CN113053466B - Method for representing fat crystal distribution sites in double-emulsion system by ternary feature vector method - Google Patents
Method for representing fat crystal distribution sites in double-emulsion system by ternary feature vector method Download PDFInfo
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Abstract
The invention provides a method for representing fat crystal distribution sites in a double-emulsion system by a ternary feature vector method. The method analyzes the potential distribution of the fat crystals in the double emulsion by adopting a ternary feature vector model, and judges and quantifies the probability density of the fat crystals appearing on the inner layer interface, the intermediate phase interface and the outer layer interface of the double emulsion according to the element values. Aiming at the problems that the distribution situation of fat crystallization sites in a double-emulsion system in a three-dimensional space is complex and difficult to characterize, the distribution situation of the fat crystallization sites in the three-dimensional space of the double-emulsion system can be accurately characterized by associating the ternary feature vector values with the distribution density probability of crystallization through dimensionality reduction and data statistics.
Description
Technical Field
The invention belongs to the technical field of double-emulsion system crystal distribution locus quantification, and particularly relates to a method for representing a fat crystal distribution locus in a double-emulsion system by a ternary feature vector method.
Background
With the progress of the research of double-emulsion liquid systems, the efficient and flexible regulation and control function of the application of fat crystals on the structural characteristics of double emulsions begins to become a new breakthrough of the leading-edge research, in oil gel or single emulsion (O/W or W/O) systems, the content, the crystal size, the crystal form and the like of the fat crystals can be measured by using conventional fat crystal characterization methods such as DSC, XRD, NMR and the like, but the three-dimensional space distribution situation of the fat crystals in the double-emulsion system is more complex: the oil phase fat crystal can be arranged in the inner layer W due to the influence of the interfacial activity of the emulsifier or the crystal molecule itself1O/W of the oil phase or outer layer of the/O interface film2Distributed at the interface film. When the stabilizer is distributed at an interface, the stabilizer can directly influence the physicochemical property of a double-emulsion interface membrane as a Pickering stabilizer, and further influences the impedance capability of double-emulsion solute exchange and interface membrane fusion.
Considering the application of double emulsion in food system, the regulation effect of fat crystallization sites on the programmed release characteristics of double emulsion is not negligible, but the fat crystallization sites in the double emulsion system are difficult to quantitatively describe, even though some recent research evidences show that: adding a small amount of fat crystals into the double-emulsion oil phase to obviously affect the structural characteristics of the double-emulsion oil phase, and compared with the content of the oil phase fat crystals, the influence of the crystallization sites on the structural responsiveness of the double-emulsion is more obvious [ Herzi, S.; essafi, W., Difference magnesium release profiles from W/O/W emulsions based on crystallized oils J Colloid Interf Sci 2018,509, 178-. It is still impossible to digitize and classify the fat crystal distribution sites and correlate them with the structure-activity relationship of the double emulsion system.
Disclosure of Invention
Aiming at the problems that the distribution condition of fat crystallization sites in a double-emulsion liquid system three-dimensional space is difficult to characterize and the evaluation of the crystallization sites cannot be digitalized and classified to be fuzzy, the invention causes the control preparation theoretical research of the oil phase crystallization sites and the regulation and control research of the crystallization sites on the double-emulsion system structure responsiveness to lack systematicness and scientificity, and is difficult to disclose the crystallization thermodynamic mechanism behind the distribution probability density of the fat crystallization sites. Aiming at providing a method for representing fat crystal distribution sites in a double-emulsion system based on a ternary feature vector method.
The invention aims to provide a method for representing fat crystal distribution sites in a double-emulsion system by a ternary characteristic vector method.
It is another object of the present invention to provide the use of the above method for characterizing the distribution sites of fat crystals in a double emulsion system.
The above purpose of the invention is realized by the following technical scheme:
the invention provides a method for representing fat crystal distribution sites in a double-emulsion system by a ternary feature vector method, which comprises the following steps:
s1, acquiring a bright field of a double emulsion system and a polarization image corresponding to the bright field, and extracting single double emulsion liquid drops;
s2, processing the polarized light Image of a single liquid drop by using Image J software, acquiring a light intensity change curve of the liquid drop along the diameter direction, and performing background elimination and baseline correction on the curve;
s3, according to the path distance passed by the light intensity change curve, superposing the bright field image to obtain the inner liquid drop and the liquid drop particle size of the corresponding liquid drop in the diameter direction, sequentially extracting the light intensity integral peak areas and the total light intensity integral peak areas of the crystallization sites at the inner interface, the middle phase and the outer interface by taking the liquid drop particle size as the attribution criterion of the crystallization sites, calculating the percentage value of the light intensity integral peak areas and the total light intensity integral peak areas of the crystallization sites, and quantifying the probability density of the distribution of the fat crystals on the inner interface, the middle phase and the outer interface;
s4, introducing each element value of a ternary characteristic vector (I, M, E) to classify the distribution sites of the fat crystals, wherein each element value I is Ai/A, M is Am/A, E is Ae/A, the value range of each element is 0-1, and I + M + E is 100%; wherein Ai is a light intensity integral peak area of a crystallization site at the inner interface, Am is a light intensity integral peak area of a mesophase crystallization site, Ae is a light intensity integral peak area of a crystallization site at the outer interface, and A is a total light intensity integral peak area.
In the method, the physical meaning of the ternary characteristic vector element value is the probability of oil phase fat crystallization at the corresponding crystallization site, and the ternary characteristic vector quantitative characterization system can characterize the crystallization site conditions and comprises the following steps: a single crystallization site for crystallization exists only at the internal interface (I, 0, 0), the intermediate phase (0, M, 0), or the external interface (0, 0, E); double crystallization sites exist at the inner and outer interfaces (I, 0, E), the inner interface and the intermediate phase are uniformly distributed (I, M, 0), and the outer interface and the intermediate phase (0, M, E) are crystallized; and more generally (I, M, E) where both the internal and external interfaces and the mesophase have a crystalline distribution.
The method selects Image J software to measure the light intensity curve to carry out digital conversion on the polarized light Image, takes the influence of a double-emulsion liquid system without crystals on the light intensity into consideration, and classifies different crystal distribution sites into ternary characteristic vectors by matching with statistical analysis, so that the controlled preparation theoretical research of oil phase crystal sites and the regulation and control research of the crystal sites on the structural responsiveness of the double-emulsion system have systematicness and scientificity; the three-element characteristic vector is introduced to reduce the dimension of the fat crystallization sites in the three-dimensional space into the binary characteristic vector element values to characterize the space distribution sites of the fat crystals in the double-emulsion system, so that the problems that the crystallization sites in the double-emulsion liquid system cannot be represented digitally and classified fuzziness are solved, and basic data support is provided for a crystallization thermodynamic mechanism after the distribution probability density of the fat crystallization sites is disclosed.
As a preferable implementation mode, the distribution sites can be classified by element value ranges according to the actual application requirements, thereby realizing the judgment of the situations of single and double crystal distribution sites. The step S4, where the classification of the fat crystal distribution locus is performed by introducing each element value of the ternary feature vector (I, M, E), includes: when all three elements are more than or equal to 0.1, the crystal distribution of the inner interface, the outer interface and the intermediate phase is the condition (I, M, E); when the two element values are less than 0.1, the double emulsion is regarded as a single crystal distribution site type (I, 0, 0), (0, M, 0) or (0, 0, E); when there is and only one element value < 0.1, the double emulsion is considered to be of double crystal distribution site type (I, 0, E), (I, M, 0) or (0, M, E).
Preferably, the double-emulsion liquid system is a system comprising two or three phases of oil, water and gas.
Further preferably, the multi-phase interface system is a water-in-oil-in-water double emulsion system or an oil-in-water-in-oil double emulsion system.
Preferably, the light intensity variation curve in step S2 is obtained by making a straight line along the diameter direction of the droplet using Image J software and using Plot Profile.
Preferably, the background elimination and baseline correction in step S2 are performed by acquiring a light intensity variation curve corresponding to a polarized Image of the double-emulsion liquid droplet without the fat crystal by using Image J software, and superimposing the light intensity variation curve of the double-emulsion liquid droplet with the fat crystal.
Preferably, the light intensity integrated peak area in step S4 is obtained by integrating the light intensity variation curve through Origin software.
Preferably, in step S3, for each double emulsion sample, at least 50 double emulsion droplets are obtained for calculating the percentage average of the light intensity integrated peak area of each crystallization site and the total light intensity integrated peak area.
Preferably, the bright field and the polarized image in step S1 are obtained at a magnification of 100 times or 400 times. Specifically, the prepared emulsion is diluted and imaged under an optical microscope (100 x, 400 x) to obtain a bright field image. And then, converting the image into a polarized lens to obtain a polarized field image of the double emulsion oil phase crystal distribution condition.
The invention reduces the dimension of the fat crystallization sites in the three-dimensional space into the space distribution sites of the fat crystals in the double-emulsion system by introducing the ternary characteristic vector, and solves the problem that the crystallization sites in the double-emulsion system can not be represented digitally and classified vaguely. Therefore, the application of the above method in characterizing the distribution sites of fat crystals in a double emulsion system is also within the scope of the present invention.
Compared with the prior art, the invention has the beneficial effects that:
the method for representing the distribution sites of the fat crystals in the double-emulsion system by the ternary feature vector method introduces the ternary feature vector to reduce the dimension of the fat crystal sites in the three-dimensional space into the element values of the binary feature vector to represent the spatial distribution sites of the fat crystals in the double-emulsion system, and solves the problem that the crystal sites in the double-emulsion liquid system cannot be represented digitally and classified vaguely.
The invention selects Image J software to measure the light intensity curve to carry out digital conversion on the polarized light Image, considers the influence of a double-emulsion liquid system without crystals on the light intensity, and classifies different crystal distribution sites into ternary characteristic vectors by matching with statistical analysis, so that the control preparation theoretical research of oil phase crystal sites and the regulation and control research of the crystal sites on the structural responsiveness of the double-emulsion system have systematicness and scientificity, and provide basic data support for the crystal thermodynamic mechanism after the distribution probability density of fat crystal sites is revealed.
Drawings
Fig. 1 is a bright field of the double emulsion of example 1 with 5% fat crystal content and its corresponding polarization microscope image (x 100): (a) hydrogenated palm oil is a fat crystal, (b) stearic acid monoglyceride is a fat crystal, (c) palmitic acid monoglyceride is a fat crystal, (d) lauric acid monoglyceride is a fat crystal, (e) hydrogenated palm oil, lauric acid monoglyceride and stearic acid monoglyceride are mixed to form a fat crystal;
FIG. 2 is a schematic structural diagram of a light intensity curve of a single double emulsion droplet polarization Image processed by Image J and a ternary eigenvector representation thereof in example 1;
fig. 3 is a microscope picture of the double emulsion in example 2 (bright field and its corresponding polarization image (x 100)): (a) 10% cocoa butter, (b) 5% monoglycerides of oleic acid, (c) 10% cocoa butter + 2% monoglycerides of oleic acid;
fig. 4 is a structural schematic diagram of a light intensity curve of a double-emulsion droplet polarization Image processed by Image J and a ternary eigenvector characterization thereof in example 2.
Detailed Description
The invention will be further described with reference to the drawings and the detailed description, which are not intended to limit the invention in any way. The starting reagents employed in the examples of the present invention are, unless otherwise specified, those that are conventionally purchased.
Example 1
This example uses Image J software, prism.8 data processing and statistical analysis, in combination with ternary eigenvectors (I, M, E) to characterize the distribution sites of fat crystals in the double emulsion system, and to verify the accuracy of the method based on the double emulsion system with known crystallization sites.
First, experiment method
1. Preparation of the double emulsion
The double emulsions prepared from oil phases of different solid-liquid oil combinations are measured, and the double emulsions are prepared by a two-step emulsification method:
(1) double emulsion (a): hydrogenated palm oil (C16 chain length monoglyceride content 90%, melting point 58-64 ℃) was mixed with soybean oil at 65 ℃ in a ratio of 1: mixing at 20 vol%, and adding 2% polyglycerol polyricinoleate (PGPR) to obtain oil phase. Using gel containing 1% (W/v%) calcium alginate as internal water phase (W1 phase), and mixing in W1: o volume ratio 3: 7, 65 ℃ and 2min high speed homogenization at 12000rpm to prepare a primary emulsion (W1/O). Taking a 1% bacterial cellulose solid particle suspension as an external water phase (W2), and taking the suspension as a suspension liquid at room temperature (W1/O): w2 volume ratio 5: high speed homogenization at 5,8000 rpm for 2min to prepare a double emulsion (W1/O/W2).
(2) Double emulsion (b): preparing a double emulsion with monoglyceride stearate GMS-C18(C18 chain length monoglyceride content 90%, diglyceride and triglyceride content 8%, melting point 56-58 ℃) as fat crystals at 65 ℃, mixing the double emulsion with soybean oil in a ratio of 1: mixing the raw materials in a volume ratio of 20 to prepare an oil phase, and preparing the double emulsion according to the preparation method.
(3) Double emulsion (c): preparing a double emulsion with monoglyceride palmitate GMS-C16 (monoglyceride content 98% chain length C16, melting point 56-58 ℃) as fat crystals at 60 ℃, mixing the double emulsion with soybean oil at a ratio of 1: mixing the raw materials in a volume ratio of 20 to prepare an oil phase, and preparing the double emulsion according to the preparation method.
(4) Double emulsion (d): preparing double emulsion by using lauric acid monoglyceride GML-C12 (the monoglyceride content of a C12 chain is 98 percent, the melting point is 52-56 ℃) as fat crystals at 60 ℃, and mixing the double emulsion with soybean oil in a ratio of 1: mixing the raw materials in a volume ratio of 20 to prepare an oil phase, and preparing the double emulsion according to the preparation method.
(5) Double emulsion (e): an oil phase was prepared by adding 4% hydrogenated palm oil, 0.5% monoglyceride laurate and 0.5% monoglyceride stearate to soybean oil at 65 ℃ and preparing a double emulsion according to the above preparation method.
2. Method for representing distribution sites of fat crystals in double-emulsion system by using ternary feature vector method
All double emulsions were observed immediately after preparation by an optical microscope and a polarizing microscope (x 100), and the obtained digital microscope photograph was subjected to Image J Image processing to obtain a light intensity change curve of the droplet in the diameter direction, and background elimination and baseline correction were performed. And (3) superposing bright field image information to obtain integrated peak areas (Ai, Am, Ae) of crystallization sites at a W1-O interface, an oil phase and an O-W2 interface, and calculating percentage averages (Ai/A, Am/A, Ae/A) of each integrated peak area and the total light intensity integrated peak area (A). And (3) introducing a ternary feature vector (I, M, E) model to analyze the potential distribution of fat crystals in the double emulsion, and finally quantifying and classifying the probability density of oil phase fat crystals appearing at the W1/O interface of the inner layer, the oil phase and the O/W2 interface of the double emulsion through the element values of the oil phase fat crystals. The method specifically comprises the following steps:
(1) and acquiring each double-emulsion liquid system bright field and corresponding polarized image data thereof, and effectively extracting the data to obtain single double-emulsion liquid system liquid drops.
(2) And analyzing the double-emulsion liquid system liquid drops by using Image J software to obtain a light intensity change curve of the treated liquid drops along the diameter direction, and selecting double-emulsion liquid without fat crystals to perform background elimination and baseline calibration to obtain more accurate light intensity of crystals under a polarizing microscope.
(3) Carrying out layer superposition analysis on the light intensity change curve and the bright field image: and combining the bright field image to obtain inner liquid drops and liquid drop particle diameters of the corresponding liquid drops in the diameter direction, and sequentially extracting total light intensity integrated peak areas (A) and integrated peak areas (Ai, Am and Ae) of all crystallization sites at a W1-O interface, an oil phase and an O-W2 interface by taking the liquid drop particle diameters as a crystallization site attribution criterion. At least 50 of the above data samples were obtained for each double emulsion sample. The integrated peak area percentage mean values (Ai/A, Am/A, Ae/A) of the inner layer W1/O interface, oil phase, outer layer O/W2 interface were obtained based on the mean values of the data samples.
(4) Introducing ternary eigenvectors (I, M and E) of crystallization sites to reduce the dimension of the fat crystallization sites in a three-dimensional space into binary eigenvector element values to characterize the space distribution sites of fat crystals in a double-emulsion system, wherein the element value I is Ai/A, M is Am/A, E is Ae/A, the value range is 0-1, and I + M + E is 100%. The physical meaning of the ternary eigenvector element value is the probability of the oil phase fat crystal appearing at the corresponding crystallization site. The ternary characteristic vector quantitative characterization system can characterize the crystallization site situations and comprises the following steps: a single crystallization site for crystallization exists only at the internal interface (I, 0, 0), the oil phase (0, M, 0), or the external interface (0, 0, E); the double-crystallization sites of crystallization exist in the internal and external interfaces (I, 0, E), the internal interface and the oil phase are uniformly distributed (I, M, 0), and the external interface and the oil phase (0, M, E); and more generally where both the internal and external interfaces and the oil phase have a crystal distribution (I, M, E).
(5) And evaluating the potential distribution probability density of the fat crystals in the double-emulsion system from two aspects of light intensity integral peak area and ternary characteristic vector value.
Second, experimental results
Fig. 1 is images of five double-emulsion bright fields of samples (a) - (e) and corresponding polarization microscopes, and fig. 2 is a structural schematic diagram of a light intensity curve of a single double-emulsion droplet polarization Image processed by Image J and a ternary characteristic vector representation thereof. Calculating to obtain hydrogenated palm oil without surface activity in fat crystals in the double emulsion (a), wherein the crystal distribution of the hydrogenated palm oil is uniform in an oil phase, and the calculated ternary characteristic vector value is (0.08, 0.85, 0.07) and is classified into a (0, M, 0) single crystal distribution site type; the double-emulsion (b) fat crystal is monoglyceride stearate (HLB value is 3-3.8) with surface activity, the crystal of the monoglyceride stearate is distributed at the internal interface of the double-emulsion or the oil phase, the corresponding ternary characteristic vector value is (0.50, 0.45, 0.05), and the monoglyceride stearate is classified as (I, M, 0) double-crystal distribution site type; the double emulsion (c) fat crystal is surface active monoglyceride palmitate (HLB value is 3.8), the crystal distribution of the double emulsion is at the inner interface or oil phase, the corresponding ternary characteristic vector value is (0.02, 0.93, 0.05), and the double emulsion is classified as (0, M, 0) single crystal distribution site type; the double-emulsion (d) fatty crystal is monoglyceride laurate with surface activity (HLB value is 4.2-5), the crystals of the monoglyceride laurate are distributed at the external interface or oil phase of the double-emulsion, the corresponding ternary characteristic vector values are (0.09, 0.31 and 0.60), and the monoglyceride laurate is classified into (0, M and E) double-crystal distribution site types; the double emulsion (E) has surface active stearic acid monoglyceride and lauric acid monoglyceride to induce the crystallization of the hydrogenated palm oil at the interface, the crystallization is distributed at the inner interface and the outer interface of the double emulsion and the oil phase, the corresponding ternary characteristic vector value is (0.28, 0.41 and 0.31), and the double emulsion is classified as the type of (I, M and E) triple crystallization distribution sites.
Example 2
This example is to verify the effectiveness of the "ternary feature vector" method to characterize fat crystal distribution sites.
First, experiment method
1. Preparation of the double emulsion
(1) Sample (a): cocoa butter and soybean oil were mixed at a ratio of 1: mixing at 10 vol%, and adding 2% polyglycerol polyricinoleate (PGPR) to obtain oil phase. Using gel containing 1% (W/v%) calcium alginate as internal water phase (W1 phase), and mixing in W1: o volume ratio 3: 7, 65 ℃ and 2min high speed homogenization at 12000rpm to prepare a primary emulsion (W1/O). Taking a 1% bacterial cellulose solid particle suspension as an external water phase (W2), and taking the suspension as a suspension liquid at room temperature (W1/O): w2 volume ratio 5: high speed homogenization at 5,8000 rpm for 2min to prepare a double emulsion (W1/O/W2).
Cocoa butter (74% C18 triglyceride, 24% C16 triglyceride, 1% free fatty acid, 0.3% diglyceride, 0.2% monoglyceride, melting point 34-37 ℃) of 10% was added as fat crystals to the liquid oil, and the theoretical value of its ternary eigenvector was (0, M, 0).
(2) Sample (b): double emulsion was prepared with 5% GMO as fat crystals, mixed with soybean oil at 1: 20 volume ratio and mixing to prepare an oil phase, and the rest of the preparation steps are the same as the sample (a).
Adding 5% of oleic acid monoglyceride GMO (the content of oleic acid monoglyceride is 70%, the content of diglyceride and triglyceride is 25%, the melting point is 37-42 ℃) into liquid oil, wherein the oleic acid monoglyceride is fat crystals at normal temperature, the HLB value is 3.8-4, the GMO is the most classical model of fat crystals generated at the interface layer in double emulsions in situ at present, the theoretical distribution site of the GMO is at the interface layer when the GMO is used as the fat crystals to be added into double emulsions independently to prepare double emulsions, and the theoretical value of the ternary characteristic vector of the GMO is (I, 0, 0) or (I, 0, E).
(3) Sample (c): blending cocoa butter and soybean oil in a ratio of 1: 10 volume percent of GMO inducer is added into the mixed solid-liquid oil, and the rest preparation steps are the same as the sample (a).
The liquid oil is added with 10 percent of cocoa butter and 2 percent of GMO, and the addition of 2 percent of GMO inducer is predicted to induce the crystallization site of the cocoa butter to change, so that the ternary eigenvector value of the cocoa butter changes significantly.
2. Method for representing fat crystal distribution sites in double-emulsion system by using ternary feature vector method as in example 1
3. Verification of effectiveness of 'ternary feature vector' method for representing fat crystal distribution sites
Element values of each sample emulsion droplet verify:
whether the amount of the ternary characteristic vector of the double emulsion prepared by separately adding cocoa butter as fat crystals in sample (a) is (0, M, 0); whether the value of the ternary characteristic vector of GMO in the sample (b) as a fat crystal is (I, 0, 0) or (I, 0, E) is independently added to prepare double emulsion; whether the sample (c) is added with 2 percent of GMO inducer induces the change of the cocoa butter crystallization site or not and whether the value of the ternary characteristic vector is changed significantly or not.
Second, experimental results
Fig. 3 is bright field and polarized light images of the samples (a), (b), and (c) double emulsion, and fig. 4 is a structural schematic diagram of the light intensity curve and its ternary eigenvector representation of the sample (a), (b), and (c) double emulsion drop polarized light Image processed by Image J.
In sample (a), the crystallization is reported to be cocoa butter with a continuous distribution as the main part by using a literature, the crystallization is characterized by (0.09, 0.87, 0.04) by using a ternary feature vector, and the classification is (0, M, 0) and accords with the distribution classification of the cocoa butter in double emulsions; in the sample (b), the crystals of the oleic acid monoglyceride which are reported to be mainly distributed on the interface by using a literature are characterized as (0.41, 0.01 and 0.59) by using a ternary eigenvector and are classified as (I, 0 and E), and the distribution classification of the oleic acid monoglyceride in the double emulsion is met; in the sample (c), the mixture of cocoa butter and monoglyceride oleate is used as an oil phase to prepare the emulsion, the monoglyceride oleate with surface activity induces the cocoa butter to crystallize at an interface by taking the monoglyceride oleate as a template, the crystallization distribution site of the cocoa butter in double emulsion is changed, the monoglyceride oleate is characterized by (0.31, 0.37 and 0.32) through a ternary feature vector and is classified into (I, M and E), and the distribution of the cocoa butter in the sample (a) is obviously different, so that the method for characterizing the fat crystal distribution site in the double emulsion by adopting the ternary feature vector method is effective and accords with the actual distribution rule.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (10)
1. A method for characterizing fat crystal distribution sites in a double-emulsion system by a ternary feature vector method is characterized by comprising the following steps:
s1, acquiring a bright field of a double emulsion system and a polarization image corresponding to the bright field, and extracting single double emulsion liquid drops;
s2, processing the polarized light Image of a single liquid drop by using Image J software, acquiring a light intensity change curve of the liquid drop along the diameter direction, and performing background elimination and baseline correction on the curve;
s3, according to the path distance passed by the light intensity change curve, superposing the bright field image to obtain the inner liquid drop and the liquid drop particle size of the corresponding liquid drop in the diameter direction, sequentially extracting the light intensity integral peak areas and the total light intensity integral peak areas of the crystallization sites at the inner interface, the middle phase and the outer interface by taking the liquid drop particle size as the attribution criterion of the crystallization sites, calculating the percentage value of the light intensity integral peak areas and the total light intensity integral peak areas of the crystallization sites, and quantifying the probability density of the distribution of the fat crystals on the inner interface, the middle phase and the outer interface;
s4, introducing each element value of a ternary characteristic vector (I, M, E) to classify the distribution sites of the fat crystals, wherein each element value I is Ai/A, M is Am/A, E is Ae/A, the value range of each element is 0-1, and I + M + E is 100%; wherein Ai is a light intensity integral peak area of a crystallization site at the inner interface, Am is a light intensity integral peak area of a mesophase crystallization site, Ae is a light intensity integral peak area of a crystallization site at the outer interface, and A is a total light intensity integral peak area.
2. The method of claim 1, wherein the double emulsion system is a system comprising two or three phases of oil, water, and gas.
3. The method of claim 2, wherein the double emulsion liquid system is a water-in-oil-in-water double emulsion system or an oil-in-water-in-oil double emulsion system.
4. The method according to claim 1, wherein the step S4 of introducing each element value of the ternary feature vector (I, M, E) to classify the distribution site of the fat crystal comprises: when all three elements are more than or equal to 0.1, the inner interface, the outer interface and the intermediate phase are in crystal distribution; when two element values are less than 0.1, the double emulsion is regarded as a single crystal distribution site type; when there is and only one element value < 0.1, the double emulsion is considered to be of the double crystal distribution site type.
5. The method according to claim 1, wherein the background elimination and baseline correction in step S2 are performed by obtaining a light intensity variation curve corresponding to a polarized Image of the double emulsion liquid droplet without the fat crystal by using Image J software, and performing the background elimination and baseline correction by superimposing the light intensity variation curve of the double emulsion liquid droplet with the fat crystal.
6. The method of claim 1, wherein the light intensity variation curve of step S2 is obtained by using Image J software to make a straight line along the diameter direction of the droplet and using Plot Profile.
7. The method of claim 1, wherein the light intensity integrated peak area of step S4 is obtained by integrating the light intensity curve with Origin software.
8. The method of claim 1, wherein in step S3, at least 50 double emulsion droplets are obtained for each double emulsion sample and used to calculate the percentage average of the integrated peak area of light intensity at each crystallization site and the integrated peak area of total light intensity.
9. The method according to claim 1, wherein the step S1 of obtaining the bright field of the double emulsion system and the corresponding polarized image is performed by magnifying the double emulsion by 100 times or 400 times under an optical microscope.
10. Use of the method of any one of claims 1 to 9 for characterizing the distribution sites of fat crystals in a double emulsion system.
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