HDX-CBZ and EPX-CBZ by-products were selected based on their appearance as the main products after electrochemical degradation of carbamazepine. The separation profile for both by-products was presented in (Figure 1). The intensity of the peak area is not the same for both products and varied strongly. Therefore, this variation may be due to the diversity of physicochemical properties under electrospray ionization conditions.
Many solvents have been applied to reconstitute of analytes after solid phase extraction and before injection to LC-TOF/MS. However, pure MeOH (Hernando et al. 2004), 25% MeOH in water (Gros et al. 2006) and 0.1% acetic acid in water plus MeOH, were used to reconstitute the residues of pharmaceuticals after SPE (Vieno et al. 2006) 27]. In the present study, MeOH-water (1:90) was used to reconstitute the residual of HDX-CBZ and EPX-CBZ after SPE.
ToF screening and confirmation
Liquid chromatography/time-of-flight/mass spectrometry is a developing technique involving the application of the electrospray ionization method to enhance the detection method. Furthermore, a sharp and nice peak exhibited good chromatographic separation in terms of sensitivity and selectivity for the quantification of target compounds in the samples. However, the by-products were analyzed in positive ionization mode (PI) because it exhibited a high signal-to-noise ratio for both by-products. The quantification and monitoring in treated samples were also considered in the PI mode. For enhancing the sensitivity of detecting trace levels of the pharmaceuticals using LC-TOF/MS, a very narrow range window of 0.02 Da has been applied to extract a target peak. It was observed that reducing the mass window plays an important role to increase the detection limit and avoiding any interferences from other organic compounds.
TOF/MS compartment has been applied for confirmation and quantification of the protonated molecular ion of an organic compound. This analysis was investigated by the elemental formula and mass errors, which has been achieved using the software of “Brucker Daltons Data Analysis program”. An example of EPX-CBZ has been presented in Figure 2.
However, the elemental formula of EPX-CBZ (C15H12N2NaO2) as sodium adduct molecular ion has a mass error of −2.9 ppm. In addition, some characteristics are shown in Table 2, including the mass measurement, retention time, mass error and elemental composition.
Table 2 Accurate mass measurements obtained by LC-TOF/MS for both by-products (HDX-CBZ and EPX-CBZ) after electrochemical oxidation treatment of carbamazepine
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)
Method validation
An external calibration curve has been built to exhibit the linearity represented by the correlation coefficient (R2). A wide range of concentrations ranged between 50 and 1000 µg/L for HDX-CBZ while it was ranged between 10 and 1000 µg/L for EPX-CBZ (Table 3 and Figure 3). Good linearity (R2 ≥ 0.993) was observed. For the calculation of instrumental quantification limit (IQL), the lowest concentration has been injected directly to the LC-ToF/MS, achieving a signal-to-noise ratio (S/N) of 10. HDX-CBZ and EPX-CBZ have IQL of 50 and 10 µg/L, respectively. The limit of quantification (LOQ) for the applied method was calculated using equation (1). They were 0.588 and 0.109 µg/L for HDX-CBZ and EPX-CBZ, respectively. Compared to the reported study by (Teixeira et al. 2013), the limits of quantification ranged between 25.9 and 39.0 µg/L, which is higher than the LOQs reported in the present study. The reason is related to the type of instrument used for method validation. However, LC-TOF/MS exhibited high sensitivity compared to the cyclic voltammetry method.
Table 3
method validation for both by-products (HDX-CBZ and EPX-CBZ)
Compound
|
Range conc. (µg/L)
|
Equation
|
IQL (µg/L)
|
LOQ (µg/L)
|
Recovery%*
|
HDX-CBZ
|
50-1000
|
y=67.47x+3692
|
50
|
0.588
|
85
|
EPX-CBZ
|
10-1000
|
y=481.5x-4453
|
10
|
0.109
|
92
|
* Recovery has been calculated for IQL values. |
Three concentrations (50, 100 and 200 µg/L) have been tested to investigate the inter-day repeatability and intra-day reproducibility in three replications. Accepted results have been obtained, in which the RSD% ≤ 6.7% for intra-day precision and 11.7% for inter-day precision as presented in Table 4. Accuracy was also considered in the present study, in which the recovery was good for both compounds through the SPE method.
Table 4
Intra-day precision and inter-day precision for both by-products (HDX-CBZ and EPX-CBZ)
By-product
|
Intra-day precision (mean±SD), n=3
|
Inter-day precision (mean.±SD), n=3
|
50 µg/L
|
100 µg/L
|
200 µg/L
|
50 µg/L
|
100 µg/L
|
200 µg/L
|
HDX-CBZ
|
50.7±1.53
|
99.3±2.52
|
201.7±2.08
|
51.0±2.65
|
98.7±3.79
|
202.7±3.21
|
EPX-CBZ
|
48.7±0.58
|
98.3±1.15
|
200.7±3.05
|
49.3±2.52
|
98.0±2.65
|
201.7±3.25
|
Effect of elution solvent
Three solvents have been examined to achieve the best recovery for both by-products. However, methyl tertiary butyl ether (MTBE), acetone and methanol were investigated separately. The lowest recovery was observed with MTBE (5 × 1 mL) while the best recovery was achieved with methanol (5 × 1 mL).
Both by-products were prepared in the concentration of IQL value for each one, then, subjected to SPE method after that eluted with 5 mL of solvent. The by-products HDX-CBZ and EPX-CBZ exhibited the highest recovery of 85% and 92%, respectively with methanol as eluent. However, low recoveries were observed with MTBA and acetone solvents compared to methanol. It could be concluded that methanol, a polar solvent, compared to other solvents, resulted in increased recovery of both by-products. Finally, methanol has been selected as the best eluent for both by-products as presented in Figure 4.
Identification of EDX-CBZ and HDX-CBZ by-products
Carbamazepine has been degraded electrochemically using different electrodes and different purposes as reported previously (García-Espinoza et al. 2018; Teixeira et al. 2013; Xu et al. 2021). The identification and quantification of the main by-products after electrochemical degradation of carbamazepine is necessary to study their fate using LC-TOF/MS before discharge to water bodies.
It is well known that in the electrochemical degradation process, a low concentration of by-products could be produced. However, the identification and quantification required an accurate and sensitive instrument, such as LC-TOF/MS.
Two main by-products (HDX-CBZ: 6.092 min and EPX-CBZ: 6.478 min) were generated during the electrochemical degradation of CBZ. It was reported that HDX-CBZ and EPX-CBZ have been formed as main by-products after electrochemical degradation of carbamazepine using novel blue-colored TiO2 nanotubes anode. The previous study was focusing on the qualitative analysis, not quantification since no standard for both by-products was calibrated and both by-products were detected as protonated molecular ions [M+H]+ (Xu et al. 2021). Figure 5 shows the mass fragmentation chromatogram of both by-products, HDX-CBZ and EPX-CBZ as sodium adduct molecular ion ([M+Na]+. High S/N ratio was obtained for both by-products due to the pre-concentration sample using solid phase extraction protocol (3 cc HLB cartridges) and also to the optimal choice for the mobile phase and elution program as explained in sections 3.2 and 3.3. A very narrow window of “0.02 Da” has been applied for the extraction of chromatographic peaks using the Bruker Daltonic Analysis software.
Monitoring the by-products after degradation process
It is well known that after the degradation of carbamazepine, the compound itself could be reduced with time due to the degradation process. The treated sample of carbamazepine has been retained and separated on a Gemini 5 µm NX 110Å C18 column (2 mm × 150 mm, Phenomenex) using liquid chromatography-time of flight/mass spectrometry. Carbamazepine chromatograms profile as extracted ion chromatogram (EIC) after 0, 20, 40 and 60 min of the electrochemical treatment process are well presented in Figure 6.
The efficiency of the electrochemical process has been evaluated by monitoring the by-products for 60 min. After 20 min of the electrochemical degradation process, two main by-products HDX-CBZ and EPX-CBZ were detected and analyzed using LC-TOF/MS at retention times of 6.092 and 6.478 min, respectively. The main HDX-CBZ and EPX-CBZ by-products were quantified based on the linear regression equation as mentioned previously. Both by-products HDX-CBZ and EPX-CBZ were formed at the maximum concentrations of 343 and 144 µg/L, respectively at 20 min of the electrochemical treatment, then, they were decreased to 17.2 and 9.8 µg/L, respectively at 40 min as shown in Figure 7. This behavior was in agreement with a previous study reported by (Xu et al. 2021). He noticed that at the first 20 min, the maximum yield of the by-products was achieved. After that, all by-products disappeared within 80 min.