Development of Liquid–Liquid Extraction‐Assisted, Derivatization‐Free Gas Chromatography Integrated With the Flame Ionization Detection (GC‐FID) Method for the Direct Determination of Fatty Acids in Cream Formulations

ABSTRACT

Marketed cream formulations contain fatty acids as core constituents, required for its texture, uniformity, shelf life, and skin penetration. Given their importance in the cosmetic and pharmaceutical industries, real‐time quantification and analysis of these compounds are crucial for assessing authenticity and ensuring compliance with regulatory specifications. In the present analysis, we employed gas chromatography with flame ionization detection (GC‐FID) due to its sensitivity, robustness, and quantitative reliability. Overcoming the traditional method for fatty acid determination, we analyzed the sample without derivatization to fatty acid methyl esters (FAMEs). The derivatization step was initially omitted to simplify sample preparation, reduce analysis time and reagent consumption, and minimize the potential errors associated with incomplete esterification or degradation of unstable fatty acids during derivatization. Direct analysis was performed by dissolving the extracted sample in isopropyl alcohol and then filtering and injecting it into the GC‐FID system under optimized chromatographic conditions. The optimized method was used for the concurrent quantification of lauric, myristic, palmitic, oleic, and stearic acids. The validation of the present work was executed in alignment with ICH Q2(R1) requirements. The method was further expanded to analyze real cream samples, thereby validating the accuracy of fatty acid profiling for routine quality control.

1. Introduction

Fatty acids are considered integral constituents of pharmaceutical formulations, particularly those intended for topical use (Ahmad and Ahsan 2020; De Carvalho and Caramujo 2018). Among various fatty acids, lauric, myristic, palmitic, oleic, and stearic acids are the most important in cream formulations (Spector 1999). The precise quantification of fatty acids is deemed important to ensure the reliability, quality, and authenticity of the formulation in accordance with regulatory specifications (Araujo et al. 2008; Meyer et al. 2017).

Among numerous methods reported for the determination, gas chromatography paired with flame ionization detection (GC‐FID) remains one of the gold standards. The other techniques employed include LC–MS/MS, NMR, FTIR, Raman, and less popular methods such as Thin Layer Chromatography (TLC) (Wu et al. 2017). The GC method is popular because it offers several advantages, including a shorter analytical workflow, high sensitivity, lower per‐analysis cost, and greater robustness with reduced sample manipulation (Zhang et al. 2015). Although it is one of the most popular techniques, it requires sample derivatization to fatty acid methyl esters (FAMEs) to enhance the volatility of fatty acids and, consequently, their easy detection (Paligová et al. 2008; Vyas et al. 2010).

However, the literature reports that the direct GC analysis of fatty acids is challenging. The derivatization, particularly FAME, is preferred owing to the fact that free fatty acids are polar and interact strongly with the column in comparison to FAME derivatives (Farajzadeh et al. 2014). In addition, the high boiling points of free fatty acids, along with the low detection rate, are overcome by FAME derivatization. Though a preferred method, it has numerous disadvantages, including, but not limited to, additional sample preparation time, the need to extract esters, and incomplete methylation, leading to underestimation of total fat and, consequently, reduced overall analytical accuracy and reproducibility (Bannon et al. 1985; Paligová et al. 2008). Additionally, the possibility of side reactions leading to artifacts cannot be overlooked. In addition, derivatization is associated with the degradation of polyunsaturated fatty acids, isomerization, the generation of undesirable byproducts, increased laboratory waste, and a greater scope for error (Gimenes de Souza et al. 2018). Additionally, they cannot be applied universally to all fatty acids, which further leads to misrepresentation in the chromatogram (Quehenberger et al. 2011). In complex samples such as cosmetics or biological fluids, co‐extraction of other acidic components, surfactants, and lipids can affect the peak shape, response, and volatility of the desired components, potentially interfering with detection (Moldoveanu and David 2018; Tammekivi et al. 2019).

To overcome this, derivatization‐free methods are usually employed. The literature also highlights the use of non‐derivatization in GC methods and their utility for efficient detection in complex matrices. Some studies include research by Hua and colleagues, who developed a derivatization‐free method for the determination of free fatty acids in human plasma using capillary‐based GC‐FID. The group's validation study revealed that the method is suitable for clinical studies, particularly for measuring plasma free fatty acid levels in patients with diabetic nephropathy during treatment effectiveness assessment (Hai‐yin et al. 2011). In other research by Lenti and group, the assessment of volatile short to medium‐chain fatty acids in wine samples via liquid–liquid extraction and direct analysis by GC‐FID was disclosed. The method was found to be reliable for comparing the fatty acid levels in white and red wine (Lenti et al. 2022). In another study by Lewis and colleagues, the researchers developed a capillary‐based GC‐FID method using polyethylene glycol (PEG) modified with nitroterephthalic acid for the determination of oleic acid in pharmaceuticals without derivatization. The method was found to be well‐suited for the simultaneous separation of 15 fatty acids in 20 min. The method was found to be robust and precise for the determination of oleic acid and related fatty acids (Botcherby 2016). “A similar study was also reported by Zhang and group, who reported GC‐FID for the determination of oleic and other fatty acids in the United States Pharmacopeia‐National Formulary) material.” The method was found to be reliable and robust (Zhang et al. 2015). In a study by Yaman and group, in their quest to understand the cytotoxicity of hemp oil and its fatty acid profiling, they employed a derivatization‐free GC‐FID method. The fatty acid comparison after simple extraction (n‐hexane and methanolic solution of 2 N KOH) was performed against the FAME standard, which was found to be reliable and precise for the estimation (Yaman et al. 2025). A similar study was conducted by Yugur and group on the estimation of fatty acids in garlic oil without any derivatization. The finding was corroborated with a FAME‐derived sample, and the analysis was found robust with a linear trend (Ugur et al. 2021).

In a similar context, we employed a modified GC‐FID method to determine the real‐time fatty acid composition in selected cream formulations. In the present analysis, we analyzed the sample without derivatization to FAMEs. The study thus presents an optimized method for the concurrent quantification of lauric, myristic, palmitic, oleic, and stearic acids. The chromatographic resolution was achieved using an RTX‐5MS capillary column under a nitrogen atmosphere, with the mobile phase maintained at a controlled temperature gradient. The validation of the present work was executed in alignment with ICH Q2(R1) requirements (Branch 2005). This was done statistically by assessments of system reliability, linear dynamic range precision, recovery performance, ruggedness, and analyte detectability. We obtained exceptional linearity across the samples (2.5–25 μg/mL [R ² > 0.999]), along with an RSD below 2% for the precision in both intraday and interday studies. The method was further corroborated by the real cream samples, which validated the accuracy of fatty acid profiling and the routine quality control implementation of the present method.

2. Materials and Methods

2.1. Chemicals

All the key markers for the fatty acids, namely, lauric acid, myristic acid, palmitic acid, oleic acid, and stearic acid, of analytical grade with an assay of 99% were procured from Sigma‐Aldrich. Analytical‐grade isopropyl alcohol (IPA) was obtained from Rankem (India), while the potassium hydroxide (KOH) in pellet form, hexane (AR), and methanol (AR) were purchased from SRL India. Double‐distilled Milli‐Q water was used wherever required during the analysis.

2.2. Instrument and Associated Conditions

GC‐FID system (Shimadzu GC‐2014) was used for quantification, utilizing C18 columns (RTX‐5MS) with dimensions of a diameter of 0.25 mm (inner), film thickness of 0.25 μm, and a length of 30 m. The carrier gas was nitrogen, and each sample was injected at 1 μL. The following instrument parameters were used: inlet temperature: 250°C; detector temperature: 270°C; column temperature: initially 150°C for 2 min and then increased to 220°C at the rate of 10°C/min for a hold time of 1 min, further increased to 230°C at 2°C/min with no hold time; again, the column temperature was increased to 270°C at the rate of 20°C/min to get the column condition for the next injection. Total run time: 17.0 min; column flow: 1.0 mL/min; split ratio: 1:5.

2.3. Preparation of Standard Stock Solution

In 5 mL of IPA, 10 mg of each fatty acid (lauric acid, myristic acid, palmitic acid, oleic acid, and stearic acid) was dissolved and sonicated for 5 min. The final stock was made with a concentration of 1000 μg/mL for all five fatty acids.

2.4. Preparation of Working Solution

The 10 mL of stock solution was transferred to a 100 mL volumetric flask, and the final solution was made up to the mark (100 mL), which resulted in a 100 μg/mL working solution. The working solution was further utilized to prepare linearity concentrations of 2.5, 5.0, 10.0, 15.0, 20.0, and 25.0 μg/mL (Zhang et al. 2025). In addition, the samples were subjected to fatty acid extraction using a standardized protocol (Gużewska et al. 2024). Briefly, 100 mg of the sample was weighed and transferred to a 20 mL reaction vial. The sample was dissolved in 10 mL of hexane, with the addition of 100 μL of KOH (2 N) prepared in methanol (11.2 g in 100 mL). The tube was centrifuged for 30 s, and the supernatant was collected and transferred to a 2 mL autosampler vial for further analysis.

The real cream samples were prepared by taking 1 g of the cream sample and transferring them into a centrifuge tube. To the tube, 10 mL of n‐hexane was added, and the mixture was sonicated for 30 min. Post 30 min, samples were centrifuged at 7000 rpm for 10 min. The supernatant was collected (organic layer) and extracted with methanol (×3). The extracts were further dried in a rotary evaporator and diluted with IPA for GC analysis.

2.5. Method Validation

The present analytical method was developed considering the ICH guidelines 2005 (Branch 2005). The validation was conducted considering numerous parameters, including suitability, robustness, linearity, precision, accuracy, and the limit of detection (LOD) and limit of quantification (LOQ).

2.5.1. System Suitability

System suitability refers to a plethora of tests that are conducted during the experiment to ensure the proper functioning and validation of the analytical system. This ensures the developed analytical method is robust and reproducible, with accuracy and reliability during analysis of the test samples. This was done by conducting experiments on retention time (RT), tailing factor, theoretical plates, and resolution at 5 μg/mL in replicates. The statistical validation was carried out using RT and %RSD (relative standard deviation) of the peak area.

2.5.2. Linearity

The linearity is defined as the outcomes obtained based on the analyte varying concentrations. The calibration curves were drawn by plotting the respective fatty acid concentrations against their peak areas. The calibration range was achieved at concentrations of 2.5–25 μg/mL, and graphs were plotted using the five linear concentrations within the selected range, followed by derivation of the regression equation from the curve.

2.5.3. Precision

Precision specifies the intimacy of agreement under the prescribed conditions between measurement sequences. The precision study was done by assessing the repeatability (intraday) and intermediate precision (interday). The %RSD was used to measure variability across replicate (triplicate) measurements. The interday precision was calculated through replicates on the same day, while intermediate precision was recorded over the assay performed on two consecutive days. Both intraday and interday values were optimized for the validation and to ensure the consistency of the present method.

2.5.4. Accuracy

The accuracy of the analytical method is defined as the percentage of analyte recovered by adding a known concentration of it. In our experiment, we employed the spiking method at varying concentrations ranging from 5 to 15 μg/mL in triplicate.

2.5.5. Robustness

To find the robustness of the analytical method, we considered the impact of varying the flow rate, inlet, and detector temperature to determine variation in the analyte concentration obtained. Each parameter was assessed in triplicate injections.

2.5.6. LOD and LOQ

LOD and LOQ were performed to identify the minimum limits that provide a detectable response. To calculate these parameters, we employed Equation (1) for the determination of LOD and Equation (2) for the estimation of LOQ utilizing the calibration curve, considering the ICH guideline

$$\mathrm{LOD}=3.3\times \mathrm{\sigma }/\mathrm{S}\dots \dots \dots \dots \dots$$ (1)

$$\mathrm{LOQ}=10\times \mathrm{\sigma }/\mathrm{S}\dots \dots \dots \dots .\dots$$ (2)

Herein, the “S” stands for slope of calibration curve (indicative of the residual standard deviation), while the standard deviation (SD) of the response is denoted by “σ”.

2.5.7. Specificity

To determine how the analyte is identified in the presence of other substances, we performed specificity analysis. This was done by comparison of the chromatograms of the blank sample with those of the fatty acid samples under evaluation (Pszonicki 1977).

The statistical analysis for all the parameters tested was done. Linearity was assessed using the calibration curve, including linear regression and the correlation coefficient (R ²). LOD and LOQ were determined statistically based on the SD and slope. Precision was calculated by intraday precision and interday precision.

3. Results and Discussion

The validated GC‐FID method developed for the simultaneous determination of fatty acids was found to be efficient. All analytes exhibited sharp, symmetrical peaks with tailing factors below 2.0 and theoretical plate counts exceeding 10,000. RT consistency demonstrated %RSD values below 0.5%, confirming excellent system stability.

3.1. System Suitability Validation

Initially, the system suitability was calculated based on RT, tailing factors, theoretical plates, and resolution. The system was deemed suitable because the %RSD for six replicate injections was less than 2. The results for all five fatty acids are shown in Table 1. The analysis revealed that the RT (elution) for all fatty acid samples was within 15 min. The %RSD (RT) was 0.18%–0.45%, within the acceptance criterion (2.0%) (Haddad et al. 2020). This indicates improved reproducibility and consistent RT measurements. The number of theoretical plates was found to exceed 10,000. This indicates a good elution. Furthermore, the resolution for each fatty acid peak was greater than 2, and the tailing factor for each fatty acid was less than 2, indicating that the system meets the suitability requirements for analysis.

TABLE 1.

System suitability parameters were analyzed for the given five fatty acids.

Peak no.	Peak name	Retention time (min)	Tailing factor	Theoretical plates	Resolution	%RSD
1.	Lauric acid	6.37	1.02	83,422	0.00	0.18
2.	Myristic acid	8.55	1.23	225,004	27.22	0.26
3.	Palmitic acid	10.91	1.22	265,592	30.08	0.13
4.	Oleic acid	13.67	1.29	284,321	29.60	0.36
5.	Stearic acid	14.05	1.53	289,467	3.62	0.45

3.2. Linearity Analysis

Next, given the linearity of the analysis, we prepared dilutions of all fatty acids at concentrations ranging from 2.5 to 25.0 μg/mL, in increments of 2.5 μg/mL. Linearity calibration was performed by considering the mean correlation coefficient. The analysis revealed (Figure S1a–e) that all fatty acids were present at linear concentrations across the range of 2.5–25 μg/mL. The increase in concentration was reflected in a proportional increase in the mean peak area, validating the linear relationship. The method of authentication was further confirmed by correlating the correlation coefficient (R ²) value, thus establishing the interrelationship. Overall, the linearity analysis data established the method's suitability for quantifying fatty acids. It was suggested that the peak area increased with increasing concentration, thus corroborating the findings (Araujo 2009). The outcome of linearity (concentration vs. peak area) is provided in Table 2. In brief, each fatty acid exhibited a linear detector response over the concentration range used during the analysis. The correlation coefficient was found to surpass 0.999. While the residual plots showed no deviation, indicating linearity, this suggests that the calibration curves derived may be suitable for quantitative assessments.

TABLE 2.

Outcome of the linearity analysis (concentration vs. peak area).

Concentration (μg/mL)	Peak area of lauric acid	Peak area of myristic acid	Peak area of palmitic acid	Peak area of oleic acid	Peak area of stearic acid
2.5	16,874	9862	4796	2116	2560
5.0	38,705	20,860	12,729	5300	6522
10.0	73,872	44,727	31,819	12,859	15,672
15.0	110,286	72,541	53,610	21,931	26,078
20.0	140,394	103,472	74,377	30,736	35,697
25.0	178,351	130,977	98,975	40,250	46,411

3.3. Precision Analysis

Next, we focused on the precision analysis of our present methodology. The analysis includes repeatability and intermediate precision. In repeatability, the technique was repeated under the same conditions, whereas in intermediate precision, it was repeated on different days. The %RSD was ≤ 2% in both repeatability and intermediate precision. For this study, three injections at 5, 15, and 25 μg/mL were administered for each fatty acid, yielding a total of nine determinations per fatty acid. The precision data for all five fatty acids are presented in the Supporting Information file (Tables S1–S5). In comparison, the consolidated data for all five fatty acids, with columns showing the %RSD for both intraday and interday studies at the three tested concentration levels, are presented in Table 3. The precision was calculated by measuring the mean peak area, the SD, and the %RSD. The analysis revealed that the %RSD for all acids was 0.3%–1.26%, which falls within the acceptance criterion of 2.0%. The low %RSD indicates high precision, enabling reliable, consistent measurement of peak area and further confirming the method's reproducibility under routine laboratory conditions.

TABLE 3.

Consolidated data for all five fatty acids, with columns showing the %RSD for both intraday and interday studies at the three tested concentration levels.

Fatty acids	Intraday %RSD			Interday %RSD
Concentration levels (μg/mL)	5	15	25	5	15	25
Lauric acid	1.26	0.32	0.39	1.24	0.16	0.48
Myristic acid	0.25	0.05	0.06	0.23	0.11	0.07
Palmitic acid	0.36	0.24	0.03	0.25	0.11	0.05
Oleic acid	1.09	0.34	0.23	0.82	0.18	0.18
Stearic acid	0.90	0.07	0.13	0.54	0.20	0.22

Next, for the calculation of intermediate precision, we injected three injections each of 5, 15, and 25 μg/mL (covering the entire range) for each of the fatty acids (a total of nine determinations for each fatty acid), respectively, on different days of sample preparation. The %RSD was measured and found to be within an acceptable range of below 2%. The interday precision data presented in the Supporting Information file (Tables S6–S10) ranged from 0.48% to 1.24% for the given fatty acids at three varying concentrations. The analysis thus revealed a high level of precision in measuring intraday peak area. Replicate injections on different days yielded similar peak areas, further indicating the reproducibility of the present method. In addition, the peak areas for the varying‐day experiments were found to be overall consistent, with relatively small but acceptable variations, thus confirming the reliability and repeatability of the present method.

3.4. Accuracy and Recovery Analysis

Next, our analysis of accuracy and recovery (Tables S11–S15) using the standard addition method at three response levels (50%, 100%, and 150%) revealed that the fatty acid samples yielded mean recoveries of 98% to 102%. The consolidated reporting of each concentration level (50%, 100%, and 150%) is made in Table 4. This indicates that the current analytical method is sufficiently competent for the accurate and reliable analysis of fatty acids in samples. Thus, in a nutshell, the recovery studies signified a high degree of trueness and efficient extraction from the cream matrix.

TABLE 4.

Consolidated reporting of the mean (%), recovery values for each concentration level (50%, 100%, and 150%) for all five fatty acids.

Fatty acids	(%) Mean recovery
	LQC 50 μg/mL	MQC 100 μg/mL	HQC 150 μg/mL
Lauric acid	98.92	99.38	100.21
Myristic acid	98.99	99.90	99.88
Palmitic acid	100.68	101.06	99.99
Oleic acid	98.88	100.34	100.31
Stearic acid	99.83	100.96	100.73

3.5. Robustness Analysis

Next, we analyzed the robustness of the present analytical method. It was done by considering the influence of variations in inlet temperature, detector temperature, and column flow on fatty acid detection. Each variation was studied in triplicate, indicating (Tables S16–S25) that variations in GC conditions were not associated with significant changes, thus signifying the method's resilience against operational fluctuations (% RSD within ±2%). The consolidated table, reflecting the quantitative effect of each tested parameter variation, includes the percentage change in peak area and %RSD for each condition (Table 5).

TABLE 5.

Consolidated table of all the fatty acids reflecting their quantitative effect on the change in peak area and retention time (%RSD) under each condition.

Fatty acids	Change in column flow		Change in inlet, detector, and temperature a
	Peak area %RSD		Retention time %RSD
Parameters	0.9 mL/min	1.1 mL/min	+10°C	−10°C
Lauric acid	0.36	0.04	0.19	0.11
Myristic acid	0.06	0.11	0.07	0.08
Palmitic acid	0.09	0.06	0.10	0.09
Oleic acid	0.07	0.30	0.16	0.10
Stearic acid	0.23	0.19	0.12	0.09

^a The temperature refers to the initial temperature of 150°C for 2 min, then increased to 220°C at the rate of 10°C/min for a hold time of 1 min, further increased to 230°C at 2°C/min with no hold time; again, the column temperature was increased to 270°C at the rate of 20°C/min.

3.6. LOD and LOQ Analyses

Lastly, we evaluated the LOD and LOQ for the fatty acid samples. This was calculated based on the data's SD and the slope of the regression line. The outcome (Table 6) revealed that the method has adequate sensitivity for analyzing fatty acids, even at low levels, in cream formulations.

TABLE 6.

Analyzed LOD and LOQ, indicating the sensitivity for the individual fatty acid samples.

	Lauric acid (μg/mL)	Myristic acid (μg/mL)	Palmitic acid (μg/mL)	Oleic acid (μg/mL)	Stearic acid (μg/mL)
LOD	1.058	1.454	1.530	1.520	1.011
LOQ	3.529	4.847	5.103	5.067	3.372

3.7. Real Sample Application

We further applied the developed methodology to real sample applications. We employed cream samples and attempted to separate and quantify the fatty acids present, thereby validating the analytical technique for efficient quality control screening and compositional evaluation. Briefly, the injection of the blank (IPA only) alone (Figure S2A) was performed, and no response was obtained, which demonstrated the non‐existence of interferences to the RT of the substances present in the sample. Next, we confirmed the RTs of the chosen fatty acids by associating their RT values with those of the co‐injected respective fatty acids. The analysis also led us to find that all the acids were resolved adequately (Figure S2B) with different RTs (lauric acid [6.37], myristic acid [8.55], palmitic acid [10.91], oleic acid [13.67], and stearic acid [14.05]), thus making their easy detection possible. All target fatty acids were efficiently separated and quantified, which demonstrated the excellent applicability of the protocol for compositional evaluation.

The analysis of Sample 1 (Figure S3; Table S26) revealed a moderate overall fatty acid content. The major fatty acids identified were stearic acid (21.16 μg/mg) and palmitic acid (14.22 μg/mg), suggesting the use of wax or heavy cream emulsifiers in the cream (Eccleston 1997). The GC‐FID analysis of Sample 2 revealed (Figure S4; Table S27) a balanced profile of all the fatty acids under consideration. The major fatty acids, palmitic (11.82 μg/mg) and stearic (12.62 μg/mg), thus increase the likelihood that coconut oil or palm‐kernel derivatives are present in the cream (Young 1983).

The analysis of Sample 3 revealed (Figure S5 and Table S28) a low concentration of fatty acids, with a total content not exceeding 3.32 μg/mg. This suggests that the formulation is either gel or water based, with low levels of palmitic acid (1.44 μg/mg) and stearic acid (1.22 μg/mg).

Next, analysis of Sample 4 revealed that it contains high concentrations of fatty acids (Figure S6 and Table S29). The predominant fatty acids included oleic acid (97.09 μg/mg) and palmitic acid (77.88 μg/mg). This is indicative that the cream may contain a high content of oleic plant oils. In addition, the lower stearic acid concentration (12.78 μg/mg) and trace amounts of residual acids indicated the use of oil‐rich emollients in the formulation (Lodén 2003). The consolidated data on the corresponding fatty acids for the sample (1–5) are shown in Table 7. The overall findings suggested that the developed GC‐FID protocol successfully differentiated four samples based on their fatty acid content and profiles.

TABLE 7.

Consolidated data of corresponding fatty acids of the sample (1–5) showing the quantified amount (μg/mg) by deploying the validated method.

Fatty acid	Sample 1	Sample 2	Sample 3	Sample 4
	Quantified amount (μg/mg)
Lauric acid	—	2.44 12.20 μg/mL	0.25 (1.27 μg/mL) (< LOQ)	0.01 (0.07 μg/mL) (ND)
Myristic acid	—	1.48 (7.40 μg/mL)	0.41 (2.05 μg/mL) (< LOQ)	0.50 (2.48 μg/mL) (< LOQ)
Palmitic acid	14.22 (71.10 μg/mL)	11.82 (59.10 μg/mL)	1.44 (7.20 μg/mL)	77.88 (389.40 μg/mL)
Oleic acid	2.24 (11.20 μg/mL)	0.97 (4.86 μg/mL) (< LOQ)	—	97.09 (485.45 μg/mL)
Stearic acid	21.16 (105.80 μg/mL)	12.62 (63.10 μg/mL)	1.22 (6.10 μg/mL)	12.78 (63.90 μg/mL)

Note: (< LOQ): Concentrations above the LOD but below the LOQ. (ND): Concentration below the LOD/LOQ, hence not detected under the sensitivity of the present method.

3.8. Comparative Analysis With the Literature

The literature indicates that fatty acids may also be determined by free‐derivatization methods. The reports, however, differ in the matrix method employed, the sample preparation method, the analytical scope, and the scope of validation. Sampson and colleagues disclosed a GC method for the quantification of seven individual free fatty acids without derivatization. The method was disclosed to have linear calibration behavior with recoveries up to 75%–80%. The method was found robust for analyzing 30 samples/day. The method was developed exclusively for plasma, and each run required a standard‐based correction and was not found to address the complex semisolid matrices, including cream (Sampson and Hensley 1975). Next, the research by Julin and group disclosed a derivatization‐free GC method for quality control of fatty acid‐based raw materials in the soap industry. The analysis was carried out in WCOT columns. The method was found to employ 15 min for each run and was able to perform rapid identification of fatty acids with C8–C24 length. The method was used exclusively for industrial raw materials to calculate percentage composition, rather than for absolute quantification in complex pharmaceuticals such as creams (Julin et al. 1983). In this league, Brooks and group disclosed underivatized GC analysis of fatty acids, primarily oleic acid, as impurities. The determination was confined to simple matrices and does not discuss the simultaneous determination of fatty acids in complex samples such as creams (Brooks et al. 1990). Next, in this league, research led by Vičanová et al. demonstrated GC analysis of underivatized free fatty acids using a phase column and programmed‐temperature vaporization. The analysis also disclosed the need for analyte‐specific validation as the method suffered from analyte loss and non‐linear calibration behavior for longer and unsaturated fatty acids, and failed to find its application in complex matrices such as cream (Vičanová et al. 1994). Similar to our present research, Oveysi and colleagues reported GC determination of fatty acids without derivatization in infant formula. The method involved the saponification and extraction of the sample prior to GC analysis. The study demonstrated the feasibility of simultaneously determining fatty acids in lipid‐rich complex food matrices. The method was robust, with expected linearity for infant formula rather than for the cream samples or the complex cream matrices (Oveysi et al. 2006).

In another similar study, Meng and group disclosed direct GC determination of long‐chain fatty acids in human serum. They employed a fast temperature program equipped with microbore columns. The method resolved C12–C26 fatty acids within 4 min, with LODs of 2.8–9.6 μg/mL and recoveries of 63.1%–97.0%. The RSD values (interday and intraday) were below 15%. The method, though demonstrated for the serum, was not applied to semisolid cream matrices (Meng et al. 2007). A similar method was further disclosed by Zhang and colleagues, which simultaneously determined oleic acid and related fatty acids in USP‐NF materials without derivatization. The method separated the 15 fatty acids on a DB‐FFAP column within 20 min. The analysis reported excellent linearity (correlation coefficient = 0.9995–0.9998) with the detection limits around 0.13–0.55 μg/mL and the quantification limits of 0.27–1.10 μg/mL. During the analysis, accuracy was acceptable (99.6% oleic acid recovery) and precision was good (RSD below 3%). The method, though robust, was found suitable for oleic acid‐based raw material but not for semisolid‐type complex cream samples(Zhang et al. 2015). Another derivatization‐free method was reported by Spandana and her group. The team reported the analysis of lauric acid in coconut oil, with a low LOD and LOQ of 0.385 and 1.168 μg/mL, respectively. Though the method was robust, it was not employed for the complex matrices (Spandana and Gurupadayya 2019). Compared with these methods, the present work presents a validated, derivation‐free GC‐FID method for the simultaneous detection of five acids in a cream sample and demonstrates its validation on the cream sample. The method we propose involves matrix novelty, no derivatization, and simultaneous estimation of five fatty acids, with acceptable sensitivity, and was found to be practical for routine use.

4. Conclusion

The present GC‐FID method was developed for the simultaneous determination of five fatty acids, which include lauric, myristic, palmitic, oleic, and stearic acids, in cream samples. In the present method, we employed a direct GC method without the derivatization of free fatty acids. This was done since in the complex samples such as cosmetics, co‐extraction of other acidic components, surfactants, and lipids can affect the peak shape, response, and volatility of the desired components, potentially interfering with detection. Consistent with the available literature reports for detection of free fatty acids in pharmaceutical samples without derivatization, we were able to achieve the analysis valid and were able to apply the method on real cream samples for further corroboration of the developed method. The chromatograms obtained from the analysis showed baseline‐resolved peaks for all five fatty acid samples, confirming their suitability for quantitative analysis. The key parameters we found to correlate include the RTs of samples relative to the standards, and we did not observe any significant coeluting interferences during analysis. The linearity analysis of fatty acids at concentrations of 2.5–25 μg/mL indicated proportionality between the detector response and concentration. The residual analysis was further performed to evaluate model adequacy. The analysis revealed that the residual plots showed both positive and negative residuals, randomly scattered around zero, with no major fluctuation (increasing or decreasing trend) with changes in concentration, suggesting a linear model. In addition, the linear performance observed was comparable to that reported in the literature for derivatization‐free fatty acid analysis (Botcherby 2016; Hai‐yin et al. 2011; Lenti et al. 2022; Ugur et al. 2021; Yaman et al. 2025; Zhang et al. 2015). Further, the method was found to have good repeatability and possess intermediate precision, demonstrated by low %RSD values: for example, Lauric acid showed intraday %RSD of 1.26%, 0.32%, and 0.39% and interday %RSD of 1.24, 0.16, and 0.48% across the three levels; similarly low %RSD values were obtained for the remaining fatty acids. This indicated acceptance of the quantitative GC‐FID method developed herein and suggested that the present method may accurately quantify fatty acids in the tested range. Considering the robustness, measured by changes in flow rate and detector temperature, revealed low %RSD values across all tested variations. The robustness outcome is thus consistent with the literature that portrays that upon optimized GC conditions, minor operational fluctuations like column, inlet, and temperature do not compromise the outcome in a significant way. Moreover, applying the method to the samples yielded quantified fatty acid levels in the cream samples, suggesting an ICH‐style validation framework.

In a nutshell, the validation of the present work was executed in alignment with ICH Q2(R1) requirements. This was done statistically by including assessments of system reliability, linear dynamic range, precision, recovery performance, ruggedness, and LOD and LOQ in cream formulations. The developed method showed excellent analytical performance, supported by satisfactory linearity, high precision, acceptable variability, and robustness, thereby reducing analytical variability. Thus, the present methodology is consistent with the literature and allows a reliable, direct determination of fatty acids with the highest acceptable precision, accuracy, linearity, and robustness. In contrast, the study focuses on a limited set of fatty acids and cream samples; therefore, it requires further studies to substantially extend its applicability to additional fatty acids and other pharmaceutical or cosmetic samples.

Funding

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Figure S1: Calibration curve of (A) lauric acid; (B) myristic acid; (C) palmitic acid; (D) oleic acid; (E) stearic acid. The intercept represents the detector's baseline response at a “0” concentration.

Table S1: The precision analysis of lauric acid.

Table S2: The precision analysis of myristic acid.

Table S3: The precision analysis of palmitic acid.

Table S4: The precision analysis of oleic acid.

Table S5: The precision analysis of stearic acid.

Table S6: Interday precision data for lauric acid.

Table S7: Interday precision data for myristic acid.

Table S8: Interday precision data for palmitic acid.

Table S9: Interday precision data for oleic acid.

Table S10: Interday precision data for stearic acid.

Table S11: Accuracy and recovery data for lauric acid.

Table S12: Accuracy and recovery data for myristic acid.

Table S13: Accuracy and recovery data for palmitic acid.

Table S14: Accuracy and recovery data for oleic acid.

Table S15: Accuracy and recovery data for stearic acid.

Table S16: Robustness (change in column flow) for the lauric acid.

Table S17: Robustness (change in column flow) for the myristic acid.

Table S18: Robustness (change in column flow) for the palmitic acid.

Table S19: Robustness (change in column flow) for the oleic acid.

Table S20: Robustness (change in column flow) for the stearic acid.

Table S21: Robustness outcome (for change in inlet and detector temperatures) for the lauric acid.

Table S22: Robustness outcome (for change in inlet and detector temperatures) for the myristic acid.

Table S23: Robustness outcome (for change in inlet and detector temperatures) for the palmitic acid.

Table S24: Robustness outcome (for change in inlet and detector temperatures) for the oleic acid.

Table S25: Robustness outcome (for change in inlet and detector temperatures) for the stearic acid.

Table S26: Quantification data of different fatty acids present in Sample 1.

Table S27: Quantification data of different fatty acids present in Sample 2.

Table S28: Quantification data of different fatty acids present in Sample 3.

Table S29: Quantification data of different fatty acids present in Sample 4.

Figure S2: Chromatograms indicating the retention time of IPA.

Figure S3: Chromatograms indicating the retention time of different fatty acids in Sample 1.

Figure S4: Chromatograms indicating the retention time of different fatty acids in Sample 2.

Figure S5: Chromatograms indicating the retention time of different fatty acids in Sample 3.

Figure S6: Chromatograms indicating the retention time of different fatty acids in Sample 4.

Data Availability Statement

The data that support the findings of this study are available in the Supporting Information of this article. Any additional clarifications or data, if needed, for this study are available upon request from the corresponding author.