CN109060700B - Method for rapidly identifying spirulina with different copper ion adsorption capacities - Google Patents
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- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
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Abstract
The invention discloses a method for quickly identifying spirulina with different copper ion adsorption capacities, which comprises the following steps: s1, collecting infrared spectrum: collecting infrared spectrums of different kinds of spirulina; s2, infrared spectrum processing: preprocessing the infrared spectrum obtained in the step S1, solving a second derivative of the preprocessed infrared spectrum to obtain a second derivative infrared spectrum, and selecting characteristic spectrum data from the second derivative infrared spectrum to perform principal component analysis; s3, cluster analysis: and selecting the principal component characteristic value obtained by the principal component analysis in the step S2 as a classification variable of the spirulina of different germplasms, and aggregating the spirulina into different categories according to the difference of copper ion adsorption capacity based on the classification variable. The method is based on infrared spectroscopy, and combines second derivative and principal component analysis and treatment, so that the spirulina with different copper ion adsorption capacity can be simply and quickly identified, and important theoretical support and practical significance are provided for searching the spirulina specie capable of efficiently removing heavy metals in the sewage.
Description
Technical Field
The invention relates to the technical field of distinguishing spirulina with different copper ion adsorption capacities, in particular to a method for quickly identifying spirulina with different copper ion adsorption capacities.
Background
With the continuous development of society and economy, environmental problems have attracted people's attention, and among them, heavy metal pollution is an important factor causing environmental problems. The fields of coal mines, steel, metallurgy and the like all bring pollution of a plurality of heavy metals such as lead, mercury, copper, chromium and the like in production activities, and cause serious influence on the environment. The common chemical precipitation method and ion exchange method can remove heavy metal pollution of water, but have large investment, higher cost and secondary pollution to the environment; the biological adsorption method has the advantages of low cost, good selectivity, large adsorption quantity and adsorption range, and common secondary pollution. Emerging bio-adsorption technology may be an effective way to address these problems.
At the end of the 20 th century, scientists at home and abroad discover that microorganisms such as bacteria, algae and yeast are suitable for serving as water adsorbents through the research on the adsorption and recovery of heavy metals in wastewater by the microorganisms. Among them, spirulina is widely used in microalgae industry, and has low cost, easy availability, large specific surface area, high density of cell adhesive and active group, high heavy metal adsorption capacity, and is more suitable for being used as an excellent biological adsorbent. However, because the contents and the proportions of the components such as lipid, protein, polysaccharide and the like in the spirulina cells under different germplasm and nutritional conditions have obvious differences, and the components are closely related to the adsorption performance of the heavy metal ions, the analysis of the components of the spirulina cells for adsorbing the heavy metals has important guiding significance for screening the biological adsorbent for efficiently adsorbing the heavy metal ions. At present, the analysis of the spirulina components mainly depends on the conventional detection technology of separation, crushing, extraction and content determination of the spirulina components. However, such methods are complicated, costly and time-consuming to perform, and it is difficult to meet the urgent requirement for rapid analysis of microalgae components.
Infrared spectroscopy (IR) is the determination of information such as the structure of a substance group and the content of a compound based on the vibrational behavior of a spectrum during the transition of energy levels that occur after molecules absorb Infrared radiation. Particularly, in recent ten years, with the rapid development of Fourier Transform technology, Fourier Transform-induced spectroscopy (FTIR) technology has significantly advanced in the fields related to microalgae, such as classification identification, growth and metabolism monitoring, breeding, water environment, food and medicine.
Therefore, with the technical progress of the infrared spectrometer, the FTIR spectrum based detection and analysis method is mature. And because the method has the advantages of simplicity, rapidness, real-time dynamic and nondestructive detection and the like, and can be combined with a chemometrics method to carry out multi-component analysis on organisms, no report about screening or identifying the copper ion adsorption capacity of the spirulina with different species based on an FTIR spectrum technology exists at present.
Disclosure of Invention
The invention provides a method for quickly identifying spirulina with different copper ion adsorption capacities, which is based on infrared spectrum and combines second derivative treatment and principal component analysis treatment to obtain a class-determining characteristic spectrum, can simply and quickly identify the spirulina with different copper ion adsorption capacities, and provides important theoretical support and practical significance for searching spirulina germplasm capable of efficiently removing heavy metals in sewage.
The invention provides a method for quickly identifying spirulina with different copper ion adsorption capacities, which comprises the following steps:
s1, collecting infrared spectrum: collecting infrared spectrums of different kinds of spirulina;
s2, infrared spectrum processing: preprocessing the infrared spectrum obtained in the step S1, solving a second derivative of the preprocessed infrared spectrum to obtain a second derivative infrared spectrum, and selecting characteristic spectrum data from the second derivative infrared spectrum to perform principal component analysis;
s3, cluster analysis: and selecting the principal component characteristic value obtained by the principal component analysis in the step S2 as a classification variable of the spirulina of different germplasms, and aggregating the spirulina into different categories according to the difference of copper ion adsorption capacity based on the classification variable.
Preferably, in step S1, the infrared spectrum is obtained by Fourier transform infrared spectrum analyzer, the number of scanning is 128, and the measuring wave number range is 900-4000cm-1Resolution of 4cm-1。
Preferably, in the infrared spectrum collection process, the spirulina powder is mixed with distilled water to prepare a suspension, and the suspension is coated on BaF2Drying the substrate on an infrared substrate at 50 ℃ overnight, and detecting by using an infrared spectrometer.
Preferably, the specific operation for preparing the spirulina powder is as follows: the spirulina is inoculated in a sterile Z-type culture medium according to the proportion of 10 percent for culture, and the culture conditions are as follows: culturing under 3000lx light at 30 + -1 deg.C in a light incubator with 12h/12h light dark period, shaking by hand 2-3 times every day until the algae grows to a stable period, centrifuging the algae liquid for 10min at 10000rpm, discarding the supernatant, retaining the precipitate to obtain algae, centrifuging with deionized water to wash the algae, drying at 60 deg.C, grinding to uniform, and sieving with 120 mesh sieve to obtain Spirulina powder.
Preferably, in step S2, the ir spectra obtained in step S1 are pre-processed using baseline correction and vector normalization.
Preferably, the infrared spectroscopy processing in step S2 is performed in Bruker Opus 6.5 software.
In order to research the identification capability of different characteristic spectra on spirulina of different germplasms, the inventor utilizes Bruker Opus 6.5 software to intercept second derivative infrared spectra of different germplasm spirulina in different wave number ranges for main component analysis and clustering, and the main component analysis and clustering are respectively carried out by 900-1200 cm--1、2800-3000cm-1、1500-1700cm-1And the full spectral band 900--1The three-dimensional clustering charts obtained as the characteristic spectrum are respectively shown in FIG. 1, FIG. 5, FIG. 6 and FIG. 7, and it can be seen from the clustering results that the wave number range of the characteristic spectrum is 2800 and 3000cm-1、1500-1700cm-1And the full spectral band 900--1Cannot distinguish different spirulina well, and 900--1The wave band can obtain good clustering effect.
Preferably, in step S2, the wave number of the characteristic spectrum is 900--1。
Preferably, the principal component analysis is performed in Eigenvector software.
Preferably, in step S3, the principal component feature values with the top 3 credibility ranks in the principal component feature values obtained in step S2 are selected as the classification variables of spirulina.
In a specific embodiment, the classification variables of spirulina obtained in step S3 may be obtained by performing clustering analysis on different spirulina samples by using different clustering methods, such as partial least squares, support vector machines, principal component regression analysis, and the like; preferably, in step S3, selecting the top 3 principal component eigenvalues with credibility ranking from the principal component eigenvalues obtained in step S2 as three-dimensional coordinate values of spirulina to make a three-dimensional clustering chart, and clustering different germplasm spirulina into different categories;
in the invention, the three-dimensional clustering graph is directly made by taking the first 3 principal components PC1, PC2 and PC3 with credibility as three-dimensional coordinate values, so that the method is simple and quick and does not need to perform further optimization processing on data.
Preferably, the copper ion adsorption capacity of different spirulina samples is determined according to a conventional method, and the copper ion adsorption capacity of different spirulina samples is compared with the clustering result to judge the accuracy of the clustering result; if the clustering result is to cluster the spirulina samples with the same quality and the same copper ion adsorption capacity into one class, and the class can be obviously distinguished from other germplasm spirulina, the clustering accuracy is high, otherwise, the clustering accuracy is low.
Preferably, the adsorption capacity of copper ions is determined using the bicyclohexanoneoxalyl dihydrazone method.
Preferably, the specific operation of measuring the copper ion adsorption capacity of the spirulina by the bicyclohexanoneoxalyl dihydrazone method is as follows: (1) adding copper standard solutions with different concentrations into a group of colorimetric tubes respectively, adding 0.7mL of ammonium citrate aqueous solution, 0.5mL of ammonia water-ammonium chloride buffer solution, 0.1mL of acetaldehyde and 1.1mL of dicyclohexyl oxalyl dihydrazone (BCO) solution, diluting to 5mL by using distilled water, mixing for 10min by using a vortex mixer, measuring absorbance at 545nm, and drawing a copper ion concentration standard curve; (2) in the adsorption experiment of copper ions, 0.04g of spirulina powder of different germplasms is weighed and dissolved in 40mL of CuSO with pH of 6 and concentration of 100mg/L4Centrifuging at 12000rpm after shaking for 12h by using a shaking table to obtain a supernatant solution to obtain a solution to be detected for adsorbing copper ions by spirulina, and determining the solution to be detected for adsorbing copper ions by spirulina according to the operation of preparing a copper ion concentration standard curve; (3) and (4) obtaining the concentration of copper ions in the solution to be detected for absorbing the copper ions by the spirulina according to the standard curve of the concentration of the copper ions, and calculating the adsorption capacity of the spirulina of different germplasms to the copper ions.
In the present invention, the adsorption capacity means: the weight (mg) of the copper ions absorbed by the spirulina with unit mass (g), the calculation method of the weight of the copper ions absorbed by the spirulina in the invention is as follows: the adsorption quality of the spirulina to the copper ions is CuSO before adsorption4Copper ion mass in solution-the mass of copper ion in the solution to be tested is adsorbed by the spirulina after adsorption.
Wherein the mass concentration of ammonium citrate in the ammonium citrate aqueous solution is 20%, the ammonia water-ammonium chloride buffer solution is obtained by dissolving 4ml of ammonia water and 4g of ammonium chloride in 100ml of deionized water, the mass concentration of acetaldehyde is 40%, and the biscyclohexanone oxalyldihydrazone alcohol solution is obtained by dissolving 0.2g of BCO in a mixed solution of 50ml of deionized water and 50ml of ethanol.
Compared with the prior art, the invention has the following advantages: by utilizing FTIR spectrum technology, specific classification variables of different germplasm spirulina are obtained by second derivative treatment and principal component analysis on the infrared spectrum of known different germplasm spirulina, and the result shows that 900-1200cm ion sources are obtained by second derivative treatment and principal component analysis treatment-1The characteristic spectrum of the wave band has good distinguishing and clustering effects on different species of spirulina. According to the identification method, the infrared spectrum is collected and the characteristic spectrum is obtained to serve as the classification variable of the spirulina of different germplasms, so that the complex procedures of separation, crushing, extraction, content determination and the like in the process of analyzing the copper ion adsorption capacity of the spirulina of different germplasms through spirulina component analysis in the prior art are omitted, and important theoretical basis and practical significance are provided for searching a novel biological adsorption material capable of efficiently removing heavy metals in sewage.
Drawings
FIG. 1 shows the characteristic spectrum of the present invention with wave number of 900-1200cm-1And obtaining the three-dimensional cluster map.
FIG. 2 is a Fourier transform infrared spectrum of different germplasm spirulina in the invention.
FIG. 3 is a second derivative infrared spectrum of spirulina of different germplasm after second derivative treatment.
FIG. 4 is a graph showing the copper ion adsorption capacity of different species of Spirulina in the present invention.
FIG. 5 shows the characteristic spectrum of the present invention with wave number 2800 and 3000cm-1And obtaining the three-dimensional cluster map.
FIG. 6 shows the characteristic spectrum of the present invention having a wavenumber of 1500--1And obtaining the three-dimensional cluster map.
FIG. 7 shows the wave number of the characteristic spectrum of the present invention is 900-4000cm-1And obtaining the three-dimensional cluster map.
Detailed Description
The invention provides a method for quickly identifying spirulina with different copper ion adsorption capacities, which comprises the following steps:
s1, collecting infrared spectrum: collecting infrared spectrums of different kinds of spirulina;
s2, infrared spectrum processing: preprocessing the infrared spectrum obtained in the step S1, solving a second derivative of the preprocessed infrared spectrum to obtain a second derivative infrared spectrum, and selecting characteristic spectrum data from the second derivative infrared spectrum to perform principal component analysis;
s3, cluster analysis: and selecting the principal component characteristic value obtained by the principal component analysis in the step S2 as a classification variable of the spirulina of different germplasms, and aggregating the spirulina into different categories according to the difference of copper ion adsorption capacity based on the classification variable.
The technical solution of the present invention will be described in detail below with reference to specific examples.
Example 1
A method for rapidly identifying spirulina with different copper ion adsorption capacities comprises the following steps:
s1, collecting infrared spectrum: collecting infrared spectra of Spirulina of different species by Nicolet 380(Thermo Scientific) infrared spectrometer, wherein the scanning frequency is 128 times, and the measuring range is 900--1Resolution of 4cm-1;
S2, infrared spectrum processing: preprocessing the infrared spectrum obtained in the step S1 by using Bruker Opus 6.5 software, solving a second derivative of the preprocessed infrared spectrum to obtain a second derivative infrared spectrum, selecting characteristic spectrum data in the second derivative infrared spectrum, and performing principal component analysis by using Eigenvector software;
s3, cluster analysis: and selecting principal component characteristic values obtained in the step S2 as classification variables of the spirulina of different germplasms, and aggregating the spirulina into different categories according to different copper ion adsorption capacities by using a partial least square method based on the classification variables.
Example 2
A method for rapidly identifying spirulina with different copper ion adsorption capacities comprises the following steps:
s1, collecting infrared spectrum: mixing Spirulina powder with distilled water to obtain suspension, and suspending 50 μ LThe floating liquid is coated on BaF2Drying overnight at 50 deg.C on infrared substrate, collecting infrared spectra of different spirulina species by Nicolet 380(Thermoscientific) infrared spectrometer, wherein the scanning times is 128 times, and the measuring range is 900--1Resolution of 4cm-1;
S2, infrared spectrum processing: preprocessing the infrared spectrum obtained in the step S1 by using Bruker Opus 6.5 software, solving a second derivative of the preprocessed infrared spectrum to obtain a second derivative infrared spectrum, and selecting the wave number of 1200cm with the wave number of 900--1Analyzing the main components of the characteristic spectrum data by utilizing Eigenvector software;
s3, cluster analysis: selecting 3 principal component characteristic values PC1, PC2 and PC3 with the highest credibility rank in the principal component characteristic values obtained in the step S2 as three-dimensional coordinate values of different germplasm spirulina to be used as a three-dimensional clustering chart;
the specific operation for preparing the spirulina powder is as follows: the spirulina is inoculated in a sterile Z-type culture medium according to the proportion of 10 percent for culture, and the culture conditions are as follows: culturing under 3000lx illumination at 30 + -1 deg.C and 12h/12h light dark period in an illumination incubator, shaking by hand 2-3 times per day until the algae grows to a stable stage, centrifuging the algae liquid for 10min at 10000rpm, discarding the supernatant, retaining the precipitate to obtain algae, centrifuging with deionized water to wash the algae, drying at 60 deg.C, grinding to uniform, and sieving with 120 mesh sieve to obtain Spirulina powder;
fourier transform infrared spectrograms of different germplasm spirulina are shown in figure 2, and second derivative infrared spectrograms of different germplasm spirulina after second derivative treatment are shown in figure 3; the wave number of this example is 900-1200cm-1The resulting three-dimensional cluster map as a characteristic spectrum is shown in FIG. 1;
the copper ion adsorption capacity of different germplasm spirulina samples in the embodiment is determined according to a bicyclohexanoneoxalyl dihydrazone method, and the specific operation is as follows: (1) adding copper standard solutions with different concentrations into a group of colorimetric tubes, respectively, adding 0.7mL ammonium citrate aqueous solution, 0.5mL ammonia-ammonium chloride buffer solution, 0.1mL acetaldehyde, 1.1mL dicyclohexylketone oxalyl dihydrazone alcoholic solution (BCO), and mixing with waterDiluting with distilled water to 5mL, mixing for 10min with a vortex mixer, measuring absorbance at 545nm, and drawing a copper ion concentration standard curve; (2) in the adsorption experiment of copper ions, 0.04g of spirulina powder of different germplasms is weighed and dissolved in 40mL of CuSO with pH of 6 and concentration of 100mg/L4Centrifuging at 12000rpm after shaking for 12h by using a shaking table to obtain a supernatant solution to obtain a solution to be detected for adsorbing copper ions by spirulina, and determining the solution to be detected for adsorbing copper ions by spirulina according to the operation of preparing a copper ion concentration standard curve; (3) obtaining copper ion concentration in the solution to be tested for absorbing copper ions by spirulina by contrasting the standard curve of copper ion concentration, and calculating the copper ion adsorption capacity of spirulina of different germplasms, wherein the obtained curve of copper ion adsorption capacity of spirulina of different germplasms is shown in figure 4;
as can be seen from comparing fig. 4 and fig. 1, the clustering result of the method for rapidly identifying spirulina with different copper ion adsorption capacities in example 1 is identical to the copper ion adsorption capacity of spirulina, which proves that the method for identifying spirulina of different copper ion adsorption capacities can effectively distinguish different spirulina according to the difference of copper ion adsorption capacities, and the adsorption capacity curve in fig. 1 has obvious boundaries in the three-dimensional classification chart in fig. 1 for the first 4 spirulina RH44, XS58, AH53, RZ22 with the last 5 spirulina HS7, RN3, WS47, YH46, AN8 (the adsorption capacity of the first 4 classes is greater than that of the last 5 classes) with the first adsorption capacity, which indicates that the germplasm spirulina with high copper ion adsorption capacity can be effectively screened by selecting classified characteristic variables in the present invention.
Comparative example 1
A method for identifying spirulina of different germplasms comprises the following steps:
s1, collecting infrared spectrum: mixing Spirulina powder with distilled water to obtain suspension, and applying 50 μ L of suspension to BaF2Drying overnight at 50 deg.C on infrared substrate, collecting infrared spectra of different spirulina species by Nicolet 380(Thermoscientific) infrared spectrometer, wherein the scanning times is 128 times, and the measuring range is 900--1Resolution of 4cm-1;
S2, infrared spectrum processing: preprocessing the infrared spectrum obtained in the step S1 by using Bruker Opus 6.5 software, and processing the preprocessed redThe second derivative is obtained by solving the second derivative through the external spectrum to obtain a second derivative infrared spectrum, and the wave number is 2800-3000cm in the second derivative infrared spectrum-1Analyzing the main components of the characteristic spectrum data by utilizing Eigenvector software;
s3, cluster analysis: selecting 3 principal component characteristic values PC1, PC2 and PC3 with the highest credibility rank in the principal component characteristic values obtained in the step S2 as three-dimensional coordinate values of different germplasm spirulina to be used as a three-dimensional clustering chart;
the specific operation for preparing the spirulina powder is as follows: the spirulina is inoculated in a sterile Z-type culture medium according to the proportion of 10 percent for culture, and the culture conditions are as follows: culturing under 3000lx light at 30 + -1 deg.C in a light incubator with 12h/12h light dark period, shaking by hand 2-3 times every day until the algae grows to a stable period, centrifuging the algae liquid for 10min at 10000rpm, discarding the supernatant, retaining the precipitate to obtain algae, centrifuging with deionized water to wash the algae, drying at 60 deg.C, grinding to uniform, and sieving with 120 mesh sieve to obtain Spirulina powder.
Comparative example 2
A method for identifying spirulina of different germplasms comprises the following steps:
s1, collecting infrared spectrum: mixing Spirulina powder with distilled water to obtain suspension, and applying 50 μ L of suspension to BaF2Drying overnight at 50 deg.C on infrared substrate, collecting infrared spectra of different spirulina species by Nicolet 380(Thermoscientific) infrared spectrometer, wherein the scanning times is 128 times, and the measuring range is 900--1Resolution of 4cm-1;
S2, infrared spectrum processing: preprocessing the infrared spectrum obtained in the step S1 by using Bruker Opus 6.5 software, solving a second derivative of the preprocessed infrared spectrum to obtain a second derivative infrared spectrum, and selecting a wave number of 1500-1700cm in the second derivative infrared spectrum-1Analyzing the main components of the characteristic spectrum data by utilizing Eigenvector software;
s3, cluster analysis: selecting 3 principal component characteristic values PC1, PC2 and PC3 with the highest credibility rank in the principal component characteristic values obtained in the step S2 as three-dimensional coordinate values of different germplasm spirulina to be used as a three-dimensional clustering chart;
the specific operation for preparing the spirulina powder is as follows: the spirulina is inoculated in a sterile Z-type culture medium according to the proportion of 10 percent for culture, and the culture conditions are as follows: culturing under 3000lx light at 30 + -1 deg.C in a light incubator with 12h/12h light dark period, shaking by hand 2-3 times every day until the algae grows to a stable period, centrifuging the algae liquid for 10min at 10000rpm, discarding the supernatant, retaining the precipitate to obtain algae, centrifuging with deionized water to wash the algae, drying at 60 deg.C, grinding to uniform, and sieving with 120 mesh sieve to obtain Spirulina powder.
Comparative example 3
A method for identifying spirulina of different germplasms comprises the following steps:
s1, collecting infrared spectrum: mixing Spirulina powder with distilled water to obtain suspension, and applying 50 μ L of suspension to BaF2Drying overnight at 50 deg.C on infrared substrate, collecting infrared spectra of different spirulina species by Nicolet 380(Thermoscientific) infrared spectrometer, wherein the scanning times is 128 times, and the measuring range is 900--1Resolution of 4cm-1;
S2, infrared spectrum processing: preprocessing the infrared spectrum obtained in the step S1 by using Bruker Opus 6.5 software, solving a second derivative of the preprocessed infrared spectrum to obtain a second derivative infrared spectrum, and selecting 4000cm infrared spectrum with the wave number of the full spectrum section 900--1Analyzing the main components of the characteristic spectrum data by utilizing Eigenvector software;
s3, cluster analysis: selecting 3 principal component characteristic values PC1, PC2 and PC3 with the highest credibility rank in the principal component characteristic values obtained in the step S2 as three-dimensional coordinate values of different germplasm spirulina to be used as a three-dimensional clustering chart;
the specific operation for preparing the spirulina powder is as follows: the spirulina is inoculated in a sterile Z-type culture medium according to the proportion of 10 percent for culture, and the culture conditions are as follows: culturing under 3000lx light at 30 + -1 deg.C in a light incubator with 12h/12h light dark period, shaking by hand 2-3 times every day until the algae grows to a stable period, centrifuging the algae liquid for 10min at 10000rpm, discarding the supernatant, retaining the precipitate to obtain algae, centrifuging with deionized water to wash the algae, drying at 60 deg.C, grinding to uniform, and sieving with 120 mesh sieve to obtain Spirulina powder.
The three-dimensional clustering charts obtained by the methods for identifying different germplasm spirulina described in comparative examples 1, 2 and 3 are shown in FIG. 5, FIG. 6 and FIG. 7, respectively, and it can be seen that the wave number range of the characteristic spectrum is 2800-3000cm-1Comparative example 1, 1500--1Comparative example 2 and full spectral band 900--1(comparative example 3) the different Spirulina species were not well distinguished, corresponding to example 1 at 900-1200cm-1The wave band can obtain good clustering effect, and the spirulina with different copper ion adsorption capacity can be accurately identified. In addition, based on biological analysis, 2800-3000cm-1Lipids and carotenoids mainly belonging to algal cells, 1500--1Mainly due to amide I and amide II of the protein, and 1200-900cm-1The polysaccharide in the main cell component is mainly attributed to that the adsorption site of the spirulina to the copper ions is mainly related to the polysaccharide active group in the cell wall, which also indicates that 1200-900cm is selected in the invention-1Rationality for distinguishing between different copper ion adsorption capacity spirulina germplasms.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (9)
1. A method for rapidly identifying spirulina with different copper ion adsorption capacities is characterized by comprising the following steps:
s1, collecting infrared spectrum: collecting infrared spectrums of different kinds of spirulina;
s2, infrared spectrum processing: preprocessing the infrared spectrum obtained in the step S1, solving a second derivative of the preprocessed infrared spectrum to obtain a second derivative infrared spectrum, and selecting characteristic spectrum data from the second derivative infrared spectrum to perform principal component analysis;
s3, cluster analysis: and selecting the principal component characteristic value obtained by the principal component analysis in the step S2 as a classification variable of the spirulina of different germplasms, and aggregating the spirulina into different categories according to the difference of copper ion adsorption capacity based on the classification variable.
2. The method as claimed in claim 1, wherein in step S1, the infrared spectrum is obtained by Fourier transform infrared spectrum analyzer, the number of scanning is 128, and the measurement wave number range is 900-4000cm-1Resolution of 4cm-1。
3. The method for rapidly identifying spirulina of different copper ion adsorption capacities as claimed in claim 1 or 2, wherein the spirulina powder is mixed with distilled water to prepare a suspension during the collection of the infrared spectrum, and the suspension is coated on BaF2Drying the substrate on an infrared substrate at 50 ℃ overnight, and detecting by using an infrared spectrometer.
4. The method for rapidly identifying spirulina of different copper ion adsorption capacities as claimed in claim 1 or 2, wherein in step S2, the infrared spectrum obtained in step S1 is preprocessed by baseline correction and vector normalization.
5. The method as claimed in claim 1 or 2, wherein in step S2, the wave number of the characteristic spectrum is 900-1200cm-1。
6. The method for rapidly identifying spirulina of different copper ion adsorption capacities as claimed in claim 1 or 2, wherein in step S3, the 3 principal component eigenvalues with the top credibility rank among the principal component eigenvalues obtained by the principal component analysis in step S2 are selected as classification variables of spirulina.
7. The method for rapidly identifying spirulina of different copper ion adsorption capacities as claimed in claim 1 or 2, wherein in step S3, the 3 principal component eigenvalues with the top credibility rank among the principal component eigenvalues obtained by the principal component analysis in step S2 are selected as three-dimensional coordinate values of spirulina to be used as a three-dimensional clustering chart, so as to cluster spirulina of different germplasms into different categories.
8. The method according to claim 1 or 2, wherein the copper ion adsorption capacity of different spirulina samples is determined, and the accuracy of the clustering result is determined by comparing the copper ion adsorption capacity of different spirulina with the clustering result.
9. The method for rapidly identifying spirulina of different adsorption capacities for copper ions according to claim 1 or 2, wherein the adsorption capacity for copper ions is measured by bicyclohexanoneoxalyl dihydrazone method.
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