Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or non-target approaches (i.e. when no reference standards are available) there is no information on retention time (RT) and collision cross section (CCS) values to facilitate identification. In-silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) was evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the empirical data. The RT model (R2=0.855) showed a deviation between predicted and empirical data of ± 2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using CCSH model (R2=0.966) was ± 4.05% with 95% confidence intervals. The CCSH model was also tested for the prediction of deprotonated molecules resulting in deviations below ± 5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCSNa, R2=0.954) with deviation below ± 5.25% for the 95% of the cases. The developed models have been incorporated in an open access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data.
Article
Prediction of Retention Time and Collision Cross Section (CCSH+, CCSH- and CCSNa+) of emerging contaminants using Multiple Adaptive Regression Splines
https://doi.org/10.21203/rs.3.rs-1249834/v1
This work is licensed under a CC BY 4.0 License
Journal Publication
published 23 Oct, 2022
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Multivariate Adaptive Regression Splines (MARS)
collision cross section prediction
retention time prediction
liquid chromatography
ion mobility separation
high resolution mass spectrometry
In the last decade, considerable effort has been devoted to enhance the performance of high resolution mass spectrometry (HRMS) suspect screening (SS) and non-target screening (NTS) strategies [1–3]. The instrumental improvements of HRMS instruments has required the development of more sophisticated algorithms to be able to handle the large amount of data generated [3, 4]. Therefore, the development of open-access scripts for data processing and in-silico prediction tools represents a step-forward into the applicability of SS and NTS in wide-scope campaigns by facilitating the identification process [5–7]. Furthermore, the establishment of community-adopted levels of confidence for the identification of compounds using chromatography coupled to HRMS has been of paramount importance for the comparison of data across studies [8].
Recently, ion mobility separation (IMS) coupled to HRMS instruments (IMS-HRMS) has proven promising for SS and NTS strategies [9]. It permits, in theory, to resolve co-eluting compounds with same nominal or exact mass that could not be previously separated with solely the chromatographic method, such as isobaric or isomeric compounds [9–11]. Moreover, it allows the removal of mass spectrometric peaks that do not correspond to the feature of interest, which is particularly beneficial in data independent acquisition (DIA) experiments [9, 10, 12]. As a consequence, there is a reduction in the necessity of data-dependent analysis since the full-spectrum HRMS acquisition can be filtered on both RT and ion mobility data [12, 13].
Collision Cross Section (CCS) values, derived from drift time (DT) measured by IMS, are known to be system and matrix-independent and, therefore, empirical CCS data can be included in home-made or online databases with an expected deviation below 2% for most cases [9, 14, 15]. However, this is not the case for chromatographic retention time (RT) which cannot easily be compared between instrumental configurations. Thus, reference standards are practically required for building a home-made database. However, SS and NTS strategies for the identification of emerging contaminants are commonly applied prior to the acquisition of the corresponding reference standards [1, 3] and, therefore, lacking any information on empirical RT and CCS. In this sense, in-silico prediction tools of either chromatographic retention data or ion mobility data are of great utility to decrease the number of candidates to investigate and, therefore, increase the chance of correct identification of features [6].
Several studies have predicted RT [6, 16–21], CCS values [7, 22–28] or both [13]. Predictors of RT have been developed mainly to model RT data in reverse-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) with prediction accuracy between approximately ± 1 to ± 3 min. However, there is no clear agreement in the literature on how to express the prediction accuracy of the models or which should be the most appropriate statistical descriptor representing the prediction power of the system developed [6]. Although CCS could be theoretically modelled from the three-dimensional and chemical structure using super-computing systems [27, 29–31], data-driven predictive models have also been developed showing predictive accuracies in the range of 3 –6 % for Travelling Wave Ion Mobility instruments (TWIMS) [22, 24, 25, 28] and Drift Tube Ion Mobility instruments (DTIMS) [23, 24, 26]. Similar prediction accuracy was obtained by Mollerup et al. in their study for the simultaneous prediction of RT and CCS [13]. However, these data-driven models were fed with data generated using different instruments depending on the output parameter. For the RT prediction they used data gathered from an ultra-high performance liquid chromatography (UHPLC)-HRMS instrument, while for CCS prediction they modelled CCS data generated with a UHPLC-IMS-HRMS instrument. Since RT variations could probably be observed across instruments, the utility of predicted RT in the identification of UHPLC-IMS-HRMS features is limited.
In general, the reported models were based on univariate or multivariate regressions [20, 28], artificial neural networks (ANNs) [13, 18, 21, 22, 24], quantitative structure-retention relationships (QSRR) [6, 17, 32], supported vector regression (SVR) [23, 26] or statistical analysis [25, 28]. However, there has been no prior exploration of Multivariate Adaptive Regression Splines (MARS) for the prediction of RT and CCS. MARS is a multivariate non-parametric regression procedure that was first proposed by Friedman [33]. One of the biggest advantages of MARS compared to the ‘black box’ methods of ANNs is that they are easy to interpret, with the interactions between variables clearly indicated [34]. MARS has previously been applied in the chemical sciences for quantitative structure-retention relationships [35]. However, the application of MARS for the prediction of chromatographic and ion mobility data of emerging contaminants has not previously been evaluated and reported in the literature.
In this work, a prediction model for both RT and CCS has been developed using MARS for the identification of candidates in SS and NTS strategies using UHPLC-IMS-HRMS. To facilitate other laboratories implementing this predictive tool in their workflows, a free online-available application has been released. This is, to best of the authors knowledge, the first application of MARS for the prediction of RT and CCS data. Additionally, it is the first parallel RT and CCS predictive model for the same instrument facilitating the identification process of emerging contaminants in SS and NTS strategies.
2.1 Chemicals and materials
A set of 556 reference standards encompassing illicit drugs, hormones, mycotoxins, new psychoactive substances, pesticides and pharmaceuticals were injected for the development of a CCS and RT library [9]. Table S1 of the Supporting Information shows the complete set of compounds used in the study with their SMILES (simplified molecular-input line-entry system) representation, and measured RT and CCS data. This database is also available on the Zenodo online repository [36]. Within this dataset, 477 protonated adducts ([M+H]+), 169 deprotonated adducts ([M-H]-) and 249 sodium adducts ([M+Na]+) were used for the development and validation of the CCS predictive models.
2.2 Instrumentation
Retention time and CCS data were obtained with a Waters Acquity I-Class UPLC system (Waters, Milford, MA, USA) coupled to a VION IMS-QTOF mass spectrometer (Waters, Milford, MA, USA), using an electrospray ionization (ESI) interface operating in positive and negative ionisation mode and following the method presented in Celma et al. (2020) [9].
The chromatographic column used was a CORTECS® C18 2.1 x 100 mm, 2.7 µm fused core column (Waters) at a flow rate of 300 μL min-1. Gradient elution was performed using H2O (A) and MeOH (B) as mobile phases, both with 0.01% formic acid. The percentage of B was initially set to 10%, and it was immediately linearly increased to 90% over 14 min, followed by a 2 min isocratic period, and then returned to initial conditions (at 16.1 min) with a 2 min equilibration of the column. The total run time was 18 min. The injection volume was 5 µL.
A capillary voltage of 0.8 kV and cone voltage of 40 V were used. The desolvation temperature was set to 550 °C, and the source temperature to 120 °C. Nitrogen was used as drying and nebulizing gas. The cone gas flow was 250 L h-1 and desolvation gas flow of 1000 L h-1. The column temperature was set to 40 °C and the sample temperature to 10 °C. MS data were acquired using the VION in HDMSe mode, over the range m/z 50-1000, with N2 as the drift gas, an IMS wave velocity of 250 m s-1 and wave height ramp of 20-50 V. Leucine enkephalin (m/z 556.27658 and m/ z 554.26202) was used for mass correction in positive and negative ionization modes, respectively. Two independent scans with different collision energies were acquired during the run: a collision energy of 6 eV for low energy (LE) and a ramp of 28-56 eV for high energy (HE). A scan time of 0.3 s was set in both LE and HE functions. Nitrogen (≥ 99.999%) was used as collision-induced dissociation (CID) gas. All data were examined using an in-house built accurate mass screening workflow within the UNIFI software (version 1.9.4) from Waters Corporation.
2.3 Retention time and Collision Cross Section Modelling
2.3.1 Molecular descriptors
A total of 1666 molecular descriptors were downloaded from Dragon v5.4 integrated within OChem website (Online Chemical Database with modelling environment, www.ochem.eu) [37]. The complete set of descriptors for the molecules used in the study is available in Table S1.
2.3.2 Prediction model
Multivariate Adaptive Regression Splines (MARS) analysis was applied to predict both RT and CCS for protonated adducts ([M+H]+) in a single multivariate model. Additionally, univariate models for individual RT and CCS for protonated adducts ([M+H]+) (CCSH) and sodium adducts ([M+Na]+) (CCSNa) were also performed. Due to the expected low correlation between RT and CCS (r=0.354), a multivariate model was not considered essential. As a further justification for this decision, the cross-validated R2 values for the multivariate model were 0.798 for RT and 0.964 for CCSH. This suggests instability on the data that is varying the accuracy of the model fits (particularly for RT). Therefore, the development of a multivariate MARS model able to predict simultaneously RT and CCS simultaneously was discarded.
MARS was able to select the most suitable molecular descriptors for each model (Table 1) and predictive interval bands were constructed for the univariate cases assuming a linear model variance structure. To meet this assumption, the square root of RT was modelled.
The CCSH prediction model was also explored for the prediction of CCS for deprotonated adducts ([M-H]-) and sodium adducts ([M+Na]+). CCSH accurately modelled [M-H]- data, but could not predict data at acceptable levels of accuracy for [M+Na]+. Therefore, a exclusive univariate model was considered for the prediction of CCS data for sodium adducts (CCSNa).
All analyses were complete using R [38] and MARS analysis was completed using the earth package with variance structure defined using the linear model (lm) option [39].
3.1. Development and validation of prediction models
3.1.1. Individual RT and CCS model development
There is no assumption of an underlying variance structure with the multivariate MARS analysis, and there was no facility to define one within the earth package at the time of implementation. However, for the univariate analyses, a linear model variance structure was defined. This meant the standard deviation was estimated as a function of the predicted response and, hence, allowed for the construction of prediction intervals.
It is essential to use prediction intervals, rather than confidence intervals, in cases where the goal is to predict future values. A prediction interval is wider than a confidence interval and, at the 95% level, will provide bounds within which 95% of predicted values should fall.
All analyses considered the whole set of 1666 molecular descriptors as possible inputs to be used in the models. The assumptions of normality, linearity and homoscedasticity were assessed for the univariate models which held those assumptions. The univariate MARS fit to RT violated the assumptions of linearity and homoscedasticity, so a square root transform was applied. This then reasonably met assumptions.
In summary, three different univariate models were developed for the prediction of RT (Equation 1), CCS data for (de)protonated molecules (CCSH) (Equation 2) and CCS data for sodium adducts (CCSNa) (Equation 3). As an example and to assist with interpretation, in equation 1, the term 0.099·max(0,(nDB-3)) is equal to 0 for nDB ≤ 3, and equal to 0.099·(nDB-3) for nDB > 3.
The univariate models obtained a cross validated R2=0.855 for the RT model, R2=0.966 for the CCSH model and R2=0.954 for the CCSNa model. Table 1 reveals that the univariate models (RT and CCSH) do not share a single descriptor, lending weight toward the argument that univariate models provide better fits to the data than previously explored multivariate model.
3.1.2. RT, CCSH and CCSNa model validation
MARS models were fitted using a 3-fold cross validation with thirty iterations. This procedure splits the data into three sections, fits the model to two of those sections (training data) and then tests the accuracy of the resulting model on the final section (test data). This procedure is then repeated thirty times, each time randomly dividing the data into three sections. The measure of accuracy used to assess goodness of fit is the cross-validated R2, which looks at the average R2 value obtained across all thirty iterations when the model was fit to the test data. This value is usually lower than the R2 for the best model fit but dramatic changes suggest volatility in the data or overfitting in the modelling procedure.
In order to perform an additional model validation and to obtain an overview of the model performance, RT and CCS data was predicted for the molecules used for model development. By comparing predicted and empirical RT data (Figure 1A, top), it was observed that the average deviation obtained using RT model (eq. 1) was ± 0.72 min as shown in Table 2. Yet, 95% of the predictions fell within ± 2.32 min. Additionally, it could also be observed that deviations in predicted data distributed normally around 0% deviation (marked as a red line in Figure 1A, bottom) The prediction accuracy obtained is an improvement for the 95% intervals in previously developed models (± 4.0 min using logKow predictor [20], ± 2.80 min using ANNs [21]) and in line with the model developed by means of ANN by Mollerup et al. (over ± 2 min deviation) [13]. The developed model herein presented also improves the prediction accuracy compared to Barron et al. where they obtained average deviation of ± 1.02 min [18]. As another way of presenting prediction accuracy, Figure 2 plots the predicted vs. empirical data with the 95% prediction intervals (blue coloured area) for the univariate MARS analysis of the . Approximately, only 8% of predicted RT were more than 2 min away from empirical ones.
Prediction accuracy for CCS data was also studied. The deviation observed for CCS data of [M+H]+ using CCSH model averaged ± 1.23 %, being ± 4.05 % within 95% of the cases (Table 2). Figure 1B, bottom shows that deviations randomly distributed around 0% (marked as a red line) value without biasing predicted data. When compared with previous models, CCS data for protonated molecules could be predicted using ANNs with an accuracy of ± 5 – 6% for 95% of the cases [13,22] or slightly over ± 5 % deviation (95% confidence interval) using machine learning [25]. Figure 3A shows the 95% prediction intervals (blue coloured area) for the univariate MARS analysis on CCSH model. The blue lines are placed at predicted values ± 2 Å2 and the purple are ± 5 Å2. It is clear that the model is still predicting well at higher values. However, since there is less data, the prediction intervals are much larger to accommodate the uncertainty. This vast improvement in the accuracy could be explained because of the larger database used for the model development as well as the better fitting of empirical data with MARS than ANNs.
Additionally, application of the CCSH model for the prediction of CCS values for deprotonated molecules was tested, yielding highly accurate predictions (Figure 1C, top). By predicting mobility data for a set of 169 molecules ionized in negative mode, it was observed that the differences between the observed and predicted CCS for the [M-H]- fell, 95% of the time, within -13.4 and 9.3 Å2, with a slight tendency to under-predict CCS values (Figure 1C, bottom). In relative terms, average deviation for [M-H]- data was ± 2.79 % (± 5.86% for the 95% of the cases, Table 2). Although these deviations seem larger than the ones observed for [M+H]+ data, this increase in the deviations observed for [M-H]- was expected since the model was developed with [M+H]+ data. However, it was assumed that the predictions of CCSH model developed with [M+H]+ data could also be extrapolated to the prediction of CCS data for [M-H]-, as no remarkable improvement was expected if a model was exclusively developed for deprotonated molecules.
Ideally, a unique model for the prediction of CCS for (de)protonated molecules and sodium adducts was intended. Therefore, the CCSH model was also tested against [M+Na]+ data. However, high deviations were observed (± 4.77 % average, ± 10.86 % for the 95% of the cases, Table 2) which could be expected due to the likely higher impact of the volume of the sodium atom in the overall CCS of the molecule. In light of this data, [M+Na]+ data required a separate model for CCS prediction that was different to the one initially developed. The procedure for CCSNa model development was equivalent to the process described above (section 2.3) but using as input a dataset of 249 CCS values for [M+Na]+ ions. The accuracy of the model was evaluated by also comparing predicted and empirical data (Table 2). Prediction deviations were ± 2.08 % on average (± 5.25 % for the 95% of the cases) showing a great improvement compared with predicted data using the CCSH model. Figure 3B depicts the predicted vs. empirical CCS values comparing the 95% prediction intervals (blue coloured area) for the univariate MARS analysis on CCSNa model. The fact that different predicted values can be obtained for both protonated molecules and sodium adducts is of great help for empirical observations of both species for a suspect substance. Hence, increased confidence on the tentative identification can be garnered by matching both of the CCS values observed with predicted data.
The CCSNa model herein presented also improves the prediction accuracy of previously developed model by the authors [22]. In that work, we evaluated the performance of the ANN predictive model for sodium adducts finding that deviations between predicted and empirical data were below 8.7% for the 95% of the cases. However, the development of an exclusive model for the sodium adducts by MARS improves the prediction accuracy.
3.1.3. Blind testing of the models
Several reference standards were purchased from different research projects during the development of the predictors based on MARS. Hence, they were not included in the training and validation datasets used. These compounds were used to verify the utility of our prediction models for chemicals not previously considered in the training steps. Thus, model applicability can be extrapolated for upcoming RT and CCS predictions of real suspect compounds. Therefore, we calculated deviations between predicted and empirical data for this dataset, and compared the observed deviations with previously calculated accuracies at different percentiles (shown in Table 2). Table 3 depicts the empirical and predicted values of RT and CCS for the different adducts observed for the additional set of 25 reference standards. Moreover, the deviation between empirical and predicted is shown and as it can be observed the RT predictions are generally in agreement with the empirical data with the 95th percentile of the observed deviations (± 4.15 min) being in the same range than that observed during validation. Furthermore, the vast majority of CCS values for [M+H]+ are in agreement with the values calculated using the CCSH model. For these compounds, 95% of the cases showed deviations below ± 3.71 %, yielding even better results than the initial database during model validation. Only 3,4-dichloroaniline shows a deviation greater than 4%, which could be explained by the small CCS value calculated. When evaluating CCSNa, higher deviations are observed concretely for the case of di(2-ethylhexyl) terephthalate and vildagliptin (-8.61% and 8.74%, respectively). These deviations could be explained because of particular chemical structures of the molecule such as the presence of an adamantyl group in vildagliptin, which has a large and rigid structure, or the high rotatability of alkyl chains in the di(2-ethylhexyl) terephthalate. However, if these adducts would be treated as outliers, 95% of the CCSNa values show deviations of ± 3.15 %, which is in great accordance with the data obtained during method validation. Finally, for [M-H]-, a small set of molecules was gathered, and all of them fit well within the ± 5.8 % deviation.
3.2. Open access prediction platform
To aid future researchers working with UHPLC-IMS-HRMS, a free online webpage incorporating these models has been released. The models are available for the scientific community through https://datascience-adelaideuniversity.shinyapps.io/Predicting_RT_and_CCS/ . Figure 4 illustrates the layout of the online platform for the prediction of RT and CCS for both (de)protonated molecules or sodium adducts.
The operational of the platform is user-friendly and easy-to-follow. As an example, the step-by-step method to obtain prediction for omeprazole is shown. First, selection of which parameter is going to be predicted need to be done (Figure 4A). In this case, CCS for protonated molecule is selected by indicating ‘Select Response: Collision Cross Section’ and ‘Sodiated: No’. After downloading the appropriate descriptors for the molecule of interest using Dragon v5.4 integrated within OChem (www.ochem.eu) [37], those can be added in the corresponding editable fields (Figure 4B). The CCS value can, then, be predicted and the output is shown together with their corresponding prediction intervals (Figure 4C). In this case, the CCS predicted value for the protonated molecule of omeprazole is 181.51 Å2 with a prediction interval of 171.93 – 190.08 Å2. The empirical value for [M+H]+ for omeprazole is 180.58 Å2, denoting that the prediction only deviated 0.52% from the empirical value.
The ease of prediction as well as the open access for this online platform is of great help for those researchers working on UHPLC-IMS-HRMS instruments who do not have an in-house developed prediction model.
Three different prediction models using Multiple Adaptive Regression Splines have been developed for the prediction of RT, CCS for (de)protonated molecules and CCS for sodium adducts. This is the first application of MARS for the prediction of RT and CCS data. In addition, the reported models are the first parallel prediction of RT and CCS data for the same instrument, facilitating the identification process of chemicals of emerging concern in SS and NTS strategies. The developed predictive models make use of a set of 26 molecular descriptors to predict RT and/or CCS values. The prediction accuracy achieved with these models bettered previously reported models in the literature by reducing the deviation between predicted and empirical to ± 2.32 min for RT, ± 4.05% for CCS of protonated molecules, ± 5.86% for CCS of deprotonated molecules and ± 5.25% for CCS of sodium adducts (95% confidence intervals). Additionally, a free access online platform has been released to enable the application of these models to third-party laboratories interested in predicting RT and CCS data.
Acknowledgements
A. Celma acknowledges the Spanish Ministry of Economy and Competiveness for his predoctoral grant (BES-2016-076914). L. Bijlsma acknowledges his fellowship funded by “la Caixa” Foundation. The project that gave rise to these results received the support of a fellowship from ”la Caixa” Foundation (ID 10 0 010434). The fellowship code is LCF/BQ/PR21/11840012. Authors from University Jaume I acknowledge the financial support of Spanish Ministry of Science, Innovation and Universities (RTI2018-097417-B-100), of Generalitat Valenciana (Research Group of Excellence Prometeo 2019/040) and of University Jaume I of Castellón, Spain (project UJI-B2018-55 and UJI-B2020-19).
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Table 1: Descriptors needed for each of the univariate MARS models for retention time (RT) and collision cross section (CCSH and CCSNa). Note that there are no similarities between the three univariate models.
|
Molecular descriptors |
||
|
RT |
CCSH |
CCSNa |
|
ALOGP |
AMR |
AMR |
|
ALOGPS_LogP |
L1m |
Har2 |
|
BEHm4 |
LPRS |
MAXDN |
|
GATS1m |
MDDD |
Mor17m |
|
Mor16m |
nRCHO |
nR09 |
|
N-068 |
PCR |
piID |
|
nDB |
Whetp |
QXXv |
|
nRNHR |
QZZm |
|
|
O-059 |
RDF065v |
|
|
STN |
ZM1v |
|
|
ALOGP: Ghose-Crippen octanol-water partition coefficient (logP) (calculation based on Viswanadhan et al. (1989) [40]; ALOGPS_LogP: Ghose-Crippen octanol-water partition coefficient (logP) (calculation based on Tetko and Tanchuk (2002) [41]; AMR: Ghose-Crippen molar refractivity; BEHm4:highest eigenvalue n. 4 of Burden matrix / weighted by atomic masses; GATS1m: Geary autocorrelation - lag 1 / weighted by atomic masses; Har2: square reciprocal distance sum index; L1m: 1st component size directional WHIM index / weighted by atomic masses; LPRS: log of product of row sums; MAXDN: maximal electrotopological negative variation; MDDD: mean distance degree deviation; Mor16m: 3D-MoRSE - signal 16 / weighted by atomic masses; Mor17m: 3D-MoRSE - signal 17 / weighted by atomic masses; N-068: Al3-N atom-centred fragment; nDB: number of double bonds; nR09: number of 9-membered rings; nRCHO: number of (aliphatic) aldehydes; nRNHR: number of secondary (aliphatic) amines; O-059: Al-O-Al atom-centered fragment; PCR: ratio of multiple path count over path count; piID: conventional bond-order ID number; QXXv: Qxx COMMA2 value / weighted by atomic van der Waals volumes; QZZm: Qzz COMMA2 value / weighted by atomic masses; RDF065v: radial distribution function - 6.5 / weighted by atomic van der Waals volumes; STN: spanning tree number (log); Whetp: Wiener-type index from polarizability weighted distance matrix; ZM1v: first Zagreb index by valence vertex degrees [42]. |
||
Table 2. Deviations at percentiles 50 (average), 95 and 99 for the predicted RT and CCS data during model validation.
Model |
Average deviation |
Deviation at 95% |
Deviation at 99% |
|
|
RT |
± 0.72 min |
± 2.32 min |
± 3.82 min |
|
|
CCSH |
[M+H]+ |
± 1.23 % |
± 4.05 % |
± 6.33 % |
|
[M-H]- |
± 2.79 % |
± 5.86 % |
± 8.39 % |
|
|
[M+Na]+ |
± 4.77 % |
± 10.86 % |
± 12.80 % |
|
|
CCSNa |
[M+Na]+ |
± 2.08 % |
± 5.25 % |
± 6.86 % |
Table 3. Empirical and predicted values of RT and CCS for additional compounds not initially included in datasets. Investigation of the deviation of predicted values.
|
Compound |
Retention Time (min) |
CCSH for [M+H]+ |
CCSNa for [M+Na]+ |
CCSH for [M-H]- |
||||||||
|
Emp. |
Pred. |
Dev (min) |
Emp. |
Pred. |
Dev (%) |
Emp. |
Pred. |
Dev (%) |
Emp. |
Pred. |
Dev (%) |
|
|
(-)-Cotinine |
0.87 |
3.05 |
2.18 |
141.48 |
136.12 |
-3.79% |
||||||
|
3,4-dichloroaniline |
7.92 |
6.21 |
-1.71 |
137.10 |
125.87 |
-8.19% |
||||||
|
3-Hydroxyphenyl diphenyl phosphate |
11.09 |
10.36 |
-0.73 |
174.31 |
178.94 |
2.66% |
184.84 |
189.67 |
2.61% |
180.89 |
178.94 |
-1.07% |
|
5,6-Dimethylbenzotriazole |
6.74 |
4.38 |
-2.36 |
129.73 |
127.21 |
-1.94% |
129.38 |
127.21 |
-1.68% |
|||
|
8-hydroxyquinoline |
1.51 |
4.57 |
3.06 |
125.01 |
123.54 |
-1.18% |
||||||
|
Amisulpride |
2.46 |
1.99 |
-0.47 |
193.15 |
189.60 |
-1.84% |
||||||
|
Antiblaze V6 |
10.84 |
14.49 |
3.65 |
208.45 |
207.32 |
-0.54% |
208.45 |
212.12 |
1.76% |
|||
|
Benzotriazole |
3.50 |
2.75 |
-0.75 |
121.49 |
117.94 |
-2.92% |
||||||
|
BClPHP phosphate a |
8.07 |
7.69 |
-0.38 |
159.22 |
157.05 |
-1.36% |
165.14 |
166.24 |
0.67% |
|||
|
Caffeine |
3.08 |
1.94 |
-1.14 |
136.62 |
136.37 |
-0.18% |
||||||
|
Chlorotoluron |
2.54 |
6.79 |
4.25 |
146.29 |
146.00 |
-0.20% |
155.35 |
157.96 |
1.68% |
|||
|
Citalopram |
6.49 |
5.21 |
-1.28 |
179.10 |
184.48 |
3.01% |
||||||
|
Di(2-ethylhexyl) terephthalate |
16.86 |
15.02 |
-1.84 |
216.36 |
197.07.23 |
-8.61% |
||||||
|
Diphenyl hydrogen phosphate |
12.46 |
5.06 |
-7.41 |
152.45 |
151.61 |
-0.55% |
161.58 |
162.65 |
0.66% |
152.18 |
151.61 |
-0.38% |
|
Diphenylcresyl phosphate |
7.36 |
11.07 |
3.72 |
175.28 |
178.08 |
1.60% |
||||||
|
Metolachlor ESA b |
7.89 |
4.99 |
-2.90 |
168.38 |
171.29 |
1.73% |
175.57 |
179.13 |
2.03% |
174.30 |
171.29 |
-1.73% |
|
Metoxuron |
5.98 |
7.04 |
1.06 |
149.83 |
150.62 |
0.53% |
158.51 |
161.17 |
1.68% |
|||
|
Mono(2-ethylhexyl) phthalate |
12.73 |
11.75 |
-0.98 |
170.91 |
167.767215 |
-1.84% |
||||||
|
Monuron |
6.68 |
5.67 |
-1.01 |
140.59 |
142.94 |
1.67% |
||||||
|
Nicotine |
0.69 |
1.11 |
0.42 |
138.34 |
134.77 |
-2.58% |
||||||
|
Niflumic acid |
11.51 |
10.86 |
-0.65 |
157.46 |
157.79 |
0.21% |
156.92 |
157.79 |
0.55% |
|||
|
Pirbuterol |
1.30 |
1.28 |
-0.02 |
153.78 |
156.91 |
2.04% |
160.02 |
165.52 |
3.44% |
|||
|
Prometon |
6.74 |
7.50 |
0.76 |
156.67 |
155.56 |
-0.71% |
||||||
|
Trietazine |
10.81 |
8.91 |
-1.90 |
150.63 |
151.12 |
0.33% |
||||||
|
Vildagliptin |
1.38 |
1.67 |
0.29 |
176.98 |
174.62 |
-1.33% |
172.29 |
187.35 |
8.74% |
|||
|
a Bis(1-chloro-2-propyl) 1-hydroxy-2-propyl phosphate; b Metolachlor ethane sulfonic acid |
||||||||||||
There is NO Competing Interest.
- TableS1.xlsx
Table S1. Complete set of compounds used in the study (SMILES, empirical RT and CCS, and molecular descriptors)