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Use of Box Behnken Design for Development of High Throughput Quantitative Proton Nuclear Magnetic Resonance Experiments for Industrial Applications Ajeet Kumar,† Manish Gupta,† Avik Mazumder,*,† Krishna Mohan Poluri,‡ and Vepa K. Rao† †

Defence Research and Development Establishment, Gwalior 474002, India Department of Biotechnology, Indian Institute of Technology Roorkee, Roorkee 247667, India

‡

S Supporting Information *

ABSTRACT: The design of experiments has been used for the development of highly efficient and accurate industrial application of quantitative nuclear magnetic resonance (qNMR) spectroscopy. Since these factors are highly dependent on the choice of proper data acquisition and data processing parameters, in this work, seven data acquisition and data processing parameters (excitation pulse width/duration, central frequency, number of scans, sample temperature, acquisition time, line broadening function, and inter scan delay) were investigated and optimized for quantitative proton NMR (qHNMR) experiments to ascertain optimal values. Box Behnken design of experiments was used to arrive at these values by performing minimum number of experiments. To test this methodology, decontamination formulation number two (DS2), a universal field decontaminating reagent of highly toxic chemical warfare agents, was used as a model mixture. Quantification of its components (methyl cellosolve and diethylene triamine) was performed with respect to dimethyl sulfone (DMS) as an internal standard. Under the optimized conditions, ∼100% recovery and maximum signal-to-noise ratio (of DMS was 39908) were obtained within a short experiment time (∼15 min). Lower limits of detection and quantification for DETA were found to be 1.92 and 6.40 mg mL−1, whereas those for MC were found to be 0.89 and 2.86 mg mL−1, respectively. Finally, this method was applied for the analysis of 14 unknown samples of DS2. A t test conducted on these samples clearly indicated that there was no significant difference (p > 0.05) between the means of the results obtained from this qHNMR method and the earlier reported classical method of analysis. This clearly suggests that this novel and comprehensive methodology can be used as an independent analytical tool for quantification of the components of complex chemical mixtures.

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industrial raw materials and finished products. Under qNMR conditions, specific detector response is independent of the chemical nature, molecular weight, or molecular structure of the analyte. Integral or area under a particular signal (Ax) is determined by the number of nuclei (NX) within the field of view of the NMR probe invoking the signal14 (eq 1). Where, Cs is a constant of proportionality referred to as spectrometer constant:15,16

INTRODUCTION Since its development, NMR spectroscopy has been traditionally used for the identification of pure analytes. To achieve this goal, NMR experiments make use of J-coupling (for determining atom connectivity), nuclear Overhauser effect (for detecting atom proximity), and signal integration (for measuring atom count).1,2 Absence of memory effects, better spectral resolution (as compared to optical spectroscopic techniques), noninvasive, nondestructive, nonaggressive nature of analysis, and ability to handle diverse types of samples (viz. solid, liquid, gases, reactive/labile, corrosive, flammable, toxic, and volume limited samples)3−5 are the hallmarks of modern NMR spectroscopy. Rapid strides in NMR technology and increased availability of NMR spectrometers have led to short method development and equilibration time, high-throughput (unattended) analysis of the samples with high degree of accuracy, precision, repeatability, and reproducibility.6,7 These advances have led to widespread applications of NMR spectroscopy in various fields of industry and research.8−13 Nowadays, quantitative nuclear magnetic resonance (qNMR) spectroscopy is being increasingly used for quality control of © XXXX American Chemical Society

Ax = Cs × NX

(1)

Therefore, absolute quantity of the analytes can be determined by mapping one (or more) of the analyte signal(s) against the area of signal(s) of some reference standard (not necessarily of the same compound).17−21 Accuracy, precision, and robustness of the method depend on the acquisition of high-quality reproducible NMR spectra22 Received: December 5, 2016 Revised: February 13, 2017 Accepted: February 25, 2017

A

DOI: 10.1021/acs.iecr.6b04697 Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

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Industrial & Engineering Chemistry Research with good resolution, line shape, and acceptable signal-to-noise ratio.23 Although nonlinearity is generally not encountered due to high dynamic range of NMR detector, selection of proper parameters for sample preparation, data acquisition, and data processing is essential. Stability of analytes, inter- or intramolecular interactions (if any), viscosity, solubility, freezing point, chemical shifts, degree of peak overlap (resolution), etc. must be taken into account while qNMR experiments are performed. Because of diversity of samples that are subjected to qNMR spectroscopy and variation of these factors from sample to sample, a single set of rules is not applicable for all of them. Thorough understanding, human judgment, and prior experience are required for each sample type. Overoptimization of these parameters can lead to low utilization of machine resource, and underoptimization leads to poor accuracy and precision. During fine optimization of the experimental parameters (of sample preparation, data acquisition, and data processing), possible interactions between them must be taken into account. The traditionally used one factor at a time (OFAT) method of optimization is a time-consuming and resource-intensive process. Moreover, it does not bring out the statistical significance of the factors or reveal interfactor interactions (if any). To overcome the above problems, tools based on design of experiments (DoE) and multivariate response surface methods (RSM) have been developed.24 They make use of statistics to bring forth a set of cumulative desirable experimental conditions, and they can also predict the effects of input (factors) on the output (responses) without actually performing all the experiments. Several applications of RSM based DoE tools can be found in the field of analytical chemistry.25−29 We propose herein the first of its kind use of RSM based DoE for optimization of various experimental parameters for quantitative proton NMR (qHNMR) spectroscopy. Since our laboratory has been working on the detection, protection, and decontamination of highly reactive and corrosive chemical warfare agents, decontamination formulation number two (DS2) was chosen as a model mixture. This is a field decontamination formulation in use by armed forces around the world for decontamination of a wide spectrum of highly toxic chemical warfare agents.30 DS2 formulation consists of sodium hydroxide (2 ± 0.35%) dissolved in a mixture of methyl cellosolve (MC, 28 ± 4%) and diethylenetriamine (DETA, 70 ± 4%) (Figure 1).

Figure 2. Mechanism of decontamination of different chemical warfare agents by DS2 formulation.

Materials. DETA (>99.9% purity), MeC (>99.9% purity), sodium hydroxide (>99.9% purity), dimethyl sulfone (99.74 ± 0.24% purity), benzoic acid (>99%), calcium formate (>99%), 3,5-dinitrobenzoic acid (99%), 1,2,4,5-tetrachloro-3-nitrobenzene (>99% purity), 1,2,4,5-tetramethylbenzene (98%), maleic acid (>99%), Wilmad-PP7 NMR sample tubes, and deuterated NMR solvents (viz. pyridine-d5, chloroform-d1, methanol-d4, acetone-d6, and D2O) were purchased from Sigma-Aldrich (Milwaukee, USA). Class ‘A’ volumetric flasks (Schott Duran, Mumbai, India) and adjustable volume micropipettes (Eppendorf, Germany) were also used. Fourteen samples of DS2 were used for the study were taken from in-house inventory. The DoE and data analysis was performed by using Microsoft Excel (Microsoft Corporation, USA) and JMP 12 software (SAS Institute Inc., USA).

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EXPERIMENTAL SECTION Two 2 mL (100 mg/mL) stock solutions A and B were prepared in pyridine-d5 (pyr-d5) by accurately weighing DETA and MC, respectively. These solutions were step diluted to obtain eight working standard solutions (A’ and B’) containing 100.00−0.39 mg mL−1 of the analytes. Another 2 mL solution C containing dimethylsulfone (15 mg mL−1) was also prepared in pyr-d5. All qHNMR experiments were conducted on BRUKER AVIII 600 MHz NMR spectrometer equipped with a 5 mm broadband (BBFO) probe (Bruker Biospin, Fallenden, Switzerland). Instrument was controlled and data was recorded using Topspin 3.2 software (Bruker Biospin, Fallenden, Switzerland). Each sample was allowed to equilibrate for 5 min inside the NMR probe preheated to the respective temperature selected for analysis. To achieve good line shape and good signal-tonoise (S/N), the samples were field frequency locked and shimmed using TopShim gradient shimming tool of the Topspin 3.2 software. Two channels of the probe were tuned and matched to proton and carbon-13 frequencies. The NMR experiments were carried out in a nonspinning mode to avoid spinning sidebands in the NMR spectra. The NMR spectra were recorded by making use of four dummy scans followed by acquisition of requisite number of transients by using standard Bruker pulse sequence (zg0ig), wherein carbon-13 decoupling was performed using composite pulse decoupling scheme GARP4 (globally optimized alternating-phase rectangular pulses) only during acquisition time period. Inverse gated

Figure 1. Composition of DS2 formulation.

In this formulation, DETA breaks the crystal lattice of sodium hydroxide and rapidly solvates the sodium ions. The highly reactive “bare” hydroxide ions thus formed generate strong conjugate base (MCate) from MC. Rapid irreversible nucleophilic substitution or elimination reactions take with the highly toxic chemical warfare agents HD, VX, GA, or GB to form stable and nontoxic products31 (Figure 2). Hence, it is essential to determine the composition of this formulation to ensure its proper functioning. For such quantification, DoE was used to maximize signal-to-noise ratio (signal of DMS taken as reference) and obtain hundred percent recovery (of MC and DETA) within the shortest possible experiment time. B


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Industrial & Engineering Chemistry Research decoupling was used (zg0ig)32 for all the experiments to avoid distortions of NMR signals that may be caused by nuclear Overhauser effect (NOE). It was also essential to acquire the FID for sufficient length of time (AQ) to allow sufficient sampling and digitization of the decaying FID to obtain good spectral resolution and avoid inaccurate measurements of signal area due to truncation artifacts in the NMR spectra. Since in the quality control laboratories, a large number of samples have to be analyzed by qHNMR. Therefore, spooler function of Topspin 3.2 software was used to automate sample injection, shimming, and tuning-matching, setting up acquisition and processing parameters for qHNMR experiment, data processing, and sample ejection steps. The method helped to streamline qHNMR analyses and eliminate tedious and repetitive manual steps that are required for performing the qHNMR experiments. When different solvents, unusual resonance, and relaxation behavior of the analyte are encountered, these experiments can be easily run on multiple samples using high throughput robotic autosamplers with little modification. Therefore, they open up an immense possibility of applications for development of automated high throughput qHNMR spectroscopy. The samples were also subjected to standard NMR experiments (1H, 13C{1H}, 1H-13C HSQC, 1 H-13C HMBC, 1H-1H COSY) recorded at 25 °C to assign the NMR signals of DMS, MC, and DETA. System suitability tests were performed to ensure good magnetic field homogeneity and proper functioning of the NMR probe and electronics. 1H NMR spectrum of a standard sample containing 1% chloroform (in acetone-d6) was recorded at regular intervals. Experiments were performed only if width at half height of CHCl3 was less than 0.7 Hz. To select proper solvent and internal standard, sample preparation protocols were optimized. Scouting studies were performed wherein 10 μL aliquots of DS2 formulation were taken in separate NMR tubes and dissolved in 500 μL aliquots of different deuterated solvents (viz. chloroform-d1, methanol-d4, pyridine-d5, acetone-d6, and dichloromethane-d2). Although the use of CDCl3 and acetone-d6 in basic environment is not chemically the most appropriate, these experiments were performed to make sure that all possibilities have been considered for sample preparation. The NMR tubes were vortexed for 2 min and kept aside at room temperature for 30 min. This experiment was performed to observe signs of phase separation, reaction, or color change (if any). The samples that did not show any physical change (viz precipitation, discoloration and heating) were subjected to 1H NMR spectroscopy to observe signal resolution and chemical changes (if any). These experiments indicated that pyr-d5 was the most suitable solvent. The optimized sample preparation protocol (Figure 3) was used to record the qHNMR and other spectroscopic data (Table 1). Since DS2 is a highly corrosive and reactive formulation, it was essential to ascertain suitable internal standard and ensure its stability during NMR experiments. Appropriate quantities of different internal standards (viz. dimethylsulfone, benzoic acid, calcium formate, 3,5-dinitrobenzoic acid, 1,2,4,5-tetrachloro-3nitrobenzene, 1,2,4,5-tetramethylbenzene, and maleic acid) were added to the NMR samples. They were studied over a period of 1 week for detecting any change in color or consistency. The 1H NMR and COSY experiments were performed to detect appearance or disappearance or changes in peak area and chemical shifts with respect to an invariant ERETIC signal (Figure 4).

Figure 3. Flowchart depicting the protocol for preparing sample for performing qHNMR of DS2.

Adequate sample concentration is also essential for performing NMR experiments. This is mainly due to a small difference of the spin populations (ΔP) present in lower (Pα) and higher (Pβ) energy states.33 Hence, samples were prepared by taking different amounts of DS2 formulation (1−25 μL) into a 5 mm NMR tube followed by 100 μL solution of internal standard (DMS, ∼15 mg mL−1). Walls of the NMR tube were washed down with 500 μL of pyr-d5. The contents were subjected to vortexing for 2 min before each qHNMR experiment. To obtain good resolution for quantitation, 32 K time-domain data points (zero filled to 64K) were acquired for all the spectra. The receiver gain of 45 was found to be optimal, and it was used for all qHNMR experiments described herein.34 As mentioned earlier, optimization performed by experimentation followed by analysis of each response is a time-consuming process. The design of experiments (DoE) tools were used for the optimization of inter-related factors AQ, flip angle (α or P0), interscan delay (D1), and sample temperature (T). Height of lock signal and line shape and line width of the DMS signal (located at 3.11 ppm)35 were checked to ensure magnetic field homogeneity. Prior to Fourier transformation, each FID was multiplied with an appropriate value of exponential line broadening (LB) function. The value of LB was adjusted using the DoE approach by monitoring the S/N of DMS signal. Errors in qHNMR performed by using peak-ratio measurement can also be introduced due to errors in integration, phase correction, and stability of the field frequency lock. The regions of integral for each signal were selected carefully, and integration was performed by taking extended ranges (±0.1 ppm) of the signals to avoid any loss of signal, and the regions were saved in the data processing computer. All subsequent qHNMR spectra were processed by recalling these saved regions. DoE technique developed by George E. P. Box and Donald Behnken,36 like other response surface methodologies (RSM), is an empirical statistical tool. It evaluates relationships (if any) between a set of experimental factors and the results.37 Various mathematical and statistical tools are used for investigating combinations of two or more factors and their levels on the results. This is especially useful when interactions are not known or improved/optimal process parameters must be determined to make a process more robust. It makes use of modeling and analysis of problems to reveal if a response of interest is influenced by one or more of these input factors k1, k2, ..., kn. The correlation between the response (Y) and the input process parameters (k1 to kn) is used to analyze a process, described as Y = f (k1, k2, ..., kn) + ε, where f is the response C


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Industrial & Engineering Chemistry Research Table 1. 1H and 13C{1H} NMR Spectroscopic Data of MC, DETA, and DMS (Internal Standard)a

a

NMR data reported in pyridine-d5 solvent and tetramethylsilane as a calibrant of the chemical shift axis at 25 °C.

Figure 4. Representative 1H-NMR spectrum of DS2 sample containing internal standard (DMS) relevant signals of DETA, MC, and DMS (labeled) was used for evaluation of stability of the sample with respect to an ERETIC signal (at −1 ppm).

function, and ε is the residual error. The correlation between the response and the input variables can be described graphically as a surface of the k1, k2, ..., kn coordinates. An investigation of these relationships (response surfaces) can be used to optimize the factors to obtain a desired response.38 A comparison between the B−B design and other response surface designs (central composite and three-level full factorial design)39−41 has demonstrated that the B−B design is slightly more efficient than the central composite design but much more efficient than the three-level full factorial designs. In addition to this, the B−B has several advantages in terms of efficiency and accuracy24,36,38,42 and ease of execution. The chances of errors are lower in B−B design as it does not allow simultaneous setting of the factors at their highest or lowest levels at the same time. This is advantageous since the extreme experimental conditions may be prohibitive or impossible to attain because of instrumental constraints. The three-level B−B designs are formed by combining 2k factorials with incomplete block designs and are therefore advantageous to optimize multiple parameters of NMR experiments with a minimal basis set in a short time span. The additional advantages of this

approach were lower consumption of reagents and sample alongside considerably lesser number of experiments (faster turn-around time).

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RESULTS AND DISCUSSION To optimize the sample preparation protocol, experiments were first performed for selection of appropriate deuterated solvent. Phase separation was observed when solutions were prepared in dichloromethane-d2 and chloroform-d1, whereas signal overlaps were observed when the methanol-d4 and acetone-d6 were used as solvents. Pyridine-d5 was found to be the most suitable solvent as a homogeneous solution was obtained and it was not highly volatile. Solvent-suppression was not required and quantitative accuracy and precision could be achieved due to the absence of any strong signal from residual proton signal of pyridine-d5 as compared to those of the analytes and internal standard. Keeping these facts in view, all qHNMR experiments were performed using pyridine-d5. The internal standard method of quantification was used to completely avoid variation of results due change in sample tube diameter. Among the different internal standards (benzoic acid, D


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essential. The offset frequency was varied by keeping the spectral width constant to ensure uniform excitation of the spins of interest. Under these conditions, a constant value of “spectrometer constant” Cs (refer to eq 1) was obtained for all the spins, and signal areas were directly proportional to the number of nuclei within the field of view of the NMR probe. To ensure restoration of the spin populations, longitudinal/ spin−lattice relaxation time (T1) was determined by inversion recovery pulse sequence. Although the large values of interscan delay determine the experiment time, the shorter values affect the accuracy of qNMR experiments. The values of T1 are related to rotational correlation time of the analytes (i.e., on rational energy and viscosity of the sample solution). Temperature variation also affect the relaxation properties of the molecules leading to variation in the values of Cs of different components of a mixture. Therefore, these experiments were performed at three different temperatures (25, 32.5, and 40 °C) suggested by the B−B design (Table S1 and Figure 5). With the increase in temperature, the values of T1 showed a slight upward trend (except in the case of DMS). This could be attributed to reduction in viscosity of the sample solution and increase in rotational partition function of the analytes and shorter correlation time of the molecules, whereas in the case of DMS, the effect of temperature change on molecular mobility was negligible owing to its small molecular cross-section or due to strong hydrogen-bonding interactions with the matrix components. The value of Cs also depends on the values of spectral width (sw) and central frequency (O1P). Hence, the value of O1P was also optimized using DoE approach by holding the sw constant (12 ppm). Error in determining peak area due to incorrect baseline was corrected automatically. The experimental factors were investigated to ascertain their effect on the responses of DETA, MC, and DMS. Experiments were performed using 62 sets of experimental factors (k1 to k7), and the experiments were executed in randomized order as to avoid bias. The objective set for these experiments was to obtain 100% recovery of the analytes, maximum possible S/N, and minimum experiment time. To ascertain the effect of these factors and estimate the experimental error on the results on the selected response (Table S1, Supporting Information), three replicate measurements were performed. The upper and lower limits of the experimental factors were chosen according to the results obtained from the screening experiments previously carried out in our laboratory. The NMR samples consisted of DS2 formulation (10 μL), internal standard DMS dissolved in pyridine-d5 (100 μL, 15.86 mg mL−1), washed down with 500 μL of pyridine-d5 (total sample volume was 610 μL). Analyte recovery, signal-to-noise ratios, and time required for performing the qHNMR experiments were recorded. It is evident from Table S1 (refer to Supporting Information) that the factors that considerably affect the results are flip angle (P0, factor k1), number of scans (NS, factor k3), and interscan delay (D1, factor k7), whereas the other factors, namely central frequency (O1P, factor k2), sample temperature (TE, factor k4), acquisition time (AQ, factor k5), and line broadening function (LB, factor k6) do not have considerable effects on the recovery, experiment time, and S/N ratio. To determine the optimum conditions at which the surface response plots were drawn, similarly, prediction profiles were plotted for DETA, and MC surface response plots were plotted. The experimental conditions at which 100% recovery with minimal experiment time and maximum S/N were found to be maximum under the

calcium formate, 3,5-dinitrobenzoic acid, 1,2,4,5-tetrachloro-3nitrobenzene, 1,2,4,5-tetramethylbenzene, and maleic acid) to be used for qHNMR experiments, commercially available dimethylsulfone (DMS) was found to be the most suitable. This was mainly due to its good solubility, stability, low vapor pressure,43 and generation of a single intense well resolved NMR signal. Experiments were also performed to determine the optimum quantity of DS2 to be used for the qHNMR experiments. Homogeneous samples could be obtained when a 10 μL aliquot of DS2 was used. The line-shapes were good, and signal-tonoise ratios (>20) were obtained. This suggested that the chosen composition was appropriate (already mentioned in the Experimental Section). Dynamic range problem was avoided by selecting a matching concentration of DS2 and DMS in the NMR samples. Once the solvent, sample quantity, and internal standards were finalized, stability of the sample solution was ascertained. These studies indicated that neither any additional signals appeared nor was there any change in appearance of the sample. The chemical shifts of the analytes and the internal standard also remained unchanged during these tests. On the basis of reported literature,2 seven factors like flip angle (P0, factor k1), central frequency (O1P, factor k2), number of scans (NS, factor k3), sample temperature (TE, factor k4), acquisition time (AQ, factor k5), line broadening function (LB, factor k6), and interscan delay (D, factor k7) were chosen as the critical variables. Each of these factors was investigated at high, middle, and low levels of each variable, which were designated as +1, 0, and −1, respectively, as shown in Table 2. Table 2. Pattern Descriptors for Seven Factors B−B Design Factors Suggested by B−B Design levels k

factors

high (+)

mid (0)

low (−)

1 2 3 4 5 6 7

P0 (μs) O1P (ppm) NS T (deg C) AQ (μs) LB D1 (sec)

12.25 4.00 48.00 40.00 4.50 3.00 60.00

7.66 2.50 28.00 32.50 3.00 1.50 30.50

3.06 1.00 8.00 25.00 1.50 0.00 1.00

More than 80% of the NMR experiment time is spent on the interscan or pulse repetition delay (τ). During this period, the spectrometer waits until spin population, that is, longitudinal magnetization, is restored (τ ≥ 5 × T1).44 The duration τ is related to the angle (αe) by which the bulk magnetization is flipped, by Ernst equation (cos αe = e−τ/T1). Although a 90° excitation pulse provides maximum transverse magnetization, a balance between pulse angle and pulse repetition delay (D1) had to be established to obtain quantitative NMR spectra within the shortest possible experiment time. Keeping in view this dependence of flip angle on the pulse repetition time, different flip angles were taken into account for our experiments. As suggested by the DoE software tool, the flip angles were varied between 90° (12.25 μs) and 22.5° (3.06 μs). To obtain quantitative accuracy and precision, uniform excitation of the spins throughout the entire spectral width, sufficient pulse repetition time (to restore spin populations), avoidance of nuclear Overhauser effect (NOE) during broadband decoupling, and sufficiently large S/N ratios (>10) are E


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Figure 5. Effect of temperature on T1 of protons (determined using standard inversion recovery experiment).

Table 3. Optimized Parameters Obtained from B−B Design of Experiments factors (ki)

P0

O1P

NS

temp

AQ

LB

D1

values

9.5 μs

2.5 ppm

24 scans

32.5 °C

3.0 μs

1.5 Hz

29.3 s

Figure 6. (a) Prediction profiler depicting importance of factors and (b−d) interaction plots showing the interplay of change in the factors on recovery and experiment time.

account for quantification purposes using NMRQUANT module of the Topspin software to minimize the errors that may arise due signal integration.

conditions mentioned in Table 3. In qHNMR method, generally a singlet is used for quantification purposes. However, in our study, all integrals of DETA and MC were taken into F


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Figure 7. Contour profiles depicting change in more than one parameter on the experiment time, signal-to-noise ratio, and recovery of MC and DETA.

qHNMR method takes care of these drawbacks of the classical method of analysis. Selection of the NMR experimental conditions (Table 3, Figures 6 and 7) for qHNMR method were made to strike a balance between the values of all the experimental factors to attain the desired S/N, recovery and experiment time. An investigation of specificity was conducted to demonstrate that an analytical procedure is able to completely discriminate and assign the signals to the analytes. Since NMR spectroscopy can unambiguously reveal the identity of organic compounds, no other supporting analytical procedures were used to demonstrate overall specificity of the technique. 1H NMR spectra of MC, DETA, DMS, and pyridine-d5 were recorded individually. Once it was confirmed that signals were well resolved from one another, 1H, 13C{1H}, 1H−1H COSY, 13 C−1H HSQC, and 1H−13C HMBC NMR spectra (refer to Supporting Information) were recorded for a DS2 sample containing DMS (internal standard). To further demonstrate specificity in assignment of the individual components, successive standard addition of DETA, MC and DMS was also performed to the NMR sample tube containing DS2 and DMS. Enhancements in 1H and 13C{1H} signal intensity and no appearance of new signals were observed to ascertain identities of the analytes. These experiments also clearly demonstrated signal resolution (peak purity) of the signals. To ascertain linearity of detector response within or at the extremes of the specified range of the analytical procedure, different concentrations of MC, DETA were analyzed with respect to a fixed concentration of internal standard DMS. A 2 mL stock solution containing MC (280 μL, 268.4 mg) and DETA (700 μL, 677.14 mg) in pyridine-d5 was prepared. Although in the intended procedure the NMR sample was contain 10 μL of DS2 (7 mg DETA and 2.8 mg MC), the calibration curves were drawn by analysis (in triplicate) using 25−500% of the test concentration (DETA 1.76−35.28 mg and MC 0.69−13.75 mg) for determination of range of analysis and content uniformity of the DS2 formulation. Linearity was evaluated from correlation coefficient, y-intercept, slope of the regression line, and residual sum of squares of the calibration curves (refer to Figure S5 of Supporting Information). The

The weight, purity, and the number of protons of DMS were specified in the pop-up window to obtain an instrument generated report wherein percentage concentrations of DETA, MC were expressed in terms of DMS (DETAqHNMRDMS and MCqHNMRDMS). This was converted to a form wherein the sum of DETA, MC, and NaOH was 100% (eq 2): Q qHNMR

NaOH

(%) =

Q qHNMR

DMS

× (100 − NaOH Titrimetry )

(DETAqHNMR

DMS

+ MCqHNMR DMS

) (2)

Responses were evaluated for maximum S/N and 100% recovery MC and DETA that can be obtained within minimum experiment time. The prediction profiler was used to obtain a number of interactive cross-sectional views of prediction model with the change in the settings of the individual factors. It also helped to ascertain the optimal settings and gauge sensitivity of the predictive model with the changes in the factors. They also helped to predict the corresponding responses with the change in values of one or two factors. By identifying the interrelationships and interaction effects among the experimental factors, the optimal values of the factors were determined interactively. The prediction profiles (Figure 6) also helped to evaluate the influence of change in one parameter on the other factor(s). In Figure 6b, an inverse relationship between D1 and recovery was observed. The optimal values of the factors (k1− k7) as obtained from B−B design are presented in Table 3. Since certified reference material of DS2 was not available, the results obtained from the qNMR method were compared with those obtained from the titrimetric method (wherein sum of DETA% and NaOH% deducted from hundred was attributed to MC). Therefore, in the classical method, error in the determination of DETA% or NaOH% leads to error in the observed concentration of MC%. Moreover, the titrimetric method is unable to ascertain the identity of DETA and MC unequivocally, and it does not detect the presence or absence of water in the samples. Therefore, tests must be performed on every sample to determine its efficiency against sulfur mustard (a highly toxic and persistent chemical warfare agent). The G


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Table 4. Results of Repeatability Experiments Carried out on DS2 Formulation Using Optimized qNMR Experimental Conditions S no.

pyr (μL)

stock (μL)

IS (μL)

DETA (mg/610 μL)

relative peak area

recovery of DETA (RSD)

MC mg (mg/610 μL)

relative peak area

recovery of MC (RSD)

1 2 3

495 500 505

15 10 5

100 100 100

5.0786 3.3857 1.6929

1.312 1.02 0.7274

99.47 (2.98) 99.64 (2.56) 99.93 (3.01)

2.01 1.34 0.67

0.6919 0.5430 0.3897

99.33 (2.43) 99.90 (2.31) 98.72 (2.52)

testing their decontamination efficiency against sulfur mustard (Table 5 and Figure 8).

limits of detection were studied based on standard deviation of the response (σ) and slope(s) of the calibration curve, obtained in the linearity study (eq 3): LOD = 3.3 × σ /s and LOQ = 10 × σ /s

Table 5. Results of t-Test for Recoveries of DETA and MC Determined by Conventional Method versus qHNMR Method Reported Herein

(3)

The slope s was estimated from the regression equation of calibration curves within the range of quantification. The residual standard deviation of a regression line or the standard deviation of y-intercepts of regression lines was used as the standard deviation. These calculated values were subsequently verified by performing experiments on serially diluted stock solutions. Experiments showed that lower limits of detection (LLOD) and quantification (LLOQ) for DETA were 1.92 and 6.40 mg mL−1, whereas those for MC were 0.89 and 2.86 mg mL−1, respectively. The effect of small, deliberate variations in method parameters that were obtained directly from the results of DoE experiments was used to ascertain reliability of the developed method during normal usage. The data gave a clear indication that the developed qNMR method tolerates well any change in the method parameters. The critical factors that affect the test results are flip angle and interscan delay (refer to Figure 6b−d). Moreover, since ∼80% of the machine time is consumed by the interscan delay, it is essential to use an optimum value of this parameter since it also determines the restoration of the spin populations. To assess whether the combination of values suggested by DoE were appropriate, accuracy of the method was estimated by measuring mean recovery with respect to the quantity of MC and DETA present in the mixture at each concentration level. Recovery tests were performed in triplicate by performing standard addition to three DS2 samples, which were also analyzed by the reported method.45 The recovery (R%) was calculated using eq 4: R (%) = (Q qHNMR

NaOH

/Q RM) × 100

DETA by NMR mean variance observations hypothesized mean difference df t stat P(T ≤ t) two-tail t critical two-tail mean variance observations hypothesized mean difference df t stat P(T ≤ t) two-tail t critical two-tail

71.0808 0.4190 14 0 18 1.6018 0.1266 2.1009 MC by NMR 26.8986 0.3539 14 0 18 −1.6507 0.1161 2.1009

DETA by titrimetry 70.4250 1.9275 14

MC by difference 27.5471 1.8070 14

(4)

where QqHNMRNaOH and QRM refer to percentage of analyte (in NaOH basis) determined by qHNMR and conventional methods, respectively. The mean recoveries were found to be >99.30% (RSD < 3.01%) as shown in Table 4. Repeatability was assessed using triplicate analysis of sample containing 10 μL of DS2 on the same day and also the next day to establish intra- and interday precision, respectively. Intermediate precision was determined over three months to establish the effects of random events on the precision of the analytical procedure. Repeatability was checked by conducting the tests by the authors A.M. and A.K. on different days. The relative standard deviation (coefficient of variation) was also determined (Table 4).

Figure 8. Graphical comparison of results obtained from the qHNMR (calibration curve and internal standard methods) methods and conventional method of analysis (plot reports the mean values of the results).

To perform optimization of experimental parameters, DoE method was used so that experimental conditions can be optimized and significant improvement in efficiency can be obtained for identification and quantification of chemicals. However, such numerical analyses usually rely on professional operations, which are not suitable for rapid tests applications by nonexperts. We thus chose the most commonly used Student’s t test to analyze the differences between two separated measurements, wherein the values of p < 0.05 means significant difference, while p > 0.05 means no significant difference in the results obtained from the two test methods. The comparison between the methods was performed to reveal whether

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APPLICATION OF THE METHOD After optimization of the experimental conditions, analysis of 14 samples of DS2 was performed in triplicate by qHNMR and conventional method. Independent aliquots were drawn from the samples at the same time. These samples were also analyzed by titrimetry and challenge experiments were carried out for H


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different analytical methodologies led to any significant differences in terms of analyte content. The comparison was carried out using 14 samples DS2. Results in Table 5 show that no statistically significant differences exist between the results. Experiments were also performed to ascertain the effects of variation of concentrations of MC and DETA by ± %5 of the recommended concentrations. The results did not show any significant change in recovery ( 10,000 times in liquid-state NMR. Proc. Natl. Acad. Sci. U. S. A. 2003, 100 (18), 10158−10163. (34) Rabenstein, D. L.; Millis, K. K.; Strauss, E. J. Proton NMR spectroscopy of human blood plasma and red blood cells. Anal. Chem. 1988, 60 (24), 1380A−1391A. (35) Szantay, C.; Demeter, A.; Gö rö g, S. Identification and Determination of Impurities in Drugs; Elsevier: Amsterdam, 2000. (36) Box, G. E. P.; Behnken, D. W. Some new three level designs for the study of quantitative variables. Technometrics 1960, 2 (4), 455− 475. (37) Montgomery, D. C. Design and Analysis of Experiments; John Wiley & Sons, 2008. (38) Myers, R. H.; Montgomery, D. C.; Vining, G. G.; Borror, C. M.; Kowalski, S. M. Response surface methodology: a retrospective and literature survey. J. Qual. Technol. 2004, 36 (1), 53. (39) Box, G. E. P.; Hunter, J. S.; Hunter, W. G. Statistics for Experimenters: Design, Innovation, and Discovery; Wiley-Interscience: New York, USA, 2005; Vol. 2. (40) Bruns, R. E.; Scarminio, I. S.; de Barros Neto, B. Statistical Design-Chemometrics; Elsevier, 2006; Vol. 25. (41) Massart, D. L.; Vandeginste, B. G.; Buydens, L. M. C.; Lewi, P. J.; Smeyers-Verbeke, J. Handbook of Chemometrics and Qualimetrics: Part A; Elsevier Science Inc., 1997. (42) Zahide, K. An Application and interpretation of second order response surface model. Ankara University Journal of Agricultural Sciences 2001, 7, 121−128. (43) Weber, M.; Hellriegel, C.; Rueck, A.; Wuethrich, J.; Jenks, P. Using high-performance 1H NMR (HP-qNMR®) for the certification of organic reference materials under accreditation guidelines Describing the overall process with focus on homogeneity and stability assessment. J. Pharm. Biomed. Anal. 2014, 93, 102−110. (44) Claridge, T. D. W. High-Resolution NMR Techniques in Organic Chemistry, 2nd ed.; Elsevier, 2016; Vol. 27. (45) Military Specification of Decontaminating Agent, DS2; US Army: USA, 1986; p 8.

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