Quantification of Menthol by Chromatography Applying Analytical Quality by Design

Quantification of Menthol by Chromatography Applying Analytical Quality by Design | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quantification of Menthol by Chromatography Applying Analytical Quality by Design Emerson Eliecer León Ávila, Ronald Andrés Jiménez Cruz, Santiago Puentes, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6873956/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Jan, 2026 Read the published version in Chromatographia → Version 1 posted 10 You are reading this latest preprint version Abstract The concept of Analytical Quality by Design (AQbD) has recently gained prominence and offers multiple advantages for the pharmaceutical industry. AQbD is an approach that employs risk management to develop robust analytical methods that adhere to regulatory requirements. Menthol is a common raw material used in formulating various pharmaceutical products, including creams, ointments, and salves. We implemented an Analytical Quality by Design (AQbD) approach to establish optimal parameters for identifying and quantifying menthol. This methodology utilizes gas chromatography coupled with an internal standard to create a reliable, reproducible, and cost-effective analytical method. In developing the method through AQbD, we first defined the Analytical Target Profile (ATP). We employed risk management tools to identify Critical Analytical Attributes (CAAs), such as resolution, retention time, theoretical plates, capacity factors, and choking factors. We also determined Critical Process Parameters (CPPs), including oven temperature and flow rate. The influence of CPPs on CAAs was assessed using a factorial experimental design. Subsequently, we established the Method Operable Region (MODR) to pinpoint the optimal conditions and specified parameters for each CAA, as indicated by response surface diagrams. The optimized method underwent validation according to international regulatory guidelines to ensure system suitability. This validation process included evaluations of linearity, accuracy, and precision, as well as the determination of limits of detection and quantification. The results confirmed that the conditions were appropriate for analyzing menthol using the gas chromatographic technique with an internal standard. Menthol thymol Analytical quality by design (AQbD) gas chromatography validation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Menthol is a tricyclic alcohol derived from the volatile oils of certain mint species. It naturally occurs as levomenthol and racementhol. This compound is mainly used as a flavoring and scent agent in various products, including cigarettes, liqueurs, cough drops, mouthwashes, toothpaste, and shampoos [ 1 ]. There has been a noticeable increase in analytical method failures during method transfers in research and development and quality control departments. Furthermore, there is growing interest in developing reliable and reproducible analytical methods. Chromatography has emerged as the standard approach for quantification in the pharmaceutical industry [ 2 ]. Analytical Quality by Design (AQbD) is a risk management-based approach that develops robust analytical methods that satisfy regulatory requirements. The AQbD approach helps create reliable methods, offering several benefits, such as reduced unexpected errors, fewer out-of-specification (OOS) and out-of-trend (OOT) results, and a lower risk of method failure during the method transfer process. AQbD offers regulatory flexibility, enhances robustness, and removes the need for revalidation in product development [ 3 ]. This study used an Analytical Quality by Design (AQbD) approach to identify and quantify menthol effectively. We employed gas chromatography combined with an internal standard. We aimed to develop a reliable, reproducible, and cost-effective analytical method, as menthol is a widely used raw material in various pharmaceutical, cosmetic, and food products. To develop the method using the Analytical Quality by Design (AQbD) approach, we started by establishing the Analytical Target Profile (ATP). We utilized risk management tools to identify the Critical Analytical Attributes (CAAs), which include resolution, retention time, theoretical plates, capacity factor, and choking factor. Additionally, we determined the Critical Process Parameters (CPPs), such as oven temperature and flow rate. We then evaluated the effect of these CPPs on the CAAs using a factorial experimental design. Subsequently, we defined the Method Operable Region (MODR) to pinpoint the optimal conditions and established specifications for each CAA by employing response surface diagrams. Materials and methods Materials used included analytical standard menthol (purity 99%), analytical grade thymol (purity > 98.5%), and chloroform (purity ≥ 99.9%) for HPLC and GC applications, all purchased from Sigma-Aldrich (St. Louis, MO, USA). Chromatographic Instrument and Conditions : The chromatographic was separated using a Thermo Scientific Trace 1310 GC gas chromatograph equipped with an AOC-20i Plus autosampler and a flame ionization detector (FID). The separation utilized an SH-I-5Sil MS column composed of silylene 1,4-bis(dimethylsiloxane)phenylene and dimethylpolysiloxane, with a film thickness of 0.25 µm and a length of 30 m. Chromatographic analysis was performed under the following conditions: gradient elution mode, with the detector temperature set at 250°C and the injection temperature set at 280°C. The split ratio was 50, and the injection volume was 10 µL, except for the oven temperature and flow rate, which AQbD determined. Quality by Design Analytical Approach (AQbD) Definition of the analytical target profile (ATP). The analytical target profile (ATP) was the fundamental axis of the AQbD methodology since it defines what will be analyzed and at what level it is necessary to do so [ 4 ]. This allowed the method to be designed and directed to ensure compliance with the ATP. The ATP included elements such as analytes, sample types, analytical techniques, analytical instruments, method quality requirements, and the method's analytical attributes. Risk assessment. The risk assessment approach was carried out through a bibliographic search related to gas chromatography and menthol quantification to identify the possible variables of the method and how they are involved in its development. Subsequently, a risk analysis was carried out using an Ishikawa or cause and effect diagram. This tool describes the qualitative relationship between the critical parameters of the method and the critical analytical attributes that are part of the quantification process. Once the method parameters affecting the process were identified and analyzed, an evaluation of these parameters was carried out using a Failure Modes and Effects Analysis (FMEA) [ 5 ]. These tools allowed determination of the critical parameters of the method, which were classified and evaluated through the scoring criteria described in supplementary material 1. Through this evaluation, we sought to evaluate and select those CMPs and CAAs determined by the risk priority number (RPN) obtained from the identified S, O, and D indices, where those parameters with an RPN > 100 were considered critical. Sample preparation The solutions handled in the AQbD section were developed as follows: 24.0 mg of menthol and 38.0 mg of thymol were taken in a volume of 10.0 mL of chloroform for the standard. Design of experiments (DOE). The chromatographic method tests were developed with a complete factorial design, which is based on a study to determine the effect of the independent or input variables on the dependent or output variables. For this case, the CMPs were taken for this study, with six oven temperatures (85, 95, 115, 130, 170, 200°C) and seven flow rates (0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0 mL/min) and thus evaluate the response that is presented in the CAAs (coleus factor, theoretical plates, retention times, resolution and capacity factor). Operational design region of the method (MODR). Once the DOE data were obtained, response surface graphs and an overlay graph were constructed with the CMPs and CAAs components to optimize the proposed tests. The Design Expert 22.0.6 program was used to develop the statistical model of the research and thus determine the best chromatographic conditions and their design space. Validation of the method. The chromatographic method was validated according to the ICH Q2 (R2) guide and FDA recommendations, considering the system's suitability, linearity, accuracy, precision, and detection and quantification limits [ 6 ]. Calibration standards. Stock solutions of menthol as analyte (5480 ppm) and the internal standard thymol (1040 ppm) were prepared by dissolving with chloroform. They were used to carry out the dilutions in the following process steps. System suitability. The parameters for system suitability of the method were determined by injecting a solution five times with a standard concentration of 356 ppm menthol and 520 ppm thymol to obtain reproducible results. For the chromatograms obtained, the peak asymmetry (tailing factor) was 2.0 and the RSD was < 5.0% [ 6 ]. Detection and quantification limits. Detection (LO) and quantification (Q) limits were established based on the standard deviation of linear response and slope (calibration curve) from 6 calibration standards (1.1, 2.2, 4.4, 5.5, 8.2, and 16.4 ppm). Linearity. Nine calibration standards (55, 110, 192, 356, 740, 986, 1507, 2000, 2740 ppm) were prepared by sequential dilutions of the menthol stock solution (10, 20, 35, 65, 90, 135, 180, 275, 365, 500 µL) and 500 µL of the thymol stock solution was added to each of these, to develop it by the internal standard method. The calibration curves were made by linear regression between the ratio of the menthol peak area divided by the thymol peak area versus the ratio of the menthol concentration divided by the thymol concentration. Additionally, to evaluate this linearity, two samples were analyzed for each concentration level [ 6 ]. Accuracy. It was performed based on a sample with a known concentration of menthol (actual value). An aliquot of 300 µL was taken from the menthol stock solution, and it was brought to a final volume of 1000 µL with 500 µL of the internal standard and 200 µL of chloroform to obtain a concentration of 1644 ppm. This entire process was performed in triplicate. The relative accuracy percentage was then determined, which should be < 5.0% for the experimental value [ 6 ]. Precision . It was determined using five solution samples with a standard concentration of 356 ppm menthol and 520 ppm thymol. To assess the degree of dispersion between these five determinations of the same sample, it is recommended that the RSD be < 5.0% [ 6 ]. Results and discussion Traditional approaches to analytical method development focus on building up the method's initial conditions, physicochemical parameters of the analyte, sample, analytical technique, and instrument one factor at a time. Additionally, this method does not allow for optimization. It does not investigate the relationships between critical parameters and method response or performance, increasing the likelihood of method failures during method transfer. Therefore, the first stage in developing a method with acceptable confidence in the quality of decisions made from the result is the definition of ATP. The ATP prospectively summarizes the requirements of measuring a quality attribute that an analytical procedure must meet. The ATP is used to define and assess the suitability of an analytical procedure in the development phase and during all changes throughout the analytical life cycle [ 7 ]. Table 1 shows the elements that were considered when creating the ATP. Element ATP Objective Justification Analyte Menthol The analyte of recent pharmaceutical interest has no history of applying the AQbD methodology for its quantification. Sample Solid Due to its physicochemical characteristics, the analyte is solid at room temperature. Therefore, to achieve volatilization of the entire sample, it is necessary to completely solubilize it before performing the analysis. Analytical technique Chromatography An analytical technique capable of quantifying, separating, purifying, and identifying a vast range of analytes using a simple theoretical foundation. Instruments GC-FID Gas chromatography is characterized by its high speed, precision, and sensitivity for separating and analyzing chemical compounds. The FID detector has a wide dynamic range, allowing quantification from traces to high concentrations. It is also a stable and robust detector that provides consistent and reproducible results. Table 1 . An analytical Target Profile (ATP) was established for the chromatographic analysis of menthol. Risk assessment. Following the guidelines in the ICH Q9 guidelines provide the principles and examples of tools for quality risk management that can be applied to different aspects of pharmaceutical quality, which indicate risk assessment is a systematic process for the evaluation, control, communication, and review of risks to quality throughout the product life cycle [ 8 ]. According to the ICH Q9 guideline, risk assessment can be performed in 3 steps: identification, analysis, and assessment. Risk identification aims to detect and prioritize all Potential Method Variables (PMV) and Potential Method Attributes (PMA). These should cover all aspects related to the analyst, materials, instruments, method, and environment that can directly or indirectly influence the quality of the method [ 9 ]. It is important to highlight that the previous literature review, the deep knowledge of the process, and the researcher's experience play a fundamental role in identifying the PMCs. Considering the above, an Ishikawa diagram was made from previous knowledge and a theoretical review of the technique, as shown in Fig. 1 . This tool allows us to qualitatively identify all those factors that can directly or indirectly affect the method's performance, considering the researcher, materials, methods, measurement, environment, and equipment. The diagram allows us to identify 24 direct or indirect variables that can potentially affect the method's performance. From this diagram, parameters related to the method and the equipment were selected. This allowed the identification of which of these variables is critical and the prioritization of them according to their severity, occurrence, and detectability through the FMEA matrix, as shown in supplementary material 2. It should be noted that this tool consists of two stages. The first focuses on identifying possible failure modes, their effects, and possible causes. In the second stage, these failure modes were classified according to their severity index (S), which is the seriousness of the consequence of this failure; their occurrence (O), which corresponds to the probability or frequency of occurrence of failure; and according to detectability (D), associated with the probability that the failure is detected before it has an impact on the process, which allowed reducing the risk of a possible failure by determining a preventive strategy [ 10 ]. Flow rate and oven temperature were the parameters with the highest NPR score and were selected as the critical method parameters. In addition, the remaining variables were considered non-critical, as they can be quickly resolved or do not significantly affect method performance or ATP compliance. However, in the case of detector and injection temperatures, they were modified during the analysis, and it was rectified that these variables were not critical. The oven temperature and the carrier gas flow rate were determined as the critical method parameters (CMPs) mainly because these parameters allow for obtaining low retention times with acceptable peak resolution, facilitating the development of a simple and economical method. The FMEA matrix allows us to identify the critical analytical attributes. Five attributes were selected because they yielded an NPR score greater than 100. Considering the above, the ideal CMP values for each of these CAAs ​​were determined through an experimental design. Method development and optimization: critical analytical attributes. A response surface plot is an advanced DOE technique that helps to better understand a method and optimize the response. The data used to construct the response surface plots are presented in Supplementary Material 3 . Retention times (Tr). This retention time is mainly influenced by temperature and flow rate. Temperature affects the volatility of the analyte and, therefore, its interactions with the stationary phase. As the temperature increases, the volatility of the analyte increases, which reduces the interactions between menthol and the stationary phase, resulting in decreased retention times. This effect is beneficial for meeting the ATP. On the other hand, the flow rate also directly impacts the Tr since a higher flow rate causes menthol to move through the column at a higher speed, reducing its interaction with it and decreasing retention times. The surface diagram presented in Fig. 2 shows that the lowest Tr are found under temperature conditions between 200 and 170°C and at a flow rate between 1.0 and 0.9 mL/min. This is because the temperature affects the retention force of menthol in the stationary phase, while the flow rate affects the speed of this analyte movement in the column. Therefore, these values ​​were the most suitable to satisfy this variable. Additionally, a short retention time improves precision and accuracy in the quantification process, reducing the analysis time and, therefore, the probability of errors. Theoretical plates (N). The theoretical plates tell us how many theoretical equilibrium stages occur during the separation, so the more theoretical plates a column has, the better its separation capacity will be; then, the theoretical plates refer to the efficiency measure and the separation capacity of the column [ 11 ]. All N values ​​obtained in the DOE exceeded the minimum required by regulatory guidelines (N > 2000). As shown in Fig. 3 , all N values ​​were above 31643, indicating that all conditions were considered significant for this parameter. A high theoretical plate value indicates that the column (SH-I-5Sil MS) has a high separation efficiency and capacity. The packing structure with uniformly sized pores allows menthol to diffuse efficiently through the stationary phase, leading to higher theoretical plate efficiency and better resolution. Resolution (Res). Resolution measures the separation between two chromatographic peaks regarding their distance and width [ 11 ]. The value of this parameter equal to zero indicates that the peaks are superimposed and not separated at all, while a value of 2.0 is the minimum for a good separation [ 2 ]. As can be seen in Fig. 4 , the optimal resolution values ​​are obtained at a temperature of 200 and 170°C regardless of the flow rate. Therefore, temperatures below these will not obtain this method's desired resolution and retention times. Temperature has a significant impact on this CAA; as temperature decreases, resolution tends to increase almost linearly [ 12 ]. This indicates that temperature and resolution have an inversely proportional relationship. However, it is important to note that resolution is a factor in peak separation, so it is especially critical for methods that separate many analytes. In the case of this method, since it is an analysis of only menthol and thymol, the temperature will not significantly decrease peak resolution [ 12 ]. Coleus factor (Tc). It is important to note that all the peaks in the DOE assays tend to have remarkably high symmetry, as shown in Fig. 5 . This indicates, on the one hand, that the SH-I-5Sil MS column is in good condition and, on the other hand, that no overloading or strong interactions of menthol with the column are observed [ 13 ]. were found below value 1, thus complying with the FDA's recommendations. Capacity factor. The capacity factor indicates the degree of retention of the analyte in the column about the dead volume [ 11 ]. In all the runs carried out at the DOE, values ​​below two were obtained, as seen in Fig. 6 , indicating that menthol does not present good retention by the column, and this is due to several factors. One of them is that, when wanting to have low retention times, menthol needs to elute more quickly, which results in lower retention of it. In addition, when high proportions of organic solvents such as chloroform are used in this case, the retention of the analytes is reduced [ 2 ]. Method Development and Optimization (MODR). MODR is the region that establishes a range allowed by the CAAs and develops a multidimensional space relating to the CAAs and CMPs variables, generating an adequate procedure for quantifying menthol based on the ATP requirements. The identification of this region in the yellow area is found in Fig. 7 and was carried out based on the criteria defined in the system suitability section. Each red point in the gray region represents a possible chromatographic method (regarding oven temperature and flow rate). An interval was selected for each CAA to meet the defined criteria and achieve a successful development in the MODR, following what was established in the ATP. For convenience, the method parameters chosen to proceed with the method validation are a temperature of 170°C and a flow rate of 0.9 mL/min (as shown in the yellow region of Fig. 7 ). Table 2 Requirements for the performance of each CAA method to obtain the MODRN = number of theoretical plates. Res = resolution. Tc = tail factor. k’= capacity factor. Tr retention time. CAA Lower limit Upper limit N 31643 374728 Res 2.00 9.91 Tc 0.78 1.00 k’ 0.50 1.00 Tr (minutes) 2.00 4.00 These chromatographic conditions ensure the suitability of the chromatographic method and are associated with shorter retention times, higher resolution, high theoretical plates, and good peak symmetry. The resulting chromatogram with these conditions is shown in Fig. 8 . Final chromatographic conditions. Gradient elution was performed because this technique is more versatile than isothermal elution, resulting in better peak resolution, shorter analysis times, and better detectability. This can be seen in supplementary material 4 , where the final chromatographic conditions are shown. Method validation System suitability . It is necessary to perform system suitability tests to assess whether the method and equipment can produce accurate and precise results. The previously identified AACs (retention times, area, number of theoretical plates, resolution) were evaluated. The results of the system suitability assessment are shown in Supplementary Material 5 . It was found that the accuracy of the retention times and peak areas were compatible for both analytes according to the RSD ≤ 2.0%. In addition, an excellent yield of theoretical plates was observed, being 67698 for menthol and 91175 for thymol. These values ​​significantly exceeded the established acceptance criterion (> 2000), which allows us to affirm that the separation efficiency of the column was adequate. Detection and quantification limit The lowest concentration at which each analyte can be detected or identified with acceptable accuracy and precision was determined by calculating the typical error from the calibration curve shown in Fig. 9 . The developed method could not quantify samples with concentrations less than 312 ppm with acceptable precision and accuracy. Likewise, it will not be able to detect any analyte in samples with concentrations less than 104 ppm. Linearity. The linearity of the calibration standards was evaluated between 55 and 2740 ppm. The results are shown in supplementary material 6, from which the calibration curve was constructed by internal standard. The linear regression equation was obtained using the least squares method, resulting in a correlation coefficient of 0.9995. The R2 value obtained meets FDA recommendations, showing a strong correlation between the variables. Accuracy. The accuracy of the developed method was determined by the recovery percentage obtained from the calibration curve performed using the internal standard method (see Table 3 ). This was calculated using the equation of the straight line of the calibration curve performed, resulting in a relative accuracy percentage of 105.58%. This value slightly exceeds the proposed limit and is considered slightly accurate. This result is due to the influence of high uncertainty of the micropipettes used, contamination, or impurities in the samples. Table 3 Data to determine the accuracy parameter. Line equation y = 1,009x-0,1061 Value of Y (mV) 589356 Theoretical concentration value (ppm) 1644 Experimental concentration value (ppm) 1735.74 Recovery percentage 105.58% Precision . The relative standard deviation was determined to show the variability of the results of the areas obtained for menthol and thymol: 1.57% for menthol and 1.46% for thymol. This is below the acceptance criterion (2.0%), which allows us to affirm that the method developed for the quantification of menthol presents the repeatability required for the analysis, which allows it to produce precise results. To observe the chromatogram in the experiments of this parameter, see Fig. 10 . Considering the results obtained from the validation parameters evaluated, it is possible to affirm that the developed method meets the quality requirements necessary for its application in the industry. Additionally, it was possible to demonstrate the benefits of applying AQbD strategies to develop analytical methods, which allow for greater regulatory flexibility regarding the method, ease in the method transfer stage, and a reduction in resource expenditure. Conclusions An AQbD strategy was developed to quantify menthol through gas chromatography using a method that includes an internal standard. In this process, the critical parameters of the method that allow compliance with the proposed ATP were determined, which were oven temperature and flow rate, for their subsequent evaluation, as well as the critical analytical attributes, including resolution, theoretical plates, retention times, choking factor, and capacity factor. This evaluation was based on a risk analysis using an Ishikawa diagram and an FMEA matrix. Subsequently, an experimental design was carried out using a complete factorial design. This type of experimental design proved efficient when evaluating two factors at many levels, simultaneously being easy to apply and interpret. This type of design allowed us to know the response factor relationships that the CAAs have on the CMPs using response surface diagrams to obtain the operable region of the method and to determine the best chromatographic conditions, which were a temperature of 170°C and a flow rate of 0.9 mL/min since they are the ones that best allow compliance with the proposed ATP. In addition, they were validated according to the regulatory guidelines that covered linearity, accuracy, precision, detection limit, and quantification. In the same way, it was possible to demonstrate the different advantages presented by the application of AQbD strategies in the development of analytical methods compared to the strategies usually used, including the development of more robust methods, ease in the method transfer stage thanks to a better understanding of the process, regulatory ease, among others. In summary, the validation of the method supports the implementation of the AQbD methodology to define the most appropriate chromatographic conditions in industrial applications. Declarations Conflict of Interest Statement The authors declare that there are no conflicts of interest regarding the publication of this article. No financial, material, or other support was received from any entities or individuals that could influence the results or interpretations of the study. 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Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 17 Jan, 2026 Read the published version in Chromatographia → Version 1 posted Editorial decision: Revision requested 08 Oct, 2025 Reviews received at journal 29 Sep, 2025 Reviewers agreed at journal 09 Sep, 2025 Reviewers agreed at journal 07 Sep, 2025 Reviews received at journal 18 Aug, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviewers invited by journal 16 Aug, 2025 Editor assigned by journal 28 Jun, 2025 Submission checks completed at journal 13 Jun, 2025 First submitted to journal 11 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6873956","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504386404,"identity":"32679d04-baab-4b7e-86e2-3dab48143ce8","order_by":0,"name":"Emerson Eliecer León Ávila","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYBACPiQ24wMGhgMwDjNOLWxIbGYDkrWwSRCnhb352IOPOTb58u29x6p5d9yRk2/PMXzAUGGd2MB++ABWLTzH0g1nbkuz3HDmXNpt3jPPjA3OvDE2YDiTntjAk5aAVYtEjpk077bDBgZAxm3etsOJG4AMCUYgo0GCxwCrFvk3ZtJ/t/03kJ+RY1YM0jJ/Ro75D8Z/eLRI8JhJM247YMBwI8eMGaSlAchgYGzAo4UnLU2yd1uygcGZM8aSc9tAfnlWLJFwLN24DYdf+NkPH5P4uc3OQL69x/DD2zZQiCVv/PChxlq2H0eIYQMJYIQSZURoGQWjYBSMglGABAB5X12eEaQDoAAAAABJRU5ErkJggg==","orcid":"","institution":"El Bosque University","correspondingAuthor":true,"prefix":"","firstName":"Emerson","middleName":"Eliecer León","lastName":"Ávila","suffix":""},{"id":504386405,"identity":"438ba396-d64f-4a58-821e-c16b311ab05c","order_by":1,"name":"Ronald Andrés Jiménez Cruz","email":"","orcid":"","institution":"El Bosque University","correspondingAuthor":false,"prefix":"","firstName":"Ronald","middleName":"Andrés Jiménez","lastName":"Cruz","suffix":""},{"id":504386406,"identity":"db299420-910f-43b7-835f-832c247021a2","order_by":2,"name":"Santiago Puentes","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Santiago","middleName":"","lastName":"Puentes","suffix":""},{"id":504386407,"identity":"aa26cb3c-5f15-47bd-899b-7b72399af0c7","order_by":3,"name":"Julian Puentes","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Julian","middleName":"","lastName":"Puentes","suffix":""}],"badges":[],"createdAt":"2025-06-11 17:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6873956/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6873956/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10337-025-04460-1","type":"published","date":"2026-01-17T16:29:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89796747,"identity":"60c6a9a9-5b52-4ae9-982e-a541ee9cbbfa","added_by":"auto","created_at":"2025-08-25 07:22:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66317,"visible":true,"origin":"","legend":"\u003cp\u003eIshikawa diagram highlighting the parameters involved in the method.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6873956/v1/1f9ee5a4273fdeb4348132f7.png"},{"id":89795702,"identity":"679fc9fb-4783-4805-86d3-486ea716af82","added_by":"auto","created_at":"2025-08-25 07:06:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":260771,"visible":true,"origin":"","legend":"\u003cp\u003eUsing Design Expert software, a three-dimensional response surface plot for the effect of oven temperature and flow rate on retention time was made.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6873956/v1/fef85cedbbfb03a195dde4a3.png"},{"id":89795686,"identity":"542ca1b1-3f83-47e1-a6a3-a77222b8a849","added_by":"auto","created_at":"2025-08-25 07:06:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":259573,"visible":true,"origin":"","legend":"\u003cp\u003eUsing Design Expert software, a three-dimensional response surface graph for the effect of oven temperature and flow rate on theoretical plates was created.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6873956/v1/d8b145e7daff5f25d70dd7a5.png"},{"id":89795679,"identity":"4c8286e7-0dab-4d6e-b092-07b58bb10f4d","added_by":"auto","created_at":"2025-08-25 07:06:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":246271,"visible":true,"origin":"","legend":"\u003cp\u003eUsing Design Expert software, a three-dimensional response surface plot for the effect of furnace temperature and flow rate on resolution was made.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6873956/v1/aef666f035febd232df4c88b.png"},{"id":89795678,"identity":"b826c791-a344-4ec4-abd9-a2e8353b96c2","added_by":"auto","created_at":"2025-08-25 07:06:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":221836,"visible":true,"origin":"","legend":"\u003cp\u003eUsing Design Expert software, a three-dimensional response surface graph for the effect of furnace temperature and flow rate on the color factor was created.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6873956/v1/bf99a2a8ac7ca1e114eee12c.png"},{"id":89797778,"identity":"6bb6e29a-4605-4432-bd28-8dd1f18c3ef0","added_by":"auto","created_at":"2025-08-25 07:30:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":174214,"visible":true,"origin":"","legend":"\u003cp\u003eUsing Design Expert software, a three-dimensional response surface graph for the effect of furnace temperature and flow rate on capacity factor was made.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6873956/v1/55028a5c05da36e5078911f5.png"},{"id":89795724,"identity":"b483e49c-fcae-4e16-b561-a5d006a1487e","added_by":"auto","created_at":"2025-08-25 07:06:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":112773,"visible":true,"origin":"","legend":"\u003cp\u003eThe overlay graph with the CMPs and CAAs variables shows where the Design Expert program defines the method operable region (MODR).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6873956/v1/a6307eb685c933561a4eca7b.png"},{"id":89796535,"identity":"64d2f68e-d4d3-424c-b019-a41cca6cff8c","added_by":"auto","created_at":"2025-08-25 07:14:30","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":21015,"visible":true,"origin":"","legend":"\u003cp\u003eChromatographic conditions with a temperature of 170 °C and a flow rate of 0.9 mL/min.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6873956/v1/dfae2ea15aa5900fdd4cb7e9.png"},{"id":89795697,"identity":"c553c12a-c02b-4e61-a851-d02e4d9d7f70","added_by":"auto","created_at":"2025-08-25 07:06:29","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":33997,"visible":true,"origin":"","legend":"\u003cp\u003eThe menthol calibration curve was obtained using internal standards from the LabSolutions program.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6873956/v1/bac20bec8edc794d62a07df9.png"},{"id":89796531,"identity":"ad86db9f-e3ab-44c1-9fd1-6001746f0723","added_by":"auto","created_at":"2025-08-25 07:14:29","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":23643,"visible":true,"origin":"","legend":"\u003cp\u003eChromatogram to evaluate the precision parameter at a concentration of 356 ppm.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6873956/v1/b6b7b0f32e73bc1b5593184c.png"},{"id":100617445,"identity":"c1d77a25-56bd-4125-8290-476132ea4453","added_by":"auto","created_at":"2026-01-19 17:53:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2177005,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6873956/v1/4a81dc8b-8831-48eb-8915-a41eddff4aa1.pdf"},{"id":89796528,"identity":"9f76cbb0-68fd-49c9-8757-6389b6af879b","added_by":"auto","created_at":"2025-08-25 07:14:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":277508,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6873956/v1/bbd1b318bcff2ba126dc999d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eQuantification of Menthol by Chromatography Applying Analytical Quality by Design\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMenthol is a tricyclic alcohol derived from the volatile oils of certain mint species. It naturally occurs as levomenthol and racementhol. This compound is mainly used as a flavoring and scent agent in various products, including cigarettes, liqueurs, cough drops, mouthwashes, toothpaste, and shampoos [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThere has been a noticeable increase in analytical method failures during method transfers in research and development and quality control departments. Furthermore, there is growing interest in developing reliable and reproducible analytical methods. Chromatography has emerged as the standard approach for quantification in the pharmaceutical industry [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAnalytical Quality by Design (AQbD) is a risk management-based approach that develops robust analytical methods that satisfy regulatory requirements. The AQbD approach helps create reliable methods, offering several benefits, such as reduced unexpected errors, fewer out-of-specification (OOS) and out-of-trend (OOT) results, and a lower risk of method failure during the method transfer process. AQbD offers regulatory flexibility, enhances robustness, and removes the need for revalidation in product development [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study used an Analytical Quality by Design (AQbD) approach to identify and quantify menthol effectively. We employed gas chromatography combined with an internal standard. We aimed to develop a reliable, reproducible, and cost-effective analytical method, as menthol is a widely used raw material in various pharmaceutical, cosmetic, and food products.\u003c/p\u003e\u003cp\u003eTo develop the method using the Analytical Quality by Design (AQbD) approach, we started by establishing the Analytical Target Profile (ATP). We utilized risk management tools to identify the Critical Analytical Attributes (CAAs), which include resolution, retention time, theoretical plates, capacity factor, and choking factor. Additionally, we determined the Critical Process Parameters (CPPs), such as oven temperature and flow rate.\u003c/p\u003e\u003cp\u003eWe then evaluated the effect of these CPPs on the CAAs using a factorial experimental design. Subsequently, we defined the Method Operable Region (MODR) to pinpoint the optimal conditions and established specifications for each CAA by employing response surface diagrams.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eMaterials used included analytical standard menthol (purity 99%), analytical grade thymol (purity\u0026thinsp;\u0026gt;\u0026thinsp;98.5%), and chloroform (purity\u0026thinsp;\u0026ge;\u0026thinsp;99.9%) for HPLC and GC applications, all purchased from Sigma-Aldrich (St. Louis, MO, USA).\u003c/p\u003e\u003cp\u003e\u003cb\u003eChromatographic Instrument and Conditions\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eThe chromatographic was separated using a Thermo Scientific Trace 1310 GC gas chromatograph equipped with an AOC-20i Plus autosampler and a flame ionization detector (FID). The separation utilized an SH-I-5Sil MS column composed of silylene 1,4-bis(dimethylsiloxane)phenylene and dimethylpolysiloxane, with a film thickness of 0.25 \u0026micro;m and a length of 30 m.\u003c/p\u003e\u003cp\u003eChromatographic analysis was performed under the following conditions: gradient elution mode, with the detector temperature set at 250\u0026deg;C and the injection temperature set at 280\u0026deg;C. The split ratio was 50, and the injection volume was 10 \u0026micro;L, except for the oven temperature and flow rate, which AQbD determined.\u003c/p\u003e\u003cp\u003e\u003cb\u003eQuality by Design Analytical Approach (AQbD)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDefinition of the analytical target profile (ATP).\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe analytical target profile (ATP) was the fundamental axis of the AQbD methodology since it defines what will be analyzed and at what level it is necessary to do so [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This allowed the method to be designed and directed to ensure compliance with the ATP.\u003c/p\u003e\u003cp\u003eThe ATP included elements such as analytes, sample types, analytical techniques, analytical instruments, method quality requirements, and the method's analytical attributes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRisk assessment.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe risk assessment approach was carried out through a bibliographic search related to gas chromatography and menthol quantification to identify the possible variables of the method and how they are involved in its development. Subsequently, a risk analysis was carried out using an Ishikawa or cause and effect diagram. This tool describes the qualitative relationship between the critical parameters of the method and the critical analytical attributes that are part of the quantification process.\u003c/p\u003e\u003cp\u003eOnce the method parameters affecting the process were identified and analyzed, an evaluation of these parameters was carried out using a Failure Modes and Effects Analysis (FMEA) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These tools allowed determination of the critical parameters of the method, which were classified and evaluated through the scoring criteria described in supplementary material 1. Through this evaluation, we sought to evaluate and select those CMPs and CAAs determined by the risk priority number (RPN) obtained from the identified S, O, and D indices, where those parameters with an RPN\u0026thinsp;\u0026gt;\u0026thinsp;100 were considered critical.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSample preparation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe solutions handled in the AQbD section were developed as follows: 24.0 mg of menthol and 38.0 mg of thymol were taken in a volume of 10.0 mL of chloroform for the standard.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDesign of experiments (DOE).\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe chromatographic method tests were developed with a complete factorial design, which is based on a study to determine the effect of the independent or input variables on the dependent or output variables. For this case, the CMPs were taken for this study, with six oven temperatures (85, 95, 115, 130, 170, 200\u0026deg;C) and seven flow rates (0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0 mL/min) and thus evaluate the response that is presented in the CAAs (coleus factor, theoretical plates, retention times, resolution and capacity factor).\u003c/p\u003e\u003cp\u003e\u003cb\u003eOperational design region of the method (MODR).\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOnce the DOE data were obtained, response surface graphs and an overlay graph were constructed with the CMPs and CAAs components to optimize the proposed tests. The Design Expert 22.0.6 program was used to develop the statistical model of the research and thus determine the best chromatographic conditions and their design space.\u003c/p\u003e\u003cp\u003e\u003cb\u003eValidation of the method.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe chromatographic method was validated according to the ICH Q2 (R2) guide and FDA recommendations, considering the system's suitability, linearity, accuracy, precision, and detection and quantification limits [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eCalibration standards.\u003c/b\u003e Stock solutions of menthol as analyte (5480 ppm) and the internal standard thymol (1040 ppm) were prepared by dissolving with chloroform. They were used to carry out the dilutions in the following process steps.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSystem suitability.\u003c/b\u003e The parameters for system suitability of the method were determined by injecting a solution five times with a standard concentration of 356 ppm menthol and 520 ppm thymol to obtain reproducible results. For the chromatograms obtained, the peak asymmetry (tailing factor) was \u0026lt;\u0026thinsp;2.0; the resolution\u0026thinsp;\u0026ge;\u0026thinsp;1.5 between the signals of the two standards; the theoretical plates were \u0026ge;\u0026thinsp;2000; the capacity factor\u0026thinsp;\u0026gt;\u0026thinsp;2.0 and the RSD was \u0026lt;\u0026thinsp;5.0% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eDetection and quantification limits.\u003c/b\u003e Detection (LO) and quantification (Q) limits were established based on the standard deviation of linear response and slope (calibration curve) from 6 calibration standards (1.1, 2.2, 4.4, 5.5, 8.2, and 16.4 ppm).\u003c/p\u003e\u003cp\u003e\u003cb\u003eLinearity.\u003c/b\u003e Nine calibration standards (55, 110, 192, 356, 740, 986, 1507, 2000, 2740 ppm) were prepared by sequential dilutions of the menthol stock solution (10, 20, 35, 65, 90, 135, 180, 275, 365, 500 \u0026micro;L) and 500 \u0026micro;L of the thymol stock solution was added to each of these, to develop it by the internal standard method. The calibration curves were made by linear regression between the ratio of the menthol peak area divided by the thymol peak area versus the ratio of the menthol concentration divided by the thymol concentration. Additionally, to evaluate this linearity, two samples were analyzed for each concentration level [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eAccuracy.\u003c/b\u003e It was performed based on a sample with a known concentration of menthol (actual value). An aliquot of 300 \u0026micro;L was taken from the menthol stock solution, and it was brought to a final volume of 1000 \u0026micro;L with 500 \u0026micro;L of the internal standard and 200 \u0026micro;L of chloroform to obtain a concentration of 1644 ppm. This entire process was performed in triplicate. The relative accuracy percentage was then determined, which should be \u0026lt;\u0026thinsp;5.0% for the experimental value [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrecision\u003c/b\u003e. It was determined using five solution samples with a standard concentration of 356 ppm menthol and 520 ppm thymol. To assess the degree of dispersion between these five determinations of the same sample, it is recommended that the RSD be \u0026lt;\u0026thinsp;5.0% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eTraditional approaches to analytical method development focus on building up the method's initial conditions, physicochemical parameters of the analyte, sample, analytical technique, and instrument one factor at a time. Additionally, this method does not allow for optimization. It does not investigate the relationships between critical parameters and method response or performance, increasing the likelihood of method failures during method transfer.\u003c/p\u003e\u003cp\u003eTherefore, the first stage in developing a method with acceptable confidence in the quality of decisions made from the result is the definition of ATP. The ATP prospectively summarizes the requirements of measuring a quality attribute that an analytical procedure must meet. The ATP is used to define and assess the suitability of an analytical procedure in the development phase and during all changes throughout the analytical life cycle [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eshows the elements that were considered when creating the ATP.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElement ATP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObjective\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJustification\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnalyte\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMenthol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe analyte of recent pharmaceutical interest has no history of applying the AQbD methodology for its quantification.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSolid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDue to its physicochemical characteristics, the analyte is solid at room temperature. Therefore, to achieve volatilization of the entire sample, it is necessary to completely solubilize it before performing the analysis.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnalytical technique\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChromatography\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAn analytical technique capable of quantifying, separating, purifying, and identifying a vast range of analytes using a simple theoretical foundation.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstruments\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGC-FID\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGas chromatography is characterized by its high speed, precision, and sensitivity for separating and analyzing chemical compounds. The FID detector has a wide dynamic range, allowing quantification from traces to high concentrations. It is also a stable and robust detector that provides consistent and reproducible results.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. An analytical Target Profile (ATP) was established for the chromatographic analysis of menthol.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRisk assessment.\u003c/b\u003e Following the guidelines in the ICH Q9 guidelines provide the principles and examples of tools for quality risk management that can be applied to different aspects of pharmaceutical quality, which indicate risk assessment is a systematic process for the evaluation, control, communication, and review of risks to quality throughout the product life cycle [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. According to the ICH Q9 guideline, risk assessment can be performed in 3 steps: identification, analysis, and assessment.\u003c/p\u003e\u003cp\u003eRisk identification aims to detect and prioritize all Potential Method Variables (PMV) and Potential Method Attributes (PMA). These should cover all aspects related to the analyst, materials, instruments, method, and environment that can directly or indirectly influence the quality of the method [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It is important to highlight that the previous literature review, the deep knowledge of the process, and the researcher's experience play a fundamental role in identifying the PMCs.\u003c/p\u003e\u003cp\u003eConsidering the above, an Ishikawa diagram was made from previous knowledge and a theoretical review of the technique, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This tool allows us to qualitatively identify all those factors that can directly or indirectly affect the method's performance, considering the researcher, materials, methods, measurement, environment, and equipment. The diagram allows us to identify 24 direct or indirect variables that can potentially affect the method's performance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFrom this diagram, parameters related to the method and the equipment were selected. This allowed the identification of which of these variables is critical and the prioritization of them according to their severity, occurrence, and detectability through the FMEA matrix, as shown in supplementary material 2. It should be noted that this tool consists of two stages. The first focuses on identifying possible failure modes, their effects, and possible causes.\u003c/p\u003e\u003cp\u003eIn the second stage, these failure modes were classified according to their severity index (S), which is the seriousness of the consequence of this failure; their occurrence (O), which corresponds to the probability or frequency of occurrence of failure; and according to detectability (D), associated with the probability that the failure is detected before it has an impact on the process, which allowed reducing the risk of a possible failure by determining a preventive strategy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFlow rate and oven temperature were the parameters with the highest NPR score and were selected as the critical method parameters. In addition, the remaining variables were considered non-critical, as they can be quickly resolved or do not significantly affect method performance or ATP compliance. However, in the case of detector and injection temperatures, they were modified during the analysis, and it was rectified that these variables were not critical.\u003c/p\u003e\u003cp\u003eThe oven temperature and the carrier gas flow rate were determined as the critical method parameters (CMPs) mainly because these parameters allow for obtaining low retention times with acceptable peak resolution, facilitating the development of a simple and economical method.\u003c/p\u003e\u003cp\u003eThe FMEA matrix allows us to identify the critical analytical attributes. Five attributes were selected because they yielded an NPR score greater than 100. Considering the above, the ideal CMP values for each of these CAAs ​​were determined through an experimental design.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethod development and optimization: critical analytical attributes.\u003c/b\u003e \u003cem\u003eA response surface plot is an advanced DOE technique that helps to better understand a method and optimize the response. The data used to construct the response surface plots are presented\u003c/em\u003e \u003cb\u003ein Supplementary Material 3\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRetention times (Tr).\u003c/b\u003e \u003cem\u003eThis retention time is mainly influenced by temperature and flow rate.\u003c/em\u003e Temperature affects the volatility of the analyte and, therefore, its interactions with the stationary phase. As the temperature increases, the volatility of the analyte increases, which reduces the interactions between menthol and the stationary phase, resulting in decreased retention times. This effect is beneficial for meeting the ATP. On the other hand, the flow rate also directly impacts the Tr since a higher flow rate causes menthol to move through the column at a higher speed, reducing its interaction with it and decreasing retention times.\u003c/p\u003e\u003cp\u003eThe surface diagram presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the lowest Tr are found under temperature conditions between 200 and 170\u0026deg;C and at a flow rate between 1.0 and 0.9 mL/min. This is because the temperature affects the retention force of menthol in the stationary phase, while the flow rate affects the speed of this analyte movement in the column. Therefore, these values ​​were the most suitable to satisfy this variable. Additionally, a short retention time improves precision and accuracy in the quantification process, reducing the analysis time and, therefore, the probability of errors.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTheoretical plates (N).\u003c/b\u003e The theoretical plates tell us how many theoretical equilibrium stages occur during the separation, so the more theoretical plates a column has, the better its separation capacity will be; then, the theoretical plates refer to the efficiency measure and the separation capacity of the column [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. All N values ​​obtained in the DOE exceeded the minimum required by regulatory guidelines (N\u0026thinsp;\u0026gt;\u0026thinsp;2000). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, all N values ​​were above 31643, indicating that all conditions were considered significant for this parameter.\u003c/p\u003e\u003cp\u003eA high theoretical plate value indicates that the column (SH-I-5Sil MS) has a high separation efficiency and capacity. The packing structure with uniformly sized pores allows menthol to diffuse efficiently through the stationary phase, leading to higher theoretical plate efficiency and better resolution.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eResolution (Res).\u003c/b\u003e Resolution measures the separation between two chromatographic peaks regarding their distance and width [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The value of this parameter equal to zero indicates that the peaks are superimposed and not separated at all, while a value of 2.0 is the minimum for a good separation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the optimal resolution values ​​are obtained at a temperature of 200 and 170\u0026deg;C regardless of the flow rate. Therefore, temperatures below these will not obtain this method's desired resolution and retention times.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTemperature has a significant impact on this CAA; as temperature decreases, resolution tends to increase almost linearly [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This indicates that temperature and resolution have an inversely proportional relationship. However, it is important to note that resolution is a factor in peak separation, so it is especially critical for methods that separate many analytes. In the case of this method, since it is an analysis of only menthol and thymol, the temperature will not significantly decrease peak resolution [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eColeus factor (Tc).\u003c/b\u003e It is important to note that all the peaks in the DOE assays tend to have remarkably high symmetry, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. This indicates, on the one hand, that the SH-I-5Sil MS column is in good condition and, on the other hand, that no overloading or strong interactions of menthol with the column are observed [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. were found below value 1, thus complying with the FDA's recommendations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCapacity factor.\u003c/b\u003e The capacity factor indicates the degree of retention of the analyte in the column about the dead volume [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In all the runs carried out at the DOE, values ​​below two were obtained, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, indicating that menthol does not present good retention by the column, and this is due to several factors. One of them is that, when wanting to have low retention times, menthol needs to elute more quickly, which results in lower retention of it. In addition, when high proportions of organic solvents such as chloroform are used in this case, the retention of the analytes is reduced [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethod Development and Optimization (MODR).\u003c/b\u003e MODR is the region that establishes a range allowed by the CAAs and develops a multidimensional space relating to the CAAs and CMPs variables, generating an adequate procedure for quantifying menthol based on the ATP requirements. The identification of this region in the yellow area is found in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and was carried out based on the criteria defined in the system suitability section. Each red point in the gray region represents a possible chromatographic method (regarding oven temperature and flow rate).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAn interval was selected for each CAA to meet the defined criteria and achieve a successful development in the MODR, following what was established in the ATP. For convenience, the method parameters chosen to proceed with the method validation are a temperature of 170\u0026deg;C and a flow rate of 0.9 mL/min (as shown in the yellow region of Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRequirements for the performance of each CAA method to obtain the MODRN\u0026thinsp;=\u0026thinsp;number of theoretical plates. Res\u0026thinsp;=\u0026thinsp;resolution. Tc\u0026thinsp;=\u0026thinsp;tail factor. k\u0026rsquo;= capacity factor. Tr retention time.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLower limit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUpper limit\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e374728\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ek\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTr (minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThese chromatographic conditions ensure the suitability of the chromatographic method and are associated with shorter retention times, higher resolution, high theoretical plates, and good peak symmetry. The resulting chromatogram with these conditions is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFinal chromatographic conditions.\u003c/b\u003e Gradient elution was performed because this technique is more versatile than isothermal elution, resulting in better peak resolution, shorter analysis times, and better detectability. This can be seen in \u003cb\u003esupplementary material 4\u003c/b\u003e, where the final chromatographic conditions are shown.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethod validation\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSystem suitability\u003c/b\u003e. It is necessary to perform system suitability tests to assess whether the method and equipment can produce accurate and precise results. The previously identified AACs (retention times, area, number of theoretical plates, resolution) were evaluated. The results of the system suitability assessment are shown in \u003cb\u003eSupplementary Material 5\u003c/b\u003e. It was found that the accuracy of the retention times and peak areas were compatible for both analytes according to the RSD\u0026thinsp;\u0026le;\u0026thinsp;2.0%. In addition, an excellent yield of theoretical plates was observed, being 67698 for menthol and 91175 for thymol. These values ​​significantly exceeded the established acceptance criterion (\u0026gt;\u0026thinsp;2000), which allows us to affirm that the separation efficiency of the column was adequate.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDetection and quantification limit\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe lowest concentration at which each analyte can be detected or identified with acceptable accuracy and precision was determined by calculating the typical error from the calibration curve shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. The developed method could not quantify samples with concentrations less than 312 ppm with acceptable precision and accuracy. Likewise, it will not be able to detect any analyte in samples with concentrations less than 104 ppm.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eLinearity.\u003c/b\u003e The linearity of the calibration standards was evaluated between 55 and 2740 ppm. The results are shown in supplementary material 6, from which the calibration curve was constructed by internal standard. The linear regression equation was obtained using the least squares method, resulting in a correlation coefficient of 0.9995. The R2 value obtained meets FDA recommendations, showing a strong correlation between the variables.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAccuracy.\u003c/b\u003e The accuracy of the developed method was determined by the recovery percentage obtained from the calibration curve performed using the internal standard method (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This was calculated using the equation of the straight line of the calibration curve performed, resulting in a relative accuracy percentage of 105.58%. This value slightly exceeds the proposed limit and is considered slightly accurate. This result is due to the influence of high uncertainty of the micropipettes used, contamination, or impurities in the samples.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eData to determine the accuracy parameter.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLine equation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1,009x-0,1061\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eValue of Y (mV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e589356\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTheoretical concentration value (ppm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1644\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental concentration value (ppm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1735.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecovery percentage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105.58%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrecision\u003c/b\u003e. The relative standard deviation was determined to show the variability of the results of the areas obtained for menthol and thymol: 1.57% for menthol and 1.46% for thymol. This is below the acceptance criterion (2.0%), which allows us to affirm that the method developed for the quantification of menthol presents the repeatability required for the analysis, which allows it to produce precise results. To observe the chromatogram in the experiments of this parameter, see Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eConsidering the results obtained from the validation parameters evaluated, it is possible to affirm that the developed method meets the quality requirements necessary for its application in the industry. Additionally, it was possible to demonstrate the benefits of applying AQbD strategies to develop analytical methods, which allow for greater regulatory flexibility regarding the method, ease in the method transfer stage, and a reduction in resource expenditure.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAn AQbD strategy was developed to quantify menthol through gas chromatography using a method that includes an internal standard. In this process, the critical parameters of the method that allow compliance with the proposed ATP were determined, which were oven temperature and flow rate, for their subsequent evaluation, as well as the critical analytical attributes, including resolution, theoretical plates, retention times, choking factor, and capacity factor. This evaluation was based on a risk analysis using an Ishikawa diagram and an FMEA matrix. Subsequently, an experimental design was carried out using a complete factorial design. This type of experimental design proved efficient when evaluating two factors at many levels, simultaneously being easy to apply and interpret. This type of design allowed us to know the response factor relationships that the CAAs have on the CMPs using response surface diagrams to obtain the operable region of the method and to determine the best chromatographic conditions, which were a temperature of 170\u0026deg;C and a flow rate of 0.9 mL/min since they are the ones that best allow compliance with the proposed ATP. In addition, they were validated according to the regulatory guidelines that covered linearity, accuracy, precision, detection limit, and quantification. In the same way, it was possible to demonstrate the different advantages presented by the application of AQbD strategies in the development of analytical methods compared to the strategies usually used, including the development of more robust methods, ease in the method transfer stage thanks to a better understanding of the process, regulatory ease, among others. In summary, the validation of the method supports the implementation of the AQbD methodology to define the most appropriate chromatographic conditions in industrial applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of Interest Statement\u003c/h2\u003e\u003cp\u003eThe authors declare that there are no conflicts of interest regarding the publication of this article. No financial, material, or other support was received from any entities or individuals that could influence the results or interpretations of the study. Furthermore, the opinions expressed in this article solely reflect the views of the authors and are not influenced by any personal, commercial, or financial interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors participated in writing and revising the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOz M, El Nebrisi EG, Yang KHS, Howarth FC, Al Kury LT (2017) Cellular and molecular targets of menthol actions. Front Pharmacol 8\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBasso J, Ramos ML, Pais A, Vitorino R, Fortuna A, Vitorino C (2021) Expediting disulfiram assays through a systematic analytical quality by design approach. 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Ars Pharmaceutica, 62(3), 315\u0026ndash;327. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dx.doi.org/10.30827/ars.v62i3.15917\u003c/span\u003e\u003cspan address=\"10.30827/ars.v62i3.15917\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"chromatographia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"chro","sideBox":"Learn more about [Chromatographia](https://www.springer.com/journal/10337)","snPcode":"10337","submissionUrl":"https://submission.nature.com/new-submission/10337/3","title":"Chromatographia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Menthol, thymol, Analytical quality by design (AQbD), gas chromatography, validation","lastPublishedDoi":"10.21203/rs.3.rs-6873956/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6873956/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe concept of Analytical Quality by Design (AQbD) has recently gained prominence and offers multiple advantages for the pharmaceutical industry. AQbD is an approach that employs risk management to develop robust analytical methods that adhere to regulatory requirements. Menthol is a common raw material used in formulating various pharmaceutical products, including creams, ointments, and salves. We implemented an Analytical Quality by Design (AQbD) approach to establish optimal parameters for identifying and quantifying menthol. This methodology utilizes gas chromatography coupled with an internal standard to create a reliable, reproducible, and cost-effective analytical method.\u003c/p\u003e\u003cp\u003eIn developing the method through AQbD, we first defined the Analytical Target Profile (ATP). We employed risk management tools to identify Critical Analytical Attributes (CAAs), such as resolution, retention time, theoretical plates, capacity factors, and choking factors. We also determined Critical Process Parameters (CPPs), including oven temperature and flow rate. The influence of CPPs on CAAs was assessed using a factorial experimental design.\u003c/p\u003e\u003cp\u003eSubsequently, we established the Method Operable Region (MODR) to pinpoint the optimal conditions and specified parameters for each CAA, as indicated by response surface diagrams. The optimized method underwent validation according to international regulatory guidelines to ensure system suitability. This validation process included evaluations of linearity, accuracy, and precision, as well as the determination of limits of detection and quantification. The results confirmed that the conditions were appropriate for analyzing menthol using the gas chromatographic technique with an internal standard.\u003c/p\u003e","manuscriptTitle":"Quantification of Menthol by Chromatography Applying Analytical Quality by Design","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-25 07:06:23","doi":"10.21203/rs.3.rs-6873956/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-08T07:47:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-29T16:55:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196836770330769464846088240783919551260","date":"2025-09-09T21:01:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220709585651231655059142222811443538262","date":"2025-09-08T00:40:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-18T07:31:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66194695344772974291992010666664217775","date":"2025-08-16T20:17:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-16T20:11:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-28T21:50:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-13T09:38:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Chromatographia","date":"2025-06-11T17:00:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"chromatographia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"chro","sideBox":"Learn more about [Chromatographia](https://www.springer.com/journal/10337)","snPcode":"10337","submissionUrl":"https://submission.nature.com/new-submission/10337/3","title":"Chromatographia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e7460872-b1e0-45dc-b327-52a409f92466","owner":[],"postedDate":"August 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T17:21:13+00:00","versionOfRecord":{"articleIdentity":"rs-6873956","link":"https://doi.org/10.1007/s10337-025-04460-1","journal":{"identity":"chromatographia","isVorOnly":false,"title":"Chromatographia"},"publishedOn":"2026-01-17 16:29:20","publishedOnDateReadable":"January 17th, 2026"},"versionCreatedAt":"2025-08-25 07:06:23","video":"","vorDoi":"10.1007/s10337-025-04460-1","vorDoiUrl":"https://doi.org/10.1007/s10337-025-04460-1","workflowStages":[]},"version":"v1","identity":"rs-6873956","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6873956","identity":"rs-6873956","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}