Method for judging and distinguishing sampling time of musk based on GC-MS (Gas Chromatography-Mass Spectrometer)

Abstract

According to the method, chemical components in musk samples at different sampling times are analyzed by using GC-MS, and the musk at different sampling times is distinguished and judged by establishing a fingerprint spectrum and an orthogonal-partial least square discriminant analysis model. Extracting chemical components of musk and carrying out GC-MS analysis; establishing a musk fingerprint spectrum by adopting a traditional Chinese medicine chromatographic fingerprint spectrum similarity evaluation system, and performing similarity evaluation; sIMCA software is used for establishing an orthogonal-partial least square discriminant analysis model for the data, chromatographic peak data is substituted into the established model for sampling time discrimination and judgment, the model is not over-fitted, and relevant parameters can prove the predictive capacity of the model. The method is accurate, simple and convenient, and can provide reference for sampling time judgment and quality evaluation of musk.

Description

Technical Field

[0001] The present invention relates to a method for analyzing musk components, and in particular to a method for distinguishing and determining the sampling time of musk based on GC-MS. Background Art

[0002] Musk is the dried secretion from the musk sac of mature male musk deer Moschus berezovskii Flerov, Moschus sifanicus Przewalski or Moschus moschiferus Linnaeus, members of the Musk Deer family. According to the Chinese Pharmacopoeia (2020 edition), musk has the effects of invigorating the mind, promoting blood circulation and menstruation, reducing swelling and relieving pain. It is widely used in clinical Chinese medicine and is often used to treat acute and severe diseases such as coma due to stroke, heart pain and stuffiness, arthralgia and blood stasis. It is an important medicine for treating closed-syndrome coma. Modern pharmacological studies have found that it has multiple activities such as anti-inflammatory, antibacterial, anti-tumor, analgesic, anti-myocardial ischemia, immune regulation, and blood circulation promotion. Its main active ingredients include macrocyclic ketones, steroids, fatty acids, etc. Among them, musk ketone is considered to be the main active ingredient and evaluation index of musk, and has clear pharmacological effects. However, natural musk resources are in short supply. Musk deer are a Class I protected species and are scarce. Furthermore, musk sampling is complex and invasive, resulting in production volumes far from meeting clinical and market demands. Musk, an animal-derived secretion, possesses a complex chemical composition, and its quality is susceptible to numerous factors, including sampling time, husbandry practices, environmental factors, and individual differences. Volatile components are particularly variable, and the level of these components directly influences the pharmacological effects of musk. Traditional visual inspection or empirical judgment alone cannot accurately determine musk maturity, chemical composition, and optimal sampling time. Therefore, scientifically and effectively evaluating musk quality has become a research priority. Traditional identification methods rely on sensory experience and physical and chemical indices, which are subject to high subjectivity and poor reproducibility. Modern analytical techniques, such as GC-MS, LC-MS, NMR, ICP-MS, and electronic noses, have been increasingly applied to musk component analysis, source tracing, quality grading, and adulteration detection. GC-MS technology is particularly widely used in volatile component analysis. It not only analyzes the types and concentrations of chemical components but also, combined with chemometrics, allows for the differentiation and traceability of musk samples collected at different times and of different quality grades. Therefore, establishing a GC-MS-based method for distinguishing musk sampling time has important practical significance and promotional value.

[0003] GC-MS, due to its high resolution, high sensitivity, and component identification capabilities, has been widely used in Chinese herbal medicine component analysis, quality control, and metabolomics research. Traditional Chinese medicine fingerprints are effective methods for evaluating the quality of Chinese medicines, identifying authenticity, distinguishing species, and conducting quality assessments. Orthogonal-partial least squares discriminant analysis, a supervised chemical pattern analysis, can better analyze the causes and differential markers of sample differences, thereby obtaining information on intergroup differences and predicting sample grouping.

[0004] By combining GC-MS with fingerprints and orthogonal-partial least squares method to distinguish and judge musk with different sampling times, the differential metabolites in musk with different sampling times are found for quality evaluation, in order to provide a basis for the further development and use of musk. At present, there is no literature research on the use of GC-MS combined with chemometrics to analyze the various chemical components in musk with different sampling times. Therefore, it is of great significance to establish a method for determining the sampling time of musk based on GC-MS. The present invention measures the chemical components of musk with different sampling times, establishes fingerprints for similarity evaluation, and combines orthogonal-partial least squares discriminant analysis to determine the differential metabolites of musk with different sampling times, which is more clear and reasonable as an indicator for quality evaluation. This method is accurate and simple, and can provide a reference for the sampling time and quality evaluation of musk. Summary of the Invention

[0005] The technical problem solved by the present invention is to provide a method for distinguishing and judging the sampling time of musk based on GC-MS combined with fingerprint and orthogonal-partial least squares, so as to quickly find metabolites causing mass differences.

[0006] The method of the present invention for distinguishing and determining the sampling time of musk based on GC-MS combined with fingerprint and orthogonal-partial least squares is carried out according to the following steps:

[0007] (1) Sample solution preparation: Accurately weigh 0.05 g of musk, add 1 mL of anhydrous ethanol, sonicate for 30 min, vortex for 1 min, centrifuge at 8000 rpm for 5 min, collect the supernatant, and filter through a 0.22 μm organic phase microporous filter to obtain the sample solution for GC-MS injection analysis.

[0008] (2) GC-MS was used for determination. The chromatographic and mass spectrometric conditions were as follows: the chromatographic column was a DB-17MS column (0.25 mm × 30 m, 0.25 μm); the temperature was programmed with an initial temperature of 50 °C and a ramp rate of 10 °C min -1 Raise to 120℃, keep for 5min, and then heat at 10℃·min -1 Raise to 180℃, keep for 25min, and heat at 10℃·min -1The temperature was raised to 280°C and held for 25 minutes. The injection volume was 1 μL, the injection port temperature was 250°C, and the split ratio was 50:1. An EI ion source was used with a source temperature of 250°C and an interface temperature of 250°C. The Q3Scan full scan mode was used, and the mass scan range was 50-600 amu.

[0009] (3) Using the analytical data obtained by GC-MS, the similarity of musk at different sampling times was evaluated using the Chinese medicine fingerprint similarity evaluation software (2012 version), and origin2022 was used to draw a three-dimensional fingerprint map; SIMCA14.1 software was used to perform orthogonal-partial least squares discriminant analysis to screen differential metabolites.

[0010] Compared with the prior art, the present invention has the following beneficial effects:

[0011] 1. The method of the present invention utilizes GC-MS to realize the identification of the chemical components of musk, collects GC-MS data of musk at different sampling times, more comprehensively characterizes the chemical components in musk, and is more accurately applied to the distinction and determination of sampling time.

[0012] 2. The present invention is simple to operate and has high detection efficiency. It uses fingerprints and orthogonal-partial least squares discriminant analysis to quickly screen key metabolites that cause differences in musk quality at different sampling times. It can be widely used to distinguish and determine the sampling time of traditional Chinese medicine. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 GC-MS total ion chromatogram of musk sample

[0014] Figure 2 3D fingerprints of musk samples at different sampling times

[0015] Figure 3 OPLS-DA model score diagram of musk samples at different sampling times

[0016] Figure 4 VIP diagram of OPLS-DA model of musk samples at different sampling times

[0017] Figure 5 Permutation test diagram of OPLS-DA model of musk samples at different sampling times DETAILED DESCRIPTION

[0018] For a better understanding of the present invention, the present invention will be further described in detail below in conjunction with specific examples. The following examples are only used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0019] Example 1 GC-MS determination of the chemical components of musk at different sampling times

[0020] 1. Instruments and reagents:

[0021] Shimadzu TQ-8040 GC-MS (Shimadzu Corporation, Japan); 321LS electronic balance (Tianmei Instrument Laboratory Equipment (Shanghai) Co., Ltd.); Milli-Q water purifier (Millipore Corporation, USA); KQ2200B ultrasonic cleaner (Kunshan Ultrasonic Instrument Co., Ltd.);

[0022] Eighteen batches of musk samples were collected from musk deer (Musk deer) using artificial extraction methods from live musk deer between May and November 2022. The samples were aliquoted into 1.5 mL EP tubes and stored in a refrigerator at −20°C. Anhydrous ethanol was chromatographically pure (Knowles).

[0023] 2. GC-MS analysis conditions

[0024] The column injection port temperature was 250°C, split injection was performed, the split ratio was 50:1, and the injection volume was 1 μL; the temperature program was: initial temperature 50°C, at 10°C·min -1 Raise to 120℃, keep for 5min, and then heat at 10℃·min -1 Raise to 180℃, keep for 25min, and heat at 10℃·min -1 The temperature was raised to 280°C and maintained for 25 min. The mass spectrometer used an EI ion source with a source temperature of 250°C, an interface temperature of 250°C, and a scan range of 50-600 amu.

[0025] 3. Identification of Musk Chemical Components

[0026] The ion peak information in the total ion current chromatogram was processed, and the chemical components of musk were qualitatively identified by comparing with reference substances and NIST14 and NIST14s databases. The peak areas were normalized to determine the relative mass fractions.

[0027] The GC-MS total ion current of musk sample obtained by the above method is as follows Figure 1 The retention time, molecular formula and other related information of the 22 chromatographic peaks were obtained according to the chromatogram, and the results are shown in Table 1.

[0028] Table 1 Qualitative results of chemical components of natural musk

[0029]

[0030]

[0031] Example 2 Establishment of musk fingerprints at different sampling times and similarity evaluation

[0032] The Chinese medicine chromatographic fingerprint similarity evaluation system (2012 version) was used to determine the common peaks and evaluate the similarity of musk fingerprints at different sampling times. The average method was used to generate a reference spectrum with a time window width of 0.1. After full spectrum peak matching, a reference spectrum was generated. The GC-MS fingerprints of each batch of musk ( Figure 2 ) and the reference chromatogram similarity results showed that a total of 10 common peaks were identified, and the similarity of the reference chromatograms of 18 batches of samples was between 0.770 and 0.995, the retention time RSD was 0.00 to 0.57%, and the peak area RSD was 0.00 to 163.57%.

[0033] Example 3 Establishing musk OPLS-DA models at different sampling times

[0034] To identify the metabolites that cause the difference in musk quality at different sampling times, an OPLS-DA classification model was established using SIMCA software. Ten groups of common peak areas of 18 batches of musk samples with different sampling times were modeled and classified according to the sampling time of musk. All 18 batches of samples fell within the 95% confidence interval, and the three parameters R of the evaluation model were evaluated. 2 X, R 2 Y and Q 2 They are 0.957, 0.958 and 0.708 respectively, indicating that the explanatory rate of the model's independent variables is 98.7%, the explanatory rate of the dependent variables is 95.8%, and the predictability of the model is 70.8%, all greater than 0.5. Therefore, the current model has good predictive ability. Figure 3 As shown, G1 and G2 can be clustered into two categories. The sampling time of the G1 group samples is concentrated in October-November 2022, while the sampling time of the G2 group samples is distributed from May to September 2022. Musk samples collected at different times have different chemical compositions, and the musk samples in the mature period of forest musk deer are richer in components, indicating that October-November is the best sampling time for musk sampling. In order to further illustrate the markers that lead to the differences in musk at different sampling times, the variable importance projection (VIP) of the data matrix was established for analysis, and the variable importance projection (VIP) > 1 was used as the screening criterion, indicating that the compound has a summary explanation rate of more than 50% for the classification. A total of 4 markers with large contributions were screened out, such as Figure 4 As shown in the figure, the VIP values of the common peaks 5 (cyclopentadecanone), peak 10 (cholesterol), peak 9 (5β-androstane-3α,17β-dione), and peak 8 (3α-hydroxy-5β-androstane-17-one) from large to small are ≥1, indicating that these common peaks play an important role in distinguishing and classifying. This shows that these four compounds are differential metabolites that are distinguished and determined at different sampling times, which has an indicative effect on the subsequent drug quality evaluation. In order to prevent the model from overfitting, 200 permutation tests were performed on OPLS-DA. Figure 5 The results show that R 2>0 and Q 2 <0, indicating that the model is not overfitting and is reliable.

Claims

1. A method for distinguishing and determining the sampling time of musk based on GC-MS combined with fingerprint and orthogonal-partial least squares, It is characterized by the following steps: (1) Extraction of musk chemical components for GC-MS analysis; (2) importing the obtained chromatographic peak data into a traditional Chinese medicine chromatographic fingerprint similarity evaluation system to establish a musk fingerprint and perform similarity evaluation; (3) Using SIMCA software, an orthogonal-partial least squares discriminant analysis model was established for the data. The chromatographic peak data were substituted into the established model to distinguish and determine the sampling time. The model was not overfitted, and the relevant parameters could prove the predictive ability of the model.

2. The method for determining the sampling time of musk based on GC-MS according to claim 1, wherein: Step (1) is as follows: accurately weigh 0.05 g of musk, add 1 mL of anhydrous ethanol, sonicate for 30 min, vortex for 1 min, centrifuge at 8000 rpm for 5 min, take the supernatant, and filter through a 0.22 μm organic phase microporous filter to obtain the test solution for GC-MS sampling analysis.

3. A method for distinguishing and determining the sampling time of musk based on GC-MS according to claim 2, characterized in that: The GC-MS analysis conditions were as follows: programmed temperature rise: initial temperature 50°C, 10°C·min -1 Raise to 120℃, keep for 5min, and then heat at 10℃·min -1 Raise to 180℃, keep for 25min, and heat at 10℃·min -1 Increase the temperature to 280°C and hold for 25 minutes. Injection volume: 1 μL, injection port temperature: 250°C, split ratio: 50:

1. Use EI ion source, source temperature: 250°C, interface temperature: 250°C, full Q3 Scan mode, mass scan range: 50-600 amu.

4. The method for distinguishing and determining the sampling time of musk based on GC-MS combined with fingerprint and orthogonal-partial least squares according to claim 1, characterized in that: Based on the chromatographic peak data obtained after GC-MS analysis, the chemical components in musk were determined, and the musk GC-MS fingerprint was constructed using the traditional Chinese medicine chromatographic fingerprint similarity evaluation system.

5. The method for distinguishing and determining the sampling time of musk based on GC-MS combined with fingerprint and orthogonal-partial least squares according to claim 1, characterized in that: An orthogonal-partial least squares discriminant analysis model for musk at different sampling times was established, and the chromatographic peak data were substituted into the established model for judgment. The model was not overfitting, and the relevant parameters could prove the predictive ability of the model.

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