CN114354464A - Method for quantitatively analyzing similarity between hyperspectral libraries of different metal nanoparticles - Google Patents
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Abstract
The invention discloses a method for carrying out quantitative analysis on similarity between hyperspectral libraries of different metal nanoparticles, which comprises the steps of acquiring hyperspectral data and preprocessing a hyperspectral picture; extracting characteristic spectrum curves of AuNPs to establish a hyperspectral library, and counting the number of the spectrum curves in the hyperspectral library; filtering the hyperspectral libraries in a one-to-one cross mode, and counting the number of spectral curves left after each hyperspectral library is filtered; and calculating a hyperspectral similarity coefficient S according to the counted number of the spectral curves, and quantitatively analyzing the similarity of hyperspectral libraries of different metal nanoparticles. The high spectral similarity coefficient S provided by the method can simply, quickly and accurately quantify the similarity between different metal nanoparticle spectrum libraries.
Description
Technical Field
The invention relates to identification of metal nano particles by utilizing a hyperspectral technology, in particular to a method for quantitatively analyzing similarity between hyperspectral libraries of different metal nano particles.
Background
A hyperspectral technology combined with a dark field microscopic imaging system is a novel method for identifying metal nanoparticles in organisms in situ. For example, AuNPs have unique surface plasmon resonance effect, so that the AuNPs can show strong spectral absorption in the visible-near infrared band of 400-1000nm, and a Charge Coupled Device (CCD) camera is simultaneously arranged in a hyperspectral imaging system provided with a unique spectrophotometer, so that the unique spectral curve of the AuNPs in the band can be recorded. In a dark-field microscopic imaging system, the shape of the spectrum formed by the surface plasmon resonance or other interactions with the nano-materials is closely related to the size, morphology, aggregation state, surface modification and surrounding dielectric environment of the metal nanoparticles. Analysis of the hyperspectral spectrum therefore helps to understand the microscopic composition of the nanoparticles and the surrounding microenvironment.
When the hyperspectral technology is used for identifying the metal nano material, the construction of a hyperspectral library is an important basic work, and the hyperspectral library is a basis for identification and classification. Meanwhile, the spectrum curve in the hyperspectral library also reflects the surface plasmon resonance condition of the metal nanoparticles, so that the judgment of the similarity of various metal nanoparticle spectrum libraries constructed under different conditions is also helpful for understanding the microscopic composition and the surrounding dielectric environment of the metal nanoparticles.
However, the existing method for judging similarity of hyperspectral libraries mostly averages all characteristic spectral curves in the hyperspectral libraries and then compares the peak positions of the averaged spectral curves. However, after all the spectral curves in the hyperspectral libraries are averaged, a large amount of information of the spectral curves in the hyperspectral libraries may be lost and wasted, and even misjudgment may occur.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to solve the technical problem of providing a more comprehensive, accurate and simple method for quantitatively analyzing the similarity between different metal nanoparticle hyperspectral libraries aiming at the defects of the prior art.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a method for quantitative analysis of similarity between hyperspectral libraries of different metal nanoparticles, comprising the steps of:
the method comprises the following steps: acquiring a hyperspectral picture of the metal nano-particles, wherein the hyperspectral picture comprises position information and spectral information of the metal nano-particles;
step two: smoothing the hyperspectral picture, and then performing spectrum correction on the smoothed hyperspectral picture by using the acquired light source spectrum;
step three: collecting characteristic spectrum curves of the metal nano particles to construct a hyperspectral library of the metal nano particles according to the hyperspectral pictures of the metal nano particles smoothed and corrected in the step two, and counting the total number of the spectrum curves in the hyperspectral library;
step four: filtering the hyperspectral libraries of the plurality of metal nano particles collected in the third step in a one-to-one crossed manner, and counting the number of spectral curves left after filtering;
step five: and calculating a similarity coefficient S of the hyperspectral libraries so as to quantitatively analyze the similarity between different metal nanoparticle spectrum libraries.
Specifically, in the first step, the metal nanoparticles are metal-element-containing nanoparticles capable of establishing a corresponding reference library and specifically identifying under a dark-field hyperspectral imaging system.
Preferably, in the first step, the metal nanoparticles are nanogold or nanosilver.
Specifically, in the second step, the hyper-spectral image is smoothed by using the Adjacent Band Averaging function in the hyper-spectral data processing software ENVI 4.8.
Specifically, in the fourth step, in the hyperspectral data processing software ENVI4.8, the hyperspectral libraries of the plurality of metal nanoparticles collected in the third step are cross-filtered one by using a Filter Spectral library function.
Specifically, in step five, S is defined as:
in NiRepresenting the number of spectral curves in the ith hyperspectral library; { Na,NbThe number of spectral curves left after the library a is filtered by the library b is represented;
representing the sum of the number of the remaining spectral curves after the n hyperspectral libraries are subjected to cross filtering; the size of the S coefficient reflects the size of similarity among the hyperspectral libraries, the larger the S coefficient is, the fewer similar spectral curves in different hyperspectral libraries are, and the smaller the similarity of the hyperspectral libraries is.
Preferably, when the S coefficient is used to determine similarity between hyperspectral libraries, the threshold for Spectral Angle Mapping is chosen to be 0.1.
Has the advantages that: the method judges the similarity between the hyperspectral libraries by calculating the S coefficient quantization, and the method for calculating the S coefficient is simple, quick and accurate. The similarity among the spectral libraries can be judged through the S coefficient, the difference of microenvironment metal nano-particles in cells can also be judged, and whether a plurality of spectral libraries can be uniformly combined into a spectral library with relatively small capacity or not is judged on the basis of the coefficient.
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The foregoing and/or other advantages of the invention will become further apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
Fig. 1 is a hyperspectral picture after smoothing and lamplight correction, and cells in the picture accumulate AuNPs.
FIG. 2 spectral library of 10, 17 and 60nmAUNPs in cells.
FIG. 3A 549, HeLa, HepG2 spectral library of AuNPs in cells.
FIG. 4 shows a unified hyperspectral library Li-M after integration and combination of Li-A549, Li-HeLa and Li-HepG 2.
Figure 5 assimilates both hyperspectral pictures of AgNPs cells and hyperspectral pictures of extracellular AgNPs.
FIG. 6 hyperspectral libraries of intracellular and extracellular AgNPs.
Detailed Description
The invention will be better understood from the following examples.
Example 1
This example provides a method for quantitatively analyzing similarity between AuNPs spectral libraries with different particle sizes in cells, comprising the following steps:
the method comprises the following steps: and acquiring hyperspectral pictures of the AuNPs, wherein the hyperspectral pictures comprise position information and spectral information of the AuNPs. Placing a sterile cover glass into a six-well plate, inoculating HeLa cells into the six-well plate, growing for 24h to adhere to the cover glass, then replacing a culture medium containing AuNPs, washing the cells for 5 times by using a PBS buffer solution after the AuNPs are absorbed by the cells, and then adding a PBS solution containing 2% paraformaldehyde to fix the cells. Imaging the HeLa cells absorbing the AuNPs by using an enhanced dark field hyperspectral microscope of Cytoviva;
step two: smoothing the hyperspectral picture by utilizing an Adjacent Band Averaging function in ENVI4.8 software, and performing spectrum correction on the smoothed hyperspectral picture by using the acquired light source spectrum to finally obtain the hyperspectral picture shown in the figure 1;
step three: and D, collecting a characteristic spectrum curve of the AuNPs as a hyperspectral library by using the hyperspectral picture obtained in the step two. As shown in FIG. 2, hyperspectral libraries of 10nm, 17nm and 60nm citric acid modified AuNPs absorbed by cells are respectively established and recorded as Li-10, Li-17 and Li-60, and the spectral curve numbers of the three hyperspectral libraries are respectively 431, 738 and 720.
Step four: in hyperspectral processing software ENVI4.8, a Filter Spectral library function is utilized to Filter hyperspectral libraries of a plurality of AuNPs collected in the third step in a one-to-one cross mode, and the number of Spectral curves left after filtering is counted. Li-10 was filtered with Li-17, leaving 21 spectral curves; li-17 was filtered with Li-10, leaving the number of spectral curves at 36; li-10 is filtered by Li-60, and the number of the remaining spectral curves is 90; li-60 was filtered with Li-10, leaving 569 spectral curves; li-17 was filtered using the Li-60 library, leaving a spectral number of 182. Li-60 was filtered through Li-17, leaving the number of spectral curves at 577. The total number of spectral curves remaining after statistical cross-filtering was 1475.
Step five: and C, calculating an S coefficient according to the number of the spectral curves obtained in the third step and the fourth step, wherein the calculation formula is as follows:
in the formula NiRepresenting the number of spectral curves in the ith hyperspectral library; { Na,NbThe number of spectral curves left after the x library is filtered by the y library is represented;
and the sum of the number of the remaining spectral curves after the cross filtering of the n hyperspectral libraries is represented. For example: in calculating the S coefficients of Li-10 and Li-60, the notation of Li-10 and Li-60 as N1And N2In step three, N is obtained1＝431，N2＝720；{N1,N2Denotes the number of spectral curves remaining after Li-10 has been filtered through Li-60, which in step four can be obtained as { N }1,N2}＝90；{N2,N1Denotes the number of spectral curves remaining after Li-60 has been filtered through Li-10, which in step four can be obtained as { N }2,N1}＝569；
Representing the sum of the number of spectral curves remaining after cross-filtering of the two hyperspectral libraries Li-10 and Li-60, is obtained in step four as
The similarity coefficient S of Li-10 and Li-60 is 0.572.
Finally, the S coefficients of the hyperspectral libraries of three AuNPs with different particle sizes are calculated and shown in Table 1. From Table 1, it can be seen that the S coefficients of the two hyperspectral libraries Li-10 and Li-17 are very small, indicating that the two hyperspectral libraries are very similar. And Li-60 has less similarity with the hyperspectral libraries of the two AuNPs, Li-10 and Li-17. This shows that the significant difference in AuNPs size has a large effect on the surface plasmon resonance absorption peak, and the total S coefficient (0.390) of these three AuNPs hyperspectral libraries with different particle sizes is large, and it is not suitable for combining into a general hyperspectral library with small relative capacity.
TABLE 1S coefficients of spectral libraries of 10, 17 and 60nm AuNPs in cells
Example 2
The present embodiment provides a method for quantitatively analyzing similarity between spectra libraries of AuNPs with the same particle size in different cells, which includes the following steps:
the method comprises the following steps: and acquiring hyperspectral pictures of the AuNPs, wherein the hyperspectral pictures comprise position information and spectral information of the AuNPs. Placing a sterile cover glass into a six-well plate, respectively inoculating three cells of A549, HeLa and HepG2 in the six-well plate, growing for 24 hours in order to grow on the cover glass in an adherent manner, then replacing a culture medium containing AuNPs, washing the cells for 5 times by using a PBS (phosphate buffer solution) after the cells absorb the AuNPs, and then adding a PBS (phosphate buffer solution) solution containing 2% paraformaldehyde to fix the cells. Imaging the HeLa cells absorbing the AuNPs by using an enhanced dark field hyperspectral microscope of Cytoviva;
step two: smoothing the hyperspectral picture by using an Adjacent Band Averaging function in ENVI4.8 software, and performing spectrum correction on the smoothed hyperspectral picture by using the acquired light source spectrum to finally obtain a preprocessed hyperspectral picture;
step three: and collecting characteristic spectrum curves of AuNPs in the A549 cells, the HeLa cells and the HepG2 cells as a hyperspectral library by utilizing the hyperspectral pictures obtained in the step two. As shown in FIG. 3, hyperspectral libraries of AuNPs in A549 cells, HeLa cells and HepG2 cells are respectively established and recorded as Li-A549 cells, Li-HeLa cells and Li-HepG2 cells, and the spectral curve numbers of the three hyperspectral libraries are respectively 409, 564 and 460 cells.
Step four: in hyperspectral processing software ENVI4.8, a Filter Spectral library function is utilized to Filter hyperspectral libraries of a plurality of AuNPs collected in the third step in a one-to-one cross mode, and the number of Spectral curves left after filtering is counted. Li-A549 was filtered with Li-HeLa, and the number of remaining spectral curves was 2; Li-HeLa was filtered with Li-A549, the number of remaining spectral curves was 0; Li-A549 was filtered through Li-HepG2, leaving a number of spectral curves of 16; Li-HepG2 was filtered through Li-A549, leaving the number of spectral curves 102; Li-HepG2 was filtered through the Li-HeLa library, leaving a spectral number of 171, and Li-HeLa was filtered through Li-HepG2, leaving a spectral number of 16. The total number of spectral curves remaining after the statistical cross-filtering was 307.
Step five: and C, calculating an S coefficient according to the number of the spectral curves obtained in the third step and the fourth step, wherein the calculation formula is as follows:
in the formula NiRepresenting the number of spectral curves in the ith hyperspectral library; { Na,NbThe number of spectral curves left after the x library is filtered by the y library is represented;
and the sum of the number of the remaining spectral curves after the cross filtering of the n hyperspectral libraries is represented. For example: in the calculation of the S coefficients of Li-HeLa and Li-A549, the Li-HeLa and Li-A549 are recorded as N1And N2In step three, N is obtained1＝0，N2＝2；{N1,N2Denotes the number of spectral curves remaining after Li-HeLa filtration through Li-A549, which can be obtained as { N } in step four1,N2}＝0；{N2,N1Denotes the number of spectral curves remaining after Li-A549 by Li-HeLa filtration, which can be obtained as { N } in step four2,N1}=2；
The sum of the residual spectrum curves after the cross filtration of the two hyperspectral libraries of Li-A549 and Li-HeLa is shown, and can be obtained in the fourth step
The similarity coefficient S of Li-a549 and Li-HeLa is 0.572.
The final calculation of the S coefficients for the hyperspectral libraries of AuNPs in three different cells is shown in table 2. From Table 2, it can be known that the S coefficients of the two hyperspectral libraries Li-A549 and Li-HeLa are very small, which indicates that the two hyperspectral libraries are very similar, and further that the microenvironment of AuNPs in the A549 cell and the HeLa cell is similar. The similarity of the Li-HepG2 and the Li-A549 and Li-HeLa of the two AuNPs is relatively small, which also indicates that the microenvironment of the AuNPs in the HepG2 is different from that of the other two cells, and the microenvironment is probably caused by the difference of the surface protein crowns of the AuNPs in the cells, the agglomeration condition of the AuNPs or the difference of dielectric environment. Meanwhile, the total S coefficient (0.107) of the three AuNPs hyperspectral libraries with different particle sizes is small, and the three AuNPs hyperspectral libraries can be combined into a general hyperspectral library with small relative capacity. The merging method comprises the following steps: in the hyperspectral data processing software ENVI4.8, the spectrum left after Li-HeLa is filtered by Li-A549 and the spectrum left after Li-HeLa is filtered by Li-HepG2 are collected, the characteristic spectra and Li-A549 are integrated to form a new unified spectrum library which is marked as Li-M (shown in figure 4), and the Li-M has all characteristic spectra of AuNPs in three cells, so that the hyperspectral library can be simultaneously applied to identifying the AuNPs in the three cells.
TABLE 2S coefficients of AuNPs hyperspectral libraries in A549, HeLa, HepG2 cells
Example 3
The embodiment provides a method for judging the degree of spectral change of AgNPs after entering cells by analyzing the similarity of in-cell and out-cell nano silver (AgNPs) spectral libraries, which comprises the following steps:
the method comprises the following steps: and acquiring a hyperspectral picture of AgNPs, wherein the hyperspectral picture comprises position information and spectral information of the AgNPs. To obtain a hyperspectral library of intracellular AgNPs, a sterile cover slip was placed in a six-well plate, HepG2 cells were seeded in the six-well plate and grown for 24h to grow adherently on the cover slip, then the culture medium containing the AgNPs was changed, the cells were washed 5 times with PBS buffer after absorbing the AgNPs, and then the cells were fixed by adding a PBS solution containing 2% paraformaldehyde. Imaging of such AgNPs-absorbed HeLa cells was performed using an enhanced dark field hyperspectral microscope from Cytoviva. In order to obtain a spectrum library of extracellular AgNPs, directly dropping the original liquid of the AgNPs on a slide to image the AgNPs;
step two: smoothing the hyperspectral picture by utilizing an Adjacent Band Averaging function in ENVI4.8 software, and performing spectrum correction on the smoothed hyperspectral picture by using the acquired light source spectrum to finally obtain a hyperspectral picture as shown in figure 5;
step three: and D, collecting a characteristic spectrum curve of the AuNPs as a hyperspectral library by using the hyperspectral picture obtained in the step two. As shown in fig. 6, hyper-spectral libraries of intracellular AgNPs and extracellular AgNPs are respectively established, and the number of spectral curves of the two hyper-spectral libraries is 720, 725 respectively;
step four: and (3) in hyperspectral processing software ENVI4.8, performing cross filtering on the hyperspectral libraries of the two AuNPs collected in the third step by using a Filter Spectral library function, and counting the number of Spectral curves left after filtering. As a result, the two do not have similar spectra.
Step five: and C, calculating an S coefficient according to the number of the spectral curves obtained in the third step and the fourth step, wherein the calculation formula is as follows:
the similarity coefficient S of the two spectral libraries can be found to be 1.
This result indicates that the spectrum of the AgNPs is greatly changed after the AgNPs are absorbed by the cells, and the change may be caused by various substances in the cells or by the behavior of the AgNPs in the cells such as aggregation. In a word, the S coefficient can judge the change of AgNPs after entering the cells, and has instructive significance for researching the change degree of the AgNPs in the intracellular spectrum.
The present invention provides a method and a method for quantitative analysis of similarity between different hyperspectral libraries of metal nanoparticles, and a plurality of methods and ways for implementing the technical solution, and the above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, many modifications and embellishments can be made without departing from the principle of the present invention, and these modifications and embellishments should also be regarded as the protection scope of the present invention. All the components not specified in the present embodiment can be realized by the prior art.
Claims (7)
1. A method for quantitative analysis of similarity between hyperspectral libraries of different metal nanoparticles, comprising the steps of:
the method comprises the following steps: acquiring a hyperspectral picture of the metal nano-particles, wherein the hyperspectral picture comprises position information and spectral information of the metal nano-particles;
step two: smoothing the hyperspectral picture, and then performing spectrum correction on the smoothed hyperspectral picture by using the acquired light source spectrum;
step three: collecting characteristic spectrum curves of the metal nano particles to construct a hyperspectral library of the metal nano particles according to the hyperspectral pictures of the metal nano particles smoothed and corrected in the step two, and counting the total number of the spectrum curves in the hyperspectral library;
step four: filtering the hyperspectral libraries of the plurality of metal nano particles collected in the third step in a one-to-one crossed manner, and counting the number of spectral curves left after filtering;
step five: and calculating a similarity coefficient S of the hyperspectral libraries so as to quantitatively analyze the similarity between different metal nanoparticle spectrum libraries.
2. The method for quantitative analysis of similarity between hyperspectral libraries of different metal nanoparticles according to claim 1, wherein in the step one, the metal nanoparticles are metal element-containing nanoparticles that can establish corresponding reference libraries and specifically identify under a dark-field hyperspectral imaging system.
3. The method for quantitative analysis of similarity between hyperspectral libraries of different metal nanoparticles according to claim 1, wherein in step one, the metal nanoparticles are nanogold or nanosilver.
4. The method for quantitatively analyzing the similarity between hyperspectral libraries of different metal nanoparticles according to claim 1, wherein in the second step, the hyperspectral picture is smoothed by using the Adjacent Band Averaging function in the hyperspectral data processing software ENVI 4.8.
5. The method for quantitative analysis of similarity between hyperspectral libraries of different metal nanoparticles according to claim 1, wherein in step four, the hyperspectral libraries of multiple metal nanoparticles collected in step three are cross-filtered one by one using a Filter Spectral library function in the hyperspectral data processing software ENVI 4.8.
6. The method for quantitative analysis of similarity between different metal nanoparticle hyperspectral libraries according to claim 1, wherein in step five S is defined as:
in the formula NiRepresenting the number of spectral curves in the ith hyperspectral library; { Na,NbThe number of spectral curves left after the library a is filtered by the library b is represented;
representing the sum of the number of the remaining spectral curves after the n hyperspectral libraries are subjected to cross filtering; the size of the S coefficient reflects the size of similarity among the hyperspectral libraries, the larger the S coefficient is, the fewer similar spectral curves in different hyperspectral libraries are, and the smaller the similarity of the hyperspectral libraries is.
7. The method for quantitative analysis of similarity between different metal nanoparticle hyperspectral libraries according to claim 4, wherein when the similarity between the hyperspectral libraries is determined by using the S coefficient, the threshold of Spectral Angle Mapping is selected to be 0.1.
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