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Fast Analytical Methodology Based on Mass Spectrometry for the Determination of Volatile Biomarkers in Saliva Miguel del Nogal Sanchez, Elena Hernandez García, Jose Luis Perez Pavon,* and Bernardo Moreno Cordero Departamento de Química Analítica, Nutricion y Bromatología, Facultad de Ciencias Químicas, Universidad de Salamanca, 37008 Salamanca, Spain ABSTRACT: We report a methodology for the rapid determination of biomarkers in saliva. The method is based on direct coupling of a headspace sampler with a mass spectrometer. The saliva samples are subjected to the headspace generation process, and the volatiles generated are introduced directly into the mass spectrometer, thereby obtaining a fingerprint of the sample analyzed. The main advantage of the proposed methodology is that no prior chromatographic separation and no sample manipulation is required. The following model compounds were studied to check the possibilities of the methodology: methyl tert-butyl ether and styrene as biomarkers of exposure and dimethyl disulfide, limonene, and 2-ethyl-1-hexanol as biomarkers of diseases. The method was applied to the determination of biomarkers in 28 saliva samples: 24 of them were from healthy volunteers, and the others were from patients with different types of illness (including different types of cancer). Additionally, a separative analysis by GC/MS was performed for confirmatory purposes, and both methods provided similar results.
A
good correlation between the salivary and serum concentrations of some compounds indicates the prospective use of saliva for monitoring the circulating levels of small molecules.14 The analysis of compounds in saliva provides a desirable and promising platform for the diagnosis of several diseases (cancer, asthma, respiratory infections, and, potentially, many others)5,6 and for monitoring exposure to environmental pollutants.7,8 Furthermore, there are many potential applications for saliva testing for drugs.9,10 In recent years several gas chromatography/mass spectrometry (GC/MS) methods for monitoring biomarkers have been reported in the literature.721 Increasing interest has been aroused as regards the use of saliva,711 exhaled breath,17,20,22 and sweat12,18 as alternative matrixes for the detection of biomarkers in comparison to blood15,16 or urine.13,15,23 Solid-phase microextraction (SPME)810,13,14,17 is the preferred sampling technique in comparison with others, such as stir-bar extraction11,12 or headspace single-drop microextraction (SDME).16 However, in general, several of the proposed chromatographic procedures are slow and time-consuming. The development of nonseparative methods for the detection of biomarkers is currently of interest, mainly owing to their fast analysis speeds. Sometimes it is not necessary to separate the individual compounds of a sample to resolve the analytical problem in hand, it sufficing to obtain a signal profile of the sample formed by all the components integrating it.24,25 In this context, different techniques such as ion mobility spectrometry, proton transfer reaction mass spectrometry (PTR-MS), selected ion flow tube mass spectrometry (SIFT-MS), and sensor arrays have addressed the determination of biomarkers.2629 Here we propose the use of direct coupling of a headspace sampler (HS) with a mass spectrometer for the rapid determination r 2011 American Chemical Society
of biomarkers in saliva samples. Additionally, a series of separative analyses by GC/MS was performed for confirmatory purposes to show the analytical potential of the nonseparative method. The method based on HS-MS has been previously used for the fast analysis of volatiles in different matrixes including water,30,31 soil,32 pharmaceuticals,25 and food.33 In this work, five biomarkers (methyl tert-butyl ether (MTBE), dimethyl disulfide, styrene, limonene, and 2-ethyl-1-hexanol) of both diseases and exposure to environmental pollutants were selected to check the possibilities of the proposed methodology. MTBE and styrene are included in an important group of airborne contaminants. 2-Ethyl-1-hexanol and limonene are biomarkers for lung cancer and liver disease, respectively.5,28 In addition, 2-ethyl-1-hexanol is known to be an indoor air pollutant, and its effects on health are of great concern.34,35 Sulfur compounds such as dimethyl disulfide are responsible for halitosis.6,36
’ EXPERIMENTAL SECTION Chemicals. Dimethyl disulfide, styrene, limonene, 2-ethyl1-hexanol, and carbon tetrachloride were supplied by SigmaAldrich (Steinheim, Germany). Methyl tert-butyl ether and methanol were purchased from Acros Organics (Geel, Belgium) and Merck (Darmstadt, Germany), respectively. The purities of the compounds were at least 97%. Standard Solutions and Samples. A set of stock solutions (5000 mg/L) of these compounds in methanol were prepared Received: October 14, 2011 Accepted: November 21, 2011 Published: November 21, 2011 379
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Analytical Chemistry and stored at 4 °C. Twenty-five standard solutions containing the five compounds studied and carbon tetrachloride, as the internal standard, were prepared in methanol by taking different volumes from the stock solutions described above. To perform the measurements of the calibration standards, 0.16 g of NaCl and 0.5 mL of ultrapure water were placed in a 10.0 mL vial (Agilent Technologies, Waldbronn, Germany). Finally, 15 μL of the above solution was added to the vials, which were sealed with Teflon/ silicone septa (Agilent Technologies). Each sample was analyzed in triplicate (three vials, one injection per vial). Unstimulated saliva samples were obtained from 28 anonymous adults of both sexes and placed directly into 10.0 mL vials sealed with silicone septum caps. The saliva samples were collected and analyzed on the same day. They were maintained at 4 °C until analysis. Samples 124 were from healthy volunteers; samples 2527 were from patients with lung, colon, and stomach cancer, respectively, and sample 28 was from a patient suffering from lymphoproliferative syndrome. To perform the measurements of these samples, 0.16 g of NaCl and 0.5 mL of saliva were placed in a 10.0 mL vial. Finally, 15 μL of methanol containing carbon tetrachloride as the internal standard was added to the vial. All the prediction samples were analyzed in triplicate. A set of four saliva samples (14) not containing any of the compounds studied were spiked with all the analytes at five different concentration levels. To perform the measurement of these samples, 0.16 g of NaCl and 0.5 mL of saliva were placed in a 10.0 mL vial. Finally, 15 μL of solution containing all the analytes and carbon tetrachloride, as the internal standard, was added to the vial. All the validation samples were analyzed in triplicate. HS-MS Measurements. HS sampling was performed with a PAL autosampler (CTC Analytics AG, Zwingen, Switzerland). This sampler is equipped with a tray for 32 consecutive samples and an oven with positions for six sample vials. The oven temperature was kept at 70 °C, and the equilibration time was set at 5 min. During this time, the vials were shaken at 750 rpm in the oven. A 2.5 mL syringe at 120 °C was used. The detector was a quadrupole mass spectrometer (HP 5973 N) equipped with an inert ion source. It was operated in electronimpact mode using an ionization voltage of 70 eV. The ion source temperature was 230 °C, and the quadrupole was set at 150 °C. The analyses were performed in the scan mode, with a sampling rate of 5. The m/z range was 45140 amu. Coupling of the headspace sampler to the mass spectrometer was accomplished by means of a gas chromatograph, whose capillary column was maintained at 240 °C along the time of analysis. In this way, the separation capacity of the column is removed, and it behaves as a simple transfer line from the injector to the mass detector. The injection port was maintained at a temperature of 250 °C, and a split ratio of 10:1 was used. The signal-recording time was 2.0 min. Immediately after analysis of a sample, the injection system was ready for the next sample because the column temperature remained constant throughout the period of sample analysis. HS-PTV-GC/MS Measurements. The HS autosampler described above was used. The experimental conditions were the same as those used for the methodology based on HS-MS. All experiments were carried out with a programmable temperature vaporizer (PTV) inlet (CIS-4, Gerstel, Baltimore, MD) using the solvent-vent injection mode. A liner (71 mm 2 mm) packed with Tenax-TA was used. The injector venting
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Figure 1. (a) HS-MS total ion current profile of saliva sample 1 spiked with the five biomarkers. (b) Mass spectrum of the sample corresponding to scan number 78 (0.77 min after the sample injection). (c) Mass spectrum representing the sum of the intensities of all the ions detected from 0.50 to 1.20 min.
temperature was 5 °C. Venting flow was adjusted to 50 mL/min and venting pressure to 5.0 psi (34 474 Pa). After 0.30 min, the split valve was closed and the liner was flash-heated at a rate of 12 °C/s to 250 °C. The analytes were transferred from the liner to the capillary column (1.5 min). The split valve was then opened (split flow 150 mL/min), and the liner temperature was held at 250 °C for 6.0 min. Cooling was accomplished with liquid CO2. To perform the gas chromatographic measurements, an Agilent 6890 GC device equipped with a low-polarity DB-VRX capillary column (20 m 0.18 mm 1 μm) from Agilent J&W was used. The initial oven temperature was 50 °C for 3.0 min; this was increased at a rate of 65 °C/min to 175 °C and then further increased at 40 °C/min to 240 °C and held for 0.50 min. The total chromatographic run time was 7.30 min. The carrier gas was helium N50 (99.995% pure, Air Liquide), and the flow rate was 1.5 mL/min. The total chromatographic run time was 7.16 min. Additionally, about 7 min was necessary before the next sample could be measured, since the column had to be cooled from the final temperature (240 °C) to the initial conditions of 50 °C. The mass detector described above was used. The different compounds were identified by comparison of the experimental spectra with those of the NIST08 database (NIST/EPA/NIH Mass Spectral Library, version 2.0). The analyses were performed in the scan mode with a sampling rate of 2. The m/z range was 45140 amu. Data Analysis. Data collection was performed with Enhanced ChemStation37 from Agilent Technologies. Partial least-squares multivariate calibration was performed using The Unscrambler, version 10.0.1, statistical package.38
’ RESULTS AND DISCUSSION Study of the Signals Obtained. The volatiles generated in the headspace were introduced into the mass spectrometer by means of a gas chromatograph whose capillary column was 380
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Table 1. Concentration Data for the Five Biomarkers in the Calibration and Validation Stepsa MTBE
dimethyl disulfide level
conc (mg/L)
styrene
level
conc (mg/L)
limonene level
conc (mg/L)
2-ethyl-1-hexanol
no.
conc (mg/L)
level
conc (mg/L)
level
1
0.694
0
0.505
0
0.449
0
0.450
0
0.591
0
2
0.694
0
0.096
2
0.048
2
0.852
2
0.394
1 2
Calibration Standards in Ultrapure Water
3
0.299
2
0.096
2
0.850
2
0.249
1
0.986
4
0.299
2
0.914
2
0.248
1
0.852
2
0.591
0
5
1.09
2
0.301
1
0.850
2
0.450
0
0.394
1
1
0.914
2
0.449
0
0.249
1
0.394
1
2
0.505
0
0.248
1
0.249
1
0.789
1
6
0.496
7
1.09
8 9
0.694 0.496
0 1
0.301 0.301
1 1
0.248 0.649
1 1
0.651 0.852
1 2
0.986 0.789
2 1
10
0.496
1
0.710
1
0.850
2
0.651
1
0.591
0
11
0.891
1
0.914
2
0.649
1
0.450
0
0.986
2
12
1.09
2
0.710
1
0.449
0
0.852
2
0.986
2
13
0.891
1
0.505
0
0.850
2
0.852
2
0.197
2
14
0.694
0
0.914
2
0.850
2
0.048
2
0.789
1
15
1.09
2
0.914
2
0.048
2
0.651
1
0.197
2
16 17
1.09 0.299
2 2
0.096 0.710
2 1
0.649 0.048
1 2
0.048 0.450
2 0
0.591 0.789
0 1
18
0.891
1
0.096
2
0.449
0
0.651
1
0.789
1
19
0.299
2
0.505
0
0.649
1
0.651
1
0.394
1
20
0.694
0
0.710
1
0.649
1
0.249
1
0.197
2
21
0.891
1
0.710
1
0.248
1
0.048
2
0.394
1
22
0.891
1
0.301
1
0.048
2
0.249
1
0.591
0
23
0.496
1
0.096
2
0.248
1
0.450
0
0.197
2
24 25
0.299 0.496
2 1
0.301 0.505
1 0
0.449 0.048
0 2
0.048 0.048
2 2
0.197 0.986
2 2
26
0.694
0
0.096
2
0.850
2
0.651
1
0.986
2
27
0.891
1
0.301
1
0.649
1
0.249
1
0.591
0
28
1.09
2
0.505
0
0.449
0
0.852
2
0.789
1
29
0.496
1
0.710
1
0.248
1
0.048
2
0.197
2
30
0.299
2
0.914
2
0.048
2
0.450
0
0.394
1
Validation Standards in Saliva
a
Concentration levels are coded from 2 (lowest) to +2 (highest).
maintained at 240 °C along the time of analysis and afforded a total ion current profile, as shown in Figure 1a, corresponding to a sample of saliva spiked with 0.496, 0.710, 0.248, 0.048, and 0.197 mg/L concentrations of MTBE, dimethyl disulfide, styrene, limonene, and 2-ethyl-1-hexanol, respectively. Figure 1b was obtained by selecting the mass spectrum occurring 0.77 min after injection, and it corresponded to the mass spectrum of all the components present in the sample arriving at the detector at the same time. The mass spectrum representing the sum of the intensities of all the ions detected during the data acquisition period is shown in Figure 1c, and it was used as the analytical signal. Some compounds showed overlapping mass spectra, and this allowed us to check the methodology under complex situations: m/z 57, which is the base peak of 2-ethyl-1-hexanol, was also seen for MTBE, m/z 94, which is the base peak of dimethyl disulfide, was also seen for limonene, and, finally, m/z 79 is an important variable of limonene and dimethyl disulfide. To check the possible existence of a matrix effect in the analysis of the saliva samples, saliva samples 14, which did not contain
any of the compounds studied, and ultrapure water were spiked with a solution of the compounds at different concentration levels. The signals for the ultrapure water sample and saliva samples were similar, and no important matrix effect was observed. Partial Least-Squares (PLS) Calibration. The calibration standards set in ultrapure water were designed using a calibration design39 at five uniformly distributed concentration levels (Table 1, nos. 125). Thus, the calibration set comprised 25 standards with uncorrelated concentrations. With the samples from the calibration set, PLS models were built for each compound. Cross-validation (leave one out) was used to select the optimum number of components. The Martens uncertainty criterion (included in The Unscrambler, version 10.0.1, statistical package) was used as the m/z variable selection technique. This eliminates all the variables whose regression coefficients have uncertainty values greater than the absolute value from the model. Of the 96 original variables (m/z 45140), only 23 were used for the PLS models (see Table 2). 381
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Table 2. Characteristics of the PLS Models explained y variance (%) compound
base peak
selected m/z variables
PLS component
calibration
validation
MTBE
73
73, 74
1
98
98
dimethyl disulfide
94
47, 61, 79, 81, 94
2
99
99
50, 51, 62, 77, 78, 103, 104, 105
2
99
98
styrene
104
limonene
68
67, 68, 93, 107
1
98
98
2-ethyl-1-hexanol
57
55, 56, 57, 69, 73
2
98
97
Table 3. Repeatability, Reproducibility, and Detection Limits Using the Univariate Estimation (UDL) and Three Multivariate Approaches (MDL13) RSD (%) compound
detection limit (mg/L)
repeatability reproducibility UDL MDL1 MDL2 MDL3
MTBE
6
15
0.01
0.2
0.1
8 10
18 16
0.05 0.09 0.007 0.1
0.2 0.08
0.2 0.1
limonene
9
17
0.02
0.1
0.09
0.2
2-ethyl-1-hexanol
7
16
0.1
0.2
0.2
0.1
dimethyl disulfide styrene
0.1
relative standard deviation (RSD), are shown in Table 3, with values not higher than 10% in the case of repeatability and 18% in the case of reproducibility. Detection Limits for PLS Models. Four different strategies were used to determine detection limits. Three of them are based on multivariate methods, and the last one corresponds to the univariate approach. The multivariate detection limit (MDL) is defined as a function of the variance of the concentration predicted by the model:40
Figure 2. Loads of the first two PLS components against the m/z ratio for the 2-ethyl-1-hexanol PLS model.
The optimum number of components and the cumulative percentage of y variance explained in the different models are shown in Table 2. The slopes of the predicted values against the reference values ranged between 0.98 and 0.99 for all the compounds in the calibration and cross-validation steps. The root mean standard error (RMSE), expressed as a relative value considering the average of the added concentration for each analyte studied, was equal to or lower than 8.2% in the calibration and the cross-validation step. As an example of the models' performance, Figure 2 depicts a plot of the PLS-component loads against the m/z ratio in the model of 2-ethyl-1-hexanol. The first two PLS components explained 99% of the x variance in the cross-validation data set (29% and 70%, respectively). The highest positive value (PLS component 1) corresponded to m/z 57, which is the base peak of the compound studied. In the case of m/z 73, the high negative loading (PLS component 2) can readily be understood by taking into account that this m/z, which was not present in the mass spectrum of 2-ethyl-1-hexanol, was used to subtract the contribution to the model by MTBE, which contains some common m/z variables with 2-ethyl-1-hexanol (e.g., m/z 57). Variable m/z 73 corresponds to the base peak of MTBE. The models were used to predict the concentration of a set of samples that were not used to build the model. This prediction group was formed by a total of 20 samples corresponding to 4 saliva samples (14), spiked at 5 different concentration levels. The concentrations are shown in Table 1 (nos. 2630). The relative prediction error values ranged between 5.7% and 11%. To study the repeatability and reproducibility of the methodology, saliva sample 1 spiked with the minimum concentration studied for each analyte was analyzed on the same day (10 replicates) and on two different days (10 replicates/day). The results, as
MDLk ¼ Δðα, βÞ varðc0, k Þ1=2
ð1Þ
where Δ(α,β) is a parameter of a noncentral Student’s t distribution with ν degrees of freedom and considers the likelihood of making errors α (false negative) and β (false positive). In this work, α = β = 0.05. Here, two expressions were used to estimate multivariate detection limits according to eq 1. The first expression (MDL1) used was developed by Faber and Bro41 and is as follows: varðcun Þ ¼ ½ð1 þ hÞðMSECÞ σ c 2
ð2Þ
where h is the leverage of the unknown sample in the calibration space, σc2 is the variance of the concentrations in the reference method, and MSEC is defined as I
MSEC ¼
∑ ð^yi yi Þ2 i¼1 I df
ð3Þ
where I is the number of samples in the calibration model, ^yi yi is the difference between the predicted and the added concentration values for the ith calibration sample, and df is the number of pseudo degrees of freedom, which can be estimated by the equation proposed by Van der Voet.42 Here it was assumed that σc2 = 0, since no reference method was used to determine the concentration of the calibration samples. Six replicates of saliva 1 spiked with the minimum studied 382
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Figure 3. Extracted ion profiles (m/z ratios 73 (a), 94 (c), and 104 (e)) for saliva sample 1 not spiked and spiked with all the biomarkers. The predicted concentrations and The Unscrambler deviations for MTBE (b), dimethyl disulfide (d), and styrene (f) in six spiked replicates of saliva sample 1 are also shown in the figure. The samples were spiked with the minimum concentrations studied for each analyte. The dotted lines represent the univariate appproach (UDL) and the three multivariate estimations (MDL13) for the detection limits.
where ^b is the slope of the calibration curve using the 25 calibration standards from Table 1. The m/z variable used in the models was the base peak for each compound (see Table 2, column 2), except for dimethyl disulfide and 2-ethyl-1hexanol owing to the existence of other compounds that also contribute to their base peaks. The selected variables for dimethyl disulfide and 2-ethyl-1hexanol were m/z 46 and 70, respectively, which are the most abundant variables in their mass spectra, with no interferences from other compounds. As can be seen from Table 3, the univariate approach (UDL) provided lower detection limits than those obtained with the three multivariate expressions (MDL13), except for 2-ethyl1-hexanol, whose limits are similar in all cases. The concentrations for several volatile organic compounds in patients with lung cancer28 are of the same order as the detection limits found here. Three extracted ion profiles (m/z ratios 73, 94, and 104) for saliva sample 1 not spiked and spiked with the minimum concentration of each analyte studied are shown in Figure 3. The predicted concentrations and The Unscrambler deviations for the set of six replicates of saliva 1 are also shown in Figure 3, as well as the univariate and multivariate detection limits (dotted lines). The results obtained are satisfactory. In this case, the multivariate approaches are a pessimistic estimation for detection limits because it is possible to quantify below these limits using the PLS models (Figures 3d,f). A suitable detection limit could
concentration for each biomarker were used to estimate the leverage. This set of samples was used to calculate the detection limits in all cases. The second expression (MDL2) used to determine MDL with eq 1 is based on the prediction uncertainty provided by The Unscrambler. This value depends on the model error, the sample leverage, and the sample’s residual x variance.38 A different alternative (MDL3) for calculating MDL, which considers that the spectral noise represents the largest source of error, was also used:43 MDL ¼ 3:3ðδxÞ
1 ¼ 3:3ðδxÞ||b|| SEN
ð4Þ
) )
where δx is an estimate of the noise level in the data, SEN is the sensitivity of the model (estimated as the inverse of the Euclidean norm of the regression coefficient vector (1/ b ). Here, δx was estimated as the square root of the mean variance in each selected m/z variable (see Table 2, column 3) taking into account the six samples described above. Finally, a univariate approach (UDL), proposed by Ortiz et al.,44 was used: UDL ¼
Δðα, βÞðvarð^yÞÞ1=2 ^b
ð5Þ 383
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probably be placed between those obtained by the univariate and multivariate approaches. Analysis of Saliva Samples. The method was applied to the analysis of 28 saliva samples: 24 of them (samples 124) were from healthy volunteers, and the others were from patients with different types of pathology. None of the biomarkers studied were detected in the samples corresponding to the healthy volunteers, except in saliva sample 24, where dimethyl disulfide was found. 2-Ethyl-1-hexanol was found in the sample corresponding to the patient suffering from lung cancer (saliva sample 25). Indeed, this compound is a biomarker of this kind of cancer,28 and it was not identified in samples from patients with other types of cancer. No other biomarker was found in the other patients (samples 2628). The concentrations obtained and their deviations
(using the equation included in The Unscrambler, version 10.0.1) are shown in Table 4. All the samples were also analyzed with HS-PTV-GC/MS for confirmatory purposes. The results were similar with both methods. The negative samples did not contain any biomarker, and the concentrations found in the positives samples were similar to those obtained with the method based on HS-MS as shown in Table 4. Figure 4 shows the total ion chromatogram for saliva samples 24 (Figure 4a) and 25 (Figure 4c). Figure 4b shows the extracted ion chromatogram for sample 24, corresponding to m/z 94. As seen from this plot, apart from dimethyl disulfide, the sample hardly displays any compound with m/z 94. In the case of the saliva from the patient suffering from lung cancer, there was no important contribution to m/z 57 coming from compounds other than 2-ethyl-1-hexanol (Figure 4d). The method based on HS-MS could afford higher concentrations than those found when GC/MS is used for samples with high concentrations of compounds also displaying the m/z ratio of interest. To eliminate this possible positive error in the determination of biomarkers using the nonseparative method, the compounds displaying important interferences with the m/z ratio of interest can be added to the experimental design as new components to be modeled. The proposed HS-MS method is very useful for the fast determination of biomarkers in saliva samples as a first option. In the case of positive samples, GC/MS could be recommended, for confirmatory purposes, owing to the possible existence of interferences.
Table 4. Concentrations (mg/L) Predicted for the Detected Biomarkers in Saliva Samples Using the Nonseparative Method and That Based on Gas Chromatography compound MTBE
method
saliva sample
saliva sample
24 (volunteer)
25 (lung cancer)
HS-MS HS-PTV-GC/MS
dimethyl disulfide
HS-MS HS-PTV-GC/MS
styrene
HS-MS
0.3 ( 0.1 0.24 ( 0.08
HS-PTV-GC/MS limonene
HS-MS HS-PTV-GC/MS
2-ethyl-1-hexanol
HS-MS
0.21 ( 0.04
HS-PTV-GC/MS
0.17 ( 0.02
’ CONCLUSIONS HS-MS coupling with multivariate calibration constitutes a reliable technique for the determination of volatile biomarkers in
Figure 4. Total ion chromatograms for saliva samples 24 (a) and 25 (c). Extracted ion chromatograms (m/z 94 and m/z 57) corresponding to saliva sample 24 (b) and that corresponding to saliva sample 25 (d). 384
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saliva samples, and it could also be used to follow therapeutic levels of drugs or illicit drug use. The fact that no chromatographic separation is required allows a reduction in the analysis time. Biomarkers in saliva samples were predicted using calibration standards in ultrapure water, because no significant matrix effect was observed. This means that both time and costs can be reduced since it is not necessary to generate individual calibration models for each type of saliva. The ability of the calibration models to predict biomarkers in saliva samples was confirmed by GC/MS. The method is rapid and simple and has good precision and accuracy, and in view of the results, it could be considered as a suitable first option for determining volatile biomarkers in saliva. For positive samples, GC/MS could be used for confirmatory purposes. If necessary, typical interferences of some pathology could be added to the calibration design as new compounds to be modeled to eliminate the positive error in the determination of biomarkers using the nonseparative method. Once the sample vial is introduced into the system, all the other steps proceed in an automatic manner, so it could be considered suitable for routine analysis. It could also be extended to new biomarkers of occupational exposure or related to different diseases.
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’ AUTHOR INFORMATION Corresponding Author
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dx.doi.org/10.1021/ac2026892 |Anal. Chem. 2012, 84, 379–385
