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Direct large-volume injection analysis of polycyclic aromatic hydrocarbons in water/ Analisis de inyeccion directa en gran volumen de hidrocarburos aromaticos policiclicos en agua/ Analise de injecao direta de grande volume de hidrocarbonos aromaticos policiclicos em agua.

Introduction

Aqueous resources pollution is an issue of current special concern. A number of pollutants can be found in water, including polycyclic aromatic hydrocarbons (PAHs) (Tian et al. 2012; Rubio et al., 2013; Rubio-Clemente et al. 2014a, 2015). These compounds constitute a group of organic pollutants formed of two or more fused benzene rings containing mainly carbon and hydrogen (Rubio-Clemente et al. 2014b; Dos Santos et al. 2018). PAHs are ubiquitous compounds in the environment (Alves et al. 2017; Segura et al. 2017). They come from anthropogenic sources, such as fossil fuel combustion, metal smelting processes and food smoking, among other human activities; and can be found in the atmosphere, soil, water and even in living beings because of their bioaccumulative properties throughout the food chain (Chizhova et al. 2013; Menezes et al. 2013; Santos et al. 2017).

The main concern related to the presence of PAHs in the environment is ascribed to their toxic potential, such as anthracene (AN), which has exhibited a high acute phototoxicity, and carcinogenic, mutagenic and teratogenic characteristics, like benzo[a]pyrene (BaP) (Rubio-Clemente et al. 2014b). In this regard, these compounds are subjected to be monitored by national and international regulations (Directive 2013; Ribeiro et al. 2015); therefore, the adoption of an analytical method aiming at their determination is required (Rubio-Clemente et al. 2017).

Due to their hydrophobicity, PAHs are poorly soluble in water, being present at ultra-trace levels in the range of ng/L or [micro]g/L; fact that limits PAH identification and quantification in aqueous matrices (Rubio-Clemente et al. 2017). Recently, several analytical techniques have been reported (Nawaz et al. 2014; Petridis et al. 2014; Ahmadvand et al. 2015; Khodaee et al. 2016). However, they use previous separation and pre-concentration procedures, being PAH analysis a tedious process. Additionally, separation and pre-concentration techniques might contaminate the sample to be analyzed and produce loses of analytes; especially when multistep procedures are performed (Buczynska et al. 2014; Anumol et al. 2015; Boix et al. 2015). Consequently, large-volume injection techniques are proposed to be used as alternative procedures (Boix et al. 2015). Sample large-volume injection techniques can also be used with reversed-phase high-performance liquid chromatography (RP-HPLC) and gas chromatography (GC), and be combined with fluorescence detector (FLD) or diode array detector, and even with mass spectrometry, finding out accurate and repeatable results within a short period of analysis, without incurring high costs, neither the contamination of the sample nor the loss of the target analytes.

On the other hand, during the development of new analytical methods, one-factor-at-a-time techniques are commonly used. However, the evaluation of several factors influencing the chromatographic system by analyzing the effect of one single parameter at a time can be an expensive task, and valuable information about the analyzed factors can be missed (Trably et al. 2004; Hanrahan & Lu 2006, Andrade-Eiroa et al. 2010; Rubio-Clemente et al. 2017). In this regard, multivariate statistical approaches can overcome these drawbacks by using principal component analysis (PCA) and design of experiments (DOE). PCA is a multivariate analytical tool that can be used to reduce a set of original variables and to extract a small number of latent factors, also called principal components (PCs), which allow the analysis of the relationships among the observed variables (Machala et al. 2001; Golobocanin et al. 2004). In turn, DOE can be used to determine the most influential factors within the considered experimental system (Ferreira et al. 2007; Dejaegher & Vander 2009, 2011).

Under this scenario, this work is focused on analyzing AN and BaP chromatographic behavior in order to optimize the system for the simultaneous analysis of the analytes of interest at ultra-trace levels in aqueous samples by means of RP-HPLC coupled with FLD and using PCA and DOE under different experimental conditions. In addition, the validation of the optimized experimental chromatographic conditions was carried out using different natural water matrices.

Materials and methods

Reagents and solutions

Anthracene (AN, 99 %) and benzo[a]pyrene (BaP, 98 %) analytical certified standards from Dr. Ehrenstorfer (Ausgburg, Germany) and gradient-grade acetonitrile purchased from Merck (Darmstadt, Germany) were used without further purification. Deionized water with a resistivity of 18.2 MQ and obtained from a Millipore purification system (Bedford, USA) was also employed.

Stock standard solutions of AN and BaP were prepared in acetonitrile at a concentration of 1000 mg/L. The working solutions used during the PCA and DOE were prepared by spiking deionized water with a small aliquot of AN and BaP for obtaining a final concentration of 20 [micro]g/L and 2 [micro]g/L, respectively.

The calibration curves were built within a range of 75 - 3000 ng/L for AN and of 30 - 3000 ng/L for BaP, using diluted standard solutions and 10 % of acetonitrile (v/v) so that the target PAH adsorption on the walls of the vials is prevented (Martinez et al. 2004).

Analytical methods

AN and BaP were identified and quantified in aqueous samples with an Agilent HPLC system series 1100/1200 (Palo Alto, USA) equipped with a G1322a vacuum degasser unit, a G1311a quaternary pump, a G1321a multiwavelength fluorescence detector, a G1316a column oven, and a G1329a autosampler. The column was a 5 [micro]m Kinetex core-shell technology C18 (150 x 4.6 mm i.d.) from Phenomenex (Torrance, USA). Unless otherwise mentioned, elution was carried out under isocratic conditions using a mobile phase composed of acetonitrile and deionized water (90:10, v/v), a flow rate of 1 mL/min, a column temperature of 35 [degrees]C, a sample injection volume of 100 [micro]L, an emission wavelength of 416 nm and excitation wavelengths of 254 nm from 0 to 3.20 min and 267 nm from 3.21 to 5 min. OpenLab CDS Chemstation software (Agilent, Palo Alto, USA) was used for chromatographic data analysis.

Statistical analysis

PCA was used to examine the behavior of AN and BaP within the chromatographic system under several experimental conditions. The objective of PCA consists of building k lineal combinations (Yk) of the considered (Xp) variables containing the major variability, being a the associated coefficients. The lineal combinations can be expressed as Eq. 1-3.

[Y.sub.1] = [a.sub.11] [x.sub.1] + [a.sub.12] [X.sub.2] + ... [a.sub.1p] [X.sub.p] (1)

[Y.sub.2] = [a.sub.21] [x.sub.1] + [a.sub.22] [X.sub.2] + ... [a.sub.2p] [X.sub.p] (2)

[Y.sub.k] = [a.sub.k1] [x.sub.1] + [a.sub.k2] [X.sub.2] + ... [a.sub.kp] [X.sub.p] (3)

The first PC ([Y.sub.1]) refers to the lineal combination of the response variables with the maximal variability. The second PC ([Y.sub.2]) is the lineal combination with the second major variability that is not correlated with the first PC. The variability grouped by the following PC ([Y.sub.k]) is decreased up to a non-statistical significant variability.

Additionally, a fractional factorial DOE was employed to find the optimal operating conditions that allow for the simultaneous identification and quantification of the target analytes.

Statgraphics Centurion XVII (Statpoint, Warrenton, USA) was used for the statistical treatment of the experimental data.

Results and discussion

Chromatographic behavior using principal component analysis

Taking into account the different factors that influence the separation of compounds in a chromatographic system for an accurate identification and quantification, the strength of the mobile phase, which was evaluated in terms of acetonitrile percentage, was selected to be analyzed. The injection volume is another parameter to be considered, particularly when determining compounds in the range of ng/L, as it is the case, since the injection of large volumes of samples may derive in the increase of the number of molecules and, therefore, in the increase of the detector analytical response. The excitation and emission wavelengths also play a main role in the quantification of compounds that exhibit an excitation and relaxation behavior under ultraviolet radiation, such as PAHs. Consequently, these two factors were also taken into consideration. Moreover, the flow rate of the mobile phase and the temperature of the column were demonstrated to influence the chromatographic system; that is, the determination of analytes due to the correlation between their elution and the pressure of the system. Thus, six factors affecting the chromatographic system were initially considered, and varied within different operational ranges. In Table 1 the factors and ranges used are listed. These ranges were selected according to different investigations (Bourdat-Deschamps et al. 2007; Lucio-Gutierrez et al. 2008; Andrade-Eiroa et al. 2010), the chromatographic expertise of the authors and previous experimental runs.

Among the different responses to be measured related to the identification and quantification of organic compounds, the retention time of AN and BaP, as well as the resolution between these two compounds, were selected for identification purposes. Since identification is not enough, in terms of regulation accomplishment, the counts of area and height of AN and BaP were also considered for quantification purposes. In order to find out the optimal experimental conditions for AN and BaP analysis in water at ultra-trace concentrations, PCA was firstly conducted to correlate the different responses selected and, therefore, to obtain those models describing the chromatographic behavior of AN and BaP. However, it is highlighted that prior to any further statistical analysis, normality of the response variables was checked. All the measured variables were verified to follow a normal distribution by using Kolmogorov-Smirnov test, with the exception of AN area and AN height. Consequently, these variables were treated and log transformations of AN area and AN height were obtained for assuring normality assumption.

Subsequently, the response variables were subjected to PCA. It was found that the first three PCs explained more than 97 % of the total variability among the seven considered response variables. These results were confirmed by the scree plot represented in Fig. 1, The scree plot displays the number of principal components versus their corresponding eigenvalues. This kind of plot indicates in a graphical way the number of PCs to be retained based on the size of their eigenvalues. The ideal pattern is a steep curve that is gradually smoothed up to a straight line, as represented by the blue line in the figure.

The number of principal components suggested to be selected corresponds to those components with eigenvalues higher than 1; that is, the components that remain above the horizontal red line. In Table 2, the estimated values of the coefficients for each extracted principal component are shown.

From Table 2, it can be observed that the coefficients having the main weights (weight > 0.5) in PC 1 are the retention times of AN and BaP, and the resolution between AN and BaP; that is, all the response variables related to the elution of the target analytes. In this regard, PC 1 can be representative of the identification index of AN and BaP. Concerning PC 2, the main coefficients are the log area of AN and the log height of AN. In turn, for PC 3, the coefficients representing the area and height of BaP are the highest ones. Therefore, PC 2 and PC 3 might be indicative of the behavior of AN and BaP, respectively, in terms of the peak area and height; that is, PC 2 and PC 3 represent the quantification index of AN and BaP, respectively. PCA tool has also been used for developing retention models in liquid chromatography and standard fingerprints, among other uses (Nikitas et al. 2012; Qi et al. 2017).

Optimization using design of experiments

Once the identification and quantification of AN and BaP were described by these indeces, corresponding to PC 1, PC 2 and PC 3, the factors statistically significant for each component were examined and the experimental conditions were optimized by using DOE; particularly, a fractional factorial design due to the high number of factors considered initially. A total of 16 runs plus 5 central points were executed within the selected operating ranges, and analysis of variance (ANOVA) test was performed for each chromatographic index. For a confidence interval of 95 %, it was observed that the block effect was not significant for the chromatographic system under the tested experimental conditions. Concerning PC 1, representing the identification index of AN and BaP, it was evidenced to be influenced negatively by the flow rate and the strength, in terms of acetonitrile content of the mobile phase. This means that as the flow rate is increased, the analytes elute faster and the resolution is, subsequently, decreased. This inversely proportional linear relationship between resolution and flow rate was also observed by Andrade-Eiroa et al. (2010) while optimizing the separation of the pairs dibenzo[a,h]anthracene-benzo[g, h, i]perylene and benzo[g, h, i]perylene-indeno[1, 2, 3-cd]pyrene. Similar reasoning can be withdrawn when considering the percentage of organic solvent in the mobile phase. An increase of the mobile phase strength leads to AN and BaP are eluted more rapidly, decreasing their retention times by the stationary phase of the chromatographic column, which results in a decrease of resolution between these two organic compounds. From these two factors, the flow rate was found to exert a higher influence in the identification index of both pollutants, since it has a coefficient associated of -2.1767 in comparison with the coefficient linked to the acetonitrile percentage (-0.1774) of the mobile phase.

With respect to PC 2, representing AN quantification index, the excitation wavelength was observed to develop a major role in AN area and height with a weight of -0.0542. This fact indicates that a decrease of the excitation wavelength results in an increase of the index describing AN quantification and, therefore, an increase of the log area and of the log height of AN. Thus, AN area is increased as well as AN height, improving AN signal detected by the FLD.

Finally, concerning PC 3, it was found that it is statistically affected by the injection volume by a weight of 0.0286. Thus, when the injection volume is increased, the amount of BaP molecules eluting is correspondingly increased with the subsequent augmentation of the area and height of BaP chromatographic peak. Additionally, for a significant level equal to 0.05, the flow rate and the excitation wavelength exerted a negative (-2.1898) and positive (0.0190) influence, respectively. On the one hand, an increase of the flow rate of the mobile phase leads to a decrease of the area of BaP peak. This can be explained from the fastest elution of the analyte molecules, reducing BaP band and, therefore, decreasing the dimensions of BaP peak. On the other hand, an increase of the excitation wavelength results in an increase of the area and height of BaP. It must be noted that, despite the non-statistically significance of the excitation wavelength for [alpha] = 0.05, it was considered in the BaP quantification index because its p-value was close to 0.05 (p-value = 0.0772).

In Fig. 2, the described magnitudes and signs of the selected factors, both the statistically and non-statistically significant ones, for the three PCs are represented through the main effect plots.

The models built describing the three chromatographic indeces representing the chromatographic behavior of AN and BaP under the experimental conditions tested with p-values lower than 0.05, corresponding to 0.0000, 0.0000 and 0.0085, respectively, are described by Eq. 4-6.

[I.sub.i] = 16.9144 - 2.1767 * FR - 0.1774 * S (4)

[I.sub.ii] = 13.8225 - 0.0542 * EW (5)

[I.sub.ii] = - 4.2625 - 2.1898 * F R + 0.0286 * IV + 0.0190 * EW (6)

where [I.sub.i] is the AN and BaP identification index, [I.sub.ii] is the AN quantification index and [I.sub.iii] is the BaP quantification index. In turn, FR, S, EW and IV represent the flow rate, strength of the mobile phase, excitation wavelength and the injection volume of the sample, respectively.

By optimizing all the principal components obtained simultaneously using multicriteria decision approach, the optimal chromatographic conditions with a desirability of 83.52 % were 1.0 ml/min, 90 %, 100 [micro]L, 230 nm, 409 nm and 25 [degrees]C for the flow rate, acetonitrile content of the mobile phase, injection volume, excitation and emission wavelengths and the column temperature, respectively. However, it is worldwide known that the absorption and fluorescence emission capacities of a substance depend on the substance itself. Additionally, absorption and fluorescence properties of the particularly tested compounds do not follow a linear relationship with the wavelengths used, since their absorption molar coefficients and absorption spectra vary with the single excitation wavelength (Rubio-Clemente et al. 2017). For example, in the case of AN, the absorption coefficient at 254 nm is log [[epsilon].sub.254] = 4.9 in ethanol. For BaP, it has a value of log [[epsilon].sub.254] = 4.6 in ethanol (Zsila et al. 2006; Jones, 1947). BaP absorption spectrum is characterized by several bands of varied intensity; a first one in the range between 245 and 305 nm, higher than 254 nm; and a second one from 320 to 410 nm (Thomas & Burgess, 2007). Moreover, AN absorption spectrum has a distinguished band around 254 nm (Thomas & Burgess, 2007). In this regard, the optimization procedure used in this work is limited when the studied system is influenced by the quadratic effects of the considered factors, as it is the case for these parameters.

Therefore, a minacious study was subsequently performed to find out the excitation and emission optimal wavelengths of AN and BaP. It was found that 416 nm was the optimal emission wavelength within the selected initial range for both of the examined analytes. The excitation wavelength was fixed at 254 nm and 267 nm during AN and BaP elution time, respectively.

On the other hand, taking into account that the column temperature was not statistically significant for the models built and considering that the analysis time can be reduced by augmenting the temperature of the column oven, reducing also the pressures in the system and improving the column efficiency, as reported by Andrade-Eiroa et al. (2010), the optimal column temperature was kept at 35 [degrees]C.

Validation

Under the optimized conditions, the proposed analytical method was validated. Good linearity values and low limits of quantification and detection of 75 and 5.54 ng/L for AN, and 30 and 4.26 ng/L for BaP were obtained. Additionally, intraday and interday precisions lower than 2 and 11%, respectively, were found for the high, medium and low levels tested. Accuracy was also verified and relative standard deviations (RSD) lower than 10 % were evidenced. Furthermore, the analysis of AN and BaP in different matrices of real natural water gave satisfactory recoveries (RSD < 13 %).

Conclusions

The results of the present study indicated that the chromatographic behavior of the selected PAHs under the experimental conditions tested may be explained by PCA using three indeces describing the elution of AN and BaP, the peak shape of AN and that of BaP, representing the former one and the latter ones the identification and the quantification of the target compounds, respectively. It was demonstrated that the identification index of the target compounds under the experimental domain studied here was defined by the flow rate and the strength of the mobile phase. Concerning the AN quantification index, the excitation wavelength was found to develop a main role. Finally, the BaP quantification index, as expected, was also influenced by the excitation wavelength; however, the injection volume and the flow rate were observed to exert also a main function.

The optimal operating conditions found using DOE that maximizing the indices referred above were 1 mL/min, 90 %, 35 [degrees]C, 100 [micro]L, and 416 nm for the flow rate, acetonitrile content of the mobile phase, column temperature, injection volume and the emission wavelength, respectively. The optimal excitation wavelengths were 254 nm and 267 nm for AN and BaP. The developed and validated method showed to be suitable for the identification and quantification of AN and BaP at ultra-trace levels in relatively clean natural water by direct injection in only 5 min of analysis.

doi: 10.11144/Javeriana.SC23-2.dlvi

Acknowledgements

This work was supported by the Colombian Institute of Science and Technology (COLCIENCIAS) and the Research Vice-rectory of Universidad de Antioquia.

Conflicts of interest

The authors state that their sole interest in the results of this research is scientific.

References

Ahmadvand M, Sereshti H, Parastar H. Chemometric-based determination of polycyclic aromatic hydrocarbons in aqueous samples using ultrasound-assisted emulsification microextration combined to gas chromatography-mass spectrometry, Journal of Chromatography A, 1413: 117--126, 2015. doi: 10.1016/j.chroma.2015.08.026

Alves CA, Vicente AM, Custodio D, Cerqueira M, Nunes T, Pio C, Lucarelli F, Calzolai G, Nava S, Diapouli E, Eleftheriadis K, Querol X, Musa BA. Polycyclic aromatic hydrocarbons and their derivatives (nitro-PAHs, oxygenated PAHs, and azaarenes) in PM 2.5 from Southern European cities, Science of the Total Environment, 595: 494-504, 2017. doi: 10.1016/j.scitotenv.2017.03.256

Andrade-Eiroa A, Dievart P, Dagaut P Improved optimization of polycyclic aromatic hydrocarbons (PAHs) mixtures resolution in reversed-phase high-performance liquid chromatography by using factorial design and response surface methodology, Talanta, 81(1-2): 265-274, 2010. doi: 10.1016/j.talanta.2009.11.068

Anumol T, Wu S, Marques M, Daniels KD, Snyder SA. Rapid direct injection LC-MS/MS method for analysis of prioritized indicator compounds in wastewater effluent, Environmental Science: Water Research &Technology, 2015(1): 632-643, 2015. doi: 10.1039/c5ew00080g

Boix C, Ibanez M, Sancho JV, Rambla J, Aranda JL, Ballester S, Hernandez F. Fast determination of 40 drugs in water using large volume direct injection liquid chromatography-tandem mass spectrometry, Talanta, 131: 719-727, 2015. doi: 10.1016/j.talanta.2014.08.005

Bourdat-Deschamps M, Daudin JJ, Barriuso E. An experimental design approach to optimise the determination of polycyclic aromatic hydrocarbons from rainfall water using stir bar sorptive extraction and high performance liquid chromatography-fluorescence detection, Journal of Chromatography A, 1167(2): 143-153, 2007. doi: 10.1016/j.chroma.2007.08.025

Buczyuska AJ, Geypens B, van Grieken R, de Wael K. Large-volume injection combined with gas chromatography/isotope ratio mass spectrometry for the analysis of polycyclic aromatic hydrocarbons, Rapid Communications in Mass Spectrometry, 28: 200-208, 2014. doi: 10.1002/rcm.6769

Chizhova T, Hayakawa K, Tishchenko P, Nakase H, Koudryashova Y. Distribution of PAHs in the northwestern part of the Japan Sea, Deep-Sea Research Part li: Tropical Studies in Oceanography, 86-87: 19-24, 2013. doi: 10.1016/j.dsr2.2012.07.042

Dejaegher B, Vander Y. The use of experimental design in separation science, Acta Chromatographica, 21: 161-201, 2009. doi: 10.1556/achrom.21.2009.2.1

Dejaegher B, Vander Y. Experimental designs and their recent advances in set-up, data interpretation and analytical applications, Journal of Pharmaceutical and Biomedical Analysis, 56(2): 141-158, 2011. doi: 10.1016/j.jpba.2011.04.023

Directive 2013. Directive 2013/39/EU of the European parliament and of the council of 12 August 2013 amending directives 2000/60/ EC and 2008/105/EC as regards priority substances in the field of water policy, Official Journal of the European Union L, 226: 1-17. Retrieved from: https://eur-lex.europa.eu/legal-content/EN/ ALL/?uri=CELEX%3A32013L0039

Dos Santos IF, Ferreira SLC, Dominguez C, Bayona JM. Analytical strategies for determining the sources and ecotoxicological risk of PAHs in river sediment, Microchemical Journal, 137: 90-97, 2018. doi: 10.1016/j.microc.2017.09.025

Ferreira SLC, Bruns RE, da Silva EGP, dos Santos WLN, Quintella CM, David JM, de Andrade JB, Breitkreitz MC, Jardim ICSF, Neto BB. Statistical designs and response surface techniques for the optimization of chromatographic systems, Journal of Chromatography A, 1158: 2-14, 2007. doi: 10.1016/j.chroma.2007.03.051

Golobocanin DD, Skrbic BD, Miljevic NR. Principal component analysis for soil contamination with PAHs, Chemometrics and Intelligent Laboratory Systems, 72(2): 219-223, 2004. doi: 10.1016/j.chemolab.2004.01.017

Hanrahan G, Lu K. Application of factorial and response surface methodology in modern experimental design and optimization, Critical Reviews in Analytical Chemistry, 36(3-4): 141-151, 2006. doi: 10.1080/10408340600969478

Jones RN. The ultraviolet absorption spectra of anthracene derivatives, Chemical Reviews, 41(2): 353-371, 1947. doi: 10.1021/cr60129a013

Khodaee N, Mehdinia A, Esfandiarnejad R, Jabbari A. Ultra trace analysis of PAHs by designing simple injection of large amounts of analytes through the sample reconcentration on SPME fiber after magnetic solid phase extraction, Talanta, 147: 59-62, 2016. doi: 10.1016/j.talanta.2015.09.025

Lucio-Gutierrez JR, Salazar-Cavazos ML, Waksman NH, Castro-Rios R. Solid-phase microextraction followed by high-performance liquid chromatography with fluorimetric and UV detection for the determination of polycyclic aromatic hydrocarbons in water, Analytical Letters, 41(1):119-136, 2008. doi: 10.1080/00032710701746758

Machala M, Dusek L, Hilscherova K, Kubinova R, Jurajda P, Neca J, Ulrich R, Gelnar M, Studnickova Z, Holoubek, I. Determination and multivariate statistical analysis of biochemical responses to environmental contaminants in feral freshwater fish Leuciscus cephalus L, Environmental Toxicology and Chemistry, 20(5): 1141-1148, 2001. doi: 10.1002/etc.5620200528

Martinez E, Gros M, Lacorte S, Barcelo D. Simplified procedures for the analysis of polycyclic aromatic hydrocarbons in water, sediments and mussels, Journal of Chromatography A, 1047(2): 181-188, 2004. doi: 10.1016/s0021-9673(04)01100-8

Menezes HC, Paiva MJ, Santos RR, Sousa LP, Resende SF, Saturnino JA, Paulo BP, Cardeal ZL. A sensitive GC/MS method using cold fiber SPME to determine polycyclic aromatic hydrocarbons in spring water, Microchemical Journal, 110: 209-214, 2013. doi: 10.1016/j.microc.2013.03.010

Nawaz MS, Ferdousi FK, Rahman MA, Alam AM. Reversed phase SPE and GC-MS study of polycyclic aromatic hydrocarbons in water samples from the river Buriganga, Bangladesh, International Scholarly Research Notices, 2014: 1-9, 2014. doi: 10.1155/2014/234092

Nikitas P, Pappa-Louisi A, Tsoumachides S, Jouyban A. A principal component analysis approach for developing retention models in liquid chromatography, Journal of Chromatography A, 1251: 134-140, 2012. doi: 10.1016/j.chroma.2012.06.049

Petridis NP, Sakkas VA, Albanis TA. Chemometric optimization of dispersive suspended microextraction followed by gas chromatography- mass spectrometry for the determination of polycyclic aromatic hydrocarbons in natural water, Journal of Chromatography A, 1355: 46-52, 2014. doi: 10.1016/j.chroma.2014.06.019

Qi X, Zhu L, Wang C, Zhang H, Wang L, Qian H. Development of standard fingerprints of naked oats using chromatography combined with principal component analysis and cluster analysis, Journal of Cereal Science, 74: 224-230, 2017. doi: 10.1016/j.jcs.2017.02.009

Ribeiro AR, Nunes OC, Pereira MFR, Silva AMT. An overview on the advanced oxidation processes applied for the treatment of water pollutants defined in the recently launched Directive 2013/39/EU, Environment International, 75: 35-51, 2015. doi: 10.1016/j.envint.2014.10.027

Rubio A, Chica EL, Penuela GA. Wastewater treatment processes for the removal of emerging organic pollutants, Ambiente & Agua-An Interdisciplinary Journal of Applied Science, 8(3): 93-103, 2013. doi: 10.4136/ambi-agua.1176 645

Rubio-Clemente A, Chica E, Penuela GA. Application of Fenton process for treating petrochemical wastewater, Ingenieria y Competitividad, 16(2): 211-223, 2014a.

Rubio-Clemente A, Chica E, Penuela GA. Petrochemical wastewater treatment by photo-Fenton process, Water, Air, & Soil Pollution, 226: 1-18, 2015. doi: 10.1007/s11270-015-2321-x

Rubio-Clemente A, Chica E, Penuela G. Rapid determination of anthracene and benzo(a)pyrene by high-performance liquid chromatography with fluorescence detection, Analytical Letters, 50(8): 1229-1247, 2017. doi: 10.1080/00032719.2016.1225304

Rubio-Clemente A, Torres-Palma RA, Penuela GA. Removal of polycyclic aromatic hydrocarbons in aqueous environment by chemical treatments: A review, Science of the Total Environment, 478: 201-225, 2014b. doi: 10.1016/j.scitotenv.2013.12.126

Santos LO, dos Anjos JP, Ferreira SL, de Andrade JB. Simultaneous determination of PAHS, nitro-PAHS and quinones in surface and groundwater samples using SDME/GC-MS, Microchemical Journal., 133: 431-440, 2017. doi: 10.1016/j.microc.2017.04.012

Segura A, Hernandez-Sanchez V, Marques S, Molina L. Insights in the regulation of the degradation of PAHs in Novosphingobium sp. HR1a and utilization of this regulatory system as a tool for the detection of PAHs, Science of the Total Environment, 590-591: 381-393, 2017. doi: 10.1016/j.scitotenv.2017.02.180

Thomas O, Burgess C. UV-visible spectrophotometry of water and wastewater (Vol. 27). Elsevier Science, The Netherlands, 2007.

Tian W, Bai J, Liu K, Sun H, Zhao Y. Occurrence and removal of polycyclic aromatic hydrocarbons in the wastewater treatment process, Ecotoxicology and Environmental Safety, 82: 1-7, 2012. doi: 10.1016/j.ecoenv.2012.04.020

Trably E, Delgenes N, Patureau D, Delgenes JP. Statistical tools for the optimization of a highly reproducible method for the analysis of polycyclic aromatic hydrocarbons in sludge samples, International Journal of Environmental Analytical Chemistry, 84(13): 995-1008, 2004. doi: 10.1080/03067310412331298412

Zsila F, Matsunaga H, Bikadi Z, Haginaka J. Multiple ligand-binding properties of the lipocalin member chicken a 1-acid glycoprotein studied by circular dichroism and electronic absorption spectroscopy: The essential role of the conserved tryptophan residue, Biochimica et Biophysica Acta (BBA)-GeneralSubjects, 1760(8): 1248-1273, 2006. doi: 10.1016/j.bbagen.2006.04.006

Ainhoa Rubio-Clemente

She is an Environmental Engineer by University of Salamanca. She has a MSc. and she is a junior researcher. She has research experiences in the field of water pollution decontamination and drinking water production using conventional and advanced treatment processes. Additionally, she has been involved in developing several analytical methods.

Edwin L. Chica

He is a Mechanical Engineer by University of Antioquia. Currently he is a MSc. PhD. associate professor and researcher at University of Antioquia, heading the Research Group 'Energia Alternativa'. He has research experiences in the field of renewable energy production and water decontamination and purification using conventional and advanced treatment processes, regarding design, scaling and performance purposes.

Gustavo A. Penuela

He is a Chemist by the National University. He is the director of the Research Group 'Diagnostico y Control de la Contaminacion' (GDCON) at University of Antioquia. He has large experience in the field of water, soil and air pollution. Additionally, he has conducted a number of researches in developing analytical methods for several purposes.

Ainhoa Rubio-Clemente, (1,2,3) Edwin L. Chica, Gustavo A. Penuela,

Edited by

Juan Carlos Salcedo-Reyes (salcedo.juan@javeriana.edu.co)

(1.) Universidad Catolica de Murcia UCAM, Facultad de Ciencias de la Salud, Avenida de los Jeronimos, 135, Guadalupe-Murcia, Spain.

(2.) Universidad de Antioquia UdeA, Facultad de Ingenieria, Sede de Investigaciones Universitarias (SIU), Grupo de Diagnostico y Control de la Contaminacion (GDCON), Calle 70, No. 52-21, Medellin, Colombia.

(3.) Tecnologico de Antioquia--Institucion Universitaria TdeA, Facultad de Ingenieria, Calle 78b No. 72A-220, Medellin, Colombia.

(4.) Universidad de Antioquia UdeA, Facultad de Ingenieria, Departamento de Ingenieria Mecanica, Calle 70, No. 52-21 Medellin, Colombia.

* ainhoarubioclem@gmail.com

Received: 07-10-2017

Accepted: 19-04-2018

Published on line: 05-06-2018

Funding:

Colombian Institute of Science and Technology (COLCIENCIAS) and the Research Vice-rectory of Universidad de Antioquia.

Electronic supplementary material: N.A.

Caption: Figure 1. Scree plot of the considered responses. Operating conditions: [anthracene] 0 = 20 [micro]g/L; [benzo[a]pyrene] 0 = 2 [micro]g/L; injection volume = 50-100 [micro]L; strength of the mobile phase= 70-90 %; excitation wavelength = 230-280 nm; emission wavelength = 408-424 nm; flow rate = 1-1.5 mL/min; column temperature= 25-35 [degrees]C.

Caption: Figure 2. Main effect plots for the identification i ndex (a), anthracene quantification index (b) and benzo[a]pyrene quantification index (c). Operating conditions: [anthracene] 0 = 20 [micro]g/L; [benzo[a]pyrene] 0 = 2 [micro]g/L; injection volume = 50-100 [micro]L; strength of the mobile phase = 70-90 %; excitation wavelength = 230-280 nm; emission wavelength = 408-424 nm; flow rate = 1-1.5 mL/min; column temperature = 25-35 [degrees]C.
Table 1. Factors and levels tested for the considered responses.

FACTOR (UNIT)                         LEVEL

Injection volume ([micro]L)         50 - 100
Strength of the mobile phase (%)     70 - 90
Excitation wavelength (nm)          230 - 280
Emission wavelength (nm)            408 - 424
Flow rate (mL/min)                   1 - 1.5
Column temperature ([micro]C)        25 - 35

Table 2. Coefficient estimated values contained in the considered
principal components.

COEFFICIENT                       PC 1      PC 2      PC 3

Retention time of AN             0.5453    0.0422    0.1432
Retention time of BaP            0.5573    0.0511    0.0965
Area of BaP                      0.0405    -0.3385   0.6593
Height of BaP                    -0.2635   -0.2974   0.5825
Resolution between AN and BaP    0.5323    0.1019    0.1420
Log Area of AN                    -0.12    0.6288    0.3016
Log Height of AN                 -0.1526   0.6219    0.2916
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Article Details
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Title Annotation:ORIGINAL ARTICLE
Author:Rubio-Clemente, Ainhoa; Chica, Edwin L.; Penuela, Gustavo A.
Publication:Revista Universitas Scientarum
Date:May 1, 2018
Words:5391
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