Infrared spectrum quantitative model of glycerol content in gel as well as establishment method and application of infrared spectrum quantitative model

Abstract

The invention discloses an infrared spectrum quantitative model for the content of glycerol in gel as well as an establishment method and application of the infrared spectrum quantitative model. The establishment method of the infrared spectrum quantitative model of the glycerin content in the gel comprises the following steps: (1) collecting mid-infrared spectrum samples of a plurality of gels with different known glycerin contents, and removing abnormal samples in the collected mid-infrared spectrum samples to obtain a sample set; the sample set comprises a correction set; (2) preprocessing the mid-infrared spectrum of the correction set; and (3) selecting one or more of modeling wave bands of 4000-400cm <-1 >, 1150-820cm <-1 > and 1550-1150cm <-1 >, selecting the number of main factors to be 2-10, and establishing an infrared spectrum quantitative model of the glycerol content in the gel for the preprocessed mid-infrared spectrum by adopting a partial least square method. The infrared spectrum quantitative model is high in accuracy and precision and good in stability and repeatability, and the glycerin content in the gel can be rapidly and accurately measured on the basis of the model.

Description

Technical Field

[0001] The invention relates to an infrared spectrum quantitative model of glycerol in a gel, and an establishment method and application thereof. Background Art

[0002] Glycerol is widely used in topical pharmaceutical preparations, cosmetics and oral solutions as a moisturizer, emollient, solvent and thickener. It can keep the preparation stable, moisturize the skin, dissolve drug ingredients and improve the taste. At present, the methods for determining the glycerol content included in the Chinese Pharmacopoeia and the current national standards include periodic acid oxidation titration, gas chromatography and high performance liquid chromatography. All three methods are lossy analysis, among which the periodic acid oxidation titration method is cumbersome to operate. However, the gel is a multi-component complex preparation. In addition to glycerol, it also contains a variety of excipients, such as gel matrix (special excipients such as carbomer and cellulose derivatives), pH adjusters, moisturizers, etc. These excipients will not only affect the distribution of glycerol in the gel, but also interfere with the extraction efficiency during the pretreatment of the preparation.

[0003] Among them, both high-performance liquid chromatography and gas chromatography require pretreatment extraction for the determination of glycerol content. The encapsulation and release characteristics of aqueous gel matrix and oily gel matrix are different for glycerol, which affects the extraction efficiency of glycerol during the dissolution process and easily leads to deviations in the measurement results. In addition, the chemical properties and physical states of different components in the gel are quite different. The complexity of the preparation increases the difficulty of developing analytical methods. The diversity of polymer excipients (such as carbomer, polycarbophil) and mixture excipients (such as light liquid paraffin, hydrogenated palm oil glyceride) not only increases the difficulty of separation, but also puts higher requirements on instruments and chromatographic columns. Polymer materials with higher viscosity increase the difficulty of eluting the chromatographic column, cause clogging or performance changes of the chromatographic column, and shorten the service life of the chromatographic column. Summary of the invention

[0004] In order to solve the shortcomings of the existing methods for detecting the glycerol content in gels, such as cumbersome operation, damage to samples and poor accuracy, the present invention provides an infrared spectroscopy quantitative model for the glycerol content in gels and a method for establishing and applying the model. The infrared spectroscopy quantitative model of the present invention has high accuracy and precision and good stability and repeatability. The method for detecting the glycerol content based on the model can realize the rapid and accurate determination of the glycerol content in the gel.

[0005] The present invention provides a method for establishing an infrared spectrum quantitative model of glycerol content in a gel, which comprises the following steps:

[0006] (1) collecting mid-infrared spectrum samples of a number of gelling agents with different known glycerol contents, removing abnormal samples from the collected mid-infrared spectrum samples, and obtaining a sample set; the sample set includes a calibration set;

[0007] (2) Preprocess the mid-infrared spectra of the calibration set; wherein, the preprocessing method includes one or more of multiplicative scatter correction, standard normal variate transformation, Savitzky-Golay filter smoothing, and derivative spectroscopy;

[0008] (3) Select the modeling band as 4000 - 400 cm -1 , 1150 - 820 cm -1 and 1550 - 1150 cm -1 for one or more segments, select the number of principal factors as 2 - 10, and establish an infrared spectroscopic quantitative model for the glycerol content in the gelling agent by partial least squares method for the preprocessed mid-infrared spectra.

[0009] In the present invention, the glycerol content in the gelling agent refers to the mass percentage of glycerol in the total mass of the gelling agent.

[0010] In step (1), the method for removing abnormal samples from the collected mid-infrared spectral samples can be a conventional removal method in the art, preferably the interquartile range method.

[0011] In the present invention, the abnormal samples can be abnormal samples defined conventionally in the art, generally including high leverage value samples and significantly abnormal prediction samples; wherein, the high leverage value samples refer to samples whose spectra deviate significantly from the average spectrum of the samples or whose reference values are much larger than the average value; the significantly abnormal prediction samples refer to samples with large prediction errors in multiple experiments after establishing the model.

[0012] In step (3), the number of principal factors in the partial least squares method is preferably 5 - 10.

[0013] In some specific embodiments, the number of principal factors in the partial least squares method is 10. This is because the gelling agent in the present invention is a complex preparation with multiple components, the corresponding number of samples is large and the complexity of the data is high. In addition, when the number of principal factors is 10, the value of the root mean square error of cross-validation (RMSECV) is the smallest during cross-validation. The smaller the value of RMSECV, the higher the prediction accuracy of the model. Generally, the value of RMSECV should be as close to 0 as possible.

[0014] In the present invention, the scanning range of the collected mid-infrared spectral samples can be a conventional mid-infrared spectral scanning range in the art, preferably 4000 - 400 cm -1 .

[0015] In the present invention, the resolution of the collected mid-infrared spectral samples is preferably 2 - 8 cm -1 , for example, 4 cm -1 .

[0016] In the present invention, the number of scans of the collected mid-infrared spectral sample is preferably 25 - 40 times, for example, 32 times.

[0017] In some preferred embodiments, the modeling band is 4000 - 400 cm -1 , 1147 - 831 cm -1 and 1533 - 1154 cm -1 or one or more of these ranges.

[0018] In some specific embodiments, the modeling band is 4000 - 400 cm -1 .

[0019] In some specific embodiments, the modeling band is 1147 - 831 cm -1 .

[0020] In some specific embodiments, the modeling band is 1533 - 1154 cm -1 .

[0021] In some specific embodiments, the modeling band is 1147 - 831 cm -1 and 1533 - 1154 cm -1 .

[0022] In the present invention, the 1147 - 831 cm -1 in the modeling band corresponds to the stretching vibration of C - O in glycerol, and 1533 - 1154 cm -1 corresponds to the bending vibration of C - H. The advantage of using the above bands as the modeling band is that: on the one hand, it can better capture the characteristic absorption peaks of glycerol; on the other hand, it can reduce the interference of other components.

[0023] In the present invention, the infrared spectrometer used for the collected mid-infrared spectral sample can be a conventional infrared spectrometer in the art. Preferably, the model of the infrared spectrometer is Shimadzu IRTracer - 100 spectrometer. Preferably, the infrared spectrometer is equipped with an Attenuated total reflectance (ATR) accessory.

[0024] In some embodiments, the pretreatment method is Multiplicative Scatter Correction (MSC).

[0025] In some embodiments, the pretreatment method is Standard Normal Variate (SNV).

[0026] In some embodiments, the preprocessing method is Savitzky-Golay filtering smoothing (S-G filtering smoothing) and derivative spectroscopy; the derivative spectroscopy is, for example, first-order derivative spectroscopy or second-order derivative spectroscopy.

[0027] In the present invention, the evaluation indexes of the infrared spectroscopy quantitative model can be the evaluation indexes conventionally used in the art. Preferably, they are selected from one or more of the correlation coefficient (R 2 ), root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), and root mean square error of prediction (RMSEP).

[0028] In the present invention, when evaluating the infrared spectroscopy quantitative model using the correlation coefficient, the correlation coefficient can be 0.998 - 1.000. Among them, the closer the correlation coefficient is to 1.000, the better the correlation of the infrared spectroscopy quantitative model.

[0029] In the present invention, when evaluating the infrared spectroscopy quantitative model using the root mean square error of calibration, root mean square error of cross-validation, and root mean square error of prediction, the smaller the root mean square error of calibration, root mean square error of cross-validation, and root mean square error of prediction, and the closer they are to 0, the better the accuracy and precision of the infrared spectroscopy quantitative model.

[0030] In some preferred embodiments, the sample set further includes an internal validation set, and the samples in the internal validation set are different from those in the calibration set; after step (3), step (4) is further included; wherein step (4) is as follows: importing the infrared spectra of the internal validation set into the infrared spectroscopy quantitative model obtained in step (3) for internal validation.

[0031] In some more preferred embodiments, the sample set further includes an external validation set, and the samples in the external validation set are different from those in the calibration set and the internal validation set; after step (4), step (5) is further included: importing the infrared spectra of the external validation set into the infrared spectroscopy quantitative model obtained in step (4) for external validation.

[0032] In the present invention, in the sample set, the classification methods of the calibration set, internal validation set, and external validation set can be the sample set classification methods conventionally used in the art, such as the random division method (Random method).

[0033] In some embodiments, the sample set includes a calibration set, an internal validation set, and an external validation set; there is no intersection among the samples in the calibration set, internal validation set, and external validation set, and they are independent of each other.

[0034] In the present invention, the calibration set can be used to establish the infrared spectroscopy quantitative model. The number of the calibration sets can be more than 20. The validation set can include an internal validation set and / or an external validation set. The validation set can be used to validate the model and evaluate the accuracy of the model. The number of the validation sets can be more than 10.

[0035] In the present invention, when establishing the infrared spectroscopy quantitative model based on the calibration set, the correlation coefficient (R 2 SEC ) and / or the root mean square error of calibration (RMSEC) of the calibration set are used to evaluate the infrared spectroscopy quantitative model. Among them, the R 2 SEC of the infrared spectroscopy quantitative model is preferably 0.998 - 1.000; the RMSEC of the infrared spectroscopy quantitative model is preferably 0.22% - 7.74%. RMSEC reflects the average error between the predicted value and the theoretical value of the model. The smaller the RMSEC and the closer the R 2 SEC is to 1, the better the fitting effect of the infrared spectroscopy quantitative model.

[0036] In the present invention, the evaluation index of the internal validation can be the test index commonly used in the art, preferably the correlation coefficient (R 2 SEP ) and / or the root mean square error of prediction (RMSEP) of the internal validation set.

[0037] In the present invention, when performing internal validation on the infrared spectroscopy quantitative model, the R 2 SEP of the infrared spectroscopy quantitative model is preferably 0.999 - 1.000; the RMSEP of the infrared spectroscopy quantitative model is preferably 0.55% - 1.64%. RMSEP reflects the average error between the predicted value and the theoretical value of the model. The smaller the RMSEP and the closer the R 2 SEP is to 1, the better the fitting effect of the infrared spectroscopy quantitative model.

[0038] In the present invention, the evaluation index of the external validation can be the test index commonly used in the art, preferably the relative standard deviation (RSD). The smaller the RSD, the better the prediction effect of the infrared spectroscopy quantitative model.

[0039] In the present invention, when performing external validation on the infrared spectroscopy quantitative model, the relative standard deviation of the infrared spectroscopy quantitative model can be less than 10%, preferably less than 3%.

[0040] In the present invention, when performing the internal verification or the external verification, it further includes performing cross-validation using the leave-one-out method to obtain the root mean square error of cross-validation (RMSECV). Performing cross-validation is to avoid overfitting of the model. Generally speaking, the closer and smaller the RMSEC and RMSECV are, and the higher the R 2 SEC value is, and the closer and smaller the RMSEP and RMSECV are, and the higher the R 2 SEP value is, at this time, it indicates that the fitting effect of the model is better.

[0041] In the present invention, the ratio of the number of infrared spectra in the calibration set to the number of infrared spectra in the internal verification set is preferably (4-8):2, for example, 7:2.

[0042] In the present invention, the ratio of the number of infrared spectra in the internal set to the number of infrared spectra in the external verification set is preferably (1-3):2, for example, 2:2.

[0043] In the present invention, the ratio of the number of infrared spectra in the calibration set, the internal verification set, and the external verification set is preferably (4-8):(1-3):2, for example, 7:2:2.

[0044] In the present invention, the gelling agent can be a conventional gelling agent containing glycerol in the art, for example, progesterone vaginal sustained-release gel or carbomer gel.

[0045] In some embodiments, the gelling agent includes one or more of glycerol, carbomer, polycarbophil, progesterone, sorbic acid, light liquid paraffin, and hydrogenated palm oil glyceride.

[0046] In some specific embodiments, the gelling agent is composed of glycerol, carbomer, polycarbophil, progesterone, sorbic acid, light liquid paraffin, hydrogenated palm oil glyceride, and water.

[0047] In some specific embodiments, in the gelling agent, the mass ratio of sorbic acid, carbomer, polycarbophil, and progesterone is 0.08:1:2:8.

[0048] In some specific embodiments, in the gelling agent, the mass ratio of light liquid paraffin and hydrogenated palm oil glyceride is 4.2:1.

[0049] In some specific embodiments, in the gelling agent, the mass ratio of sorbic acid, carbomer, polycarbophil, progesterone, light liquid paraffin, hydrogenated palm oil glyceride, and water is 0.08:1:2:8:4.2:1:83.72.

[0050] In the present invention, the gel refers to a thick liquid or semi-solid preparation in the form of a solution, suspension or emulsion prepared from a drug and an adjuvant capable of forming a gel.

[0051] In the present invention, when the glycerol content in the gel is too high, although it cannot maintain the gel state, it still has significance. Considering that too high glycerol content will affect the infrared spectrum, in the present invention, pretreatment of the infrared spectrum and adjustment of model parameters (increasing the number of main factors, adjusting the spectral range) are adopted to improve the accuracy and reliability of the infrared spectrum in the analysis of the glycerol content in the gel, so that even when a gel with a high glycerol content (such as above 30%) is used for testing, high accuracy and reliability can be ensured.

[0052] The present invention provides an infrared spectrum quantitative model for the glycerol content in a gel, which is obtained by the method for establishing the infrared spectrum quantitative model for the glycerol content in a gel as described above.

[0053] The present invention provides a method for detecting the glycerol content in a gel, which comprises the following steps:

[0054] (1) Collect mid-infrared spectra of the gel;

[0055] (2) Import the mid-infrared spectra collected in step (1) into the infrared spectrum quantitative model for the glycerol content in a gel as described above to obtain the content of glycerol in the gel.

[0056] In the present invention, the glycerol content in the gel refers to the mass percentage of glycerol in the total mass of the gel.

[0057] In step (1), the scanning range of the collected mid-infrared spectra can be 4000 - 400 cm -1 .

[0058] In step (1), the resolution of the collected mid-infrared spectra can be selected according to the routine in the art. Preferably, the resolution of the collected mid-infrared spectra is 2 - 8 cm -1 , for example, 4 cm -1 .

[0059] In step (1), the number of scans of the collected mid-infrared spectra can be selected according to the routine in the art. Preferably, the number of scans of the collected mid-infrared spectra is 25 - 40 times, for example, 32 times.

[0060] In step (1), the infrared spectrometer used for collecting the mid-infrared spectral samples can be an infrared spectrometer conventionally used in the art. Preferably, the model of the infrared spectrometer is Shimadzu IRTracer-100 spectrometer. Preferably, the infrared spectrometer is equipped with an Attenuated Total Reflectance (ATR) accessory.

[0061] In step (1), the gelling agent can be a conventional gelling agent containing glycerol in the art, such as progesterone vaginal sustained-release gel or carbomer gel.

[0062] In some embodiments, the gelling agent includes one or more of glycerol, carbomer, polycarbophil, progesterone, sorbic acid, light liquid paraffin, and hydrogenated palm oil glyceride.

[0063] In some specific embodiment schemes, the gelling agent is composed of glycerol, carbomer, polycarbophil, progesterone, sorbic acid, light liquid paraffin, hydrogenated palm oil glyceride, and water.

[0064] In the present invention, the gelling agent can be a conventional gelling agent containing glycerol in the art, such as progesterone vaginal sustained-release gel or carbomer gel.

[0065] In some embodiments, the gelling agent includes one or more of glycerol, carbomer, polycarbophil, progesterone, sorbic acid, light liquid paraffin, and hydrogenated palm oil glyceride.

[0066] In some specific embodiments, the gelling agent is composed of glycerol, carbomer, polycarbophil, progesterone, sorbic acid, light liquid paraffin, hydrogenated palm oil glyceride, and water.

[0067] In some specific embodiments, in the gelling agent, the mass ratio of sorbic acid, carbomer, polycarbophil, and progesterone is 0.08:1:2:8.

[0068] In some specific embodiments, in the gelling agent, the mass ratio of light liquid paraffin and hydrogenated palm oil glyceride is 4.2:1.

[0069] In some specific embodiments, in the gelling agent, the mass ratio of sorbic acid, carbomer, polycarbophil, progesterone, light liquid paraffin, hydrogenated palm oil glyceride, and water is 0.08:1:2:8:4.2:1:83.72.

[0070] In the present invention, preferably, the components of the gelling agent in the detection method of the glycerol content in the gelling agent are the same as the types of components of the gelling agent used in the method for establishing the infrared spectral quantitative model of the glycerol content in the gelling agent.

[0071] In some preferred embodiments, the content of glycerol in the gelling agent is not higher than 17%, and the percentage is the mass percentage of glycerol in the total mass of the gelling agent.

[0072] On the basis of conforming to the common knowledge in the art, the above preferred conditions can be arbitrarily combined to obtain various preferred examples of the present invention.

[0073] The reagents and raw materials used in the present invention are all commercially available.

[0074] The positive and progressive effects of the present invention are as follows:

[0075] (1) The infrared spectroscopic quantitative model of the present invention has high accuracy and precision, and good stability and repeatability.

[0076] (2) The detection method of the glycerol content obtained based on this model can achieve rapid and accurate determination of the glycerol content in the gelling agent: The present invention combines infrared spectroscopy technology with multivariate analysis methods to rapidly detect the glycerol content in the gelling agent. Samples are directly taken for testing without any pretreatment, and non-destructive analysis is carried out. The detection of one sample can be completed in only 3 minutes throughout the testing process, greatly improving the detection efficiency. This detection method has strong specificity and good repeatability, effectively solving the problems of low detection efficiency and many interferences in the traditional method in complex matrices. At the same time, this method has wide universality and is suitable for rapid analysis of various gelling agent samples.

[0077] (3) Aiming at the characteristics of high efficiency, high accuracy and high throughput of this method, it can meet the real-time monitoring requirements in the production process and provide an efficient and reliable analysis means for the pharmaceutical field. Description of the Drawings

[0078] Figure 1 It is the infrared spectrogram of gelling agents with different glycerol contents collected in Example 1.

[0079] Figure 2 It is the infrared spectroscopic quantitative model of the glycerol content in the gelling agent established in Example 1. Detailed Embodiments

[0080] The present invention will be further illustrated by the following examples, but the present invention is not limited to the scope of the described examples. The experimental methods without specific conditions noted in the following examples are carried out according to conventional methods and conditions, or selected according to the product specifications.

[0081] Instrument Information:

[0082] IRTracer-100 spectrometer (Shimadzu Corporation, Japan), equipped with an ATR (Attenuated total reflectance) accessory, the data analysis and processing software is TQ Analyst, weighing is performed using a one-hundred-thousandth balance (Mettler Toledo), an 85-2 constant temperature magnetic stirrer, a PB-10 pH meter, and purified water obtained from an ultrapure water instrument (Millipore integral10).

[0083] Purchase source of raw materials:

[0084] Progesterone (China Resources Zizhu, batch number: HV-2203001-716, purity 99.6%), glycerol (Zhejiang Zhuchang, batch number: 20230710, purity >99%), Carbopol 974P (Lubrizol, batch number: 0000157751) and Polycarbophil (Lubrizol, batch number: 0000151341), sorbic acid (Sichuan Boli, batch number: 230201, purity >99%), hydrogenated palm oil glyceride (Jiangxi Alpha, batch number: JS240501) and light liquid paraffin (Jiangxi Alpha, batch number: AY10240601).

[0085] In the following examples or effectiveness examples, the preparation method of a gel sample with a known glycerol content (hereinafter referred to as the self-made sample) includes the following steps:

[0086] S1. Preparation of blank gel:

[0087] 1) Preparation of polymer phase: Heat 83.72 g of purified water and 0.08 g of sorbic acid to 75 °C, cool to room temperature after complete dissolution, add 1 g of Carbopol and 2 g of Polycarbophil in sequence and stir with a magnetic stirrer for 2 h to fully swell and disperse Carbopol and Polycarbophil; adjust the pH to 3.0 ± 0.1 with 1 M NaOH solution; add 8 g of progesterone, stir evenly and heat to 60 °C for use as the polymer phase reserve.

[0088] 2) Preparation of oil phase: Heat 4.2 g of light liquid paraffin and 1 g of hydrogenated palm oil glyceride to melt, stir evenly, and keep at 60 °C.

[0089] 3) Emulsification: Add 94.8 g of oil phase to 5.2 g of polymer phase, emulsify at 60 °C for 30 min to obtain blank gel (100 g).

[0090] Store the above blank gel in a PE tube at room temperature, wrap it with tin foil outside for future use.

[0091] S2. On the same day of analysis, the final addition of glycerol in the blank gel was carried out to prepare a gelling agent containing different glycerol contents (mass percentage of the total mass of the gelling agent, 0 - 100%).

[0092] In the following examples and effect examples, the sample set was obtained based on the following steps:

[0093] (1) Collection of infrared spectra

[0094] The self - made samples with different glycerol contents prepared above were respectively placed on the ATR accessory to collect infrared spectra. Collection conditions: Using the instrument - built - in background as the reference, set the scanning range to 4000 - 400 cm -1 , the resolution was 4 cm -1 , the number of scans was 32 times, and the average spectrum was taken. Three parallel samples were prepared for each glycerol content point, and each parallel sample was measured in parallel 3 times. The infrared spectra of the gelling agent containing glycerol are shown in Figure 1 .

[0095] (2) Selection of calibration set, internal validation set and external validation set

[0096] The random division method (Random method) was used to randomly select samples in the sample set for classification to obtain the calibration set, internal validation set and external validation set, so that the sample number ratio was close to 7:2:2, as shown in Table 1. There was no intersection among the samples in the calibration set, internal validation set and external validation set, and they were independent of each other. Among them, the self - made samples in the calibration set and internal validation set were prepared on the day when the blank gel was prepared, and the self - made samples in the external validation set were prepared 7 days after the blank gel was prepared.

[0097] Table 1 Results of sample set division

[0098]

[0099] Establishment of infrared spectroscopic quantitative model for glycerol content in Example 1

[0100] (1) Based on the sample set obtained above, an infrared spectroscopic quantitative model was established;

[0101] (2) The mid - infrared spectra of the calibration set in the sample set were pre - processed, and the pre - processing method was MSC;

[0102] (3) The modeling waveband was selected as 4000 - 400 cm -1 , the number of main factors was selected as 10, and the partial least squares method was used to establish an infrared spectroscopic quantitative model for the glycerol content in the gelling agent based on the pre - processed mid - infrared spectra.

[0103] Establishment of infrared spectroscopic quantitative model for glycerol content in Example 2

[0104] In Example 2, the preprocessing method is SNV, and the number of principal factors is 5; the others are the same as in Example 1.

[0105] Establishment of infrared spectral quantitative model for glycerol content in Example 3

[0106] In Example 3, the preprocessing methods are Savitzky-Golay (S-G) filtering smoothing and first derivative spectroscopy, and the number of principal factors is 6; the others are the same as in Example 1.

[0107] Establishment of infrared spectral quantitative model for glycerol content in Example 4

[0108] In Example 4, the preprocessing methods are S-G filtering smoothing and second derivative spectroscopy, and the number of principal factors is 6; the others are the same as in Example 1.

[0109] Establishment of infrared spectral quantitative model for glycerol content in Example 5

[0110] In Example 5, the preprocessing method is multiplicative scatter correction (MSC), the number of principal factors is 3, and the modeling waveband is 1533 - 1154 cm -1 ; the others are the same as in Example 1.

[0111] Establishment of infrared spectral quantitative model for glycerol content in Example 6

[0112] In Example 6, the preprocessing method is SNV, the number of principal factors is 6, and the modeling waveband is 1533 - 1154 cm -1 ; the others are the same as in Example 1.

[0113] Establishment of infrared spectral quantitative model for glycerol content in Example 7

[0114] In Example 7, the preprocessing methods are S-G filtering smoothing and first derivative spectroscopy, the number of principal factors is 7, and the modeling waveband is 1533 - 1154 cm -1 ; the others are the same as in Example 1.

[0115] Establishment of infrared spectral quantitative model for glycerol content in Example 8

[0116] In Example 8, the preprocessing methods are S-G filtering smoothing and second derivative spectroscopy, the number of principal factors is 2, and the modeling waveband is 1533 - 1154 cm -1 ; the others are the same as in Example 1.

[0117] Establishment of infrared spectral quantitative model for glycerol content in Example 9

[0118] In Example 9, the preprocessing method is MSC, the number of principal factors is 5, and the modeling waveband is 1147 - 831 cm -1 ; the others are the same as in Example 1.

[0119] Establishment of Infrared Spectroscopy Quantitative Model for Glycerol Content in Example 10

[0120] In Example 10, the pretreatment method is SNV, the number of main factors is 4, and the modeling waveband is 1147 - 831 cm -1 ; others are the same as in Example 1.

[0121] Establishment of Infrared Spectroscopy Quantitative Model for Glycerol Content in Example 11

[0122] In Example 11, the pretreatment method is Savitzky-Golay (S-G) filter smoothing and first derivative spectroscopy, the number of main factors is 6, and the modeling waveband is 1147 - 831 cm -1 ; others are the same as in Example 1.

[0123] Establishment of Infrared Spectroscopy Quantitative Model for Glycerol Content in Example 12

[0124] In Example 12, the pretreatment method is Savitzky-Golay (S-G) filter smoothing and second derivative spectroscopy, the number of main factors is 3, and the modeling waveband is 1147 - 831 cm -1 ; others are the same as in Example 1.

[0125] Establishment of Infrared Spectroscopy Quantitative Model for Glycerol Content in Example 13

[0126] In Example 13, the pretreatment method is multiplicative scatter correction (MSC), the number of main factors is 5, and the modeling wavebands are 1147 - 831 cm -1 and 1533 - 1154 cm -1 ; others are the same as in Example 1.

[0127] Establishment of Infrared Spectroscopy Quantitative Model for Glycerol Content in Example 14

[0128] In Example 14, the pretreatment method is SNV, the number of main factors is 10, and the modeling wavebands are 1147 - 831 cm -1 and 1533 - 1154 cm -1 ; others are the same as in Example 1.

[0129] Establishment of Infrared Spectroscopy Quantitative Model for Glycerol Content in Example 15

[0130] In Example 15, the pretreatment method is Savitzky-Golay (S-G) filter smoothing and first derivative spectroscopy, the number of main factors is 8, and the modeling wavebands are 1147 - 831 cm -1 and 1533 - 1154 cm -1 ; others are the same as in Example 1.

[0131] Establishment of Infrared Spectroscopy Quantitative Model for Glycerol Content in Example 16

[0132] In Example 16, the preprocessing method is S-G filter smoothing and second derivative spectroscopy, the number of principal factors is 9, and the modeling band is 1147 - 831 cm -1 and 1533 - 1154 cm -1 ; Others are the same as in Example 1.

[0133] In Examples 1 - 16, the conditions in the method for establishing the infrared spectroscopy quantitative model of glycerol content based on the calibration set and the evaluation parameters of the established model (calibration set evaluation parameters R 2 SEC and RMSEC) are shown in Table 2.

[0134] Table 2 Establishment of the infrared spectroscopy quantitative model of glycerol content

[0135]

[0136] In Example 17, the internal validation set is used to evaluate the infrared spectroscopy quantitative model

[0137] The infrared spectra of the internal validation set are respectively imported into the infrared spectroscopy quantitative models of glycerol content established in Examples 1 - 16, and the TQ Analyst analysis software is used for the test of the internal validation set to obtain the model parameters. The results are shown in Table 3. Among them, R 2 SEP and RMSEP are the evaluation parameters of the internal validation set, and RMSECV is the evaluation parameter of the leave-one-out cross-validation.

[0138] Table 3 Evaluation of the infrared spectroscopy quantitative model of glycerol content based on the internal validation set

[0139] Number <![CDATA[R 2 SEP > RMSEP / % RMSECV / % Example 1 1.000 0.815 1.000 Example 2 0.999 1.130 0.999 Example 3 1.000 0.626 0.999 Example 4 1.000 0.649 0.999 Example 5 1.000 0.822 0.999 Example 6 0.999 1.500 0.999 Example 7 0.999 1.640 0.998 Example 8 0.999 1.620 0.997 Example 9 1.000 0.854 1.000 Example 10 1.000 1.140 0.999 Example 11 1.000 0.923 0.999 Example 12 0.999 1.170 0.999 Example 13 1.000 0.785 1.000 Example 14 1.000 0.708 1.000 Example 15 0.999 1.540 0.998 Example 16 1.000 0.550 1.000

[0140] According to the R 2 value, PLS number of principal factors, RMSEC, RMSECP, and RMSECV in Table 3, it can be seen that it is better to establish the calibration model by using four different spectral ranges of MSC, SNV, smoothing and derivative spectrum combination processing, and the calibration effects are basically similar.

[0141] From the above evaluation using the internal validation set, for each band, it is initially screened that the four models established by MSC (4000 - 400 cm -1 ) in Example 1, MSC (1147 - 831 cm -1 ) in Example 9, MSC (1533 - 1154 cm -1 ) in Example 5, and SNV (1147 - 831, 1533 - 1154 cm -1 ) in Example 14 have better modeling effects.

[0142] Example 18 Evaluation of the Infrared Spectroscopy Quantitative Model Using an External Validation Set

[0143] Nine self-made samples with different known glycerol contents were used as the external validation set. The four models established in Example 1, Example 5, Example 9, and Example 14 were externally validated using the external validation set respectively. The results are shown in Table 4.

[0144] Table 4 Evaluation of the Infrared Spectroscopy Quantitative Model for Glycerol Content Based on the External Validation Set

[0145]

[0146] Note: The recovery rate in Table 4 is the ratio of the theoretical value to the predicted value, expressed as a percentage, reflecting the relative relationship between the two, and helping to evaluate the accuracy and reliability of the model.

[0147] Combined with the external validation results, for the partial least squares model after SNV treatment in Example 14, with the spectral range of 1147 - 831, 1533 - 1154 cm -1 , the calibration effect of the model is the best (the minimum RSD is only 1.51%).

[0148] The correlation diagram between the predicted value and the theoretical value of the infrared spectroscopy quantitative model for glycerol content obtained in Example 14 is shown in Figure 2 . Among them, R 2 is 1, and SD is 3.02. It can be seen that the coincidence of the predicted value and the theoretical value is good, indicating that the error of the model is small and the stability is good.

[0149] Effect Example 1

[0150] Based on the infrared spectroscopy quantitative model SNV (1147 - 831 cm -1 , 1533 - 1154 cm -1 ) established in Example 14, the following methodological investigations were carried out on this model:

[0151] (1) Accuracy investigation

[0152] According to the above preparation method of the self-made samples, gel samples with glycerol contents of 24.70%, 65.76%, and 74.56% were prepared respectively. Based on the theoretical value of the known glycerol content and with the measured value (predicted value) as a reference, the accuracy of the method for determining glycerol content based on the infrared spectroscopy quantitative model was expressed by the relative error percentage. The smaller the absolute value of the relative error rate (the ratio of the absolute error to the theoretical value), the higher the accuracy of the quantitative method. The results are shown in Table 5. It can be seen from Table 5 that when using the infrared quantitative model established by the present invention to detect the glycerol content in the gel, the relative error rate can reach within 1.5%, and the accuracy is relatively high.

[0153] Table 5 Accuracy Results

[0154]

[0155] (2) Precision Investigation

[0156] According to the above preparation method of the self-made sample, a gel sample with a glycerol content of 51.39% was prepared. The infrared spectrum of the sample was collected, and the content of glycerol was determined using the infrared quantitative detection model for glycerol content. The scan was repeated 6 times, and the RSD value was calculated, as shown in Table 6. It can be seen from Table 6 that when using the infrared quantitative model established in the present invention to detect the glycerol content in the gel, the RSD in the precision investigation was only 0.33%, indicating relatively high precision.

[0157] Table 6 Precision Test (Multiple Tests of the Same Sample)

[0158]

[0159] (3) Repeatability Investigation

[0160] According to the above preparation method of the self-made sample, a gel sample with a glycerol content of 16.89% was prepared (the same batch but divided into six portions for detection). The infrared spectrum of the sample was collected, and the content of glycerol was determined using the infrared quantitative detection model for glycerol content. The RSD value was calculated, as shown in Table 7. It can be seen from Table 7 that when using the infrared quantitative model established in the present invention to detect the glycerol content in the gel, the RSD in the repeatability investigation was only 1.76%, indicating good repeatability.

[0161] Table 7 Repeatability Test

[0162]

[0163] (4) Stability Investigation

[0164] According to the above preparation method of the self-made sample, a gel sample with a glycerol content of 17.74% was prepared. The infrared spectra of the sample were collected at 0 h, 2 h, 4 h, 6 h, 8 h, and 10 h after sample preparation, respectively. The content of glycerol was determined using the infrared spectrum quantitative model for glycerol content, and the RSD value was calculated, as shown in Table 8. As the storage time prolongs, due to the volatilization of water in the sample, the glycerol content in the self-made sample will further increase; although glycerol as a humectant can slow down the volatilization of water in the sample to a certain extent, thereby maintaining a certain stability, there will still be a small amount of water loss in the self-made sample, resulting in an increase in the glycerol content. It can be seen from Table 8 that when using the infrared spectrum quantitative model established in the present invention to detect the glycerol content in the gel, although the glycerol content in the self-made sample also increases with the prolongation of the storage time, the RSD in the stability investigation was only 2.70%, indicating that the overall stability is good.

[0165] Table 8 Stability study (tests conducted at different test times)

[0166]

[0167] As can be seen from Tables 5 - 8, for the method of detecting the glycerol content in the gel based on the established infrared spectroscopy quantitative model in the present invention, the deviation of the accuracy determination is within ±1.5%; the precision is RSD less than 0.5%; the repeatability is RSD not more than 2%, and the stability is RSD not exceeding 3%. According to the requirements for accuracy, precision, repeatability, and stability in the "Guidelines for Validation of Analytical Methods" in the Chinese Pharmacopoeia: the deviation of the accuracy determination is within ±5%; the precision is RSD less than 2%; the repeatability is RSD not more than 2%, and the stability is RSD not exceeding 3%. By comparison, the precision, repeatability, stability, and accuracy of the method for determining the glycerol content in the gel in the present invention meet the above - mentioned guidelines and can be applied to detect the glycerol content in the gel.

[0168] Effect Example 2 Determination of the glycerol content in different types of gels using the infrared spectroscopy quantitative model

[0169] Using the infrared spectroscopy quantitative model SNV (1147 - 831 cm -1 , 1533 - 1154 cm -1 ) obtained in Example 14 to respectively determine the glycerol content in commercially available progesterone vaginal sustained - release gel (purchased from Crinone, product batch number C22122) and carbomer gel (purchased from Qingtai, model CBM - IⅣ, production date 20240101), which specifically includes the following steps:

[0170] Take an appropriate amount of the commercially available gel sample, collect the infrared spectrum of the sample; determine the glycerol content therein based on the above - mentioned model. The test results are shown in Table 9.

[0171] Among them, the theoretical value of the glycerol content in the progesterone vaginal sustained - release gel is known from the prescription calculation.

[0172] For the theoretical value of the glycerol content in the carbomer gel, since the prescription of the carbomer gel is unknown, the content determination is carried out by the NMR internal standard method, with maleic acid as the internal standard, and the glycerol quantitative peak position (the chemical shift information of the quantitative peak is: 2 - CH, m 3.73 - 3.83 ppm) has no interference with other excipients.

[0173] Table 9 Determination of the glycerol content in commercially available gel samples

[0174]

[0175] Although the specific embodiments of the present invention have been described above, those skilled in the art should understand that this is only an example, and the protection scope of the present invention is defined by the appended claims. Without departing from the principles and essence of the present invention, those skilled in the art can make various changes or modifications to these embodiments, but these changes and modifications all fall within the protection scope of the present invention.

Claims

1. A method for establishing an infrared spectroscopy quantitative model for glycerol content in a gel, comprising the following steps: (1) collecting mid-infrared spectrum samples of a number of gelling agents with different known glycerol contents, removing abnormal samples from the collected mid-infrared spectrum samples, and obtaining a sample set; the sample set includes a calibration set; (2) preprocessing the mid-infrared spectrum of the calibration set; wherein the preprocessing method includes one or more of multivariate scattering correction, standard normal variable transformation, Savitzky-Golay filter smoothing and derivative spectrum; (3) Select the modeling wavebands as 4000 - 400 cm -1 , 1150 - 820 cm -1 and 1550 - 1150 cm -1 For one or more of these wavebands, select the number of principal factors as 2 - 10, and use partial least squares method to establish an infrared spectroscopic quantitative model for the glycerol content in the gelling agent based on the preprocessed mid-infrared spectrum.

2. The method for establishing an infrared spectroscopy quantitative model for the glycerol content in the gel as claimed in claim 1, characterized in that, In step (3), the number of principal factors is 5-10; And / or, in step (1), the method for eliminating abnormal samples in the collected mid-infrared spectrum samples is the interquartile range method.

3. The method for establishing an infrared spectroscopy quantitative model for the glycerol content in the gel as described in claim 1, characterized in that, In step (3), the modeling waveband is 4000-400 cm -1 , 1147-831 cm -1 and 1533-1154 cm -1 or one or more segments thereof; Preferably, the modeling waveband is 4000-400 cm -1 ; Preferably, the modeling band is 1147 - 831 cm -1 ; Preferably, the modeling band is 1533 - 1154 cm -1 ; Preferably, the modeling waveband is 1147 - 831 cm -1 and 1533 - 1154 cm -1 .

4. The method for establishing an infrared spectroscopic quantitative model for the glycerol content in the gel as claimed in claim 1, characterized in that, In step (1), the collection satisfies one or more of the following conditions ad: a. The infrared spectrometer used for the collected mid-infrared spectrum samples is a Shimadzu IRTracer-100 spectrometer; preferably, the infrared spectrometer is equipped with an attenuated total reflection accessory; b. The scanning range of the collected mid-infrared spectral samples is 4000 - 400 cm -1 ; c. The resolution of the collected mid-infrared spectral sample is 2 - 8 cm -1 , for example, 4 cm -1 ; d. The number of scans of the collected mid-infrared spectrum sample is preferably 25-40 times, for example 32 times.

5. The method for establishing an infrared spectroscopy quantitative model for the glycerol content in the gel as claimed in claim 1, characterized in that, In step (1), the sample set also includes an internal validation set, and the samples in the internal validation set are different from the calibration set. Step (3) further includes step (4): importing the infrared spectra of the internal validation set into the infrared spectrum quantitative model obtained in step (3) to perform internal validation; Preferably, in step (1), the sample set also includes an external validation set, and the samples in the external validation set are different from the calibration set and the internal validation set. Step (4) further includes step (5): importing the infrared spectrum of the external validation set into the infrared spectrum quantitative model obtained in step (4) for external validation.

6. The method for establishing an infrared spectroscopy quantitative model for the glycerol content in the gelling agent according to claim 5, characterized in that, The internal validation set and the external validation set satisfy one or more of the following conditions ac: a. The ratio of the number of infrared spectra in the calibration set to the number of infrared spectra in the internal validation set is (4-8):2, for example, 7:2; b. The ratio of the number of infrared spectra in the internal set to the number of infrared spectra in the external validation set is (1-3):2, for example, 2:2; c. The ratio of the number of infrared spectra in the calibration set, the internal validation set and the external validation set is (4-8):(1-3):2, for example, 7:2:

2.

7. An infrared spectroscopic quantitative model for the glycerol content in a gelling agent, characterized in that, The method is obtained by establishing an infrared spectrum quantitative model of glycerol content in the gel according to any one of claims 1 to 6.

8. A method for detecting the glycerol content in a gelling agent, characterized in that, It includes the following steps: (1) collecting mid-infrared spectra of the gel; (2) The mid-infrared spectrum collected in step (1) is introduced into the infrared spectrum quantitative model of glycerol content in the gel according to claim 7 to obtain the glycerol content in the gel.

9. The method for detecting the glycerol content in the gel as claimed in claim 8, wherein It meets one or more of the following conditions: a. In step (1), the scanning range of the collected mid-infrared spectrum is 4000 - 400 cm -1 ; b. In step (1), the number of scans of the collected mid-infrared spectrum is 25-40 times, for example, 32 times; c. In step (1), the resolution of the collected mid-infrared spectrum is 2 - 8 cm -1 , for example, 4 cm -1 .

10. The method for detecting the glycerol content in the gel as claimed in claim 8, characterized in that, In step (1), the gel is progesterone vaginal sustained-release gel or carbomer gel; Or, the gelling agent comprises one or more of glycerol, carbomer, polycarbophil, progesterone, sorbic acid, light liquid paraffin, and hydrogenated palm oil glyceride; preferably, the gelling agent consists of glycerol, carbomer, polycarbophil, progesterone, sorbic acid, light liquid paraffin, hydrogenated palm oil glyceride, and water; Preferably, the content of glycerol in the gelling agent is not higher than 17%, and the percentage is the mass percentage of glycerol in the total mass of the gelling agent.

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