Assessing the influence of neglected GC-FID variables on the multiple responses using multivariate optimization for the determination of ethanol and acetonitrile in radiopharmaceuticals

Abstract

Analytical gas chromatography in line with a flame ionization detector (GC-FID) method was developed and validated for direct determination of organic solvents in [18F]fluoro-ethyl-tyrosine ([18F]FET), [18F]fluoromisonidazole ([18F]FMISO) and [18F]fluorothymidine ([18F]FLT). Variables of the splitless time (min) and injection temperature (°C) on the response of analysis time and resolution were optimized with the assistance of a two-level full factorial design and desirability function of Derringer. The proposed procedure was validated following the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Q2 (R1) guideline. Excellent linearity, R2 > 0.990, indicated that approximately 99% of the response variance could be predicted from ethanol and acetonitrile concentrations ranging from 0.5 to 6.0 mg mL−1 and 0.1 to 0.8 mg mL−1, respectively. The proposed procedure has proved to be selective, sensitive, and accurate (90–110%), with excellent repeatability and precision (RSD < 2%). In the robustness analysis, the findings from the calculated Standardized Effects Values (SE) were insignificant (p > 0.05) and demonstrated that the proposed method was robust for a splitless time of 1.0 ± 0.5 min and an injection temperature of 210 ± 10 °C. The proposed method was also successfully used for the quantitative determination of ethanol and acetonitrile in [18F]FET, [18F]FMISO, and [18F]FLT. Both solvents were well separated (R, 4.1–4.3) within 4.5 min. Therefore, the proposed method is relevant for routine quality control analysis of all 18F-radiopharmaceutical derivatives for the direct determination of ethanol and acetonitrile.

Introduction

Organic solvents play an indispensable role in most radiopharmaceutical production phases to facilitate reactions, separation, purification and drying. Therefore, it is almost impossible to remove them entirely in the final product formulation [1,2,3,4,5]. The aforementioned has sparked interest in the community due to organic solvents' potential ability to pose a health risk due to their toxicity [6]. The International Conference on Harmonization of Technical Specifications for the Registration of Pharmaceuticals for Human Use (ICH) Guideline Q3C, updated in 2018, listed acetonitrile and methanol as Class 2 solvents due to their inherent toxicity and potentially harmful to human health. In the meantime, acetone, ethanol, and dimethyl sulfoxide as Class 3 are moderately harmful in large quantities [7].

In pursuance of overcoming the potential effects of organic solvents on the patient, the international and local regulatory authorities have decided that organic solvents' determination is compulsory in radiopharmaceutical quality control practices. The previously mentioned is achieved by limiting the amount of Class 2 solvents in the final formulation of radiopharmaceuticals to a maximum of 0.41 mg mL−1 and 5 mg mL−1 for Class 3 solvents [2, 3, 7].

The current consensus is that the analytical gas chromatography method has been identified as the most efficient method for analyzing organic solvents [8, 9]. With the development of gas chromatography technology, now it is possible to determine the concentration of organic solvents which remained in the final formulation in a short time with high specificity and sensitivity [10,11,12].

Over the last ten years, much research has examined, and different pharmacopeias have discussed several gas chromatography analyses [13]. As the gas chromatography equipment and its sub-component may differ one to another, depending on the specification, the optimal procedure also may be different. The influence of variation in the column temperature, injection split ratio, carrier gas flow (mL min−1), and carrier gas on gas chromatography responses have been extensively investigated in previous research [2, 3, 10, 14,15,16].

Work in this area is extensive but is primarily concerned with the influence of variation in the column temperature, injection split ratio, carrier gas flow (mL min−1), and carrier gas on gas chromatography responses. Limited number of studies have been conducted to investigate the effect of the splitless time and injection temperature on gas chromatography multiple responses to the analysis time and resolution in the radiopharmaceutical field [13]. To our knowledge, these two variables are always overlooked, and the method’s potential has not yet been identified. The splitless time parameter is repeatedly used for analyzes involving adulteration in food products, pesticides, and environmental monitoring, to name a few [17,18,19,20,21,22].

Therefore, this study is set to explore the influence of splitless time and injection temperature on multiple responses to analysis time and resolution using multivariate analysis for method optimization. The present study's goal was to test the hypothesis that variation in the splitless time and injection temperature could significantly result in rapid analysis time and excellent resolution of adjacent peaks. At the latter stage, this recent work focuses on optimizing and validating an improved gas chromatography analytical method for the determination of ethanol and acetonitrile in [18F]fluoro-ethyl-tyrosine ([18F]FET), [18F]fluoromisonidazole ([18F]FMISO), and [18F]fluorothymidine ([18F]FLT).

Materials and methods

General

[18F]fluoride produced via the 18O(p,n)18F nuclear reaction on a 16.5 meV cyclotron PETtrace® (GE Healthcare Technologies, USA) was supplied by the National Cancer Institute (NCI) and delivered in the aqueous form to the Centre of Diagnostic Nuclear Imaging (CDNI), Universiti Putra Malaysia (UPM). The no-carrier-added [18F]fluoride solution was then transferred to the Scintomics GRP 4 V module (Scintomics GmbH, Germany) to produce [18F]FET, [18F]FMISO, and [18F]FLT.

Reagent and chemicals

Ethanol and acetonitrile with the purity of 99.9% and 99.5%, respectively, were purchased from Merck (Darmstadt, Germany) and used to prepare analytical solutions. Reagent kits and sterile cassettes used in the preparation of [18F]FET, [18F]FMISO, and [18F]FLT were purchased from ABX Advanced Biochemical Compounds (Radeberg, Germany). All precursors and eluents were stored following ABX Advanced Biochemical Compounds instruction.

Instrumentation

All experiments were performed on a Shimadzu (Japan) GC-2010Plus AF gas chromatography equipped with an FID-2010Plus flame ionization detector (FID) and an integrated AOC-20i autoinjector. The system was controlled by LabSolutions (Version 5.82). Chromatographic separation was performed on the Agilent J&W DB-200 column ((35% trifluoropropyl)-methylpolysiloxane column, mid-polarity), 30 m in length, 0.53 mm inner diameter, and 1 µm film thickness. For each injection, a sample volume of 1 µL was used.

The ultrapure nitrogen gas, 99.9995% purity (Air Products, Malaysia), was used as the carrier gas and set at 30 mL min−1. For combustion of the FID, as a rule of thumb, the ratio of air to hydrogen gas was 10:1; the airflow (Air Products, Malaysia) was set at 400 mL min−1, while for hydrogen gas, 99.9992% purity (Air Products, Malaysia) it was set at 40 mL min−1. The temperature of the FID detector was set at 250 °C.

The carrier gas flow was set at 2.4 mL min−1. The column temperature was set at 50 °C and hold for 1 min before linearly increased at 10 °C min−1 to 90 °C. In an attempt to test the current study hypothesis, the splitless time and injection temperature were evaluated using multivariate optimization of the two-level full factorial design.

Optimization of the experimental factors

Multiple responses to the analysis time and resolution of adjacent peaks directly affected by the splitless time and injection temperature were assessed using a two-level full factorial design. To allow these responses to be optimized simultaneously, the desirability function was used [2, 23]. Each response (yi) was converted to an individual desirability function (di), which varies between 0 and 1 (0 ≤ di ≤ 1). The individual desirability (di) was then determined using the Eqs. (1) and (2) for the analysis time and resolution of adjacent peaks.

$$di = \left( {\frac{ymax - yi}{{ymax - ymim}}} \right)^{S = 1} , ymim \le yi \le ymax$$
(1)
$$di = \left( {\frac{yi - ymim}{{ymax - ymim}}} \right)^{S = 1} , ymim \le yi \le ymax$$
(2)

yi is any experimental response, while ymim and ymax are the lowest and highest experimental response, and S is the weight (when equal to 1 = linear desirability function). The two individual desirability scores, di; analysis time and resolution, were then combined into a single global desirability D.

Analysis of Variance (ANOVA) is a statistical method used to analyze variations among or between groups. It is also commonly used for the treatment of gas chromatographic data [24]. In this study, a statistical analysis of a Factorial ANOVA was carried out on data to ascertain which factors significantly impact the response. All statistical analyses were performed using the SPSS Statistics 26 (IBM, USA) software.

Validation of the proposed procedure

The procedure was validated following the ICH analytical procedure validation guideline [25]. The matrix effect, linearity, precision (repeatability and intermediate precision), the limit of detection (LOD) and quantification (LOQ), sensitivity, selectivity, accuracy (recovery), and robustness of the proposed procedure were evaluated.

Application of the procedure

Production of [18F]FET, [18F]FMISO, and [18F]FLT were performed in Scintomics GRP 4 V automated synthesis module (Scintomics, Germany) at CDNI, UPM. Two consecutive productions were carried out for each of the 18F-derivatives. Before the sample collection in a sterile vial, samples were passed through and filtered using a 0.22 µm membrane filter attached to the end of the dispensing line (Millipore, USA). The automated synthesis module usually controlled the step. Samples were then analyzed using the proposed procedure. The peaks obtained from the samples were determined by comparing the retention times with the ethanol and acetonitrile standards under the same chromatographic conditions and with the test samples.

Results and discussion

Optimization of the experimental factors

Experiments were executed using a 0.9% (w/v) NaCl solution spiked with ethanol and acetonitrile concentrations equivalent to 1 mg mL−1 for the simulation of a radiopharmaceutical matrix. The two-level full factorial design was performed on two-levels: high ( +) and low (−). Table 1 shows the coded and actual values of the selected factors at this level, the central point triplicates, and the di values for the resolution and analysis time. The factors were optimized using a two-level full factorial design, which includes the central point triplicates for estimating the experimental error.

Table 1 Experimental factors with individual desirability values for the analysis time and resolution response

However, in contrast to previous studies, splitless time and injection temperature were selected as factors [2, 3]. There are no previous literature reports about whether the splitless time and injection temperature directly affected both the analysis time and resolution, particularly in radiopharmaceutical analysis. Analysis time and resolution of adjacent peaks were chosen as responses to assess the proposed method's suitability in terms of rapid analysis time and separation efficiency.

The influence of column temperature was not evaluated here as the preliminary results have shown that at the isothermal temperature of below 100 °C, the separation between ethanol and acetonitrile was poor and not convincing (Fig. 1). It was surprising because both solvents have a relatively low boiling point, below 100 °C, which can usually be quite satisfactorily separated under isothermal conditions [26]. When the column was set isothermally between 100 and 200 °C, ethanol and acetonitrile formed a single peak (Fig. 1). The finding is expected since the temperature was set at too high, the lighter components will co-eluate, resulting in poor resolution [27]. It was interesting to note that throughout these experimental designs, the temperature control program was set at 50 °C and held for 1 min before the column temperature was linearly increased at 10 °C min−1 to 90 °C.

Fig. 1
figure1

GC-FID chromatogram of 0.9% (w/v) NaCl solution spiked with ethanol and acetonitrile (1 mg mL−1) at isothermal column temperature of 50, 100, and 200 °C

The statistical analysis of Factorial ANOVA revealed a relatively low observed power, 0.61. However, there was a significant interaction between the splitless time and injection temperature on the analysis time (p < 0.05). The Partial Eta squared revealed about 56% of the variance in the analysis time can be predicted from the splitless time. In comparison, only about 33% was predicted for the injection temperature (Table 2).

Table 2 Statistical analysis of factorial ANOVA

As the interaction between these factors on the analysis time was significant, further Games-Howell posthoc analysis was performed to determine the combination of experimental matrices that yield statistically significant results on the analysis time. It is unsurprising to find that the increase in the splitless time increased the analysis time. Our most intriguing finding is the combination of 0.5 min splitless time and 220 °C of injection temperature produced a resolution of more than 4 (R > 4), analysis time within 4.5 min with a precision of relative standard deviation of less than 3%. On the contrary, no significant difference was found on the interaction between splitless time and injection temperature on the resolution (p > 0.05). The finding of this statistical analysis reinforces the general belief that only the injection split ratio affects resolution [2].

After the validation and optimization of the two-individual desirability (di) for analysis time and resolution, both functions could be combined into the overall desirability (D), which was computed using Eq. (3)

$$D = \sqrt {diAtdiR}$$
(3)

diAt and diR are denoted as the individual desirability for the analysis time and resolution, respectively.

From the calculation, the optimum conditions for the ethanol and acetonitrile in the sample to be well resolved with a rapid analysis time, within 5 min and highest D (0.57) were: 0.5 min of the splitless time at 220 °C of injection temperature before the solvents entering the column (Fig. 2). Under these conditions, the resolution values were between 4.1 and 4.3, with a relative standard deviation (RSD) of less than 3%. The statistical analysis of Two-Way ANOVA performed earlier significantly strengthened the optimum conditions obtained using the overall desirability (D). In summary, the optimal experimental conditions used for validation in the later part were as follow (Table 3).

Fig. 2
figure2

GC-FID chromatogram of 0.9% (w/v) NaCl solution spiked with ethanol and acetonitrile (1 mg mL−1) under optimized chromatographic conditions

Table 3 Optimal experimental conditions

Validation of the proposed procedure

Following a statistical analysis of Student’s t-test, the matrix slopes and standard calibration curves for both solvents showed a significant result of the matrix effect (p < 0.05). It could be due to the interaction between the matrix and both solvents, which produces a significant difference in response. For this reason, fortified samples were used to construct the calibration curve.

In the linearity study, Levene's test was performed to support homoscedasticity (p > 0.05) before the use of the ordinary least squares method (OLSM) for slope, intercept, residual, and correlation coefficient (r) estimation. Notably, a close correlation existed between the standard solution at the five concentrations levels and the area's response under the curve for both solvents, r > 0.990. Logistic regression analyses were performed to assess the association between concentration levels and response. Through the regression analysis (R2), about 99% of the response's variance can be predicted from the concentration.

All calibration points were found within the prediction interval (Fig. 3a), and the present value of R2 was 0.992. The studentized residual plot (Fig. 3b) also shows all the computed residues within the ± 2 limits and appears normally distributed and centered at zero. The response factor for ethanol tends to become unstable at a medium level, with values were exceeded ± 5% (Fig. 3c). Nevertheless, superior results were seen in the back-calculated relative errors (%RE) against standard solutions concentration (Fig. 3d). From the author's point of view, this proposed method exhibits excellent performance in almost all cases.

Fig. 3
figure3

Linearity study for the calibration curve of ethanol by GC-FID in the calibration range, 0.5–6.0 mg mL−1

In Fig. 4, all calibration points are found within the prediction interval (Fig. 4a), and the present value of R2 0.998 for concentration ranges from 0.1 to 0.8 mg mL−1. The studentized residual plot (Fig. 4b) also shows all the computed residuals within the ± 2 limits with the back-calculated %RE were lower than 5% (Fig. 4d). In the case of the acetonitrile response factor, excellent results were achieved compared to ethanol's response factor (Fig. 4c).

Fig. 4
figure4

Linearity study for the calibration curve of acetonitrile by GC-FID in the calibration range, 0.1–0.8 mg mL−1

The LOD and LOQ were calculated adopting the equation: LOD = 3.3 × sa/b, and LOQ = 10 × sa/b, where sa is the standard deviation of the calibration curve intercept, while b is the calibration curve slope. The estimated LOD and LOQ were subsequently verified by the independent analysis of solutions prepared solutions at these ranges (n = 6). The LOD and LOQ were higher for ethanol than acetonitrile as the calibration curve's concentration range was not similar [3]. The LOD and LOQ, as presented in Table 4, demonstrated excellent precision (RSD below 3%) and accuracy (recovery from 93 to 103%) for both solvents.

Table 4 Analytical figures obtained from the proposed procedure of GC-FID analytical

Selectivity (α) is defined for peak pairs and is determined by the ratio of retention factors of the more retained component (k2), acetonitrile, and the less retained component (k1), ethanol in this experiment [28]. It was calculated following Eq. (4). The selectivity (α) should be greater than one. If the selectivity (α) is equal to one, it indicates that the sample's two components cannot be separated and that their peaks overlap.

$${\text{Selectivity }}\left( \alpha \right) = k_{2} / \, k_{1}$$
(4)

In this experiment, the selectivity (α) was 4.88, indicating higher selectivity and therefore means better separation. The method has also proved to be sensitive. The method's sensitivity was greater for acetonitrile than ethanol, as the method could detect the smallest difference for acetonitrile. The finding was also supported by the LOD previously presented, and in agreement with Lozano & Cantero, as the higher the analytical sensitivity, the lower the detection limit [29].

The assessment of precision for repeatability and intermediate precision was carried out by the RSD % acquired from the recovery analysis. A statistical analysis of the Student's T-test was then carried out, and the analysis did not demonstrate any significant differences in the result (p > 0.05). In the accuracy assessment, the recovery values for ethanol and acetonitrile ranged from 93 to 96% and 97 to 103%, respectively. Good agreement was found when comparing results from this work against published data [2, 3, 10]. Table 4 above summarized the regression analysis, LOD, and LOQ, assessment of precision and recovery.

Another integral element for validating the proposed method is the robustness of the method. Robustness is best described as the analytical method's ability to remain unaffected, even in the presence of minor changes that have been intentionally introduced under experimental conditions [30, 31]. In pursuance to assess robustness, the two-level full factorial design approach was adopted in this study [30]. To date, the two-level full factorial design has been the approach of choice due to its efficiency for assessing robustness when the number of factors is not high [30]. The standardized effects values (SE) were calculated as in Eq. 5 and compared to the critical value of the Student's t-test (α = 0.05). SE is the ratio between the effect value and the dispersion of the value (S effect) obtained during the optimization process. In order to obtain the calculated SE, one may have to refer to the previous work by Ferreira et al., which discussed the robustness evaluation in depth using the two-level full factorial experimental design [30].

$$SE = {\text{Effect value }}/S_{{{\text{effect}}}}$$
(5)

In assessing the robustness of the proposed method, the experimental matrix was developed based on adjusting factors associated with the optimized conditions, considering the retention time of ethanol and acetonitrile as a response. The two factors and their interactions are not significant for the established experimental matrix (Table 5). One of the key findings of this study showed that the proposed method is robust for the splitless time of 1.0 ± 0.5 min and an injection temperature of 210 ± 10 °C. All the standardized effect values attained were below 4.30, the critical value of Student’s t-test for the degree of freedom of 2 at a confidence level of 95%. The proposed method is, therefore, robust for the experimental matrix set out in the robustness analysis.

Table 5 Standardised effects from the robustness analysis

Application of the procedure

Following the production of [18F]FET, [18F]FMISO, and [18F]FLT, ethanol, and acetonitrile in the samples were evaluated using the method developed. Ethanol and acetonitrile were well resolved and determined within 4.5 min, with an overall analysis time of fewer than 5 min, including sample injection and separation. The separation of the two peaks was excellent. The total analysis time of fewer than 5 min was acceptable, as the procedures described in the literature required a total separation time of 7 min [3].

From our perspective, although the splitless time and injection temperature have always been overlooked in the previous studies, the present results showed that the combination of these two factors also contributes to rapid analysis time and excellent resolution. Although the splitless time should be long enough to allow most of the vaporized sample to be transferred to the column, an excessive splitless time of more than 1.5 min may produce tailing peaks and broad peaks, which are always mistakenly thought due to the column aging [32]. On the other hand, opening the split vent too early, as a result of shorter splitless time, may risk losing the analyte [33]. Therefore, in this study, 0.5 min is the optimum splitless time to achieve good reproducibility and sensitivity in the splitless injection mode. In the meantime, the injection temperature must be relatively high enough to allow the sample to vaporize completely, but not too high to cause the sample's degradation, especially for the organic solvent, which has a low boiling point.

The tabulated data in Table 6 below showed that the proposed method could provide rapid analysis time. Ethanol and acetonitrile were well resolved and determined within 4.5 min. The separation between the two peaks was excellent compared to Klok and Windhorst [13] method. The difference in retention times between ethanol and acetonitrile, 0.4 min observed in the respective method, was too small for a good baseline separation. Table 7 shows the concentration values for radiopharmaceuticals, and all the samples tested were within the range of acceptance, except for one [18F]FMISO sample with a relatively high concentration of ethanol.

Table 6 Comparison of the results with the other literature
Table 7 Ethanol and acetonitrile concentration in [18F]FET, [18F]FMISO and [18F]FLT samples

Conclusion

The experimental factors of splitless time and injection temperature were successfully optimized through a multivariate approach. Although these two factors are doubtful of contributing to a substantial impact, the proposed method nevertheless contributes to rapid analysis time and excellent resolution (R > 1.5). The proposed method has been validated to meet all acceptance criteria and provides excellent linearity and resolution of an adjacent peak, adequate analysis time, and robustness. The proposed method also has been successfully used for the quantitative determination of ethanol and acetonitrile in [18F]FET, [18F]FMISO, and [18F]FLT. The proposed method is relevant for the direct determination of ethanol and acetonitrile for any 18F-radiopharmaceutical derivatives.

Availability of data and materials

The datasets used and analyzed during the current study are available on a reasonable request from the corresponding author.

Code availability

LabSolutions (Version 5.82) (Shimadzu, Japan) and SPSS Statistics 26 (IBM, USA).

Abbreviations

[18F]:

Fluorine-18

[18F]FET:

[18F]fluoro-ethyl-tyrosine

[18F]FLT:

[18F]fluorothymidine

[18F]FMISO:

[18F]fluoromisonidazole

%RE:

Percentage of relative error

CDNI:

Centre for Diagnostic Nuclear Imaging

d i :

Individual desirability

d iAt :

Individual desirability for analysis time

d iR :

Individual desirability for resolution

D :

Global desirability

FID:

Flame ionization detection

GC:

Gas chromatography

GC-FID:

Gas chromatography flame ionization detection

ICH:

The International Conference on Harmonisation of Technical Specifications for the Registration of Pharmaceuticals for Human Use

IJ:

Injection temperature

LOD:

Limit of detection

LOQ:

Limit of quantification

min:

Minute

mg mL 1 :

Milligram per milliliter

OLSM:

Ordinary least squares method

r:

Correlation coefficient

R:

Resolution

R2 :

Regression analysis

RSD:

Relative standard deviation

RSDIP:

Relative standard deviation from intermediate precision

RSDr:

Relative standard deviation from repeatability

S :

Weight

SE:

Standardized effects

ST:

Splitless time

UPM:

Universiti Putra Malaysia

w/v :

Weight by volume

y i :

Response

y mim :

Lowest experimental response

y max :

Highest experimental response

References

  1. 1.

    Tankiewicz M, Namieśnik J, Sawicki W (2016) Analytical procedures for quality control of pharmaceuticals in terms of residual solvents content: challenges and recent developments. TrAC—Trends Anal Chem 80:328–344

    Article  Google Scholar 

  2. 2.

    dos Santos Costa CL, Ramos DP, da Silva JB (2019) Multivariate optimization and validation of a procedure to direct determine acetonitrile and ethanol in radiopharmaceuticals by GC-FID. Microchem J 147:654–659

    Article  Google Scholar 

  3. 3.

    Mihon M, Tuta CS, Ion AC, Niculae D, Lavric V (2017) Fast method for the determination of residual solvents in radiopharmaceutical products. Rev Chim 68(4):666–670

    Article  Google Scholar 

  4. 4.

    Otero R, Carrera G, Dulsat JF, Fábregas JL, Claramunt J (2004) Static headspace gas chromatographic method for quantitative determination of residual solvents in pharmaceutical drug substances according to European Pharmacopoeia requirements. J Chromatogr A 1057(1–2):193–201

    Article  Google Scholar 

  5. 5.

    Cheng C, Liu S, Mueller BJ, Yan Z (2010) A generic static headspace gas chromatography method for determination of residual solvents in drug substance. J Chromatogr A 1217(41):6413–6421

    Article  Google Scholar 

  6. 6.

    Witschi C, Doelker E (1997) Residual solvents in pharmaceutical products: acceptable limits, influences on physicochemical properties, analytical methods and documented values. Eur J Pharm Biopharm 43:215–242

    Article  Google Scholar 

  7. 7.

    International Council for Harmonisation Of Technical Requirements for Pharmaceuticals for Human Use, “Ich Q3C(R7) Impurities Residual Solvents,” ICH Harmon Guidel, 4, 2002, 2018.

  8. 8.

    Asfaw AA, Aspromonte J, Wolfs K, Van Schepdael A, Adams E (2019) Overview of sample introduction techniques prior to GC for the analysis of volatiles in solid materials. J Sep Sci 42(1):214–225

    Article  Google Scholar 

  9. 9.

    Ahn J-W, Pandey SK, Kim K-H (2011) Comparison of GC-MS calibration properties of volatile organic compounds and relative quantification without calibration standards. J Chromatogr Sci 49(1):19–28

    Article  Google Scholar 

  10. 10.

    Nader M, Reindl D, Eichinger R, Beheshti M, Langsteger W (2011) Improved quality control of [18F]fluoromethylcholine. Nucl Med Biol 38(8):1143–1148

    Article  Google Scholar 

  11. 11.

    Farajzadeh MA, Dehghani H, Yadeghari A, Khoshmaram L (2017) Extraction and preconcentration of residual solvents in pharmaceuticals using dynamic headspace–liquid phase microextraction and their determination by gas chromatography–flame ionization detection. Biomed Chromatogr 31(2):e3788

    Article  Google Scholar 

  12. 12.

    Meinert C, Meierhenrich UJ (2012) A new dimension in separation science: comprehensive two-dimensional gas chromatography. Angew Chemie Int Ed 51(42):10460–10470

    Article  Google Scholar 

  13. 13.

    Klok RP, Windhorst AD (2006) Residual solvent analysis by gas chromatography in radiopharmaceutical formulations containing up to 12% ethanol. Nucl Med Biol 33(7):935–938

    Article  Google Scholar 

  14. 14.

    Bernardoni F, Halsey HM, Hartman R, Nowak T, Regalado EL (2019) Generic gas chromatography flame ionization detection method using hydrogen as the carrier gas for the analysis of solvents in pharmaceuticals. J Pharm Biomed Anal 165:366–373

    Article  Google Scholar 

  15. 15.

    Jemmali Z, Chartier A, Dufresne C, Elfakir C (2016) Talanta optimization of the derivatization protocol of pentacyclic triterpenes prior to their gas chromatography—mass spectrometry analysis in plant extracts Oleane Lupane. Talanta 147:35–43

    Article  Google Scholar 

  16. 16.

    Kilian K, Pekal A, Szkutnik W, Pyrzyńska K (2014) A fast method for the determination of residual solvents in 18FDG and 11C-methionine samples. Microchem J 115:95–99

    Article  Google Scholar 

  17. 17.

    Fiorini D, Paciaroni A, Gigli F, Ballini R (2010) A versatile splitless injection GC-FID method for the determination of mineral oil paraffins in vegetable oils and dried fruit. Food Control 21(8):1155–1160

    Article  Google Scholar 

  18. 18.

    Hermida-Ameijeiras A, Simal-Gándara J, López-Hernández J, Simal-Lozano J (1995) Evaluation of the performance of a multiresidue gas chromatographic method for the determination of aliphatic hydrocarbons in mussels of Galicia (N.W. Spain). Water Res 29(9):2118–2124

    Article  Google Scholar 

  19. 19.

    Pérez Pavón JL, Casas Ferreira AM, Fernández Laespada ME, Moreno Cordero B (2009) In situ derivatization reaction and determination of ibuprofen in water samples using headspace generation-programmed temperature vaporization-gas chromatography-mass spectrometry. J Chromatogr A 1216(39):6728–6734

    Article  Google Scholar 

  20. 20.

    Evans O, Kauffman P, Vonderheide AP, Wymer LJ, Morgan JN (2009) The determination of pesticidal and non-pesticidal organotin compounds in water matrices by in situ ethylation and gas chromatography with pulsed flame photometric detection. Microchem J 92(2):155–164

    Article  Google Scholar 

  21. 21.

    Liao H et al (2018) Determination of ethyl carbamate in daily foods and cigarette smoke by gas chromatography coupled tandem mass spectrometry. Tob Sci Technol 51(4):53–59

    Google Scholar 

  22. 22.

    Chen H, Yin J, Zhu M, Cao C, Gong T, Xian Q (2016) Cold on-column injection coupled with gas chromatography/mass spectrometry for determining halonitromethanes in drinking water. Anal Methods 8(2):362–370

    Article  Google Scholar 

  23. 23.

    Derringer G, Suich R (1980) Simultaneous optimization of several response variables. J Qual Technol 12(4):214–219

    Article  Google Scholar 

  24. 24.

    Psodorov D, Lazic V, Acanski M, Psodorov D, Popovic S, Plavsic D, Pastor K, Suput D (2018) Fatty acid profile changes in ricotta filled pastry during storage investigated by a GC/MS-ANOVA. Chem Ind Chem Eng Q 24(2):149–155

    Article  Google Scholar 

  25. 25.

    International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use, ICH Topic Q2 (R1) (2006). Validation of analytical procedures: text and methodology 333 (7574)

  26. 26.

    Raymond P, Scott W (2018) Introduction to analytical gas chromatography. CRC Press, Boca Raton

    Google Scholar 

  27. 27.

    D. Bhanot, “Temperature control of the gas chromatographic column,” Lab-Training.Com, 2014.

  28. 28.

    Valcárcel M, Gómez-Hens A, Rubio S (2001) Selectivity in analytical chemistry revisited. TrAC Trends Anal Chem 20(8):386–393

    Article  Google Scholar 

  29. 29.

    Lozano D, Cantero M (1997) Difference between analytical sensitivity and detection limit. Am J Clin Pathol 107(5):619

    Article  Google Scholar 

  30. 30.

    Ferreira SLC, Caires AO, da Borges TS, Lima AMDS, Silva LOB, dos Santos WNL (2017) Robustness evaluation in analytical methods optimized using experimental designs. Microchem J 131:163–169

    Article  Google Scholar 

  31. 31.

    Bosque-Sendra JM, Nechar M, Cuadros Rodríguez L (1999) Decision protocol for checking robustness with previous outlier detection in the validation of analytical methods. Fresenius J Anal Chem 365(6):480–488

    Article  Google Scholar 

  32. 32.

    Restek, “Operating hints for using split/splitless injectors injectors,” Tech Guid: 36, 2002.

  33. 33.

    Watson DW (2019) Optimizing sensitivity in GC—FID analysis. LCGC North Am 37(2):124

    Google Scholar 

Download references

Acknowledgements

The authors sincerely acknowledge the financial support of Universiti Putra Malaysia (UPM) through Putra Grant (9580700) and the Ministry of Higher Education, Malaysia (Hadiah Latihan Persekutuan – SLPP).

Funding

The authors gratefully acknowledge the financial support of Universiti Putra Malaysia (UPM) via Putra Grant (9580700).

Author information

Affiliations

Authors

Contributions

All authors contributed to the study conception and design. Hishar Hassan performed material preparation, data collection, and analysis. Hishar Hassan wrote the first draft, and all authors made comments. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Hairil Rashmizal Abdul Razak.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hassan, H., Othman, M.F., Zakaria, Z.A. et al. Assessing the influence of neglected GC-FID variables on the multiple responses using multivariate optimization for the determination of ethanol and acetonitrile in radiopharmaceuticals. SN Appl. Sci. 3, 251 (2021). https://doi.org/10.1007/s42452-021-04246-8

Download citation

Keywords

  • Organic solvent analysis
  • Quality control
  • Gas chromatography-flame ionization detection (GC-FID)
  • Splitless time
  • Injection temperature
  • Radiopharmaceutical