Chemometric approach to optimize the operational parameters of ESI for
the determination of contaminants of emerging concern in aqueous
matrices by LC-IT-TOF-HRMS
Keila Letícia Teixeira Rodrigues
⁎
, Ananda Lima Sanson, Amanda de Vasconcelos Quaresma,
Rafaela de Paiva Gomes, Gilmare Antônia da Silva, Robson José de Cássia Franco Afonso
Institute of Exact and Biologic Sciences, Chemistry Department, Federal University of Ouro Preto— UFOP, 35400-000 Ouro Preto, MG, Brazila b s t r a c t
a r t i c l e i n f o
Article history: Received 24 March 2014
Received in revised form 12 June 2014 Accepted 12 June 2014
Available online 30 June 2014 Keywords:
Contaminants of emerging concern LC-IT-TOF-HRMS
ESI
Multivariate optimization Doehlert design Kohonen neural network
Contaminants of emerging concern are organic compounds used in large quantities by the society for various pur-poses. They have shown biological activity at low concentrations, which gives great environmental relevance. The difficulty to detect and quantify contaminants of emerging concern in the environment stimulates the develop-ment of appropriate analytical methods. In this work a chemometric approach to positive and negative electrospray ionization (ESI) optimization for the simultaneous determination of contaminants of emerging concern in water samples by liquid chromatography-ion trap-time offlight-high resolution mass spectrometry (LC-IT-TOF-HRMS) was applied. Three types of phase modifiers were used: formic acid, ammonium hydroxide and formic acid/ammonium formate. The effects of operational parameters such as mobile phase modifier con-centrations, mobile phaseflow rate, heating block temperature and drying gas flow rate were evaluated by the 24− 1fractional factorial experimental design, resolution IV, in the screening phase and by Doehlert experimental
design. Initial factorial experimental design studies indicated that the phase modifier ammonium hydroxide was more efficient compared to the other evaluated modifiers. It provided higher ion intensities to the majority of analytes. Doehlert experimental design allowedfinding a region indicative of the optimum experimental condi-tions for most analytes. The best experimental condition observed was 3.5 mM ammonium hydroxide concentra-tion; 0.0917 mL/min of mobile phase; 300 °C heating block temperature; and drying gas at 200 kPa. These optimized parameters resulted in decreased detection limits of the method. The optimized method was applied to the evaluation of water samples coming from the Rio Doce basin— Minas Gerais/Brazil utilizing multivariate exploratory techniques such as principal component analysis and Kohonen neural network. In this way, the use of chemometric approach showed to be a promising way to optimize the simultaneous determination of twenty-one contaminants of emerging concern in aqueous matrices by LC-IT-TOF-HRMS using ESI.
© 2014 Elsevier B.V. All rights reserved.
1. Introduction
The contaminants of emerging concern are the indicators of anthro-pogenic activity and are associated with a diverse set of organic com-pounds that are used in large quantities by the society for various purposes[1–3]. The growing interest in these substances occurs mainly because they may have biological activity at low concentrations, which gives them great environmental relevance[4–7]. Many analytical methods are being developed and refined to detect and quantify them
[8–12]. A multi-residue analytical method is advantageous to reduce cost and time while simultaneously obtaining information on the
occurrence of a broad number of compounds[13]. Therefore, a system-atic study to optimize the analytical method to the simultaneous detec-tion of contaminants of emerging concern is important.
Optimization of both ionization processes and ion transportation is of crucial importance in order to achieve high sensitivity, low detection limits and acceptable accuracy in liquid chromatography–mass spec-trometry (LC–MS) analysis[14]. The amount of ions reaching the detec-tor depends on the efficiency of ionization promoted by the interface used, which can be improved by adjusting their operational parameters
[15–17].
Chemometric methods provide powerful tools for designing or optimizing experiments and to statistically process the data with the purpose of obtaining the maximum information[18–21]. Multivariate statistical methods most used in chemistry can be conveniently classi-fied according to how one decides which experiments are to be execut-ed. All methods require the user to supply minimum and maximum values for each factor that defines the experimental domain to be
⁎ Corresponding author. Tel.: +55 31 91594240.
E-mail addresses:keilaleticia17@hotmail.com(K.L.T. Rodrigues),
ananda_lima@hotmail.com(A.L. Sanson),amanda.qui.ufop@gmail.com(A.V. Quaresma), rafaela_p_gomes@hotmail.com(R.P. Gomes),gilmare@gmail.com(G.A. da Silva), robsonafonso@iceb.ufop.br(R.J.C.F. Afonso).
http://dx.doi.org/10.1016/j.microc.2014.06.017 0026-265X/© 2014 Elsevier B.V. All rights reserved.
Contents lists available atScienceDirect
Microchemical Journal
investigated during the optimization procedure. Optimization proce-dures are frequently performed by experimental designs. The most commonly used designs to determine response surfaces are the full and fractional factorial, primarily in the screening step, followed by op-timization with more complex central composite, Box–Behnken, Doehlert and mixture designs[22–24].
In this work the possible parameters and their interactions that could influence the ionization efficiency of electrospray ionization (ESI) source on the systematic study of simultaneous detection of, initially, twenty-five contaminants of emerging concern were investigated. The compounds studied included hormones, phthalates, pharmaceutical compounds, detergents by-products and plastic additives. The 24− 1 fractional factorial experimental design, resolution IV, for screening and Doehlert experimental design for optimization were employed. The parameters evaluated were the type of mobile phase modifier (formic acid, ammonium hydroxide and formic acid/ammonium for-mate), phase modifiers concentrations, mobile phase flow rate, heating block temperature and drying gasflow rate; this procedure has not been previously described in the literature. The optimized method was ap-plied to the evaluation of water samples coming from the Rio Doce basin— Minas Gerais/Brazil and the results treated by multivariate ex-ploratory techniques such as principal component analysis (PCA)[25]
and Kohonen neural network (Self-Organising Maps, SOM)[26], for the determination of contamination profile.
2. Materials and methods 2.1. Chemicals and reagents
The solid-phase extraction (SPE) employed 500 mg Strata-X car-tridges (Phenomenex). LC grade methanol and the ethyl acetate were purchased from J. T. Backer (Phillipsburg, USA) and water was produced using an ion exchange purification system (TKA Wasseraufberei-tungssysteme, Niederelbert, Germany). The buffers formic acid and am-monium hydroxide were purchased from Synth®. Amam-monium formate and the standards of acetylsalicylic acid, acetaminophen, azithromycin, bezafibrate, cimetidine, ciprofloxacin, clarithromycin, diclofenac, diltia-zem, gemfibrozil, ibuprofen, miconazole, naproxen, ranitidine, sulfa-methoxazole, trimethoprim, caffeine, bisphenol-A, bis-(2-ethylhexyl) phthalate, diethyl phthalate, 4-nonylphenol, 4-octylphenol, estrone, 17α-ethinylestradiol and 17β-estradiol were purchased from Sigma Aldrich (St. Louis, MO, USA).
The stock solutions were composed of a mixture of the contaminants of emerging concern standards, at a concentration of 1000μg/L, and the appropriate working standard solutions were prepared in methanol and stored in amber glass-polyethylene stopper bottles at 4 °C. For the eval-uation by the 24− 1fractional factorial experimental design and by the Doehlert experimental design was used a working standard solution at a concentration of 50μg/L.
2.2. Instrumentation
Liquid chromatography was performed on a Shimadzu Prominence system equipped with a high-pressure binary solvent delivery system (LC-20AD) and a SIL 20AC auto-sampler, according to operational con-ditions described in §2.2in the Supplementary information.
The high resolution mass spectrometry (HRMS) detection was per-formed using a Shimadzu LC-IT-TOF-HRMS, a tandem ion trap (IT) and a time-of-flight (TOF) sequential mass spectrometer, working at high resolution (10,000 FWHM) and high mass accuracy (b5 ppm) in the following conditions: electrospray ionization (ESI) at−3.5 kV (negative) and + 4.5 kV (positive) with nebulizer gas at 1.5 L/min, curved desorption line (CDL) interface at 200 °C, octapole ion accumu-lation time of 100 ms and MS scan in the range 100–800 m/z.
2.3. Optimization procedures
The 24− 1fractional factorial design with central point was used in
the screening step in order to assess the parameters that influence the system (supplied in Fig. S.1 in the Supplementary information) and to delimit the experimental area that should be explored further. To refine the experimental region, the significant effects were investigated by using the Doehlert experimental design. This approach allows extracting maximum information from the system being investigated in a more efficient way.
The criterion for the selection of variables was based on the influence that they would provide the signal strength of the system studied, con-sidering the function of each parameter. The mobile phase composition can influence the chromatographic separation of analytes and it could also be important at the ionization. Other instrumental parameters such as: mobile phaseflow rate, heating block temperature and drying gasflow rate could contribute to increase the detectability of the method. So, all these variables can and/or should be studied. In the work the type of mobile phase modifier (formic acid, ammonium hy-droxide and formic acid/ammonium formate), phase modifiers concen-trations, mobile phaseflow rate, heating block temperature and drying gasflow rate was investigated.
Three fractional factorial experimental designs were employed in the screening step, one for each type of mobile phase modifier. The mo-bile phase concentration varied according toTable 1and other parame-ters varied likewise for all three designs. Mobile phaseflow rate varied from 0.10 to 0.30 mL/min, heating block temperature varied from 200 to 300 °C and drying gasflow rate varied from 100 to 200 kPa. Each pa-rameter was varied across a high and low setting with triplicates in cen-tral point.
The results of the fractional factorial designs demonstrated that the parameters evaluated had significant effects on the response of most contaminants of emerging concern studied. From these, it was selected that the most appropriate mobile phase modifier and the parameters previously studied were again investigated utilizing the Doehlert exper-imental design as follows: mobile phase modifier concentrations (1.00 to 6.00 mM, in 5 levels), mobile phaseflow rate (0.05 to 0.30 mL/min, in 7 levels), heating block temperature (200 to 300 °C, in 3 levels) and drying gasflow rate was kept constant at 200 kPa. The number of experiments was 17, includingfive replicates of the central point to estimate the re-peatability.Table 2shows the applied Doehlert experimental design.
The analytical curvefitting of the method was conducted using the optimized conditions pointed out by the Doehlert design. The adjust-ment of the curve relating the chromatographic peak areas of analytes was obtained with increasing concentrations. The working standard so-lutions used were 1, 5, 10, 30, 50,100, 150 e and 200μg/L.
The ion chromatogram was divided into appropriate operational segments. High-resolution scan mass spectra were obtained in all seg-ments and a selected ion monitoring (SIM) was supplied in Table S.1 in the Supplementary information.
2.4. Sample analysis 2.4.1. Sample collection
The surface water samples were collected monthly from the Rio Doce basin— Minas Gerais/Brazil at twenty-four sampling points
Table 1
Coded and decoded values (between parentheses) of modifiers and concentrations (mM) tested by fractional factorial design for screening of the operating parameters of LC-IT-TOF-HRMS for the determination of the contaminants of emerging concern using ESI.
Type of mobile phase modifier Concentration (mM)
Formic acid −1 (2.6) 0 (14.4) 1 (26.1)
Ammonium hydroxide −1 (1.5) 0 (3.0) 1 (4.5) Formic acid/ammonium formate −1 (26.1/1.6) 0 (104.4/16.7) 1 (182.7/31.8) Note: (−1) = lowest level; (+1) = highest level; (0) = central point.
located in different villages and towns, as shown inFig. 1. The sampling spots were coded with the same code used by the“Águas de Minas” monitoring project, of the Instituto Mineiro de Gestão das Águas— IGAM, an agency of the state government responsible for planning and promoting directed actions for preserving the quantity and quality of water of Minas Gerais/Brazil. The IGAM was responsible for the sample collecting of this work.
The sampling was conducted in July of 2012 (dry period— D) and January of 2013 (rainy period— R). The collected samples were ran-domly taken at the water surface, preferably at the main river channel, in glassflasks, transferred to 1 L amber glass bottles and preserved by the addition of 1% v/v HPLC-grade methanol. The samples were transported to the laboratory in cooling boxes and prepared for analysis after.
2.4.2. Sample preparation
Thefirst step of sample preparation consisted of consecutive filtra-tions of the water samples under vacuum through 8μm blue bandpass and 1.2μm glass-fiber filters, to remove suspended particulate matter and to avoid clogging of the SPE cartridge. The sample pH was adjusted to 2.0 by the addition of 30% (v/v) HCl and the analytes were extracted using SPE cartridges (Strata-X, 500 mg), preconditioned with 5 mL of ethyl acetate followed by 5 mL of methanol and 5 mL of water. The sam-ples (usually 500 mL) were then loaded onto the cartridges at a rate lower than 5 mL/min. After that, the cartridges were dried for 20 min under vacuum and eluted with 10 mL of ethyl acetate. The extracts col-lected in amber glassflasks were dried under nitrogen and resuspended in 500μL of methanol, concentration factor of 1000. The solutions were transferred to sealed cap vials and analyzed by ESI-LC-IT-TOF-MS.
The extraction recovery and the matrix effect on the ion signals of the analytes in the ESI source were determined accordingly, as described in §2.4.2in the Supplementary data.
Table 2
Coded and decoded values (between parentheses) for the operational variables of LC-IT-TOF-HRMS for the optimization experiments determined according Doehlert experimen-tal design: x1— ammonium hydroxide concentrations (mM), x2— mobile phase flow rate
(mL/min) and x3— heating block temperature (°C).
Experiment x1 x2 x3 1 1 (6.00) 0 (0.1750) 0 (250) 2 0.5 (4.75) 0.866 (0.3000) 0 (250) 3 0.5 (4.75) 0.289 (0.2167) 0.817 (300) 4 −1 (1.00) 0 (0.1750) 0 (250) 5 −0.5 (2.25) −0.866 (0.0500) 0 (250) 6 −0.5 (2.25) −0.289 (0.1333) −0.817 (200) 7 0.5 (4.75) −0.866 (0.0500) 0 (250) 8 0.5 (4.75) −0.289 (0.1333) −0.817 (200) 9 −0.5 (2.25) 0.866 (0.3000) 0 (250) 10 0 (3.50) 0.577 (0.2583) −0.817 (200) 11 −0.5 (2.25) 0.289 (0.2167) 0.817 (300) 12 0 (3.50) −0.577 (0.0917) 0.817 (300) 13a 0 (3.50) 0 (0.1750) 0 (250)
Note: a = Central point withfive replicates.
Fig. 1. Map of sampling stations in the Rio Doce basin— Minas Gerais/Brazil. Source: Adapted fromhttp://www.igam.mg.gov.br/images/stories/mapoteca/Mapas/qualidade/2013/2_ trimestre/doce-2otrim-2013.pdf.
2.4.3. Software and data treatment
The data acquisition and peak integration were performed using the software LCMS solution from the Shimadzu Corporation.
Optimization calculations were performed using spreadsheets, available at the website of the Laboratory of Theoretical and Applied Chemometrics, State University of Campinas[27].
The software used to perform the multivariate analysis of Kohonen neural network was freely available on the Internet at pagehttp:// www.cis.hut.fi/projects/somtoolbox/ [28]. The data set was organized into a matrix of 45 samples (45 lines) and 17 variables (17 columns), corresponding to the contaminants detected in the samples. Before processing, the entire data set was autoscaled for all variables. This pre-processing step ensures that all variables have the same level of im-portance, allowing users to assess the significance of all variables in the samples[29]. The Kohonen maps were created and initialized linearly. The Kohonen neural network was trained with the data using the batch training algorithm, the neighborhood function used in training was the Gaussian, the structure was hexagonal and the shape of the map was planar. During the data training, architectures with several or-ders were tested (from 5 × 5 to 10 × 10) for the evaluation of the groups of samples and the architecture that had the best sample distribution in groups (which was more informative) was chosen.
For comparison, a PCA analysis was also performed using the same set of data through the computing environment GNU Octave 3.6.4, free-ly available on the Internet at pagehttp://www.gnu.org/software/ octave/; before data processing, the entire data set was autoscaled for all variables.
3. Results and discussion 3.1. Optimization procedures
Analysis of data gained from screening experiments allowed an in-sight into the basic operation of the ESI source when exposed to deter-mined conditions according to 24− 1fractional factorial design.Table 3
shows the levels of the variables that presented better performance (increased peak areas) for each mobile phase modifier used.
A comparison among the best assays for each mobile phase modifier is shown inFig. 2. It can be verified that all the twenty-five compounds were detected at least in one of the mobile phase modifiers studied. Am-monium hydroxide proved to be the best modifier since twenty-three compounds were detected while with the formic acid and formic acid/ ammonium formate modifiers only sixteen and seventeen compounds, respectively, were detected. In addition, ammonium hydroxide modi fi-er showed highfi-er area intensities to the majority of the compounds. So, the mobile phase modifier ammonium hydroxide was the most efficient compared to the other ones.
Table 4shows that all variables investigated in the 24− 1fractional factorial design with central point using ammonium hydroxide as mo-bile phase modifier revealed to be significant for most analytes studied in the system, with significant coefficients at a 95% confidence level.
According toTable 4, the mobile phaseflow rate was the variable with the highest influence on the system using the ESI, to most analytes.
This parameter influences the formation of the droplets during spraying process, which promotes the transfer into the gas phase ions, resulting in greater efficiency of ionization. Still, a lower flow favors the formation of spray and improves the solvent dying efficiency. The heating block temperature was the variable that less influenced the system.
Doehlert design was applied for the optimization of the factors affecting ESI source performance. Analysis of variance (ANOVA) was performed on the models to determine the statistical significance of the coefficients, related to the parameters mobile phase modifier con-centrations, mobile phaseflow rate, heating block temperature and interaction between themselves. ANOVA calculations or the total varia-tion in response calculavaria-tions, examine the overall significance of each term in the model compared to the residual error. Terms found to have a probability value of less than 0.05 are considered to be significant.
Table 5resumes the ANOVA results obtained from the Doehlert ex-perimental design to the contaminants of emerging concern evaluated. ANOVA coefficients for all elements were statistically valid with 95% confidence level, however most of them showed lack of fit at the same confidence level (only acetaminophen, sulfamethoxazole and trimetho-prim exhibited an acceptable modeling).
Although most models have not been adjusted, it was possible to find a region indicative of the optimum experimental conditions for most analytes. The best conditions for most analytes were observed when these were subjected to the conditions of assay 12: 3.5 mM am-monium hydroxide concentration, 0.0917 mL/min of mobile phase flow rate, 300 °C heating block temperature and drying gas flow rate at 200 kPa.
Fig. 3compares the results of screening experiments determined ac-cording to 24− 1fractional factorial experimental design resolution IV
with the Doehlert experimental design in the operational conditions that provided higher peak areas for most analytes and shows the improvement for majority of emerging contaminants studied when the response surface methodology was used.
The adjusted analytical curve equations for twenty-one contami-nants of the twenty-three emerging contamicontami-nants of concern detected in the ammonium hydroxide modifier based on the multioptimized LC-IT-TOF-HRMS procedure are shown in Table S.2 in the Supplementa-ry information. Linear and quadratic models with the determination co-efficients (R2) ranging from 0.990 to 0.998, with concentrations from
1.0 to 200μg/L, according to the contaminant evaluated were found.
3.2. Sample analysis
After the analysis of the samples collected (results supplied in Table S.3 and Table S.4 in the Supplementary information) by the multioptimized LC-IT-TOF-HRMS procedure the PCA method for con-tamination profile investigation was applied. However, the necessity of 15 PC to represent 99.28% of sample variability was found.
In this way, the SOM algorithm was applied, since it provides an ease viewing and interpreting of data, compared with other approaches, such as PCA.
A Kohonen neural network with hexagonal grids was obtained after performing the multivariate analysis from the data and architectures of several orders were evaluated (from 5 × 5 to 10 × 10) and the arrange-ment 8 × 8 with 64 neurons had the best sample distribution in the map.
Fig. 4presents the formation of 9 different groups (I to IX) that were circled. It is important to mention that samples located at the same neu-ron or at neighboring neuneu-rons form groups with similar characteristics. The map of the variables is shown inFig. 5, where the color bars beside the maps indicate the intensity of each parameter evaluated; the white colors in these bars mean higher values and a higher importance in the formation of the groups for each variable and the black colors mean lower values.
Table 3
Coded and decoded values (between parentheses) of the operational parameters studied: x1— mobile phase modifiers concentrations (mM), x2— mobile phase flow rate (mL/min);
x3— heating block temperature (°C) and x4— drying gas flow rate (kPa), that provided the
highest peak areas on the determination of the majority of contaminants of emerging concern by LC-IT-TOF-HRMS in the screening experiments, determined according 24− 1
fractional factorial design, for the mobile phase modifiers formic acid, ammonium hydrox-ide and formic acid/ammonium formate buffer.
Mobile phase modifier x1 x2 x3 x4
Formic acid −1 (2.6) −1 (0.1) −1 (200) −1 (100) Ammonium hydroxide +1 (4.5) −1 (0.1) +1 (300) +1 (200) Formic acid/ammonium formate −1 (26.1/1.6) −1 (0.1) −1 (200) −1 (100)
ThroughFigs. 4 and 5 it is possible to evaluate the profile of microcontaminants contamination of the samples. The formation of nine groups of samples, according to their microcontaminant contents
can be noticed. From these formations, three groups were composed by only one sample due to the fact that RD099R sample (formation IX) was the only one contaminated with trimethoprim, RD056D (formation VII) was unique with very prominence caffeine values, and also presenting high values of gemfibrozil, naproxen and estrone and, lastly, RD053D (formation IV) was the sample that presented the lower levels of all contaminants studied, except to diclofenac and sulfamethoxazole that were presented at intermediate levels within the range detected.
It was also verified that the samples of the rainy period are situated basically on the right of sample group map (natural waters) obtained by Kohonen neural network and the ones of the dry period on the left (Fig. 4); the samples were separated according to the seasonal
4 6 7 89 10 11 11 12 13 14 15 15 15 16 17 18 18 18 19 19 19 20 21 21 22 23 24 24 25 3.00E+06 4.00E+06 5.00E+06 6.00E+06 7.00E+06 8.00E+06 P e a k A rea (1) clarithromycin (2) azithromycin (3) ibuprofen (4) diethylphthalate (5) 17- estradiol (6) acetaminophen (7) bisphenol A (8) estrone (9) 17- ethinylestradiol (10) 4-octylphenol (11) cimetidine (12) miconazole (13) diclofenac (14) 4-nonylphenol (15) trimethoprim (16) bezafibrate (17) naproxen 1 2 3 4 4 5 5 6 6 7 7 10 11 12 12 13 13 16 16 17 18 19 20 20 2123 23 24 25 25 0.00E+00 1.00E+06 2.00E+06
Formic acid Formic acid / ammonium formate Ammonium hydroxide Mobile phase (18) dietilhexilftalato (19) acetylsalicylic acid (20) sulfamethoxazole (21) ranitidine (22) gemfibrozil (23) ciprofloxacin (24) caffeine (25) diltiazem
Fig. 2. Peak area of analytes (standard solution at a concentration of 50μg/L) obtained in the best assay performed in the 24− 1fractional factorial design with central point for each phase
modifier evaluated: formic acid — Essay 1: x1= 2.6 mM, x2= 0.1 mL/min, x3= 200 °C and x4= 100 kPa; formic acid/ammonium formate— Essay 1: x1= 26.1/1.6 mM, x2= 0.1 mL/min,
x3= 200 °C and x4= 100 kPa and ammonium hydroxide— Essay 2: x1= 4.5 mM, x2= 0.1 mL/min, x3= 200 °C and x4= 200 kPa. Definitions: x1— mobile phase modifiers
concen-trations (mM), x2— mobile phase flow rate (mL/min); x3— heating block temperature (°C) and x4— drying gas flow rate (kPa).
Table 4
p-Values obtained by the analysis of the effects of operational variables ammonium hy-droxide concentration— x1,mobile phaseflow rate — x2, heating block temperature—
x3and drying gasflow rate — x4, proposed by the 24− 1fractional factorial design using
ammonium hydroxide mobile phase modifier for the optimization of operating parame-ters of LC-IT-TOF-HRMS for the determination of contaminants of emerging concern. Bold values are significant at a confidence level of 0.95.
Emerging contaminant p Parameter
x1 x2 x3 x4 17α-Ethinylestradiol 0.0324 0.0059 0.0490 0.0224 17β-Estradiol 0.6763 0.0281 0.7814 0.1620 4-Nonylphenol 0.2129 0.0160 0.6645 0.1539 4-Octylphenol 0.3151 0.0042 0.9438 0.0172 Acetaminophen 0.0437 0.1465 0.7452 0.2073 Acetylsalicylic acid 0.0281 0.3684 0.2175 0.1052 Bezafibrate 0.0449 0.0167 0.8531 0.6659
Bis (2-ethylhexyl) phthalate 0.0023 0.0011 0.8518 0.0608
Bisphenol-A 0.0449 0.0167 0.8531 0.6659 Caffeine 0.0015 0.0023 0.0823 0.0825 Cimetidine 0.0105 0.0050 0.8095 0.1494 Diclofenac 0.1318 0.0443 0.4050 0.5112 Diethylphthalate 0.0367 0.5205 0.0023 0.4231 Diltiazem 0.6307 0.0381 0.8891 0.3275 Estrone 0.0025 0.0084 0.0132 0.0033 Gemfibrozil 0.0242 0.6286 0.4971 0.1861 Ibuprofen 0.0010 0.0001 0.1807 0.0005 Miconazole 0.4419 0.0275 0.7719 0.6196 Naproxen 0.2415 0.0117 0.9384 0.3348 Ranitidine 0.0670 0.0096 0.0977 0.0307 Sulfamethoxazole 0.0105 0.0001 0.0028 0.4151 Trimethoprim 0.2073 0.0080 0.1025 0.3402 17α-Ethinylestradiol 0.0324 0.0059 0.0490 0.0224 Table 5
Contaminants of emerging concern ANOVA results by models proposed by Doehlert ex-perimental design for the optimization of operating parameters of LC-IT-TOF-HRMS. Bold values are significant at a confidence level of 0.95.
Analyte p Parameter Explained variance
Regression model Lack offit 4-Octylphenol 1.1220 × 10−5 0.0160 0.9681
Acetaminophen 4.4970 × 10−5 0.2724 0.9562
Acetylsalicylic acid 7.6169 × 10−7 0.0070 0.9826
Bezafibrate 0.0294 0.0002 0.8223
Bis (2-ethylhexyl) phthalate 0.0003 0.0322 0.9345
Bisphenol-A 0.0040 0.0032 0.8747 Caffeine 0.0054 0.0009 0.8648 Diclofenac 0.1342 0.0001 0.7165 Diethylphthalate 0.0027 0.0002 0.8862 Gemfibrozil 0.0044 0.0030 0.8954 Miconazole 0.0006 0.0011 0.9386 Ranitidine 0.0057 0.0482 0.8632 Sulfamethoxazole 9.2447 × 10−5 0.5385 0.9483 Trimethoprim 3.8019 × 10−8 0.1005 0.9911
conditions. This happened mainly due to greater contamination during the rainy period by synthetic hormone 17α-ethinylestradiol and plasti-cizers 4-nonylphenol and 4-octylphenol that maybe are leaching from the soil of agricultural crops, since these compounds are also found in the formulation of various pesticides, second Moreira 2011[10]. The profile of microcontaminants contamination of the samples in the dry
period was characterized by higher contents of bisphenol-A and pharmaceuticals like bezafibrate, diclofenac, ibuprofen, ranitidine and sulfamethoxazole, although a sample of the rainy period (RD077R) presented among the other microcontaminants found, high content of this compound, that is probably most used in the dry period is more frequent because of respiratory diseases.
1.00E+07 1.50E+07 2.00E+07 2.50E+07 3.00E+07 3.50E+07 Pe a k A re a Screening
Doehlert experimental design
0.00E+00 5.00E+06
Fig. 3. Peak area of contaminants of emerging concern (standard solution at a concentration of 50μg/L) under the best condition of the screening step compared to optimized conditions by Doehlert experimental design. Screening— essay 2: x1= 4.5 mM of ammonium hydroxide, x2= 0.1 mL/min, x3= 200 °C and x4= 200 kPa; Doehlert design— essay 12: x1= 3.5 mM of
ammonium hydroxide, x2= 0.917 mL/min, x3= 300 °C and x4= 200 kPa.
4. Conclusions
An efficient multioptimization methodology was developed to find the best operational parameters of ESI for the simultaneous determi-nation of 21 contaminants of emerging concern in aqueous matrices by LC-IT-TOF-HRMS using 24− 1fractional factorial experimental design,
res-olution IV, in the screening phase, and Doehlert experimental design, as the response surface methodology. The experimental parameter values: mobile phase modifiers concentrations, mobile phase flow rate, heating block temperature and drying gasflow rate were optimized to obtain maximized chromatographic signals for the majority of contaminants of emerging concern evaluated. These optimized parameters resulted in de-creasing of the detection limit of the method, being applied to the microcontaminant evaluation in natural water samples from Rio Doce basin (Minas Gerais, Brazil). Through the data treatment by Kohonen neu-ral network it was possible to describe the contamination profile of these bodies of water. Therefore the use of chemometric approaches showed to be a promising way for the optimization of the simultaneous determina-tion of contaminants of emerging concern in aqueous matrices by LC-IT-TOF-HRMS using ESI; this investigation is inedited.
Acknowledgments
The authors would like to thank the Foundation for Research Support of the State of Minas Gerais FAPEMIG (03864-09 and CEX - APQ-00711-09), the National Research Council of Brazil (CNPq), the Coordina-tion for Improvement of Higher Level Personnel (CAPES), Financier of Studies and Projects (FINEP) and the Federal University of Ouro Preto (UFOP) for thefinancial support of this work.
Appendix A. Supplementary data
Supplementary data to this article can be found online athttp://dx. doi.org/10.1016/j.microc.2014.06.017.
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