CN116687355A - Method for monitoring permeation behavior of active component in skin based on Raman spectrum - Google Patents
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Classifications
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
- A61B5/0075—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/44—Detecting, measuring or recording for evaluating the integumentary system, e.g. skin, hair or nails
- A61B5/441—Skin evaluation, e.g. for skin disorder diagnosis
Abstract
The application relates to the relevant technical field of active component monitoring, in particular to a method for monitoring permeation behavior of an active component in body skin based on Raman spectrum. The method comprises the following steps: acquiring initial Raman spectrum information of the body skin without the active component by adopting a confocal microscopic Raman spectrometer; wherein the initial raman spectrum information contains depth information of human skin in vivo; defining different layers of the body skin by utilizing the Raman spectrum information; acquiring target Raman spectrum information of the body skin after the application of the active component for a preset time by adopting a confocal microscopic Raman spectrometer; the target Raman spectrum information contains depth information of human body in-vivo skin; tracking penetration of the active component in the body skin based on the active component chemical features, the different layers of the body skin, the initial raman spectrum information and the target raman spectrum information.
Description
Technical Field
The application relates to the relevant technical field of active component monitoring, in particular to a method for monitoring permeation behavior of an active component in body skin based on Raman spectrum.
Background
The skin has important barrier function in life activity process, can effectively prevent water loss in vivo, exogenous substances from being absorbed and prevent infection caused by microorganisms on the surface of the skin, and can protect the body from exogenous chemical and physical factors, thus constructing a first defense line for body surface immunity together with mucous membrane.
The skin is composed of three main components of epidermis, dermis and subcutaneous tissue, and appendages such as sweat glands, sebaceous glands and hair follicles. Wherein the epidermis is composed of a stratum corneum, a stratum granulosum, a stratum spinosum and a stratum basale. The stratum corneum, which is the outermost layer of the skin, is considered the primary source of skin barrier function, and its "brick wall" structure, consisting of the intercellular lipid matrix of ceramides, fatty acids and sterols and the filiform keratin-forming cells embedded therein, is the primary limiting barrier for the passage of exogenous molecules into the body, allowing only molecules smaller than 500 Da to pass through. The thickness of the stratum corneum, the composition and structure of surface lipids, proteins, affects the barrier function of the skin.
The penetration behavior of the active ingredient of a drug or cosmetic can be known during the course of treatment or use of the cosmetic, and the effect of the drug or cosmetic can be improved even better. However, the prior art lacks a method for understanding the penetration behavior of active ingredients in the skin of the body.
Disclosure of Invention
In view of the above, the present application provides a method for monitoring permeation behavior of active components in body skin based on raman spectrum, so as to solve the problems in the related art.
The application adopts the following technical scheme:
in a first aspect, an embodiment of the present application provides a method for monitoring permeation behavior of an active ingredient in skin based on raman spectroscopy, including:
acquiring initial Raman spectrum information of the body skin without the active component by adopting a confocal microscopic Raman spectrometer; wherein the initial raman spectrum information contains depth information of human skin in vivo;
defining different layers of the body skin by utilizing the Raman spectrum information;
acquiring target Raman spectrum information of the body skin after the application of the active component for a preset time by adopting a confocal microscopic Raman spectrometer; the target Raman spectrum information contains depth information of human body in-vivo skin;
tracking penetration of the active component in the body skin based on the active component chemical features, the different layers of the body skin, the initial raman spectrum information and the target raman spectrum information.
In some embodiments, the defining the different layers of bulk skin using the raman spectral information comprises:
the different layers of the body skin are defined by utilizing the characteristic that the different characteristic peaks of the human skin due to the different compositions of the different layers and the characteristic curves included in the Raman spectrum information.
In some embodiments, further comprising:
the interface of the body skin surface with air is determined using a multi-gradient descent algorithm.
In some embodiments, the tracking penetration of the active ingredient in the skin of the human body comprises:
the penetration of the active ingredient in the human body skin is obtained by a multivariate curve resolution-least squares regression (MCR-ALS) algorithm.
In some embodiments, the active component comprises components of multiple structures.
In some embodiments, further comprising:
the product was applied to the skin surface of a human body by Standard D-square disks for optical focusing.
In some embodiments, acquiring the targeted raman spectrum information of the body skin after a predetermined period of time has elapsed after application of the active ingredient comprises:
the active ingredient applied to the skin surface is rinsed.
In some embodiments, the Raman spectrometer uses 532 nm laser as excitation light source, the power is 2.68 mW, the exposure time is 0.05 s, the exposure times are 100 times, the ocular and objective lens times are 50 times, and the scanning range is 400-4000 cm-1.
According to the technical scheme, a confocal micro-Raman spectrometer is adopted to obtain initial Raman spectrum information of body skin without active components; wherein the initial raman spectrum information contains depth information of human skin in vivo; defining different layers of the body skin by utilizing the Raman spectrum information; acquiring target Raman spectrum information of the body skin after the application of the active component for a preset time by adopting a confocal microscopic Raman spectrometer; the target Raman spectrum information contains depth information of human body in-vivo skin; tracking penetration of the active component in the body skin based on the active component chemical features, the different layers of the body skin, the initial raman spectrum information and the target raman spectrum information. Thus, the penetration behavior of the active ingredient in the skin of the body can be understood.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for monitoring permeation behavior of an active ingredient in body skin based on Raman spectrum provided by an embodiment of the application;
fig. 2 is a view showing a monitoring result provided by the embodiment of the application.
Fig. 3 is a partial flow chart of a method for monitoring permeation behavior of an active ingredient in body skin based on raman spectroscopy according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail below. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, based on the examples herein, which are within the scope of the application as defined by the claims, will be within the scope of the application as defined by the claims.
Firstly, the application scene of the embodiment of the application is described, the skin has important barrier function in the life activity process, can effectively prevent the water loss in the body, the absorption of exogenous substances and the prevention of the infection caused by microorganisms on the surface of the skin, and can protect the body from being influenced by exogenous chemical and physical factors, and a first defense line of body surface immunity is constructed together with mucous membrane.
The skin is composed of three main components of epidermis, dermis and subcutaneous tissue, and appendages such as sweat glands, sebaceous glands and hair follicles. Wherein the epidermis is composed of a stratum corneum, a stratum granulosum, a stratum spinosum and a stratum basale. The stratum corneum, which is the outermost layer of the skin, is considered the primary source of skin barrier function, and its "brick wall" structure, consisting of the intercellular lipid matrix of ceramides, fatty acids and sterols and the filiform keratin-forming cells embedded therein, is the primary limiting barrier for the passage of exogenous molecules into the body, allowing only molecules smaller than 500 Da to pass through. The thickness of the stratum corneum, the composition and structure of surface lipids, proteins, affects the barrier function of the skin.
The Raman spectrum has the characteristics of non-invasiveness, capability of providing fingerprint characteristics of chemical substances and small influence of water on the sample, and is very suitable for composition and structure research of biological samples. Confocal microscopy Raman spectroscopy has been widely used in the fields of tumor detection, cultural relics archaeology, public security law and the like in recent years. The technology is also applied to skin penetration research, and the safety problem of the sun-screening cosmetics to human bodies is researched, and the confocal microscopic Raman spectrum technology is required to be adopted for testing on the skin. When the active ingredients of the medicine or the cosmetic are applied to the skin, the confocal raman technique can be adopted to scan the skin layer by layer from the outermost layer to the inside of the skin, so that the longitudinal distribution information of the medicine/the active ingredients can be obtained. The microscopic Raman spectrum can reflect the differences of chemical compositions and molecular structures of substances on a molecular level, and compared with the traditional dispersive Raman spectrometer, the microscopic Raman spectrum has greatly improved resolution, sensitivity and the like.
Based on the reasons, the application provides a method for monitoring the permeation behavior of an active ingredient in human skin based on Raman spectrum, which can be used for rapidly, nondestructively and low-cost detection of the permeation behavior of an active ingredient in human skin by a micro-confocal Raman spectrum imaging technology.
Examples
Fig. 1 is a flowchart of a method for monitoring permeation behavior of an active ingredient in body skin based on raman spectroscopy according to an embodiment of the present application. Referring to fig. 1, the method may specifically include the steps of:
step 101, acquiring initial Raman spectrum information of body skin without active components by adopting a confocal micro-Raman spectrometer; wherein the initial raman spectrum information contains depth information of human skin in vivo;
step 102, defining different layers of the body skin by utilizing the Raman spectrum information;
step 103, acquiring target Raman spectrum information of the body skin after the application of the active component for a preset time by adopting a confocal micro-Raman spectrometer; the target Raman spectrum information contains depth information of human body in-vivo skin;
step 104, tracking the penetration condition of the active component in the human body based on the chemical characteristic structure of the active component, different layers of the body skin, the initial Raman spectrum information and the target Raman spectrum information.
According to the scheme, the permeation behavior of the active component in the skin of the human body can be detected rapidly, nondestructively and at low cost through the micro-confocal Raman spectrum imaging technology.
Specifically, in the raman spectrum information acquiring process of the intrinsic depth information of skin of the present application, different layers of the skin of the subject may be defined by using the raman spectrum information, and specifically, different layers of the skin of the subject may be defined by using characteristics of different characteristic peaks of the skin of the subject due to different compositions of the different layers and characteristic curves included in the raman spectrum information. In this way, depth stratification information including the skin itself can be obtained based on raman spectral information: stratum corneum, stratum hyaline, stratum granulosum, stratum spinosum, stratum granulosum, and stratum basale; and the raman spectral intensities and spatial distribution of protein, lipid, polysaccharide and nucleic acid information at different depths of stratification.
Further, the interface of the body skin surface with air can be determined using a multi-gradient descent algorithm. And determining the interface as a depth information starting point so as to facilitate subsequent comparison.
FIG. 2 is a diagram showing the monitoring results provided by an embodiment of the present application; referring to fig. 2, in the present application, the process of acquiring raman spectrum information includes:
the preparation method of the human skin sample comprises the steps of dividing human body in a region of 1X 1cm < 2 > on the human body skin (on the inner side of the front part of the forearm), cleaning the front end of the arm according to the human body efficacy test requirement and standard, recovering the skin to the optimal physiological level in the environment with the temperature of 22+/-2 ℃ and the humidity of 50+/-10% for half an hour, and carrying out a Raman test at a surface selection point.
The Raman spectrometer uses 532 nm laser as excitation light source, the power is 2.68 mW, the exposure time is 0.05 s, the exposure times are 100 times, the ocular and objective lens multiple are 50 times, and the scanning range is 400-4000 cm-1.
The active component in the present application may refer to an active component in a cosmetic raw material product, and also refers to an active component in a cosmetic finished product preparation, and the cosmetic active component includes, but is not limited to: vitamin C, small molecule collagen peptide, blue copper peptide, nicotinamide, resveratrol, etc.
In practical applications, for better testing, solutions of active ingredients of different concentrations may be applied, and control tests may be performed, specifically, the active ingredient concentration is 0.1-10%, for example, 0.3%,0.5%,1%, 5%,2%,5%, etc.
It should be emphasized that when the test sample (solutions of active components with different concentrations) is applied to the skin before and after the application, the optical confocal plane needs to be determined, and the optical focusing is performed by attaching the Standard D-square dispersions to the skin surface of the human body, namely: the keratolytic tape is adhered to the surface of human skin for optical focusing.
In practical application, for better control of the test variables, the test temperature may be preset to be 22±2 ℃ (i.e. the temperature range required for human efficacy test). The reaction pressure was 101.325KPa (i.e., normal atmospheric pressure). The specific acquisition time may be: before, 0.5, 2, 4, 6, 8 or 10 hours after use. The active components applied to the skin surface need to be cleaned prior to collection to avoid the effect of the active components on the skin surface on the detection structure.
Thereafter, the penetration of the active ingredient in the body skin is tracked based on the chemical features of the active ingredient, the different layers of the body skin, the initial raman spectrum information and the target raman spectrum information. In particular, the penetration of the active ingredient in the skin of the human body can be obtained by the multivariate curve resolution-least squares regression MCR-ALS algorithm. Namely: comparing the difference between the initial Raman spectrum information and the target Raman spectrum information, and determining the penetration condition of the active component in different layers of human body skin based on the difference and the chemical characteristic structure of the active component.
Specifically, the human body Raman medium-high order dimension reduction algorithm is combined with the MCR-ALS algorithm as follows:
the interference caused by disturbance of a subject is recovered through a high-order dimension reduction algorithm (particularly a sample sparsity algorithm and calculation of a distance sparse sample in an MDS algorithm), and a variable far from a coordinate origin in an S-plot is selected as a significant variable for identifying and contributing to cosmetic active component information by adopting an MCR-ALS (multi-component curve resolution-least squares regression) algorithm. Based on this criterion, 10 variables were screened as characteristic components in combination with p < 0.05. By combining the feature variables screened from the two scaled OPLS-DA models, 17 feature components are finally obtained for locating the tracking of specific active components in the human body's skin.
Based on the feature components screened, 6 new OPLS-DA models were reconstructed and validated. Compared with the original OPLS-DA model constructed by all formula components, the novel OPLS-DA model constructed eliminates redundant information, reduces orthogonal components used for fitting, and ensures that the recognition rate of external verification of the reconstructed model can reach more than 90%.
With specific reference to fig. 3, the scheme provided by the implementation of the application comprises the following steps:
step 401, baseline correction;
specifically, baseline correction is one of the key steps in preprocessing raman spectral data, and is an effective method for eliminating fluorescence interference. The traditional polynomial fitting and uniform spline fitting methods are simple in principle and easy to implement, but the flexibility is limited by the uncertainty of the fitting order and the interior nodes. The step adopts a Raman spectrum baseline correction algorithm based on median filtering. Firstly, screening wave trough points through smoothing pretreatment, differential calculation and threshold value setting, and adaptively selecting inner nodes of a sample according to the wave trough position of spectrum data; the spectral data is then processed using a median filtering algorithm to better fit the spectral values to the baseline. The algorithm overcomes the defect that the traditional baseline correction method needs to manually select the inner node according to different Raman spectrums, avoids the influence of random noise in spectrum data on baseline fitting, further improves the spectrum baseline correction effect, and can better eliminate Raman signal baseline drift without over-fitting and under-fitting phenomena. Thus, the method can provide more accurate and reliable information for further analysis of spectral data.
Step 402, outlier judgment and interference item rejection;
specifically, outliers are classified into two categories by the cause of the generation: the first type of outliers are extreme manifestations of overall intrinsic variability, which are of the same general population as the rest of the observations in the sample; the second type of outliers is the result of accidental deviations from the test conditions and test methods, or errors in observation, recording, calculation, which are not of the same general population as the rest of the observations in the sample. And performing outlier judgment and interference item rejection on the target Raman spectrum information based on the requirement.
In the face of single outlier situation, selecting proper outlier test rules (glabros test, dixon test, etc.) according to actual conditions; and determining an appropriate level of significance; and finally, calculating the value of the corresponding statistic by the observed value, and judging according to the comparison result of the obtained value and the critical value.
When a plurality of outliers are determined, if the number of allowed outliers to be detected is greater than 1, the inspection rule is reused for inspection. If the detected outlier is beyond the upper limit, the inspection is stopped, and the sample should be treated carefully, otherwise, the inspection is continued for the remaining observation value after the detected outlier is removed by adopting the same detection level and the same rule.
After outliers are removed, new observed values are added or replaced by proper interpolation values; and the detected outliers (outliers) should be rejected or corrected.
Step 403, analyzing algorithm characteristic information;
step 404, active component signal stripping and tracking;
specifically, extracting features of the target Raman spectrum information to obtain target features; processing the initial Raman spectrum information to obtain initial characteristics; and analyzing the target characteristic and the initial characteristic based on the chemical characteristic structure of the active component and different layers of the body skin to obtain the penetration condition of the active component in the body skin.
Further, the above steps may be considered as constructing a raman spectrum database model of the active ingredient based on the chemical characteristics of the active ingredient and the different layers of the body skin and the initial characteristics, which can simulate the target raman spectrum information collected under various distribution conditions of the various active ingredient components. Based on the method, the actual target Raman spectrum information can be compared with a Raman spectrum database model of the active component, and the penetration condition of the active component in the human body skin can be obtained.
Specifically, a raman spectrum database model of the active component is first created in the algorithm, which includes a step of normalizing input data and a process of using data comparison. The algorithm then uses a fitting function to specifically tag the raman spectra of the active component in the library and the raman spectra of the species under test, and to correlate the tag to the active component being tested. Finally, the database is used for query prediction of the spectrum.
Step 405, data verification.
Specifically, the data authenticity is verified through binary coding of the spectrum data, namely, the spectrum data is coded and transcoded into numbers 0 and 1 to be used for representing spectrum shape characteristics, slope characteristics, amplitude characteristics, gradient characteristics and the like; the spectra of the active components and the samples to be tested in the database are verified.
Thus, through the algorithm, the penetration condition of the active component in the human body skin can be effectively tracked.
It should be noted that in the description of the present application, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present application, unless otherwise indicated, the meaning of "plurality" means at least two.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (9)
1. A method for monitoring permeation behavior of an active ingredient in body skin based on raman spectroscopy, comprising:
acquiring initial Raman spectrum information of the body skin without the active component by adopting a confocal microscopic Raman spectrometer; wherein the initial raman spectrum information contains depth information of human skin in vivo;
defining different layers of the body skin by utilizing the Raman spectrum information;
acquiring target Raman spectrum information of the body skin after the application of the active component for a preset time by adopting a confocal microscopic Raman spectrometer; the target Raman spectrum information contains depth information of human body in-vivo skin;
tracking penetration of the active component in the body skin based on the active component chemical features, the different layers of the body skin, the initial raman spectrum information and the target raman spectrum information.
2. The method for monitoring permeation behavior of an active ingredient in a body skin based on raman spectrum according to claim 1, wherein said defining different layers of the body skin using said raman spectrum information comprises:
the different layers of the body skin are defined by utilizing the characteristic that the different characteristic peaks of the human skin due to the different compositions of the different layers and the characteristic curves included in the Raman spectrum information.
3. The method for monitoring the permeation behavior of an active ingredient in body skin based on raman spectroscopy according to claim 1, further comprising:
the interface of the body skin surface with air is determined using a multi-gradient descent algorithm.
4. The method for monitoring the permeation behavior of an active ingredient in human skin based on raman spectroscopy according to claim 1, wherein said tracking the permeation of the active ingredient in human skin comprises:
and obtaining the penetration condition of the active component in the human body skin through a multi-curve resolution-least square regression algorithm.
5. The method for monitoring the permeation behavior of an active ingredient in the skin of a human body based on raman spectroscopy according to claim 4, wherein the obtaining the permeation situation of the active ingredient in the skin of the human body by a multivariate curve resolution-least squares regression algorithm comprises:
preprocessing target Raman spectrum information in a baseline correction mode to eliminate fluorescence interference;
performing outlier judgment and interference item rejection on the target Raman spectrum information;
extracting features of the target Raman spectrum information to obtain target features;
processing the initial Raman spectrum information to obtain initial characteristics;
and analyzing the target characteristic and the initial characteristic based on the chemical characteristic structure of the active component and different layers of the body skin to obtain the penetration condition of the active component in the body skin.
6. The method for monitoring the permeation behaviour of an active ingredient in body skin based on raman spectroscopy according to claim 1, wherein said active ingredient comprises a plurality of structural components.
7. The method for monitoring the permeation behavior of an active ingredient in body skin based on raman spectroscopy according to claim 1, further comprising:
the sampling tape is adhered to the surface of human skin in order to perform optical focusing.
8. The method for monitoring permeation behavior of an active ingredient in a body skin based on raman spectroscopy according to claim 1, wherein the step of obtaining the target raman spectroscopic information of the body skin after a predetermined time period of application of the active ingredient comprises:
the active ingredient applied to the skin surface is rinsed.
9. The method for monitoring the permeation behavior of an active ingredient in body skin based on Raman spectrum according to claim 1, wherein the Raman spectrometer uses 532 nm laser as an excitation light source, the power is 2.68 mW, the exposure time is 0.05 s, the exposure times are 100, the ocular and objective lens multiples are 50 times, and the scanning range is 400-4000 cm-1.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102725624A (en) * | 2009-12-17 | 2012-10-10 | 不列颠哥伦比亚癌症分社 | Apparatus and methods for in vivo tissue characterization by Raman spectroscopy |
| CN106604677A (en) * | 2014-09-04 | 2017-04-26 | Rsp系统公司 | Method and apparatus for transdermal in vivo measurement by raman spectroscopy |
| CN110008836A (en) * | 2019-03-06 | 2019-07-12 | 华东师范大学 | A kind of feature extracting method of histopathologic slide's high spectrum image |
| CN116035532A (en) * | 2023-01-17 | 2023-05-02 | 杭州时光肌生物科技有限公司 | Method for detecting content of blue copper peptide in cosmetics on basis of Raman spectrum in vivo |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102725624A (en) * | 2009-12-17 | 2012-10-10 | 不列颠哥伦比亚癌症分社 | Apparatus and methods for in vivo tissue characterization by Raman spectroscopy |
| CN106604677A (en) * | 2014-09-04 | 2017-04-26 | Rsp系统公司 | Method and apparatus for transdermal in vivo measurement by raman spectroscopy |
| CN110008836A (en) * | 2019-03-06 | 2019-07-12 | 华东师范大学 | A kind of feature extracting method of histopathologic slide's high spectrum image |
| CN116035532A (en) * | 2023-01-17 | 2023-05-02 | 杭州时光肌生物科技有限公司 | Method for detecting content of blue copper peptide in cosmetics on basis of Raman spectrum in vivo |
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