A method for detecting the content of phosphodiesterase in cosmetics

Abstract

The present invention discloses a method for detecting the boson content in cosmetics, which relates to the technical field of cosmetics detection. The method comprises the following steps: gray-scaling a fluorescent image, marking potential edge pixels according to gradient amplitude; reducing the potential edge pixels, eliminating invalid edge pixels, dilating non-potential edge pixels, and generating a final actual contour based on the intersection of the potential contour and the effective contour; accurately extracting the intersection of the potential contour and the effective contour by combining microfluidic chip technology with fluorescent markers, adopting gradient analysis and morphological optimization, further screening out effective edge pixels by establishing a coordinate system and structural element window analysis, and finally determining a precise final contour based on the overlap of the potential contour and the effective contour. This method not only improves detection efficiency, but also significantly enhances the accuracy and reliability of the detection results.

Description

Technical Field

[0001] The present invention belongs to the technical field of cosmetics detection, and in particular relates to a method for detecting the content of phosphatase in cosmetics. Background Art

[0002] Boswellia is a natural compound extracted from the frankincense tree and is widely used in cosmetics because of its various biological activities such as anti-inflammatory and antioxidant properties;

[0003] As an efficient and precise analytical tool, microfluidic chips have been widely used in fields such as bioanalysis and environmental monitoring. Fluorescence image analysis using microfluidic chips combined with fluorescent markers offers a new approach for detecting the presence of benzophenone in cosmetics. To test benzophenone in cosmetics using a microfluidic chip, the sample is loaded onto the chip, and microchannels and microvalves control sample flow and mixing. Utilizing the characteristic of benzophenone that produces a fluorescent signal when excited by light of a specific wavelength after binding to a specific fluorescent marker, the cosmetic sample is processed through the microfluidic chip to fully react with the fluorescent marker. Fluorescence images of the reaction are collected, and machine learning algorithms are used to analyze the image features. A relationship model between fluorescence intensity, image morphology, and benzophenone content is established, enabling accurate benzophenone detection.

[0004] However, when detecting the content of benzophenone in cosmetics, traditional methods mainly rely on chemical analysis and low-resolution image detection. However, the chemical analysis method usually requires a complex sample pretreatment process, is time-consuming, and may damage the sample. On the one hand, the low-resolution image detection method cannot provide enough detailed information, making it difficult to accurately identify and quantitatively analyze the content of benzophenone. On the other hand, the existing detection method is easily affected by background interference when detecting the fluorescence image of the fluorescence filter, resulting in reduced accuracy and reliability of the detection results of the fluorescence image contour. Based on this, a method for detecting the content of benzophenone in cosmetics is proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for detecting the content of bosphorin in cosmetics, which solves the technical problem that the detection method is easily affected by background interference when detecting the fluorescent image of the fluorescent filter, resulting in reduced accuracy and reliability of the detection results of the fluorescent image contour.

[0006] A method for detecting the content of phosphatidylcholine in cosmetics comprises the following steps:

[0007] Step 1: Acquire fluorescence images of the reaction area of ​​the microfluidic chip;

[0008] Step 2: Grayscale the fluorescence image, calculate the gradient component of each pixel, obtain the gradient amplitude corresponding to each pixel, and mark the potential edge pixels according to the gradient amplitude;

[0009] Step 3: Perform a reduction operation on the potential edge pixels to eliminate invalid edge pixels, and perform an expansion operation on the non-potential edge pixels to obtain valid edge pixels among the non-potential edge pixels;

[0010] Step 4: Generate the final actual contour based on the intersection of the potential contour and the effective contour.

[0011] As a further solution of the present invention, the specific method of obtaining the gradient amplitude corresponding to each pixel point is:

[0012] The gradient components GAe and GBe of each pixel in the grayscale image of the microfluidic chip fluorescence image in the horizontal and vertical directions are obtained using the formula: , calculate and obtain the gradient amplitude Fe corresponding to each pixel in the fluorescence image, where e refers to the pixel, e=1, 2, ..., a, a refers to the total number of pixels in the grayscale image of the fluorescence image, a is a positive integer, and a satisfies a≥2.

[0013] The gradient amplitude Fe corresponding to each pixel in the fluorescence image is compared with the preset threshold Y1. The pixel points with a gradient amplitude Fe greater than the preset threshold Y1 are marked as potential edge pixels in the fluorescence image. Otherwise, no mark is made, thereby obtaining the potential edge pixels in the fluorescence image. The specific value of the preset threshold Y1 is the square root of the sum of the mean and standard deviation squares of the gradient amplitude Fe.

[0014] As a further solution of the present invention, a specific method of performing a reduction operation on potential edge pixels and eliminating invalid edge pixels is as follows:

[0015] Draw a circular structuring element window with a radius of R, move the circular structuring element window on the fluorescence image, overlap the center point O of the circular structuring element window with the potential edge pixel point, and obtain the number n of edge pixels in the circular structuring element window. When the ratio between n and a is greater than the preset value Y2, no processing is performed. When the ratio between n and a is less than or equal to the preset value Y2, the potential edge pixel point overlapping with the center point of the circular structuring element window is marked as an invalid edge pixel point and eliminated. All potential edge pixels in the fluorescence image are traversed, all invalid edge pixels among the potential edge pixels are eliminated, and the remaining potential edge pixels are marked as valid edge pixels. The specific value of the preset value Y2 is greater than 0.2 and less than 0.4.

[0016] As a further solution of the present invention, a specific method of performing an expansion operation on non-potential edge pixels to obtain valid edge pixels from the non-potential edge pixels is as follows:

[0017] The circular structuring element window is moved on the fluorescence image, and the center point O of the circular structuring element window is coincident with the non-potential edge pixel point. When all the edge pixels in the circular structuring element window are excluded from the non-potential edge pixel point coincident with the center point O, and all the remaining edge pixels are potential edge pixels, the non-potential edge pixel point coincident with the center point O is marked as a valid edge pixel point. Otherwise, no processing is performed. All the non-potential edge pixels in the fluorescence image are traversed, and the valid edge pixels therein are marked to obtain the valid edge pixels among the non-potential edge pixels.

[0018] As a further solution of the present invention, the specific method of drawing a circular structure element window with a radius R is:

[0019] Taking the center point z of the fluorescence image as the origin, a two-dimensional coordinate system is established in the fluorescence image to obtain the two-dimensional coordinates corresponding to each pixel in the fluorescence image. The straight-line distance between each pixel and the center point z of the fluorescence image is calculated, and the maximum value of the straight-line distance Le is taken as the radius R of the structure element window. A circular structure element window with a radius of R is drawn.

[0020] As a further solution of the present invention, the specific method of generating the final actual contour according to the intersection of the potential contour and the effective contour is as follows:

[0021] The potential contour of the fluorescence image is drawn according to the potential edge pixels in the fluorescence image, the effective contour of the fluorescence image is drawn according to all the effective edge pixels in the fluorescence image, the potential contour and the effective contour are binarized respectively to obtain the final edge pixels, and the final contour of the fluorescence image is obtained according to the final edge pixels.

[0022] As a further solution of the present invention, the specific method of performing binarization processing on the potential contour and the effective contour to obtain the final edge pixel point is:

[0023] The potential edge pixel points on the edge contour line of the potential contour are marked as 1, and the other potential edge pixel points are marked as 0. Similarly, the effective edge pixel points on the edge contour line of the effective contour are marked as 1, and the other effective edge pixel points are marked as 0, thereby obtaining binary images corresponding to the potential contour and the effective contour respectively. The edge pixel points marked as 1 in the binary images of the potential contour and the effective contour are taken as the final edge pixel points, and the final contour of the fluorescence image is obtained according to the final edge pixel points.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The present invention combines microfluidic chip technology with fluorescent markers, adopts gradient analysis and morphological optimization, and accurately extracts the intersection of potential contours and effective contours. This method not only enhances the clarity and stability of the contour, but also ensures the accuracy of the final contour by eliminating noise and invalid edges.

[0026] By establishing a coordinate system and performing structural element window analysis, valid edge pixels are further screened, and the final, precise contour is determined by combining the overlap between the potential and effective contours. This series of operations not only improves detection efficiency but also significantly enhances the accuracy and reliability of test results. It provides an efficient and feasible technical solution for the precise detection of bosphorin content in cosmetics, significantly promoting the development of cosmetics testing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Schematic diagram of the framework structure of the method of the present invention;

[0028] Figure 2 Schematic diagram of the structural element window structure of the present invention. DETAILED DESCRIPTION

[0029] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0030] Example 1: Please refer to Figure 1 and Figure 2 The present application provides a method for detecting the content of bosaicin in cosmetics, comprising the following steps:

[0031] Step 1: Use a high-resolution microscope equipped with appropriate fluorescence filters to image the reaction area of ​​the microfluidic chip. By setting the excitation wavelength of the microscope to the excitation wavelength of the fluorescent marker, the fluorescence image is collected to obtain fluorescence signal information;

[0032] It should be noted that before collecting the fluorescence image of the microfluidic core, an appropriate amount of cosmetic sample is first taken and diluted with a specific buffer solution to reduce the interference of the sample matrix. The diluted sample is injected into the sample channel of the microfluidic chip, and a solution containing a fluorescent marker is injected into another channel. The sample and the fluorescent marker are mixed in the reaction area through the microvalves and microchannels of the microfluidic chip. The reaction is carried out under appropriate temperature and time conditions to allow the boson and the fluorescent marker to fully combine, thereby achieving pretreatment of the sample and reaction with the microfluidic chip, preparing for the collection of the fluorescence image of the microfluidic chip, and facilitating the collection of the fluorescence image of the microfluidic chip. The equipment and methods used above are all existing and mature technologies, so they will not be described in detail here.

[0033] The specific binding of fluorescent markers to bosera improves the detection accuracy and sensitivity of bosera, and the interference of other components in the matrix on the detection is reduced by diluting the sample and using buffer solution.

[0034] Step 2: Grayscale the fluorescence image to obtain a grayscale image of the microfluidic chip. Then, the Sobel operator is used to obtain the gradient components GAe and GBe of each pixel in the grayscale image of the microfluidic chip fluorescence image in the horizontal and vertical directions. According to the gradient components GAe and GBe of each pixel in the horizontal and vertical directions, the gradient amplitude Fe corresponding to each pixel is obtained, where e refers to the pixel, e=1, 2, ..., a, a refers to the total number of pixels in the grayscale image of the fluorescence image, a is a positive integer, and a satisfies a≥2. Potential edge pixels in the fluorescence image are marked according to the gradient amplitude Fe. The specific method is as follows:

[0035] Fluorescence images are grayscaled to convert the original color image into a black and white image. Grayscale simplifies the image data and highlights the edges of the fluorescence signal in the image, laying the foundation for subsequent processing.

[0036] By formula: , calculate and obtain the gradient amplitude Fe corresponding to each pixel in the fluorescence image. The gradient amplitude reflects the intensity of the image brightness change at the pixel. The larger the gradient amplitude, the more likely the corresponding pixel is a potential edge pixel.

[0037] Compare the gradient amplitude Fe corresponding to each pixel in the fluorescence image with the preset threshold Y1, and mark the pixel points with the gradient amplitude Fe greater than the preset threshold Y1 as potential edge pixels in the fluorescence image. Otherwise, no mark is made, thereby obtaining the potential edge pixels in the fluorescence image;

[0038] The specific value of the preset threshold Y1 is the square root of the sum of the mean and standard deviation squares of the gradient amplitude Fe, that is, , where Fp is the mean of the gradient amplitude Fe, and C is the standard deviation of the mean;

[0039] Gradient calculation can effectively capture edge information in images, especially when the image has a lot of details. It can accurately extract the edges of the area where the Bose element is located. By calculating the gradient amplitude, significant edge features can be extracted from the image and the interference of background noise can be reduced.

[0040] Step 3: With the center point z of the fluorescence image as the origin, a two-dimensional coordinate system is established in the fluorescence image to obtain the two-dimensional coordinates Se (Sxe, Sye) corresponding to each pixel in the fluorescence image. According to the two-dimensional coordinates corresponding to each pixel, the straight-line distance Le between each pixel and the center point z of the fluorescence image is calculated. The maximum value of the straight-line distance Le is taken as the radius R of the structure element window, and a circular structure element window with a radius of R is drawn (such as Figure 2 ), the circular structure element window is moved and analyzed on the fluorescence image, the potential edge pixels on the fluorescence image are reduced, invalid edge pixels among the potential edge pixels are analyzed and eliminated, and valid edge pixels are obtained. At the same time, the non-potential edge pixels on the fluorescence image are analyzed, and the valid edge pixels among the non-potential edge pixels are expanded, thereby obtaining all valid edge pixels in the fluorescence image;

[0041] By defining the movement and analysis of the structural element window, the potential edge pixels are reduced and invalid edge pixels are eliminated. The valid edge pixels among the non-potential edge pixels are expanded to expand the edge area and obtain clearer valid edge pixels.

[0042] pass ; Where (zx, zx) is the coordinate of the center point z of the fluorescence image;

[0043] The specific method of reducing the potential edge pixels on the fluorescence image is as follows:

[0044] The circular structuring element window is moved on the fluorescence image, and the center point O of the circular structuring element window is overlapped with the potential edge pixel point. The invalid edge pixel points among the potential edge pixels are analyzed and eliminated to obtain the valid edge pixel points, thereby realizing the reduction operation of the potential edge pixel points. The specific method is as follows:

[0045] When the center point O of the circular structuring element window coincides with the potential edge pixel point, the number n of edge pixels in the circular structuring element window is obtained. When the ratio between n and a is greater than a preset value Y2, no processing is performed. When the ratio between n and a is less than or equal to the preset value Y2, the potential edge pixel point that coincides with the center point of the circular structuring element window is marked as an invalid edge pixel point and removed. All potential edge pixels in the fluorescence image are traversed, and all invalid edge pixels among the potential edge pixels are marked and removed, and the remaining potential edge pixels are marked as valid edge pixels. The specific value of the preset value Y2 is greater than 0.2 and less than 0.4.

[0046] The specific method of performing the expansion operation on the valid edge pixels among the non-potential edge pixels is as follows:

[0047] The circular structuring element window is moved on the fluorescence image, and the center point O of the circular structuring element window is overlapped with the non-potential edge pixel point. When all the edge pixels in the circular structuring element window are excluded from the non-potential edge pixel point overlapped with the center point O, and all the remaining edge pixels are potential edge pixels, the non-potential edge pixel point overlapped with the center point O is marked as a valid edge pixel point. Otherwise, no processing is performed. All the non-potential edge pixels in the fluorescence image are traversed, and the valid edge pixels therein are marked to obtain the valid edge pixels among the non-potential edge pixels, and to perform the dilation operation on the valid edge pixels among the non-potential edge pixels; thereby, all the valid edge pixels in the fluorescence image are obtained.

[0048] Thresholding and reduction operations remove invalid noise from potential edges, ensuring a more accurate edge region. Dilation operations fill in gaps in edge regions, ensuring the continuity and integrity of the contour.

[0049] Step 4: Draw the potential contour of the fluorescence image based on the potential edge pixels in the fluorescence image, and then draw the effective contour of the fluorescence image based on all the effective edge pixels in the fluorescence image. The final contour of the fluorescence image is obtained based on the potential contour and effective contour of the fluorescence image. The specific method is as follows:

[0050] The potential contour of the fluorescence image is drawn based on the potential edge pixels in the fluorescence image. The set of these potential edge pixels can be directly drawn on the fluorescence image to represent the preliminary edge area. This potential contour may contain some noise or inaccurate edges because it mainly relies on gradient information.

[0051] The effective contour of the fluorescence image is drawn based on all the valid edge pixels in the fluorescence image. The effective contour represents more accurate image edge information because it has been screened by valid edge pixels and eliminated by invalid edge pixels. These valid edge pixels are obtained after the potential contour is screened, edge enhanced, and expanded, and can better reflect the true Bose region.

[0052] Calculate the overlapped part of the potential contour and the effective contour, perform binarization on the potential contour and the effective contour respectively, and obtain the binary images corresponding to the potential contour and the effective contour respectively. The specific method is as follows:

[0053] The potential edge pixels on the edge contour line of the potential contour are marked as 1, and the other potential edge pixels are marked as 0. Similarly, the effective edge pixels on the edge contour line of the effective contour are marked as 1, and the other effective edge pixels are marked as 0. Then, the binary images corresponding to the potential contour and the effective contour are obtained, which can clearly distinguish the edge and non-edge areas.

[0054] The edge pixels marked as 1 in both the potential contour and the effective contour binary images are taken as the final edge pixels, and the final contour of the fluorescence image is obtained based on the final edge pixels. That is, the intersection of the two binary images is taken as the final contour of the fluorescence image. This intersection represents the area that both consider to be the edge.

[0055] The intersection of the potential contour and the effective contour is extracted through logical AND operation, inaccurate edge information is eliminated, the error is reduced, and the advantages of the potential contour and the effective contour are combined to make the final contour more stable and accurate.

[0056] By combining microfluidic chip technology with fluorescence image analysis, an efficient and accurate method for detecting the content of bosphorin was proposed. This method uses gradient analysis, morphological operations, and the intersection calculation of potential contours and effective contours to solve the problems of complex operations, insufficient sensitivity, and high costs in traditional detection methods. Through multi-source information fusion, it not only improves the accuracy and sensitivity of detection, but also effectively removes background noise and interference, ensuring the stability and reliability of the test results, providing a low-cost, high-precision bosphorin content detection solution for the cosmetics industry.

[0057] Through high-resolution microscopy imaging and image processing technology, the problems of low efficiency, insufficient accuracy, and susceptibility to background interference in traditional methods for detecting the content of phosphatase in cosmetics have been effectively resolved. During the detection process, the sample is first pretreated to reduce matrix interference, and then a clear fluorescent image is obtained using fluorescent labeling and high-resolution imaging. Subsequent grayscale processing and Sobel operator gradient calculation are used to accurately identify potential edge pixels. By establishing a coordinate system and structural element window analysis, valid edge pixels are further screened, and finally the precise final contour is determined by combining the overlapping parts of the potential contour and the effective contour. This series of operations not only improves detection efficiency, but also significantly enhances the accuracy and reliability of the test results. It provides an efficient and feasible technical solution for the precise detection of the content of phosphatase in cosmetics, and has strongly promoted the development of cosmetics testing technology.

[0058] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0059] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for detecting the content of bosaicin in cosmetics, characterized in that: The following steps are involved: Step 1: Acquire fluorescence images of the reaction area of ​​the microfluidic chip; Step 2: Grayscale the fluorescence image and obtain the gradient components GAe and GBe in the horizontal and vertical directions of each pixel in the grayscale image of the microfluidic chip fluorescence image, using the formula: , calculate the gradient amplitude Fe corresponding to each pixel in the fluorescence image, where e refers to the pixel, e=1, 2, ..., a, a refers to the total number of pixels in the grayscale image of the fluorescence image, a is a positive integer, and a satisfies a≥2, and mark the potential edge pixels according to the gradient amplitude; Step 3: Draw a circular structuring element window with a radius of R, move the circular structuring element window on the fluorescence image, and overlap the center point O of the circular structuring element window with the potential edge pixel point to obtain the number n of edge pixels in the circular structuring element window. When the ratio between n and a is greater than the preset value Y2, no processing is performed. When the ratio between n and a is less than or equal to the preset value Y2, the potential edge pixel point that overlaps with the center point of the circular structuring element window is marked as an invalid edge pixel point and removed. All potential edge pixels in the fluorescence image are traversed, all invalid edge pixels among the potential edge pixels are removed, and the remaining potential edge pixels are marked as valid. Edge pixel points, the specific value of the preset value Y2 is greater than 0.2 and less than 0.4, invalid edge pixel points are eliminated, the circular structure element window is moved on the fluorescence image, and the center point O of the circular structure element window is coincident with the non-potential edge pixel point. When all edge pixels in the circular structure element window are excluded from the non-potential edge pixel points coincident with the center point O, and all remaining edge pixels are potential edge pixels, the non-potential edge pixel points coincident with the center point O are marked as valid edge pixels, otherwise no processing is performed, and all non-potential edge pixels in the fluorescence image are traversed, and the valid edge pixels therein are marked to obtain the valid edge pixels among the non-potential edge pixels; Step 4: Generate the final actual contour based on the intersection of the potential contour and the effective contour.

2. The method for detecting the content of bosine in cosmetics according to claim 1, characterized in that: The specific method of marking potential edge pixels according to the gradient amplitude is as follows; The gradient amplitude Fe corresponding to each pixel point in the fluorescence image is compared with the preset threshold Y1, and the pixel points with a gradient amplitude Fe greater than the preset threshold Y1 are marked as potential edge pixels in the fluorescence image. Otherwise, no mark is made, thereby obtaining the potential edge pixels in the fluorescence image.

3. The method for detecting the content of bosine in cosmetics according to claim 2, characterized in that: The specific method of drawing a circular structure element window with a radius of R is: Taking the center point z of the fluorescence image as the origin, a two-dimensional coordinate system is established in the fluorescence image to obtain the two-dimensional coordinates corresponding to each pixel in the fluorescence image. The straight-line distance between each pixel and the center point z of the fluorescence image is calculated, and the maximum value of the straight-line distance Le is taken as the radius R of the structure element window. A circular structure element window with a radius of R is drawn.

4. The method for detecting the content of bosine in cosmetics according to claim 1, wherein: According to the intersection of the potential contour and the effective contour, the specific method of generating the final actual contour is: The potential contour of the fluorescence image is drawn according to the potential edge pixels in the fluorescence image, the effective contour of the fluorescence image is drawn according to all the effective edge pixels in the fluorescence image, the potential contour and the effective contour are binarized respectively to obtain the final edge pixels, and the final contour of the fluorescence image is obtained according to the final edge pixels.

5. The method for detecting the content of bosine in cosmetics according to claim 1, characterized in that: The specific method of binarizing the potential contour and the effective contour to obtain the final edge pixel points is as follows: The potential edge pixel points on the edge contour line of the potential contour are marked as 1, and the other potential edge pixel points are marked as 0. Similarly, the effective edge pixel points on the edge contour line of the effective contour are marked as 1, and the other effective edge pixel points are marked as 0, thereby obtaining binary images corresponding to the potential contour and the effective contour respectively. The edge pixel points marked as 1 in the binary images of the potential contour and the effective contour are taken as the final edge pixel points, and the final contour of the fluorescence image is obtained according to the final edge pixel points.

6. The method for detecting the content of bosine in cosmetics according to claim 2, characterized in that: The specific value of the preset threshold Y1 is the square root of the sum of the square of the mean and standard deviation of the gradient amplitude Fe.

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