

The three light sources (LED, CWLD, and FPL, with a centralavelength of 400 nm) have been used to develop the fluorescence

tudies of the prepared aqueous solutions of the pyridinium-based

[bpy][Cl]. Two main overlapping peaks can be seen at around 497 and 524 nm. (a)LED, (b) CWLD, and (c) FPL.

IL analyzed. Each approach has been examined individually in thefollowing subsections.

To successfully correlate the fluorescence emission with the ILconcentration, Gaussian functions were employed to fit the dif-ferent contributions in the spectra and the area under the curve(AUC) of both peaks (Peak 1 and Peak 2) for every sample has been

calculated by peak integration. These results are illustrated in Fig. 3.

Fig. 3 shows that the AUC initially increases almost linearly withthe concentration of the IL irrespective of the excitation source. This

176 J.C. Cancilla et al. / Sensors and Actu

(a)

AU

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150012009006003000

(c)

AU

C (

a.u.

)

[Bpy][Cl] concentration (mM)

(b)

AU

C (

a.u.

)

Fig. 3. Fluorescence emission results after electronically exciting with (a) LED, (b)CWLD, and (c) FPL. The AUC (a.u.) versus [bpy][Cl] concentration (mM) in water isshown for both emission peaks (Peak 1: 497 nm (full squares; continuous line) andPeak 2: 524 nm (empty circles; dashed line)).

cctts

easrpeett

HNN, Lc, Lcd, and Lci. The HNNs tested were from 3 to 11 dueto the size of the database. Lc and Lcd were optimized analyzing

an be noticed for both emission peaks. Once the IL reaches a certainoncentration (around 200 mM), the AUC starts to change its risingrend until it ends up decreasing (near 650 mM). This fact is dueo the inner filter effect [21], which will be analyzed in subsequentections.

The information provided by the three different light sources isquivalent. The curves are smoother when the laser-based sourcesre employed showing that the data obtained with these lightources have a higher quality (more reliable, repetitive, and accu-ate). This is due to the greater stability and intensity that lasersrovide versus LEDs, implying that the IL samples require a lowerxposure time and thus the better signal-to-noise ratio in the finalmission spectra. Nonetheless, it is clear that all three representa-ions show the same nonlinear behavior and can be used to relate

he [bpy][Cl] concentration with its fluorescence emission signal.

ators B 198 (2014) 173–179

3.3. Inner filter effect

When analyzing any of the fluorescence studies (see Fig. 3), itcan be seen that there is an interesting fact that takes place: thereis a given concentration of [bpy][Cl] which can be associated witha fluorescence emission signal maximum (or a largest AUC) whichappears around 650 mM. This phenomenon, known as the innerfilter effect [21], is triggered by the fact that as the concentrationof the IL aqueous solutions increases, and the distance betweenmolecules reduces, the emitted fluorescence by a molecule maythen be absorbed by others in the vicinity instead of being detectedby the spectrometer. This is plausible thanks to an existing over-lap between the absorption (see Fig. 4) and fluorescence emissionspectra of [bpy][Cl] [21].

The [bpy][Cl] absorption spectrum shown in Fig. 4 proves thatthere is a wavelength overlap between absorption and emissionthat would produce the mentioned inner filter effect. The net effectis to yield a lower fluorescence intensity than expected by extrap-olation of the linear section which corresponds to more dilutedsolutions (samples with concentrations below 200 mM; see Fig. 3).

Additional evidence that could help support the existence ofthis inner filter effect would be the fact that as the concentrationincreases, the ratio between the calculated AUC for both emissionpeaks (AUC Peak 2/AUC Peak 1) also increases. The emission of Peak2 is found around 524 nm, which is longer than the emitting wave-length of Peak 1, which appears near 497 nm (see Fig. 2). Therefore,Peak 1 corresponds to a more energetic emission photon than Peak2. For this reason, it might be possible that the 497 nm photonsemitted by [bpy][Cl] would more likely be absorbed by other neigh-boring IL molecules, resulting in a larger AUC for the less energeticPeak 2. This is confirmed in Fig. 5, where AUC Peak 2/AUC Peak 1 isrepresented as a function of [bpy][Cl] concentration for the threelight sources employed.

As can be observed in Fig. 5, the AUC ratio clearly increasesas the IL concentration rises (i.e., Peak 2 becomes relatively largerthan Peak 1 as [bpy][Cl] concentration grows). Although initially,at extremely low concentrations, the AUC of Peak 1 is larger thanthat of Peak 2, this tendency rapidly shifts (around 40 mM), untilit reaches a linear trend (near 100 mM). This circumstance helpscorroborate the presence of an inner filter effect.

3.4. Artificial neural network model

Due to the fact that the fluorescence emission of [bpy][Cl] isnot linearly proportional to its concentration for the entire rangestudied (see Fig. 3), ANNs have been employed to estimate its con-centration using data from the fluorescence spectra obtained. Thesealgorithms have been selected because they are known to excel atfinding nonlinear relations between independent and dependentvariables in databases.

3.4.1. Parameter selection and optimizationThree MLP models have been designed to estimate the concen-

tration of [bpy][Cl] in aqueous solutions within the concentrationrange studied. Each one has been created using the data from themeasurements of every individual light source employed (AUCsfrom both emission peaks and IL aqueous solution concentrations)to be able to compare the results from all three experimentalapproaches. A series of parameters have been selected and opti-mized to obtain an accurate estimative mathematical tool (seeTable 1). The selected parameters were the input nodes, outputneurons, and training function, while the optimized ones were the

the range from 0.001 to 1, and the same was done for Lci from2 to 100. The combination of optimized values was chosen after

J.C. Cancilla et al. / Sensors and Actuators B 198 (2014) 173–179 177

800700600500400

Abs

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nce

(a.u

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λ (nm)

F lengt

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ig. 4. Absorption spectrum of a [bpy][Cl] aqueous solution (1370 mM) in the wave

tudying the accuracy of the MLPs through a meticulous exper-mental design (vide supra). The parameters that offered lowest

PEs (Eq. (1)) were chosen.Two input nodes have been defined for each MLP, which are the

UC of both peaks (Peak 1 and Peak 2; Fig. 3). They are used tostimate the concentration of the analyzed IL in aqueous solution,hich is the single output neuron of the ANNs (see Table 1).

The reason behind the selection of the training function trainBRBayesian regulation function) for the MLPs is that this functionmproves the typical ANN generalization by updating the weightsf the network after analyzing the errors and the sum of the squaresf the weights of the network, which permits discovering the mostmportant parameters of the ANN as well as their optimization (seeable 1). Over-fitting models are avoided thanks to trainBR, andnding the optimum network topology is simplified [30,32].

.4.2. Statistical resultsIn order to assure that the mathematical models designed are

ble to generalize well and are applicable inside the whole rangef data analyzed, a K-fold cross-validation (K = 6 for all three MLPs)est has been employed [25,33].

Analyses of the results have led to define a slightly more

estricted [bpy][Cl] concentration range for which the estimation isrecise. Lower concentrations than 72 mM provide large MPEs and,herefore, the fluorescence-ANN combination employed is usefulnd highly accurate within the range of 72–1370 mM.

able 1elected and optimized ANN parameters for the three designed MLPs.

Parameter Selection or value

LED CWLD FPL

Selection Input nodes AUC of Peaks 1 and 2Output neuron [bpy][Cl] concentrationTraining function TrainBR

Optimization HNN 7 7 7Lc 1.0 0.001 0.001Lcd 0.989 1.0 0.001Lci 93.2 25.7 17.0

h range 350–850 nm, where both fluorescence peaks are observed (see Fig. 2).

The results shown in Table 2 are obtained from the averages ofthe six ANNs (K = 6) for the three light sources used in the concen-tration range 72–1370 mM. As can be seen in Table 2, the resultsobtained from the MLPs using the data from the measurementsdone with the LED provide higher IL concentration estimatingerrors than the laser measurements. The main reason behind thisfact is the different quality of the data, which undoubtedly dependson the light source employed. The lasers offer smoother emissionspectra (see Fig. 2), with an improved signal-to-noise ratio (higherquality; vide supra), when compared to the ones attained withthe LED. This, combined with the higher stability and intensity ofthe lasers, leads to more accurate and repetitive results (observ-able when analyzing the results depicted in Fig. 3), which, in theend, helps the ANN create more reliable and accurate estimativemodels. When comparing both laser sources, the FPL seems to bea bit more effective and accurate to fulfill the [bpy][Cl] concen-tration estimation than the CWLD. Nevertheless, all three lightsources offer data that can be adequately interpreted with ANNsto accurately estimate [bpy][Cl] concentrations within the range72–1370 mM.

To sum up, the possibility of accurately determining the con-centration of [bpy][Cl] in aqueous solutions may lead to the designof reliable sensors based on the combination of fluorescence andANNs. The control of chemical reactions which involve this orsimilar ILs [16–18] is therefore conceivable. Analyzing poten-tial polluting sources or wastewater quality are other possible

applications.

Table 2Statistical performance of the MLPs designed from 72 to 1370 mM. The data shownis the result of the average values of each ANN of the K-fold cross-validation test(K = 6).

LED CWLD FPL

R2 0.997 >0.999 >0.999MPE (%) 4.9 2.5 1.7

178 J.C. Cancilla et al. / Sensors and Actuators B 198 (2014) 173–179

Table 3Relavant issues to consider when selecting the light source. Clear advantages marked in bold.

LED CWLD FPL

Lower spectra quality Higher spectra quality Higher spectra qualityLess repetitive results/data Intermediate situation More repetitive results/dataLower intensity and stability Intermediate situation Higher intensity and stabilityHighly inexpensive Intermediate situation, but much closer to LED Very expensive (equipment and maintenance)Easy and inexpensive maintenance Intermediate situation Specialized staff for maintenanceEasy to operate Intermediate situation Specialized staff for correct operationLow or no safety concerns Important safety concerns

Easy and fast set up Intermediate situation

Less accurate estimations with ANNs Intermediate situation

150012009006003000

1,0

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2,0

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Fig. 5. AUC ratio of both peaks (AUC Peak 2/AUC Peak 1) versus [bpy][Cl] concen-tration (mM). Results obtained from the measurements carried out with (a) LED, (b)CWLD, and (c) FPL.

Important safety concernsComplex set upMore accurate estimations with ANNs

3.5. LED, CWLD, and FPL comparison for fluorescence studies

Even though all three light sources can be employed todetermine the concentration of the analyzed IL using thefluorescence-ANN combination, there are various advantages anddrawbacks of the different approaches. To make the adequate lightsource selection there are a few points that should be taken intoconsideration (Table 3).

When higher accuracy and quality of data are required, employ-ing an FLP is the better option due to its high intensity and stability.Nonetheless, when limitations are set by an economical factor ortime, LEDs or CWLDs are a much faster, inexpensive, and easier-to-maintain choice, without a great loss in terms of concentrationestimation accuracy. Therefore, depending on the requirements,any one of the light sources can be selected to adequately study thefluorescence of emitting compounds, and relate it with its concen-tration using ANNs.

4. Conclusion

Light emitting diode (LED), continuous wave laser diode(CWLD), and femtosecond pulsed laser (FPL), all centered at awavelength of 400 nm, have been successfully employed as lightsources to assess the concentration of N-butylpyridinium chlo-ride ([bpy][Cl]) in aqueous solutions through fluorescence studies.Artificial neural networks (ANNs) have been used to analyze theemission spectra for the design of [bpy][Cl] concentration esti-mating mathematical models within the range of concentrations72–1370 mM. These nonlinear algorithms have been used becauseof the inner filter effect observed in the concentration range stud-ied. The concentration of the pyridinium-based ionic liquid hasbeen successfully estimated using the areas under the curve ofthe two observed emission peaks (497 and 524 nm) of the spectraattained after the measurements with the three light sources. Nev-ertheless, the statistical results obtained are more accurate whenthe data from the lasers was employed, mainly due to their higherquality (mean prediction errors were 4.9%, 2.5%, and 1.7% for LED,CWLD, and FPL, respectively, although R2 correlation coefficientswere greater than 0.99 in all three cases). These results imply thatfluorescence studies in combination with ANNs may be an interest-ing source for the design of sensors that can accurately estimate theconcentration of numerous fluorescence emitting compounds forprocess monitoring, environmental control, and many other fieldsand applications.

Acknowledgements

The research leading to these results has achieved fund-ing from the European Union Seventh Framework Programme

(FP7/2007–2013) under grant agreement no. HEALTH-F4-2011-258868.

This work was supported by Spanish MINECO through GrantsCTQ2008-02578/BQU, CTQ2012-37404-C02-01 and Consolider

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AUUL CSD2007-00013. The facilities provided by the Centro deáseres Ultrarrápidos (UCM) are gratefully acknowledged.

Materials for experimentation were partially financed by IJMdC131655.

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Biographies

John C. Cancilla finished his Bachelor’s Degree in Biochemistry from ComplutenseUniversity of Madrid (UCM) in 2012, as well as his Master’s in Biochemistry,Biomedicine, and Molecular Biology also from UCM in 2013. He is currently work-ing on his Ph.D. with professor Torrecilla and collaborates on the LCAOS EuropeanProject. His main role in LCAOS is the design of mathematical models, mainly basedon neural networks, to achieve an early diagnosis of lung cancer through breathanalysis.

Pablo Díaz-Rodríguez He received his Bachelor’s Degree in Chemistry from UCMin 2012 and is currently finishing his Master’s in Chemical Science and Technologyfrom UCM as well. Additionally, he participates in the LCAOS European Project andis developing his work on ionic liquids and artificial neural networks.

Jesús G. Izquierdo 2002 B.S. in Chemistry (Physical Chemistry) from UniversidadComplutense de Madrid (Spain). 2007 Ph.D. in Chemistry from Universidad Com-plutense de Madrid (Spain). He is working as Superior Technician at Utrafast LaserCenter facility from Universidad Complutense de Madrid. He is an expert in the useof molecular beams, laser spectroscopy, ion and photoelectron imaging and ultrafastlasers techniques. He has also experience in laser desorption/ionization and matrixassisted laser desorption ionization coupled to time-of-flight mass spectrometryand on ultrafast pulsed laser deposition and materials ultrafast laser micromachin-ing. He is member of the Spanish Royal Society of Chemistry and the Spanish RoyalSociety of Physics. He is treasurer of the Spanish Specialized Group of Atomic andMolecular Physics (GEFAM).

Luis Banares received his B.S. degree in Chemistry in 1985 from Universidad Com-pultense de Madrid, Spain and received his Ph.D. from that same university in 1990.He had postdoctoral stays at the California Institute of Technology (1990–1992) andthe Institute of Physics at Wurzburg University, Germany (1995–1996) with Ful-bright and Alexander von Humboldt fellowships, respectively. He joined the facultyof the Physical Chemistry Department at Universidad Complutense de Madrid in1992 as an Assistant Professor, becoming a full Professor since 2007. His researchinterests are related to experimential and theoretical chemical reaction dynam-ics and femtochemistry. His work focuses on the understanding of fundamentaltime-resolved chemical reactions and photodissociation processes at a molecularlevel.

José S. Torrecilla is a Professor of the Chemical Engineering Department of the Com-plutense University of Madrid (UCM). He received his Ph.D. in Chemical Engineeringfrom UCM in 2000. After that, in 2005, he obtained his Advanced Technician inOccupational Risk Prevention Degree, specialized on Industrial Hygiene, Occupa-tional Safety, Ergonomics, and Applied Psycho-Sociology. From all of his main linesof research, it is worth highlighting the modeling of complex systems and the designof chemometric tools used in many fields such as health, chemistry, engineering, andfood technology. He has collaborated with numerous universities as well as national

and international research facilities. The impact of his research can be measured withthe great number of published articles in prestigious international journals, somebooks related with his research lines, and the participation and coordination of agreat number of competitive projects inside the American, European, and nationalframeworks.