Environ. Sci. Technol. 2008, 42, 2988–2991
Detection of Trace Triclocarban in Water Sample Using Solid-Phase Extraction-Liquid Chromatography with Stochastic Resonance Algorithm SHAOFEI XIE,† HAISHAN DENG,‡ B I N G R E N X I A N G , * ,† A N D S U Y U N X I A N G † Center for Instrumental Analysis, China Pharmaceutical University (Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education), Nanjing 210009, China, and Jiangsu Key Laboratory for TCM Formulae Research, College of Pharmacy, Nanjing University of Chinese Medicine, Xianlin Avenue 138, Nanjing 210046, China
Received November 13, 2007. Revised manuscript received January 7, 2008. Accepted January 28, 2008.
Based on the theory of stochastic resonance, a new algorithm was presented to enhance the signal-to-noise ratio (SNR) of weak HPLC/UV signals of triclocarban in water samples with a new method for optimizing the system parameters. The method was applied to experimental weak signals of HPLC/ UV, which was embedded in the noise background, and the SNR was enhanced greatly. The algorithm was employed to detect triclocarban residue in water with solid-phase extraction-high-performance liquid chromatography. The limit of detection and the limit of quantification were improved from 10 ng L-1 and 50 ng L-1 to 1 ng L-1 and 5 ng L-1, respectively. The results showed an excellent quantitative relationship between concentrations and chromatographic responses.
Introduction Noise has traditionally been considered a nuisance in qualitative and quantitative analysis. Especially in trace and ultratrace analyses, noise is the primary obstacle to improve instrumental detectability. A great number of chemometrics techniques have been employed to improve the performance of analytical instruments. Traditional methods (1, 2) filter out the noise to improve analytical signals, but they may result in negligible loss of useful information (3). Alternatively, stochastic resonance (SR) shows the conductive aspect of noise and renders a seemingly counterintuitive but much better approach for solving the problem by utilizing noise to amplify a weak input signal. Frequently used filtering and smoothing methods eliminate the ‘useless’ part or noise from the signal, but SR transfers the energy of noise to create a useful signal and retains the integrity of the signal. Stochastic resonance is a nonlinear phenomenon whereby the noise can enhance the detection of a weak signal. It generally occurs in bistable dynamical systems attacked by a weak signal corrupted by noise where the signal can be * Corresponding author phone: +86 25 83271180; e-mail:
[email protected]. † China Pharmaceutical University. ‡ Nanjing University of Chinese Medicine. 2988
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amplified by the assistance of noise (4). It has been shown in numerous research works that signal strength increases as noise decreases by the cooperation of appropriate noise, weak signals, and a nonlinear system with designated threshold (5–8). Stochastic resonance has attracted considerable interest in the past decade due to its potential technological applications for optimizing the output signalto-noise ratio (SNR) in nonlinear dynamical system. There have been several applications in improving analytical detection limits for trace analysis. The algorithm based on the theory of stochastic resonance (SRA) can be used to determine weak analytical signals quantitatively. Application has been successful in the analysis of the Raman spectra from tetrachloromethane (CCl4) that traditionally produced high-level noise and chromatographic signals from carbon dioxide (CO2) (3). In our previous works, SRA was used to determine phenazopyridine, glyburide, and roxithromycin in plasma (9–11). Also, it was used to determine Sudan I in pepper-based products (12) and chloramphenicol residues in milk (13). However, the theory of stochastic resonance is seldom employed to deal with the problems in environmental science. Triclocarban (TCC; 3,4,4′-trichlorocarbanilide; CAS# 10120-2; the structure is provided in the Supporting Information) is an antimicrobial compound commonly added for its sanitizing properties to a wide range of household and personal care products including bar soaps, detergents, body washes, cleaning lotion, and wipes. Triclocarban has a number of properties suggestive of potential adverse environmental behavior. Triclocarban, as a nonagricultural pesticide released into wastewater, is toxic to humans and other animals. It triggers methemoglobinemia and causes a reduction in the rate at which exposed mammals conceive, in the number of offspring born, as well as in the survival rate and body weight of the young (14). Its polychlorinated aromatic structure suggests a potentially significant resistance to biotransformation and biodegradation (15). Despite its extensive use over several decades, environmental occurrence data on TCC are scarce. This is due in part to a lack of analytical techniques offering the desired sensitivity, selectivity, affordability, and ease of use. Halden et al. (16–19) reported the method detection limit of TCC at 3 ng L-1 by using solid-phase extraction (SPE) and liquid chromatography electrospray ionization mass spectrometry (LC/ESI/ MS). In this paper, stochastic resonance algorithm was at first applied to the chromatographic signals of TCC detected by direct injection of the solutions into the HPLC/UV system. The weak chromatographic peak of TCC was amplified and the system parameters were optimized by using the index I which was proposed in this work. The parameters obtained by the optimization method with index I would achieve preferable SNR while the shape of the original signal would also be kept well-within the mean. The improved algorithm was used to detect the TCC residue in water with SPEHPLC/UV. The limit of detection (LOD) was improved to 1 ng L-1, and the limit of quantification (LOQ) was improved to 5 ng L-1. Linearity, precision, and recoveries were also determined.
Theory and Algorithm Theory of Stochastic Resonance. The nonlinear Langevin equation has been frequently employed to describe the phenomenon of SR. It has the following formula (3, 20–24) dx/dt ) -U ′(x) + MI(t) + Cξ(t) 10.1021/es702856u CCC: $40.75
(1)
2008 American Chemical Society
Published on Web 03/15/2008
where I(t) ) S(t) + N(t) denotes an input signal embedded in a noisy environment with the signal S(t) and the intrinsic noise N(t), x is the particle displacement, and ξ(t) is the external noise added to induce SR. M and C are adjustable parameters. U(x) is the simplest double-well potential with constants a and b characterizing the system (3, 23). 1 1 U(x) ) - ax2 + bx4 2 4
(2)
The symmetric double well shows that the minima are located at ( xm, where xm) (a/b)1/2. A potential barrier separates the minima with the height given by ∆U ) a2/4b. The barrier top is located at xb ) 0. When the input signal, noise, and nonlinear system cooperate well, the signal will extract energy from the noise in order to surmount the energy barrier and hop from one potential well to another. Consequently, the strength of the signal will be increased and that of the noise will be decreased. Therefore, the output signal of the system will have a better SNR than the input. In order to perform quantitative determination, M and C in equation 1 were set to 1 and 0, respectively, to keep the property of intrinsic noise and reduce SR algorithm variables. Only parameters a and b of the system are modulated to match the input signal including real signal and intrinsic noise to achieve SR (3, 23–25). Equation 1 can be solved by a fourth-order Runge–Kutta method (3, 17, 26). The calculation procedure starts with the normalization of the input signal to the interval [-1, 1]. The output signal is then obtained by substituting the normalized signal into eq 3 (see Supporting Information). The final results can be given by inverse normalization of the output signals. Optimization Method of System Parameters. In eq 2, the parameters a and b not only define the height of the potential barrier (∆U ) a2/4b) but also affect the profile of the potential well. Therefore, it is necessary to optimize the parameters a and b in order to get a good output result. The main feature of SR is to improve the SNR of output signal, which has always been adopted as an indicator of optimization for evaluating the quality of output signal in the theory of SR. Nevertheless, the chromatographic peak achieved by SR with the parameters optimized by that indicator is often distorted in chromatographic analysis. To solve this problem, Zhang et al. have employed the ratio of peak height to peak half-width as an evaluating indicator to optimize the system parameters, which has been shown to improve the peak shape distortion (9). In this paper, an alternative idea has been considered in order to give attention to both the SNR and the shape of output chromatographic peak. An experimental formula for optimizing system parameters was proposed in the idea based on a mass of experimental results. The experimental formula can be expressed as follows: I)
|
SNRi LA -γ -1 SNRmax RA
|
(4)
Where I is an index for considering simultaneously the SNR and the shape of the chromatographic peak in the output signal. The maximal I will achieve an optimized output signal not only with satisfactory SNR but also with good peak shape. SNRi is the signal-to-noise ratio defined as a ratio of the standard deviation of peak area in the output signal to that of baseline area. SNRmax is the maximal SNR with parameters a and b. Parameter γ is the control coefficient of peak shape, while LA and RA are left side and right side of peak area, respectively. In eq 4, SNRi/SNRmax is the control part of SNR to restrict the item of SNR within [0, 1], while γ|(LA/RA) 1| is the control part of peak shape. Using the ratio of left side and right side of the peak area is an effective method to ensure good chromatographic peak shape. If the chromatographic peak in the output signal is a Gaussian peak, the
FIGURE 1. Chromatogram of 1 ng L-1 TCC in water. value of |(LA/RA) - 1| is 0, while the value of |(LA/RA) - 1| for a common chromatographic peak is within [0, 1]. Parameter γ can control the proportion of peak shape in the system output. Thus, a simple arithmetical operation between the control part of SNR and that of peak shape will obtain the optimal index I. The maximal I is searched by adjusting a and b within the range of [0, 1] simultaneously. The output signal can be improved with the optimized parameters a and b that are obtained via the optimization method with index I. The index I is a comprehensive criterion which contains both information of SNR and peak shape. The maximal I indicates the greater SNR and the better peak shape, with the corresponding a and b as the best system parameters. Thus, the output signal with the greater SNR but the poor peak shape or with the better peak shape but the smaller SNR will be discarded. In practical applications, the optimum system parameters a and b can be found easily and rapidly. The proposed algorithm is apt to implement adaptive stochastic resonance with convenient calculation in a short time period.
Experimental Section Reagents and Standards. Triclocarban (99% purity) was obtained from the Hunan Dajie Technoloht Co., Ltd. (Hunan, China). Methanol (HPLC-grade) was purchased from Merck (Darmstadt, Germany). Distilled water was used throughout the study. Chromatographic and Detection Conditions. The chromatographic system was the Shimadzu HPLC LC-10ATVP series (Tokyo, Japan) equipped with an LC-10 ATVP pump, a 7725 manual injector, an SPD-10 AVP detector, and an N2000 workstation (Zhejiang University). Separation was carried out at room temperature on a reversed-phase Dikma Diamonsil C18 (250 mm × 4.6 mm I.D., 5 µm) column. The mobile phase consisted of methanol and distilled water (80:20, v/v). The flow rate was 1.0 mL min-1, the detection wavelength was 281 nm, and the injection volume was 20 µL. TCC Solution Preparation. Stock solution of TCC was prepared by dissolving the compound in methanol at a concentration of 1000 mg L-1. Working standards for direct HPLC/UV analysis were prepared by diluting the stock solution with methanol, and those for SPE-HPLC/UV analysis were prepared by diluting the stock solution with distilled water. All of the solutions were stored at 4 °C. SPE Procedure. Aliquots (200 mL) of water samples were extracted using Oasis HLB (60 mg) cartridges (Waters Corp; Milford, MA) that were pre-equilibrated with 2 mL of methanol in acetone (50%) followed by 2 mL of methanol VOL. 42, NO. 8, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 1. Calibration Curve of TCCa concentration (ng L-1)
peak area (f) acquired by SRA
5
10
20
50
100
8547
17272
30105
70425
133080
a Linear regression curve ) 1302 ((18.75) c - 3702 ((956.8); R ) 0.9997, SD ) 1474.
FIGURE 2. Peak of TCC was amplified by SRA: (a) specially enlarged chromatogram of Figure 1 (1 ng L-1 TCC in water); (b) chromatogram of 1 ng L-1 TCC in water obtained by SRA. and 6 mL of water. Following loading of the sample, cartridges were washed with 1 mL of water containing 5% methanol by volume, and analytes were eluted with 3 mL of methanol in acetone (50%) at a flow rate of 0.5 mL min-1. Extracts were dried under nitrogen at 40 °C and reconstituted in 200 µL of methanol.
Results and Discussion Optimization of System Parameters a and b. When a quantitative determination is performed in practice, the optimization of the system parameters is essential and complicated. Although all samples in a series have different strengths, the same parameter set will be used to keep the quantitative relationship of the output signals of the series. When the input signal is fixed, the selection of the nonlinear system parameters a and b will directly affect the quality of final output signal. Therefore, the system parameters a and b must be optimized to obtain satisfactory output signals. The optimization index proposed in this paper takes into consideration comprehensively the SNR and the peak shape in output signal. The discussion in the previous literature (3, 9, 13) helps to limit the range of the parameters to be searched. Small a and b should be considered first (3, 9). In this work, a was limited within the range of 0 to 0.1, and b was limited within the range of 0 to 0.001. The optimization speed of system parameters a and b could be increased with the smaller range of parameters. Parameter γ affects the peak shape and the intensity of output signal obtained by SRA. The selection of γ depends on the noise interference with the signal. Parameter γ can be adjusted to promote the role of peak shape in the index I if the chromatographic peak shape is poor in output signal. The better peak shape can be obtained by SRA with the larger γ-value, while the better SNR can be obtained by SRA with the smaller γ-value. In this work, γ was set to 1. The optimized values of the two parameters (a ) 6 × 10–3, b ) 1 × 10–6) can be obtained with the index I. LOD and LOQ. A total of 20 µL of TCC working solution was injected into the chromatographic system, and a UV detector was used. As a result, the limit of detection (LOD) and the limit of quantification (LOQ) of TCC solutions were originally 10 ng L-1 and 50 ng L-1, respectively. The chromatogram of TCC solution at the concentration of 1 ng L-1 is given in Figure 1. The peak is too weak to meet the requirement for quantitative or even qualitative analysis, and it is impossible to detect TCC accurately in this condition. 2990
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FIGURE 3. (A) Original chromatograms of TCC in water; (B) chromatograms of TCC obtained by SRA. Therefore, a new and effective method for dealing with the data of the chromatographic signal should be adopted. Since the retention time of TCC was around 18 min, a section of signals during the period of 16.5∼19 min was chosen to perform stochastic resonance (a ) 6 × 10-3, b ) 1 × 10-6). Figure 2b shows the signal of TCC can be amplified significantly by SRA and the detection could be practiced accurately. After the application of SRA, the LOD and LOQ were improved to 1 ng L-1 and 5 ng L-1, respectively. Calibration Curve. A set of TCC calibration solutions was prepared at concentrations of 5, 10, 20, 50, and 100 ng L-1 by diluting the appropriate volumes of the primary stock
solution with methanol. Equal volumes (20 µL) were injected into the HPLC/UV system, and the chromatograms were recorded separately. The signals during the period of 16.5–19 min were chosen to perform stochastic resonance (a ) 6 × 10-3, b ) 1 × 10-6). As presented in Table 1 and Figure 3, the results show good linearity between concentration and peak area acquired by SRA over the concentration range from 5 to 100 ng L-1. Precision, Recovery, and QC. The precision was determined at concentrations of 5, 20, and 100 ng L-1. Intraday precision was determined by repeated analysis of water samples on one day (n ) 5), and interday precision was determined by repeated analysis of water samples on three consecutive days (n ) 5 series per day). The intra- and interday precisions are summarized in Table S1 (see Supporting Information). The results demonstrate that the precisions are acceptable. Recovery of TCC was determined at concentrations of 5, 20, and 100 ng L-1. The recovery was defined as the ratio of measured concentration to added concentration. Table S2 (see Supporting Information) shows the recoveries are satisfactory. Quality control (QC) was determined at concentrations of 5, 20, and 100 ng L-1, and Table S3 (see Supporting Information) shows QC is satisfactory. The quantitative analysis of TCC in water samples shows that SRA can not only improve the limit of detection and limit of quantification but also keep a good quantitative linearity between concentration and peak strength acquired by SRA. The optimal parameters a and b can be obtained rapidly by the optimization method proposed in this paper. The adaptive stochastic resonance can be realized by this optimization method, which will provide a convenient method for SRA application. Stochastic resonance renders an entirely new way for improving the instrumental detectability in HPLC/UV analysis, and it is effective in the detection of a weak signal. The method achieved good sensitivity for the determination of TCC residue in water samples. It can be expected that SRA will be an effective tool for trace environmental analysis.
Supporting Information Available The Supporting Information contains the structure of TCC, the calculation procedure of SRA, and tables concerning precision, recovery, and QC data. This material is available free of charge via the Internet at http://pubs.acs.org.
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