A method for analyzing in-situ fragrance release regularity of hand cream based on GC-IMS technology

Abstract

This invention discloses a method for analyzing the in-situ fragrance release pattern of hand cream based on GC-IMS technology, belonging to the field of cosmetic testing. The method includes: uniformly applying a quantitative sample of scented hand cream to the skin surface; collecting volatile fragrance components at multiple preset time points after application using headspace sampling, and detecting them using headspace gas chromatography-ion mobility spectrometry (GC-IMS) to obtain GC-IMS sample data; obtaining fingerprint spectra of characteristic fragrance components based on the sample data, and analyzing the dynamic release pattern of fragrance on the skin surface through fingerprint spectrum comparison and cluster analysis. This invention achieves real-time, in-situ, and visual monitoring of the fragrance release process of hand cream on the skin surface using GC-IMS technology, and can identify the three stages of fragrance release: the initial burst period, the stable release period, and the decay period. It can be applied to the fragrance design and formulation optimization of hand cream products, as well as the screening and efficacy evaluation of fragrance sustained-release fixatives.

Description

Technical Field

[0001] This invention belongs to the field of cosmetic testing, specifically relating to a method for analyzing the in-situ fragrance release pattern of hand cream based on GC-IMS technology. Background Technology

[0002] Hand cream is a common skincare product in daily life. Its fragrance components not only enhance the user experience but also have a positive impact on mood. However, traditional fragrance analysis methods, such as sensory evaluation and gas chromatography-mass spectrometry (GC-MS), while providing detailed information on fragrance components, lack real-time monitoring of the dynamic release process of fragrance on the skin surface.

[0003] Gas chromatography-ion mobility spectrometry (GC-IMS) combines the high separation capability of gas chromatography with the high sensitivity of ion mobility spectrometry, enabling real-time monitoring of the release behavior of aroma components. It offers excellent visualization for comparing different samples, providing a novel analytical tool for hand cream fragrance research. This method is particularly suitable for analyzing samples from different processes or under varying conditions.

[0004] In the development of hand cream products, the addition of fragrance is a key technical step in enhancing product added value and creating brand differentiation. However, fragrance is a complex system composed of various volatile organic compounds. Its stability, release behavior, and changes after long-term storage in the complex emulsion matrix of hand cream are key factors affecting the consistency of sensory quality and shelf life of the product. Currently, the aroma analysis of scented hand creams relies heavily on traditional technologies such as GC-MS. While these methods can accurately identify components, the pretreatment is complex and it is difficult to achieve in-situ, rapid capture, and intuitive characterization of aroma fingerprints. This limits the ability to monitor aromas online during production and to finely analyze the evolution of flavor patterns over the product's shelf life. Therefore, the industry urgently needs to introduce a technical method that can efficiently, non-destructively, and in real-time detect trace amounts of volatile flavor substances in complex matrices to achieve precise control and scientific evaluation of the aroma quality of scented hand creams. Summary of the Invention

[0005] The technical problem to be solved by the present invention is that the existing technology cannot provide real-time, in-situ, and intuitive characterization of the dynamic release process of fragrance in scented hand cream on the skin surface.

[0006] To address the aforementioned issues, this invention provides a method for analyzing the in-situ fragrance release patterns of hand cream based on GC-IMS technology. This method enables precise, real-time monitoring and analysis of the dynamic release behavior of fragrance components during hand cream use, providing a scientific basis for product fragrance design and optimization. This invention simulates actual hand cream usage scenarios, performing in-situ, timed sampling on the skin surface where the hand cream is applied. Combining the high sensitivity and rapid detection capabilities of GC-IMS technology, the release kinetics of characteristic fragrance components on the skin surface under different fragrance types and concentrations are systematically analyzed, constructing a fragrance release pattern analysis system based on GC-IMS fingerprinting and release curves. See below for details: In a first aspect, the present invention provides a method for analyzing the in-situ fragrance release pattern of hand cream based on GC-IMS technology, comprising the following steps: S1. Skin application and headspace sampling: A quantitative sample of scented hand cream is evenly applied to a designated area on the skin surface. At multiple preset time points after application, the application area is covered with a gas enrichment component and the air is blown away through a syringe to complete the collection of volatile fragrance components on the skin surface and headspace sampling. S2. GC-IMS detection: The collected volatile aroma components were detected using headspace gas chromatography-ion mobility spectrometry to obtain GC-IMS sample data; S3. Data Acquisition and Processing: Acquire the GC-IMS sample data and process it to obtain fingerprint spectrum and / or peak volume data of characteristic aroma components for subsequent analysis; S4. Release Pattern Analysis: Based on the fingerprint spectrum and / or peak volume data, the dynamic release pattern of the aroma on the skin surface is analyzed by comparing fingerprint spectra, clustering analysis and / or drawing release curves.

[0007] Optionally, the characteristic aroma components are leaf alcohol, leaf alcohol acetate, linalool, 2-phenylethanol, benzyl acetate, geraniol, and linalool acetate.

[0008] Optionally, the preset time points in S1 include 0 min, 30 min, 60 min, 120 min, and 180 min after application.

[0009] Optionally, the headspace sampling operation in S1 is as follows: the gas sampling device is aligned with the smear area, and under relatively stable indoor environmental conditions, the headspace gas is injected into the headspace gas chromatography-ion mobility spectrometry inlet at a uniform speed within 10 seconds using a syringe.

[0010] Optionally, the gas chromatographic conditions for GC-IMS detection in S2 are as follows: the column is an MXT-5 with dimensions of 15 m × 0.53 mm × 1 μm; the carrier gas is high-purity nitrogen with a purity ≥99.999%; the column temperature program is initially 40℃ and held for 2 min, then increased to 240℃ at a rate of 10℃ / min and held for 20 min; the carrier gas flow rate program is initially 2 mL / min and held for 2 min, then increased to 10 mL / min within 2 min to 10 min, then increased to 150 mL / min within 10 min to 20 min and held until the end.

[0011] Optionally, the ion mobility spectrometry conditions for GC-IMS detection in S2 are as follows: drift tube length 53 mm, electric field strength 500 V / cm, drift tube temperature 45℃; drift gas is high-purity nitrogen with a purity ≥99.999%, flow rate 75 mL / min; injection port temperature 80℃.

[0012] Optionally, the data acquisition and processing in S3 includes: qualitative analysis of characteristic aroma components using GC-IMS Library Search software and the NIST database, and semi-quantitative analysis by calculating characteristic peak volumes.

[0013] Optionally, the release pattern analysis in S4 includes: comparing and analyzing the release intensity differences of different concentrations or types of fragrances at different time points through fingerprint spectrum analysis; and / or, classifying volatile components according to release characteristics through cluster analysis to identify key components affecting initial release, mid-term stability and persistence; and / or, plotting release curves through peak volume data of characteristic aroma components to identify the initial burst period, stable release period and decay period of aroma release.

[0014] Secondly, this invention also provides an application of a method for analyzing the in-situ fragrance release pattern of hand cream based on GC-IMS technology in the fragrance design and formulation optimization of hand cream products.

[0015] Compared with the prior art, the present invention has the following beneficial effects: First, this invention applies gas chromatography-ion mobility spectrometry (GC-IMMS) to study the dynamic fragrance release pattern of hand cream on the skin surface. By uniformly applying a quantitative sample of hand cream to the skin surface and collecting volatile fragrance components at multiple preset time points using headspace sampling, real-time, in-situ, and non-destructive monitoring of the entire process of fragrance components from initial application to complete dissipation is achieved. This overcomes the limitation of traditional GC-MS, which can only perform static analysis, and solves the technical problem that existing technologies cannot perform real-time, in-situ, and intuitive characterization and pattern analysis of the dynamic release process of fragrance in scented hand cream on the skin surface.

[0016] Secondly, this invention obtains gas chromatography-ion mobility spectrometry fingerprints of characteristic fragrance components in hand creams, and combines fingerprint comparison and cluster analysis to achieve visualized characterization and multi-dimensional analysis of fragrance release patterns. This method can intuitively present the differences in release intensity of different concentrations or types of fragrances at different time points, and through cluster analysis, classifies volatile components according to their release characteristics, identifying key components affecting initial release, mid-term stability, and persistence, thereby accurately revealing the influence of fragrance concentration, fragrance type, and formulation matrix on fragrance release kinetics.

[0017] Third, this invention uses peak volume data of characteristic aroma components to plot release curves, enabling the identification of three stages in aroma release: the initial burst phase, the stable release phase, and the decay phase. Based on this method, the supersaturation release phenomenon of high-concentration fragrances and the persistence advantage of medium- and low-concentration fragrances can be discovered. This provides reliable data support and theoretical guidance for the fragrance design, fragrance concentration selection, and sustained-release formulation development of hand creams, significantly improving the scientific rigor and precision of aroma quality control.

[0018] Fourth, the method of this invention features simple sample pretreatment, requiring no complex extraction or derivatization steps, and offers rapid detection, with a single analysis completed within 30 minutes. It is suitable for rapid analysis and comparison of large batches of samples, possessing high practical value and promising prospects for widespread application. This method can be directly applied to the fragrance design and formulation optimization of hand cream products, as well as the screening and efficacy evaluation of fragrance sustained-release fixatives, providing the cosmetics industry with an efficient, objective, and quantifiable means of evaluating fragrance quality. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a structural diagram of the surface gas enrichment component provided by the present invention.

[0021] Figure 2 This is a schematic diagram of in-situ sampling of the fragrance of the scented hand cream provided by the present invention.

[0022] Figure 3 GC-IMS volatile matter fingerprint spectra of jasmine fragrance hand creams of different concentrations provided by this invention.

[0023] Figure 4 shows the GC-IMS topographic map of volatile organic compounds in jasmine fragrance hand creams of different concentrations at different sampling time points provided by the present invention (with a 25% concentration fragrance sample as a reference). Figure 4A The fingerprint spectrum at 0 min provided by this invention. Figure 4B The fingerprint spectrum at 30 min provided by this invention. Figure 4C The fingerprint spectrum at 60 min provided by this invention. Figure 4D The fingerprint spectrum at 120 min provided by this invention. Figure 4E The fingerprint spectrum at 180 min provided by this invention.

[0024] Figure 5 The release curves of volatile substances from samples at various sampling time points are provided by this invention.

[0025] Figure 6 This invention provides a clustering heatmap of volatile substance signal values ​​of jasmine fragrance hand cream at different sampling time points. Attached image description:

[0027] Figure 5 In the Chinese: (A) geraniol; (B) geraniol acetate; (C) linalool; (D) phenylethanol; (E) benzyl acetate; (F) linalool acetate; (G) geraniol. Detailed Implementation

[0028] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Example 1 This embodiment provides a method for analyzing the in-situ fragrance release pattern of hand cream based on GC-IMS technology. A hand cream sample with jasmine fragrance as the fragrance ingredient is used, and the method of this invention is applied to analyze the release pattern of its fragrance on the skin surface to verify the feasibility and effectiveness of the method of this invention.

[0030] I. Experimental Materials and Instruments 1. Material Preparation The experimental hand cream was prepared in the laboratory using an oil-phase matrix, an aqueous-phase matrix, and a fragrance. The fragrance was a self-prepared jasmine fragrance, the formula of which is shown in Table 1. The specific base of the hand cream is as follows: Fragrance: Benzyl acetate, methylhexylcinnamaldehyde, methyl dihydrojasmonate, linalool, linalyl acetate, 10% tetradecanal, 10% leaf alcohol, phenethyl acetate, terpineol, phenethyl alcohol, geraniol, ionone, jasmine base; Emulsifiers: Stearyl alcohol polyether-21 (HLB 15.5), Stearyl alcohol polyether-2 (HLB 4.5); Oil phase: petrolatum, shea butter (Shanghai Maclean's Reagent), oil-soluble lanolin, cetearyl alcohol, 318 caprylic / capric triglyceride, nut oil, DM350, DC345; Moisturizers: Glycerin, Butylene Glycol, Sodium Hyaluronate (Shanghai Yuanye Biotechnology Reagent), Allantoin; Preservative: Phenoxyethanol (Guangdong Jinlong Chemical Import & Export Co., Ltd.); Solvent: Deionized water; 2-Butanone, 2-pentanone, 2-hexanone, 2-heptanone, 2-octanone, 2-nonanone, and other reagents were all of analytical grade and were obtained from Beijing Sinopharm Chemical Reagent Co., Ltd.

[0031] Table 1 Jasmine Fragrance Formula

[0032] The self-prepared jasmine fragrance was diluted with anhydrous ethanol to five concentration gradients: 6.25%, 12.5%, 25%, 50%, and 100%. These were then used to add fragrance to subsequent hand creams, with each addition amounting to 0.1% of the hand cream base mass. By setting different concentration gradients, the influence of fragrance concentration on fragrance release behavior could be systematically investigated, providing a data foundation for subsequent analysis of the intrinsic relationship between concentration and release patterns.

[0033] The experimental instruments and equipment are as follows: ME204 electronic analytical balance (Shanghai Mettler Toledo Instruments Co., Ltd.), FJ200-SH digital display high-speed dispersion homogenizer (Shanghai Huxi Industrial Co., Ltd.), heated magnetic stirrer (IKA Group, Germany / IKA (Guangzhou) Instrument Equipment Co., Ltd.), 524G digital display thermostatic magnetic stirrer (Shanghai Meiyingpu Instrument Manufacturing Co., Ltd.), HH-4AS digital display thermostatic magnetic stirring water bath (Changzhou Guoyu Instrument Manufacturing Co., Ltd.), FlavorSpec® gas chromatography-ion mobility spectrometry (GC-IMS, GAS, Germany), and MXT-5 chromatographic column (15 m × 0.53 mm × 1 μm, Restek, USA).

[0034] 2. Preparation of hand cream Oil phase preparation: Take a clean beaker and add stearyl alcohol polyether-21, stearyl alcohol polyether-2, petrolatum, shea butter, oil-soluble lanolin, cetearyl alcohol, caprylic / capric triglyceride, nut oil, and DM350. Place the beaker in a water bath, stir and raise the temperature to 85°C until all raw materials are completely melted and mixed evenly to form a transparent oil phase.

[0035] Aqueous phase preparation: In another beaker, weigh glycerol, butylene glycol, sodium hyaluronate, allantoin and deionized water, heat to the same temperature of 85°C until all raw materials are completely dissolved and mixed evenly to form a transparent aqueous phase.

[0036] Emulsification process: The aqueous phase was slowly added to the oil phase, and stirred at 480 rpm for 5-10 minutes using a constant-temperature magnetic stirrer. Then, it was homogenized at 12000 rpm for 3 minutes using a high-speed homogenizer to form a uniform emulsion. After the emulsion cooled to 50℃, DC345 was added and stirred until homogeneous. When the temperature dropped to 45℃, jasmine fragrance and preservative phenoxyethanol at different concentration gradients were added, and homogenization was repeated to obtain a series of scented hand cream test samples with different fragrance concentrations. Specifically, the self-prepared jasmine fragrance was diluted with anhydrous ethanol to five concentration gradients: 6.25%, 12.5%, 25%, 50%, and 100%. Each gradient was added to the hand cream base at a rate of 0.1% (w / w), and homogenized to obtain a series of test samples. By strictly controlling the temperature and homogenization conditions of the preparation process, the stability and consistency of the hand cream base were ensured, thereby guaranteeing the comparability and reliability of the subsequent fragrance release pattern analysis results.

[0037] II. Methods for Analyzing Aroma Release Patterns The method of this invention was used to analyze the fragrance release patterns of the series of hand cream samples prepared above, specifically including the following steps: S1. Skin application and headspace sampling: Different concentrations of jasmine-scented hand cream were collected as test samples. Before testing, volunteers were required to refrain from using any skincare products or perfumes on the test area (inner forearm) for at least 24 hours to ensure no residual fragrance components remained on the skin. During testing, 0.1 mL of jasmine-scented hand cream was drawn into a 2 mL sampling tube and evenly applied to the inner forearm of the volunteer, with the time of application recorded. This quantitative application method ensures consistency in sampling volume across different samples and time points, thereby eliminating experimental errors introduced by variations in sample dosage and guaranteeing the accuracy of the release pattern analysis results.

[0038] Volatile aroma components released from the skin surface were collected using headspace sampling at multiple preset time points after application. In this embodiment, the preset time points included 0 min, 30 min, 60 min, 120 min, and 180 min after application. By setting multiple time points covering the period from the initial application to 180 min, the entire dynamic process of aroma change from initial burst to stable release and then to decay and dissipation can be fully captured, providing comprehensive time-series data for subsequent analysis of release patterns.

[0039] Aromas themselves exhibit a clear temporal evolution structure, typically divided into top, middle, and base notes, with their release intensity and characteristics gradually changing over time. Top notes exhibit high volatility, appearing rapidly within 10 or 15 minutes, while base notes last longer. The study *What's Hot, What's Not: The Trends of the Past 20 Years in the Chemistry of Odorants* (Angewandte Chemie International Edition, 2020) reveals that natural fruity aroma compounds, such as short-chain carboxylic acids, esters, and thiols, are volatile and highly water-soluble, and are usually perceived as top notes in fragrances. *Fragrances and sensory evaluation techniques* (Nonfood Sensory Practices: Woodhead Publishing, 2022: 217-233) also points out that the time interval between aroma sampling and evaluation must be strictly controlled; evaluation of top notes should be conducted within seconds of sampling, while persistence assessment can be performed several hours later. Furthermore, the study "Examination of VOC Concentration of Aroma Essential Oils and Their Major VOCs Diffused in Room Air" (International Journal of Environmental Research and Public Health, 2022) systematically revealed the dynamic release patterns of volatile organic compounds (VOCs) from four essential oils during indoor diffusion using a three-time-point sampling design (0-5 min, 30-35 min, and 60-65 min). Based on the release behavior, the VOCs were clearly divided into two categories: one category consisted of rapidly volatile components (α-pinene, γ-terpinene), which had the highest concentration at the initial diffusion stage (0 min) and then rapidly decreased; the other category consisted of slowly released components (linalool, citronellol, etc.), whose concentrations reached their peak only at 30 min or even 60 min, exhibiting a significant delayed release characteristic. This time design was scientifically sound, not only fully capturing the release trajectories of different volatile components but also successfully distinguishing their release characteristics, providing crucial time-resolved data for concentration assessment, safety analysis, and subsequent clinical research in aromatherapy.

[0040] Therefore, selecting multiple time points helps to fully capture the entire process of fragrance from its initial burst to its gradual dissipation, and the 30-minute sampling interval supports dynamic monitoring of fragrance release, which is also consistent with the GC-IMS testing method (total duration 30 minutes). Thus, this experiment selected five time points: 0, 30, 60, 120, and 180 minutes, covering the immediate response after application (0 minutes), short-term release (30 and 60 minutes), and medium- to long-term fragrance retention (120 and 180 minutes). This not only reflects the intensity characteristics of the initial fragrance release but also systematically evaluates its longevity performance, aligning with the conventional practice of focusing on "long-term effects" in fragrance performance evaluation. Furthermore, this time frame coincides with the time span of actual hand cream use, providing release curve data that closely reflects real-world applications for product fragrance design.

[0041] like Figure 1 and Figure 2 As shown, the headspace sampling operation was as follows: A commercially available skin volatile gas collector (in this embodiment, the standard part of the GAS SkinCollect S-100 manufactured by GAS Dortmund, Germany, was used as the epidermal gas enrichment component) was attached to the application area, and the operation was carried out under relatively stable indoor environmental conditions. To ensure data comparability, tests at multiple time points (0 min, 30 min, 60 min, 120 min, 180 min) for the same test sample, as well as tests between samples of different concentrations, were all completed continuously and without interruption on the same day. During the test, the indoor ambient temperature was maintained within the range of 18℃ to 23℃, and the relative humidity was maintained at around 40%, with minimal fluctuations in temperature and humidity. In specific operation, the sample was left to stand for 10 seconds to allow the fragrance on the skin surface to fully evaporate into the enrichment component. The headspace gas was then purged at a uniform speed using an airtight syringe within 10 seconds, and the gas in the enrichment component was blown into the sample inlet of the instrument connected to the other end. The gas was then injected into the sample inlet of the gas chromatography-ion mobility spectrometry (GC-IMS) instrument for detection. This purging sampling method can effectively enrich trace volatile components released on the skin surface, achieving in-situ, non-destructive sampling and truly reflecting the actual release of fragrance on the skin surface.

[0042] S2. GC-IMS detection: The collected volatile aroma components were detected using headspace gas chromatography-ion mobility spectrometry (GC-IMS) to obtain GC-IMS sample data.

[0043] The detection conditions for headspace gas chromatography-ion mobility spectrometry are as follows: (1) Gas chromatography conditions: The chromatographic column was MXT-5 with a size of 15m × 0.53mm × 1μm; the carrier gas was high-purity nitrogen with a purity of ≥99.999%; the column temperature program was initially 40℃ and held for 2min, then the temperature was increased to 240℃ and held for 20min within 8min. The carrier gas flow program employed a linear ramp-up mode: an initial flow rate of 2 mL / min, maintained for 2 min; then, from 2 min to 10 min, the flow rate linearly increased from 2 mL / min to 10 mL / min; subsequently, from 10 min to 20 min, the flow rate linearly increased from 10 mL / min to 150 mL / min; finally, the flow rate was maintained at 150 mL / min until the end of the analysis. The six-way valve was in the injection position from 0 to 40 s, with the first 10 s of sampling occurring at ports V1-6, followed by 30 s of gas sample delivery to the column; from 40 s to 30 min, the column was in the analysis position (isotropic elution followed by IMS detection). These chromatographic conditions enabled the effective separation of complex aroma components, laying the foundation for accurate qualitative and quantitative analysis.

[0044] (2) Ion mobility spectrometry conditions: drift tube length 53 mm, electric field strength 500 V / cm, drift tube temperature 45℃; drift gas is high-purity nitrogen with a purity ≥99.999%, flow rate 75 mL / min; injection port temperature is 80℃, and the temperature at the connection between the GC and IMS systems is 60℃. The total instrument running time is 30 min. Data acquisition: signal integration time 20 s; acquisition frequency 1 spectrum / s; detector sensitivity: automatic gain control (AGC). The IMS detector is equipped with dedicated software tools (GC-IMS LibrarySearch, version 1.01, GASmb H, Dortmund, Germany). The atmospheric pressure chemical ionization of the ion source in this detector is caused by primary ions generated by β-irradiation 3H bound in the metal (300 MBq). All measurements are performed in positive operating mode. These ion mobility spectrometry conditions enable rapid and highly sensitive detection of the separated components, meeting the requirements for the detection of trace aroma components.

[0045] In addition, standard solutions (C4-C9 ketone solutions) were tested using the same GC-IMS method as described above. The resulting standard solution fingerprints were used for subsequent calibration and qualitative analysis. The GC-IMS Library Search software allows the calculation of retention indices based on homologous ketone series using the NIST 2014 retention index database. 1 mL each of 2-butanone, 2-pentanone, 2-hexanone, 2-heptanone, 2-octanone, and 2-nonanone were mixed and serially diluted with ultrapure water to the required concentration (16 ppm) to obtain the calibration solution. The calibration solution was analyzed using a detector, allowing for correlation of retention indices based on retention time. Subsequent data processing and analysis were performed using VOCal, a substance identification application software program connected to the NIST 2020 database and the IMS database.

[0046] S3. Data Acquisition and Processing: Raw GC-IMS data of samples at each time point were acquired. First, the compound signal regions in the spectra were selected and integrated using VOCal analysis software to calculate their peak volumes for subsequent semi-quantitative analysis. Second, the Gallery Plot plugin built into VOCal analysis software was used to generate fingerprint spectra for visually comparing the differences in volatile substances among different samples; simultaneously, its Reporter plugin was used to generate topographic difference maps for visualizing the differences between samples.

[0047] S4. Analysis of Release Pattern: Using ketone compounds (C4-C9) as external standards, the retention times of standard substances (C4-C9 ketones) with known retention indices (RI) were measured under the same chromatographic conditions. A retention time (Rt)-retention index (RI) calibration curve was constructed using VOCal analysis software. The RI value of unknown compounds was then calculated from the Rt of the test results chromatogram, thus completing the preliminary retention index correction.

[0048] To eliminate interference from skin background and system environment and accurately identify the characteristic fragrance components of hand cream, this invention includes a blank control experiment. Specifically, blank samples were collected on clean skin without any hand cream applied, following the same S1 step (gas enrichment and headspace sampling) as the test samples, and then subjected to GC-IMS detection in step S2. The GC-IMS spectra of the obtained blank group were compared and analyzed with those of all scented hand cream samples. The comparison results showed that the blank group only had a small amount of weak signal in the retention time range of 100-200 s, with no obvious signal peaks in other areas, indicating a pure detection environment. After eliminating the above-mentioned blank background signal, the additional high-frequency signal peaks in the sample spectra were analyzed using VOCal analysis software connected to the NIST and IMS databases. The calculated RI values ​​and migration times were manually compared with the standard substances in the databases. Only when the retention index (RI) and migration time (Dt) of the signal peak highly matched those of the standard substances in the database were they identified as characteristic fragrance components of hand cream. Based on this screening principle, the characteristic markers of the scented hand cream of interest in this invention were ultimately determined to be phytol, phytol acetate, linalool, 2-phenylethanol, benzyl acetate, geraniol, and linalool acetate (qualitative data are shown in Table 2). Linalool and phytol were identified as monomers and dimers, respectively, due to their high proton affinity or concentration, resulting in multiple signals in a single analysis. These signals included protonated monomers, multi-molecular polymers, proton-bound dimers, and trimers. Furthermore, due to limitations in the GC-IMS spectral library, some corresponding unknown compounds were not identified.

[0049] Table 2 Qualitative data on characteristic compounds in scented hand creams

[0050] It should be noted that in GC-IMS analysis, due to the high proton affinity of some compounds (such as linalool and leaf alcohol) or their high concentration in the sample, protonated monomers and their multi-molecular polymers (such as dimers) may be formed simultaneously during ionization. Therefore, the same compound may correspond to multiple signal peaks in the fingerprint spectrum or topographic map. Generally, monomer peaks have a relatively lower migration index and are located in the left region of the spectrum, while polymer peaks have a higher migration index and are located in the right region. The "characteristic aroma component peak volume data" described in this invention includes the peak volume of at least one of these monomer peaks and / or polymer peaks, serving as the basis for semi-quantitative analysis of the component.

[0051] Based on the peak volume data of each characteristic aroma component obtained in S3 (see Table 3 for specific semi-quantitative data), the release pattern was analyzed.

[0052] Table 3 Semi-quantitative data on characteristic compounds in scented hand creams

[0053] Specifically: By comparing the fingerprint spectra of different samples at different time points (e.g. Figure 3 As shown in Figure 4, the differences in release intensity of different concentrations or types of fragrances at different time points can be analyzed intuitively. The dynamic changes in fragrance release can be observed more clearly through the topographic difference map (as shown in Figure 4).

[0054] Furthermore, Excel was used to plot the release curves of characteristic aroma components over time (e.g., Figure 5 As shown in the figure, the aroma release can be characterized by three stages: the initial burst, the stable release, and the decay.

[0055] Furthermore, the peak volume data was logarithmically processed using TBtools software, followed by normalization to scale it to the [0,1] range. Then, hierarchical cluster analysis was performed only on columns (i.e., different samples or time points), with Euclidean distance calculated for the clustering distance. Other parameters remained at their default software settings, and a cluster heatmap was plotted (e.g.,...). Figure 6 (As shown). Through this cluster analysis, volatile components can be classified according to their release characteristics, and key components affecting initial release, intermediate stability, and persistence can be identified.

[0056] Through the above multi-dimensional data analysis, the influence of fragrance concentration, fragrance type and formulation matrix on aroma release dynamics can be analyzed.

[0057] The analysis results of this embodiment are described in detail below with reference to specific graphs: GC-IMS fingerprint spectra of jasmine-scented hand creams of different concentrations obtained in S3 on the skin surface ( Figure 3 The characteristic regions were compared using both the topographic difference map and the topographic difference map, and the color depth and peak volume were compared. Figure 3 In the image, each horizontal row represents all signal peaks of a sample at a specific time point, from top to bottom showing the volatile component signals of different concentrations of jasmine hand cream at 0, 30, 60, 120, and 180 minutes. Each vertical column represents the signal peaks of the same volatile substance in different samples; the darker the color, the greater the peak intensity and the higher the relative content. The fingerprint spectrum allows for a direct comparison of the aroma release intensity differences between different samples and at different time points, achieving a visual characterization of the aroma release pattern.

[0058] Based on GC-IMS sample data, fingerprint spectra of characteristic fragrance components in hand cream were obtained. Through fingerprint spectra comparison and cluster analysis, the dynamic release pattern of fragrance on the skin surface was analyzed. Specifically: This embodiment uses fingerprint analysis to compare and analyze the differences in release intensity of different concentrations of fragrance at different time points. Figure 3 As can be seen, except for benzyl acetate (column 3 from the left), the signal values ​​of all compounds decreased rapidly with increasing time. This is reflected in the graph: at 0 min, the compound spot was a bright red, but by 30 min, the red had largely faded. Only linalyl acetate (column 1 from the left), geraniol (column 2 from the left), and 2-phenylethanol (column 4 from the left) still showed strong signals, appearing as light blue and bright white. This phenomenon was not clearly observed in the 6.25% and 12.5% ​​low-concentration samples, where the initial signal values ​​of these compounds were lower, appearing as dark blue, making it difficult to discern differences in compound signals over time. Further semi-quantitative data processing and compound release curves are needed to more accurately observe the changes in each compound across different samples. It is worth noting that in the fingerprint spectrum, the signal peak of benzyl acetate was adjacent to an unidentified peak; therefore, the selection of the benzyl acetate signal peak did not form a standard circular spot, but this does not affect the subsequent analysis of the test results.

[0059] Furthermore, by comparing the GC-IMS topographic maps at different time points in Figure 4, the dynamic changes in aroma release can be clearly observed. The vertical axis of the spectrum represents retention time (Rt / s), and the horizontal axis represents migration index (Dt / au). The red box in the figure represents the qualitative region of the substance peak signal. The red spots in the fingerprint spectrum indicate that the substance signal value here is higher than that of the 25% concentration fragrance sample, that is, the content of the qualitative substance is higher at this location. The appearance of blue spots indicates that it is lower. Figure 4A The results showed that at 0 min, the volatile component peak signals of the hand cream samples prepared with high-concentration fragrances (50%, 100%) were enhanced, appearing as bright red spots, but the brightness was not obvious, indicating that the signal intensity of the detected substances was slightly higher than that of the 25% concentration fragrance sample (refer to the spectrum), but not significantly. In contrast, the samples prepared with relatively low-concentration fragrances (6.25%, 12.5%) showed obvious blue in the same region, indicating to some extent that the initial volatile component signal intensity of the sample increased with the increase of the concentration of the raw fragrance used, but this increase was not linear but tended to be gradual. Figure 4CThe fingerprint spectra at 60 min show that the differences between samples have become similar, with fewer differential spots appearing in the qualitative regions of the spectra for each concentration of fragrance and flavoring samples. The differential spots appearing on the left side of the spectra (the left edge of the red-framed qualitative region) and the long tailing peaks appearing between the upper and lower red frames (retention time 350-650 s) are not identified qualitative peaks. This may be related to changes in the sample introduction environment, and the peak sites may not match the compound database, making qualitative characterization of the peak signals impossible; therefore, further analysis is not recommended. At this time point, not only are there fewer differential spots between samples, but the overall peak signal also shows a significant decrease compared to earlier times, indicating a decrease in the overall compound concentration, which is consistent with the changes in the content of each compound in the release curve. Figure 4D (120 min) and Figure 4E At 180 min, bright red spots were observed in the 6.25% and 12.5% ​​low-concentration flavor samples, indicating that at this time, the low-concentration flavor samples actually had a higher concentration of volatile compounds than the high-concentration flavor samples. Figure 5 The changes in compound E content and the retention times (Rt) of each compound in Table 2 indicate that the differential spot is benzyl acetate. This discrepancy is likely due to benzyl acetate's high boiling point and its compatibility with the oil matrix, making it more easily soluble or retained within the lipid matrix of the hand cream, rather than rapidly migrating to the surface and evaporating. This tendency to "retain" makes its release behavior more susceptible to the influence of the "microenvironment" between the hand cream and the skin, resulting in a near-jumping, chaotic release pattern. Higher concentrations can be observed even with longer evaporation times, suggesting that benzyl acetate may be a key aroma component affecting fragrance longevity, i.e., a "base note" substance. The contents of the remaining compounds were very low at time points of 120-180 min, indicating almost complete release.

[0060] Combined with the compound release curve ( Figure 5 This demonstrates that there is a significant critical concentration effect in the release of fragrance from scented hand creams. That is, when the concentration of the raw fragrance exceeds a certain threshold (25%), the initial release intensity of the main fragrance components tends to increase slowly, while the decay rate accelerates. Figure 5 The data shows the peak signal changes of four main aroma compounds: linalool, phenylethyl alcohol, linalyl acetate, and geraniol. The initial signal value increased by more than 130% when the fragrance concentration increased from 12.5% ​​to 25%. Among these, linalyl acetate (… Figure 5 F) and geraniol (F) Figure 5The increase in G) even exceeded 500%. However, the increase was lower when the concentration increased from 25% to 100%, not exceeding 25%. Furthermore, the difference in compound signal values ​​at 30 minutes was small. This meant that while the initial (0-minute) increase in compound signal was low after the fragrance concentration increased, the aroma signal decay rate increased significantly between 0 and 30 minutes. For the four compounds mentioned above, the high-concentration sample showed a 5-10% higher compound signal decay rate compared to the low-concentration sample during this period. The intensity of the compound peak signal also indicates its concentration. Therefore, the changes in the initial content of these main aroma substances indicate the existence of an optimal fragrance addition concentration range. Excessively increasing the concentration of the raw fragrance does not linearly improve its initial aroma effect. It is worth noting that these four main aroma compounds are released gradually over time. The decay rate is extremely rapid between 0 and 30 minutes, gradually decreasing from 30 minutes until the release curve remains almost horizontal after 120 minutes. Based on the characteristics of relatively slow release and long-lasting fragrance, linalool, phenylethyl alcohol, linalyl acetate, and geraniol are likely the "body fragrance" substances in this fragrance, which establishes the woody and floral characteristics of the hand cream in this embodiment. At the same time, the fragrance release pattern of this scented hand cream is defined as: initial burst period (0-30 min), stable release period (30-120 min), and decay period (after 120 min).

[0061] Unlike the aroma compounds mentioned above that are gradually released over time, leaf alcohol and leaf alcohol acetate ( Figure 5 A and Figure 5 B) The release is rapid and complete, with a high signal peak appearing at the initial 0 min, followed by a rapid decline in signal value, reaching its lowest point at 30 min, and then showing no further significant downward trend. This phenomenon may be related to the properties of its compounds; the boiling points of leaf alcohol (boiling point 156℃) and leaf alcohol acetate (boiling point 174.2℃) are both lower than those of other aroma substances in the qualitative analysis results, and the lowest retention values ​​(RI) recorded in Table 2 also indicate their more volatile characteristics. Combining the significant initial high signal and rapid, complete decay in the release curve, it is judged that leaf alcohol and leaf alcohol acetate are likely the "top note" substances in this fragrance, establishing the fresh and clean initial characteristics of the product.

[0062] In summary, the release intensity of characteristic aroma components in scented hand creams is significantly positively correlated with fragrance concentration. High-concentration fragrances exhibit a stronger initial aroma burst, but are accompanied by a faster decay rate; while medium- and low-concentration fragrances, although initially weaker, show superior aroma persistence. Furthermore, the jasmine-scented hand cream in this configuration follows a release pattern of initial burst (0-30 min), stable release (30-120 min), and decay (after 120 min). This method visualizes and characterizes the aroma release patterns of scented hand creams with different fragrance concentrations.

[0063] This embodiment uses TBtools software to process GC-IMS volatile component data using logarithmic and normalization methods, and then analyzes the data by plotting cluster heatmaps. The results are as follows: Figure 6 As shown in the figure. Cluster analysis categorized the different samples according to their aroma compound concentrations. Samples prepared at 25% and 100% raw fragrance concentrations at 0 min and 30 min of the initial aroma burst were most similar, followed by the 50% sample. Geraniol, linalyl acetate, phenylethyl alcohol, and linalool contributed significantly, exhibiting similar red colors in the 25% and 100% concentration samples, while appearing light blue in the 12.5% ​​and 6.25% raw fragrance concentration samples. Therefore, these four compounds are likely the key differentiating aroma compounds between samples with different fragrance concentrations. Furthermore, the high similarity of the cluster heatmaps between the 25% and high-concentration fragrance hand creams further supports the aforementioned conclusion that "an optimal fragrance addition concentration range exists," and it can be determined that this optimal fragrance addition concentration is between 12.5% ​​and 50%. However, the stability of this cluster was broken after a long period of time (after 120 min), at which point the aroma substances were almost completely released. This may be due to the unstable decrease in the concentration of aroma substances causing the deviation in the clustering between samples.

[0064] The release kinetics of different volatile components differ significantly. Highly volatile components such as leaf alcohol and leaf alcohol acetate exhibit rapid decay characteristics at all concentrations; moderately volatile components such as linalool and geraniol show relatively flat release curves, and can maintain a high late-signal intensity, especially at low and medium concentrations, which makes an important contribution to the persistence of the aroma's base notes. This differentiated release characteristic provides a scientific basis for selecting appropriate components for different aroma stages in product development.

[0065] In hand cream product development, sustained-release fixatives are often added to prolong fragrance persistence. Because the method of this invention can accurately and quantitatively characterize the change in the release intensity of characteristic fragrance components (such as linalool and benzyl acetate) over time during the application of hand cream to the skin, it has the potential to serve as an effective technical means for screening and evaluating the efficacy of sustained-release fixatives. By establishing an efficacy database of different candidates, a scientific basis can be provided for selecting the optimal sustained-release fixative for product development, potentially avoiding the subjectivity and uncertainty of traditional sensory evaluation, and improving R&D efficiency and product quality control.

[0066] Example 2 This embodiment demonstrates how the analytical method described in Example 1 can be applied to the fragrance design and formulation optimization of hand cream products.

[0067] Using the GC-IMS-based method for analyzing the fragrance release patterns of hand creams established in Example 1, a series of hand cream samples with different fragrance concentrations were tested, and the results were as follows: Figure 3 The fingerprint spectrum and semi-quantitative data are shown in Table 3. Analysis of this data can provide the following guidance for the fragrance design of hand cream products: First, fingerprint analysis was used to compare and analyze the differences in release intensity of different concentrations of fragrance at different time points. From... Figure 3 It can be seen that 100% high-concentration jasmine fragrance has the strongest initial aroma burst at 0 minutes, but it decays rapidly; while the 6.25%, 12.5%, 25%, and 50% concentration fragrances, although having lower initial release intensities, still maintain a identifiable aroma intensity at 120 and 180 minutes. This finding suggests that in product design, if a strong first impression and immediate olfactory impact are desired, a higher concentration of fragrance can be chosen; if a long-lasting, delicate fragrance experience is desired, medium to low concentrations of fragrance should be selected.

[0068] Secondly, the three-stage characteristics of aroma release are identified through release curves. Combining the semi-quantitative data in Table 3, the release rate and decay pattern of each characteristic component at different time points can be calculated. Taking linalool as an example, at a concentration of 6.25%, the release amount changes little from 0 min to 30 min, and shows a slow decay from 30 min to 180 min; while at a 100% concentration, the release amount drops sharply from 0 min to 30 min, and the decay slows down after 30 min. Based on this, the formulation can be adjusted accordingly. For example, aroma components that need to be enhanced during the initial release phase can have their concentration appropriately increased or high-volatility components added; aroma components that need to be maintained during the stable release phase should focus on controlling the proportion of medium-volatility components; and aroma components that need to be prolonged during the decay phase should focus on the proportion of low-volatility components.

[0069] Furthermore, cluster analysis was used to identify key components that affect initial release, intermediate stability, and persistence. Figure 6 Cluster analysis results showed that linalool and linalool acetate mainly affect the initial release intensity of the aroma; linalool monomers and geraniol determine the mid-term release characteristics of the aroma; and linalool acetate and phenethyl alcohol contribute significantly to the persistence of the aroma. In product design, the proportions of various components can be adjusted according to the target aroma quality. For example, to enhance aroma persistence, the content of low-volatile components such as linalool acetate and phenethyl alcohol can be appropriately increased.

[0070] Finally, by establishing a correlation model between fragrance concentration and release behavior, the critical concentration point where the initial release of aroma tends to plateau can be identified. Table 3 shows that for most characteristic aroma components (such as linalool, phenylethyl alcohol, benzyl acetate, linalyl acetate, and geraniol), except for highly volatile components (such as leaf alcohol and leaf alcohol acetate), when the fragrance concentration increases from 50% to 100%, the initial release intensity at 0 min increases significantly, not proportionally to the increase in fragrance concentration (50%), and some components even show stagnation or slight decrease, exhibiting a near-saturated release state. Simultaneously, the aroma decay rate of the high-concentration (100%) sample accelerates. This indicates the existence of a critical concentration range (approximately 50% in this study). When the raw fragrance concentration exceeds this range, simply increasing the concentration significantly reduces the effect on increasing the initial aroma release, i.e., a "release saturation" phenomenon occurs. Based on these findings, the method of this invention can provide accurate data support for formulation optimization. In formula design aimed at achieving longer-lasting aromas, the following optimization case can be referenced: Analysis of the release behavior of benzyl acetate based on the method of this invention revealed that, after adjusting the fragrance concentration from 100% to 50%, although the immediate release intensity of benzyl acetate decreased from 4154 au to 3143 au in the initial application stage (0 min), it increased from 111.87 au to 132.38 au in the later application stage (180 min), resulting in an improvement in aroma longevity of approximately 18.3%. This indicates that by moderately reducing the fragrance concentration, the longevity of the aroma can be significantly improved by sacrificing a small amount of initial burst power.

[0071] Based on the analysis conclusions of the clustering heatmap in Example 1 (see...), Figure 6 Based on this, it can be determined that the optimal concentration range for this jasmine fragrance in hand cream is between 12.5% ​​and 50%. Within this concentration range, it can maintain a good initial fragrance intensity while achieving better fragrance longevity and release stability.

[0072] In summary, by applying the method of this invention, the fragrance design and formula optimization of hand cream products can be scientifically guided, realizing the transformation from experience-based formula design to data-driven precision design, and significantly improving the efficiency and success rate of product development.

[0073] Finally, it should be noted that this method includes, but is not limited to, the analysis of the release pattern of jasmine fragrance in hand cream, and its technical approach and implementation steps are universal. For other fragrance types (fruity, woody, fougère, etc.) and other cream-based cosmetics (body lotion, face cream, etc.), by adaptively optimizing the sampling time point setting and GC-IMS detection conditions (column temperature program, carrier gas flow rate, etc.) based on the volatile component characteristics of the target fragrance and the differences in the product matrix, in-situ, dynamic monitoring and pattern analysis of fragrance release behavior on the skin surface can also be achieved. Therefore, this invention provides a universally applicable method for studying the dynamics of fragrance release, which can provide a reliable analytical tool for fragrance design, fragrance screening, and formulation optimization of various scented cosmetics.

[0074] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for analyzing the in-situ fragrance release pattern of hand cream based on GC-IMS technology, characterized in that, Includes the following steps: S1. Skin application and headspace sampling: A quantitative sample of scented hand cream is evenly applied to a designated area on the skin surface. At multiple preset time points after application, the application area is covered with a gas enrichment component and the air is blown away through a syringe to complete the collection of volatile fragrance components on the skin surface and headspace sampling. S2. GC-IMS detection: The collected volatile aroma components were detected using headspace gas chromatography-ion mobility spectrometry to obtain GC-IMS sample data; S3. Data Acquisition and Processing: Acquire the GC-IMS sample data and process it to obtain fingerprint spectrum and / or peak volume data of characteristic aroma components for subsequent analysis; S4. Release Pattern Analysis: Based on the fingerprint spectrum and / or peak volume data, the dynamic release pattern of the aroma on the skin surface is analyzed by comparing fingerprint spectra, clustering analysis and / or drawing release curves.

2. The analytical method according to claim 1, characterized in that, The characteristic aroma components are leaf alcohol, leaf alcohol acetate, linalool, 2-phenylethanol, benzyl acetate, geraniol and linalool acetate.

3. The analytical method according to claim 1, characterized in that, The preset time points in S1 include 0 min, 30 min, 60 min, 120 min and 180 min after application.

4. The analytical method according to claim 1, characterized in that, The headspace sampling operation in S1 is as follows: the gas sampling device is aimed at the smeared area, and under relatively stable indoor environmental conditions, the headspace gas is injected into the headspace gas chromatography-ion mobility spectrometry inlet at a uniform speed within 10 seconds using a syringe.

5. The analytical method according to claim 1, characterized in that, The gas chromatographic conditions for GC-IMS detection in S2 are as follows: the column is an MXT-5 with dimensions of 15 m × 0.53 mm × 1 μm; the carrier gas is high-purity nitrogen with a purity ≥99.999%; the column temperature program is initially 40℃ and held for 2 min, then increased to 240℃ at a rate of 10℃ / min and held for 20 min; the carrier gas flow rate program is initially 2 mL / min and held for 2 min, then increased to 10 mL / min within 2 min to 10 min, then increased to 150 mL / min within 10 min to 20 min and held until the end.

6. The analytical method according to claim 1, characterized in that, The ion mobility spectrometry conditions for GC-IMS detection in S2 are as follows: drift tube length 53 mm, electric field strength 500 V / cm, drift tube temperature 45℃; drift gas is high-purity nitrogen with a purity ≥99.999%, flow rate 75 mL / min; injection port temperature 80℃.

7. The analytical method according to claim 1, characterized in that, The data acquisition and processing in S3 includes: qualitative analysis of characteristic aroma components using GC-IMS Library Search software and the NIST database, and semi-quantitative analysis by calculating characteristic peak volumes.

8. The analytical method according to claim 1, characterized in that, The release pattern analysis in S4 includes: comparing and analyzing the release intensity differences of different concentrations or types of fragrances at different time points through fingerprint spectrum analysis; and / or, classifying volatile components according to release characteristics through cluster analysis to identify key components affecting initial release, mid-term stability and persistence; and / or, plotting release curves through peak volume data of characteristic aroma components to identify the initial burst period, stable release period and decay period of aroma release.

9. The application of the method for analyzing the in-situ fragrance release pattern of hand cream based on GC-IMS technology as described in any one of claims 1 to 8 in the fragrance design and formulation optimization of hand cream products.

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