CN109389065B - Red tide algae distinguishing method based on asymmetric spectrum shape structure feature extraction - Google Patents

Red tide algae distinguishing method based on asymmetric spectrum shape structure feature extraction Download PDF

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CN109389065B
CN109389065B CN201811132730.1A CN201811132730A CN109389065B CN 109389065 B CN109389065 B CN 109389065B CN 201811132730 A CN201811132730 A CN 201811132730A CN 109389065 B CN109389065 B CN 109389065B
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algae
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毛志华
陈鹏
刘航
朱乾坤
黄海清
龚芳
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Second Institute of Oceanography MNR
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    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
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    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/6486—Measuring fluorescence of biological material, e.g. DNA, RNA, cells
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    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction
    • G06F2218/10—Feature extraction by analysing the shape of a waveform, e.g. extracting parameters relating to peaks

Abstract

The invention discloses a red tide algae distinguishing method based on asymmetric spectrum shape structure characteristic extraction, which comprises the steps of firstly obtaining original fluorescence spectrum data of each algae species, carrying out normalization processing on spectrum intensity in the original fluorescence spectrum data, secondly searching all peaks of each fluorescence spectrum by adopting a local maximum peak searching algorithm, filtering all peaks by using a filtering algorithm, then fitting an asymmetric peak function by using a Levenberg-Marquardt iterative algorithm, establishing a Bi-Gaussian mixed function model, decomposing the asymmetric peak function, finally substituting fluorescence data corresponding to 685nm and 730nm into a model, calculating to obtain peak height, left half Gaussian width and right half Gaussian width corresponding to 685nm and 730nm, further obtaining a spectrum shape description index, carrying out cluster analysis on the spectrum shape description index, and realizing the distinguishing of different types of algae. The invention adopts a Bi-Gaussian mixture model, can provide more pigment spectrum information, and can well distinguish algae from species level.

Description

Red tide algae distinguishing method based on asymmetric spectrum shape structure feature extraction
Technical Field
The invention belongs to the technical field of red tide algae species distinguishing, and particularly relates to a red tide algae distinguishing method based on asymmetric spectral shape structure feature extraction.
Background
Phytoplankton is mostly a tiny volume of algae (algae) that absorb carbon dioxide CO2 for photosynthesis (photosynthesis) to release oxygen O2In most water ecosystems (aquatics)ecosystems), they are the bottom most food chain. They are a major source of ocean and freshwater primary productivity. The phytoplankton composition may vary with spatial and temporal differences. Thus, confirmation of phytoplankton composition requires high frequency, high spatial resolution measurements in space and time. The main red tide algae in the sea area of China are dinoflagellates and diatoms, and it is important to correctly and effectively distinguish the dinoflagellates and diatoms in the sea area of China.
Considering that different types of algae have different pigment structures capable of exciting different characteristic spectrums, a Bi-Gaussian model is used for separating the characteristic spectrums, and then the characteristic spectrums are described by using a spectrum description index, so that the algae are effectively distinguished. Compared with the traditional band ratio method and PCA principal component analysis method, the Bi-Gaussian mixture model can provide more pigment spectrum information because the pigment spectrum information is represented based on the spectrum shape, and the traditional methods are not suitable for complex mixture spectrum models because the pigment spectrum information is sensitive to the change of background fluorescence and is easy to shift due to the change of emission spectrum of a representative organism.
Disclosure of Invention
In order to realize the discrimination of the red tide algae, the invention provides a red tide algae discrimination method based on the extraction of asymmetric spectral shape structure characteristics. The method uses a Bi-Gaussian model for separating characteristic spectrums, and describes the characteristic spectrums by using a spectrum description index, so that algae can be effectively distinguished.
The purpose of the invention is realized by the following technical scheme:
a red tide algae distinguishing method based on asymmetric spectral shape structure feature extraction is characterized by comprising the following steps:
s1: acquiring original fluorescence spectrum data of each algae species;
s2: carrying out normalization processing on the spectrum intensity in the original fluorescence spectrum data by using the following formula;
Figure BDA0001813969560000011
wherein x is*Denotes the normalized spectral intensity, xiIs the raw spectral intensity, xmaxIs the maximum intensity, x, of the original spectrumminIs the minimum intensity of the original spectrum;
s3: firstly, searching all peaks of each fluorescence spectrum by adopting a local maximum peak searching algorithm, then filtering all peaks by using a filtering algorithm, and fitting an asymmetric peak function by using a Levenberg-Marquardt iterative algorithm; and finally, establishing a Bi-Gaussian mixture function model, and decomposing the asymmetric peak function, wherein the formula of the Bi-Gaussian mixture function model is as follows:
Figure BDA0001813969560000021
wherein, x, y0,xc,H,w1And w2Respectively represent wavelength, baseline, peak position, peak height, left half-gaussian width and right half-gaussian width;
s4: respectively substituting the fluorescence data corresponding to 685nm and 730nm into a formula (2), calculating to obtain the peak height, the left half-Gaussian width and the right half-Gaussian width corresponding to 685nm and 730nm, and then calculating the spectrum shape description index log10[w2/w1,w1/H,w2/H,w2 */w1 *,w1 */H*,w2 */H*]；
S5: and performing cluster analysis on the spectrum shape description indexes by using a cluster analysis algorithm to distinguish different types of algae.
Further, the original fluorescence spectrum data of the algae species is obtained by a laser-induced fluorescence detection device.
Furthermore, the fitting wave band range of the Bi-Gaussian model is 620 nm-800 nm.
Further, the filtering algorithm in S3 is mean filtering.
Further, the spectral shape description index is based on the principle of spectral shape representation, and different peak width ratios and different peak width to height ratios are used to describe the waveform.
The invention has the beneficial effects that: considering that different types of algae have different pigment structures and can excite different characteristic spectra, the Bi-Gaussian model is used for separating the characteristic spectra, compared with the traditional band ratio method and PCA principal component analysis method, the Bi-Gaussian model can provide more pigment spectrum information because the pigment spectrum information is represented based on the spectrum shape, and the traditional methods are not suitable for complex mixed spectrum models because the pigment spectrum information is sensitive to the change of background fluorescence and is easy to shift due to the change of emission spectra of representative organisms. And a novel spectral description index method is provided for characteristic fluorescence spectrum identification, and the method can well distinguish algae from species level.
Drawings
FIG. 1 is a functional diagram of a Bi-Gaussian mixture model;
FIG. 2 shows the results of fitting decomposition spectra by a Bi-Gaussian model;
FIG. 3 is a histogram of spectral shape description index values for eight algae.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments, and the objects and effects of the present invention will become more apparent, and the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In order to carry out the red tide algae discrimination method of the present invention, first, algae species culture was carried out, 8 red tide algae species were co-cultured in this example for experiments, as shown in table 1, belonging to 7 genera of 2 phyla of dinoflagellate and diatom, each strain of algae species was cultured in an incubator of a 500 ml Erlenmeyer flask, and the algae species were separately cultured according to the f/2 method, the culture temperature was controlled at 20.5 ℃, the salinity was controlled at 35psu, the illumination was controlled at 85, 110 and 150Wm-2, and the illumination period was controlled at 12: the cultivation period of the algae is about 18 days after 12 hours.
TABLE 1 laboratory-cultured 8 species of red tide algae
Algae seed English name Abbreviations Belong to Door with a door panel
Fucus carotovora Chaetoceros debilis Cd Chaetoceros Diatom door
Alternaria hainanensis Thalassiosira rotula Tr Thalassiosira Diatom door
Prorocentrum donghaiense Prorocentrum donghaiense Pd Prorocentrum Dinoflagellate door
Prorocentrum dentis (Fr.) Kuntze Prorocentrum dentatum Pt Prorocentrum Dinoflagellate door
Red Haka algae Akashiwo sanguinea As Akashiwo Dinoflagellate door
Gymnodinium sp Gymnodinium simplex Gs Gymnodinium Dinoflagellate door
Karenia mikimotoi Karenia mikimotoi Km Karenia Dinoflagellate door
Alexandrium tamarense (Levl.) Ramat Alexandrium tamarense At Alexandrium Dinoflagellate door
The specific red tide algae distinguishing method comprises the following steps:
s1: raw fluorescence spectra data were obtained for each algal species. In the embodiment, each original spectrum is converted into matrix data of 3648 rows and 2 columns, and data of a spectrum range of a 620 nm-800 nm wave band is mainly researched in a centralized manner;
s2: carrying out normalization processing on the spectrum intensity in the original fluorescence spectrum data by using the following formula;
Figure BDA0001813969560000031
wherein x is*Denotes the normalized spectral intensity, xiIs the raw spectral intensity, xmaxIs the maximum intensity, x, of the original spectrumminIs the minimum intensity of the original spectrum;
s3: firstly, searching all peaks of each fluorescence spectrum by adopting a local maximum peak searching algorithm, then filtering all peaks by using a filtering algorithm, and fitting an asymmetric peak function by using a Levenberg-Marquardt iterative algorithm; and finally, establishing a Bi-Gaussian mixture function model (a model schematic diagram is shown in figure 1), decomposing an asymmetric peak function, wherein the formula of the Bi-Gaussian mixture function model is as follows:
Figure BDA0001813969560000032
wherein, x, y0,xc,H,w1And w2Respectively represent wavelength, baseline, peak position, peak height, left half-gaussian width and right half-gaussian width;
s4: respectively substituting the fluorescence data corresponding to 685nm and 730nm into a formula (2), calculating to obtain the peak height, the left half-Gaussian width and the right half-Gaussian width corresponding to 685nm and 730nm, and then calculating the spectrum shape description index log10[w2/w1,w1/H,w2/H,w2 */w1 *,w1 */H*,w2 */H*]；
S5: and performing cluster analysis on the spectrum shape description indexes by using a cluster analysis algorithm to distinguish different types of algae.
Eight red tide algae species cultured in a laboratory are subjected to a distinguishing experiment by adopting an asymmetric spectrum shape structure characteristic extraction method.
The result of the fluorescence spectrum decomposed by using the Bi-Gaussian model is shown in FIG. 2, 685nm corresponds to the chlorophyll a fluorescence peak, and a shoulder is arranged at 730 nm. The fitting error is about ± 0.02. The fitting results found that different types of spectral shapes, particularly the fitting peak shape at 730nm, could be separated, with clear distinction. In addition, c.debilis and Thalassiosira rotula in diatoms were also found to have similar fluorescence characteristics. The parameters of the spectrum of the fluorescent component decomposed by the Bi-Gaussian mixture model are shown in Table 2.
TABLE 2 spectra of fluorescent components decomposed by Bi-Gaussian mixture model
Figure BDA0001813969560000041
The calculated spectral shape description index values were subjected to cluster analysis, and the results are shown in fig. 3. The clustering distance is inversely proportional to the similarity between the classes. The larger the distance, the larger the difference between the species. As can be seen, 8 species of algae were successfully classified into 6 categories: 1) cd, Tr; 2) pd, Pt; 3) gs; 4) at; 5) km; and 6) As. Where Cd and Tr belong to the diatoms, they are classified into a large group. For other algal species, all but Pd and Pt were difficult to distinguish, and the rest were successfully distinguished. For Pd and Pt, since they both belong to the genus protozobium, their bio-optical properties are very similar and therefore difficult to distinguish. While others were successfully differentiated by describing the index by spectral shape.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and although the invention has been described in detail with reference to the foregoing examples, it will be apparent to those skilled in the art that various changes in the form and details of the embodiments may be made and equivalents may be substituted for elements thereof. All modifications, equivalents and the like which come within the spirit and principle of the invention are intended to be included within the scope of the invention.

Claims (5)

1. A red tide algae distinguishing method based on asymmetric spectral shape structure feature extraction is characterized by comprising the following steps:
s1: acquiring original fluorescence spectrum data of each algae species;
s2: carrying out normalization processing on the spectrum intensity in the original fluorescence spectrum data by using the following formula;
Figure FDA0001813969550000011
wherein x is*Denotes the normalized spectral intensity, xiIs the raw spectral intensity, xmaxIs the maximum intensity, x, of the original spectrumminIs the minimum intensity of the original spectrum;
s3: firstly, searching all peaks of each fluorescence spectrum by adopting a local maximum peak searching algorithm, then filtering all peaks by using a filtering algorithm, and fitting an asymmetric peak function by using a Levenberg-Marquardt iterative algorithm; and finally, establishing a Bi-Gaussian mixture function model, and decomposing the asymmetric peak function, wherein the formula of the Bi-Gaussian mixture function model is as follows:
Figure FDA0001813969550000012
Figure FDA0001813969550000013
wherein, x, y0,xc,H,w1And w2Respectively represent wavelength, baseline, peak position, peak height, left half-gaussian width and right half-gaussian width;
s4: respectively substituting the fluorescence data corresponding to 685nm and 730nm into a formula (2), calculating to obtain the peak height, the left half-Gaussian width and the right half-Gaussian width corresponding to 685nm and 730nm, and then calculating the spectrum shape description index log10[w2/w1,w1/H,w2/H,w2 */w1 *,w1 */H*,w2 */H*]；
S5: and performing cluster analysis on the spectrum shape description indexes by using a cluster analysis algorithm to distinguish different types of algae.
2. The method for differentiating red tide algae based on the asymmetric spectral shape structure feature extraction as claimed in claim 1, wherein the original fluorescence spectrum data of the algae species is obtained by a laser-induced fluorescence detection device.
3. The method for differentiating red tide algae based on asymmetric spectral shape structure feature extraction as claimed in claim 1, wherein the fitting band range of the Bi-Gaussian model is 620 nm-800 nm.
4. The method for differentiating red tide algae based on the asymmetric spectral shape structure feature extraction as claimed in claim 1, wherein the filtering algorithm in S3 is mean filtering.
5. The method of claim 1, wherein the spectral shape description index is based on the principle of spectral shape representation, and different ratios of peak width and peak width to height are used to describe the waveform.
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