CN106442463B - Algal cell count and algal species discrimination method based on line scanning Raman microscopy - Google Patents
Algal cell count and algal species discrimination method based on line scanning Raman microscopy Download PDFInfo
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- 238000001530 Raman microscopy Methods 0.000 title claims description 12
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Abstract
本发明公开了一种基于线扫描拉曼显微成像的藻细胞计数及藻种判别方法，包括制备藻细胞检测计数板、藻细胞样本的预处理、藻细胞成像、基于藻细胞拉曼成像的藻细胞计数以及藻种的识别。本发明提供的一种基于线扫描拉曼显微成像及多元数据挖掘的藻细胞计数及藻种判别分析方法，可实现藻液中细胞的计数及藻种的快速判别分析，易于实现藻细胞的准确计数及藻种的同步快速识别。
The invention discloses a method for counting algal cells and discriminating algal species based on line scanning Raman microscopic imaging. Algal cell count and identification of algal species. The invention provides a method for counting algal cells and discriminating analysis of algal species based on line scanning Raman microscopic imaging and multivariate data mining, which can realize the counting of cells in algal liquid and the rapid discriminative analysis of algal species, and is easy to realize the identification and analysis of algal cells. Accurate counting and simultaneous rapid identification of algal species.
Description
Technical field
The invention belongs to environmental monitoring and protection technique fields, and in particular to a kind of to scan Raman mapping based on line
Frustule counts and algae method of discrimination.
Background technique
With economic rapid development, have at 88.6% in the lake that China has investigated to later period the 1990s
In eutrophic state.The situation of in the 21st century, lake eutrophication presentation high speed development.China has occurred and that eutrophy at present
The area of lake of change reaches 5000km2, have occur eutrophication condition area of lake reach 14000km2.Due to eutrophication
Water body faces the problem of frequently breaking out algae wawter bloom, therefore, can be to China resident using eutrophication water as drinking water source
Safe drinking water cause to seriously threaten.
Cause in volume wawter bloom in freshwater lake eutrophication, it is most commonly seen with spring and summer microalgae wawter bloom, such as the Taihu Lake in China
Basin, the biomass of microalgae account for 90% or more of algae total biomass；The microalgae wawter bloom of Dian Chi does not subside throughout the year, water quality corruption,
Algae toxin content is high.With the aggravation of water pollution, more and more lakes, reservoir even river also break out water often
Magnificent phenomenon, therefore, it is necessary to distinguish whether the wawter bloom of eutrophication water is caused by microalgae and determined the quantity of microalgae.
Currently, algae researcher can count by microscope to frustule and the identification of algae, but for counting
Accuracy and algae identification, be largely limited to the accumulation of experience of experimenter in terms of frustule.
With the development of computer image processing technology, a kind of cell count and recognition methods based on frustule imaging is developed, it should
Method needs to carry out frustule optical imagery, and is counted using complicated image processing techniques to frustule, and for
The identification of the type of frustule is based only upon the differentiation between cell size.In addition to tradition counts identification based on microscopical frustule
Outside method, the method that the prior art can be used for frustule counting further includes based at flow cytometer, micro-imaging digital picture
Reason, optical detecting method and its improved method be combineding with each other, these methods improve the counting of frustule to a certain extent
Precision and recognition accuracy.But these methods are there is certain defective, such as need to carry out complicated pretreatment (ultrasound,
Filtering, dyeing etc.), directly it cannot count or identify, meanwhile, it needs to establish standard curve in advance using indirect method.
Therefore, find that a kind of quick, simple and effective frustule counts and algae method of discrimination is for those skilled in the art
It is necessary for member.
Summary of the invention
It is excavated in view of this, the purpose of the present invention is to provide one kind based on line scanning Raman mapping and multivariate data
Frustule count and algae discriminant analysis method, realize algae solution in the counting of cell and the simultaneously and rapidly discriminant analysis of algae.
For achieving the above object, the present invention provides the following technical solutions:
Frustule based on line scanning Raman mapping counts and algae method of discrimination, includes the following steps:
1) it prepares frustule detection tally: etching sensing chamber on a silicon substrate, the sensing chamber is 1mm*1mm*
The sensing chamber is divided into 3*3 small sensing chamber with lines in sensing chamber bottom, in sensing chamber by the cuboid slot of 0.1mm
Two opposite side etch sample inlet channel and sample export channel respectively again；
2) pretreatment of frustule sample: algae solution sample is carried out sufficiently to be diluted to frustule not phase mutual respect with ultrapure water
Folded, the sample after dilution is added to the inspection of tally by the covered on frustule detection tally from sample inlet channel
It surveys in room, extra sample is then flowed out from sample export channel, frustule detection tally is placed on horizontal station,
Standing is adsorbed on sensing chamber bottom, nonvoluntary travelling to frustule as much as possible；
3) frustule is imaged: selection 532nm laser, 50 times/100 times telephoto lens, and the large area based on line scanning is total
Burnt Raman image splicing carries out quick Overlap-scanning to the sample in frustule tally, and sample room large area imaging is spliced, with
Feature peak height, peak area, peak height or the peak area ratio and characteristic peak frequency of frustule move automated imaging and obtain based on line scanning
The Raman image figure of large area splicing frustule；
4) frustule based on frustule Raman image counts: the Raman of the large area splicing frustule based on line scanning at
As figure, clustering is carried out to the frustule Raman signatures information in four angle imaging regions of sensing chamber respectively, realizes that algae is thin
Born of the same parents count respectively, find out four region frustule number mean values, and 9 times of this mean value are the quantity of frustule in the sensing chamber, into
And realize that frustule counts, the frustule Raman signatures information includes feature peak height, peak area or ratio, peak frequency displacement；
5) identification of algae:
Firstly, the confocal Raman spectra standard database of different algae frustules is established, the confocal drawing including different algaes
The Raman signatures information of graceful spectrum, algae；By the confocal Raman spectra standard database of the different algae frustules, based on sentencing
Other formula least-squares algorithm, support vector classification or artificial neural network, establish algae discriminant analysis model；
Based on step 4) frustule count as a result, by it is a certain cluster sample characteristic information be averaged, as this
The center of class sample scans for comparing and then to algae from the confocal Raman spectra standard database of the different algae frustules
Kind is identified；Alternatively, using all spectroscopic datas in a certain cluster as forecast set, the algae discriminant analysis based on aforementioned foundation
Model identifies the cluster frustule.
Preferably, step 3) is apparent in order to make frustule imaging, is filtered to frustule image and baseline correction
Pretreatment, the final Raman image figure for obtaining large area scan based on line and splicing frustule.
Preferably, the preprocessing process of the step 3) filtering and baseline correction is carried out according to two methods, a kind of method
It is to be handled based on Raman image figure using two-dimensional filtering and baseline correction algorithm；Another method is in imaging
Single Raman spectrum based on, handled using one-dimensional filtering and baseline correction algorithm, be filtered into wavelet filtering, smooth filter
Wave；Baseline correction is selected as wavelet transformation, asymmetric least square, smoothing spline fitting or is calculated based on genetic groups baseline correction
Method.
The beneficial effects of the present invention are: it is provided by the invention a kind of based on line scanning Raman mapping and multivariate data
The frustule of excavation counts and algae discriminant analysis method is, it can be achieved that the quick discrimination of the counting of cell and algae divides in algae solution
Analysis, it is easy to accomplish the accurate counting of frustule and the simultaneously and rapidly identification of algae.
Detailed description of the invention
In order to keep the purpose of the present invention, technical scheme and beneficial effects clearer, the present invention provides following attached drawing:
Fig. 1 shows scan the frustule counting and algae discriminant analysis that Raman mapping and multivariate data excavate based on line
Scheme.
Fig. 2 indicates that frustule detects tally；Wherein 1 --- quartz substrate；2 --- sample channel；3 --- quartz cover glass
Piece；4 --- algae solution deposition region；5 --- sample channel；6 --- scanning laser and scattered signal are collected；7 --- telephoto lens；
8 --- incident laser and scattered signal collect channel.
Fig. 3 indicates the foundation of algae discrimination model and the identification of algae to be measured.
Fig. 4 indicates the blue-green algae cell 1522cm scanned based on line-1Neighbouring characteristic peak area Raman image schematic diagram.
Fig. 5 indicates the blue-green algae cell 1522cm scanned based on line-1Neighbouring feature frequency displacement Raman image schematic diagram.
Fig. 6 indicates blue-green algae cell 1522cm-1Neighbouring feature frequency displacement Raman original image effect.
Blue-green algae cell 1522cm after Fig. 7 expression processing-1Neighbouring feature frequency displacement Raman image effect.
Specific embodiment
In order to keep the purposes, technical schemes and advantages of the embodiment of the present invention clearer, below with reference to the embodiment of the present invention
In attached drawing, clear, complete description is carried out to specific implementation of the invention.Obvious, described embodiment is the present invention
In a part of the embodiment, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art exist
Every other embodiment obtained under the premise of creative work is not made, protection scope of the present invention is belonged to.
Embodiment 1
The first step prepares frustule detection tally using micro-processing method
On a silicon substrate, using lithographic methods, frustule sensing chamber, detection are processed according to tally structure shown in Fig. 2
Room side length is 1mm*1mm, and including 3*3 small sensing chamber, the depth of sensing chamber is 0.1mm.I.e. when algae solution fills with sensing chamber,
Algae solution volume is 0.1mm3.Then quartz cover slip is covered in sensing chamber, in case the detection and counting of frustule.
Second step, sample preprocessing and Preparatory work of experiment
The dilution that algae solution sample is carried out to certain multiple, shakes up, and takes a certain amount of algae solution using pipettor, drips in frustule
The injection port 2 of tally 1, algae solution are distributed to each sensing chamber 4 under siphonage, and extra algae solution blotting paper is from sample
Channel 5 is sucked out.Frustule tally is placed on to horizontal station, a period of time is stood, adsorbs frustule as much as possible
At sensing chamber bottom, nonvoluntary travelling facilitates confocal Raman to carry out the Overlap-scanning of large area, and then can be to frustule counting and algae
The identification of kind.
Third step, frustule imaging
532nm laser is selected, 50 times of telephoto lens, 1% laser power, the time of integration is 2 seconds.Laser light quartz
Coverslip focuses on the algae solution sample of sensing chamber, and the confocal Raman image splicing of the large area based on line scanning counts frustule
The sample of plate is quickly scanned.Sample room large area imaging splicing, with frustule characteristic peak 1522cm-1(belong to carotenoids
Element) it is basic automated imaging.With blue-green algae cell 1522cm-1For neighbouring characteristic peak area Raman image, as shown in figure 4, algae
Cell characteristic significantly shows.For clarity show frustule feature, need to frustule image further
Pretreatment, including filtering and baseline correction, obtain the large area imaging figure of frustule in tally.With the single Raman in imaging
It based on spectrum, is handled using one-dimensional filtering and baseline correction algorithm, the final pretreatment for realizing frustule image.Its
Middle filtering uses wavelet filtering；Baseline correction asymmetric least square baseline correction algorithm.
4th step, the frustule based on frustule Raman image count
Based on Fig. 4, with 1522cm-1Blue-green alge Characteristic Raman is imaged in neighbouring feature frequency displacement, as shown in figure 5, due to
There are certain error of fitting in imaging process, there are biggish errors for frequency displacement Raman image, i.e., the place of frustule does not go out
Feature frequency shift information is showed.The theoretically not no region of frustule, frustule Raman signatures peak area is zero.Therefore, it is based on algae
Cell imaging (Fig. 4) and frequency displacement imaging (Fig. 5), consider peak error of fitting, generate error frequency displacement characteristic peak area it is smaller or
It is very big, it is based on Matlab platform, primitive character frequency displacement imaging (Fig. 6) is pre-processed, obtains relatively clear frustule frequency
It moves into as (Fig. 7).
Based on the Raman image figure of large area splicing frustule, respectively in four angle imaging regions of sensing chamber
Frustule raman characteristic peak frequency displacement carries out clustering, realizes that frustule counts respectively, finds out four region frustule number mean values,
9 times of this mean value are the quantity of frustule in the sensing chamber, and then realize that frustule counts.
The identification of 5th step, algae
Firstly, the confocal Raman spectra database of different algae frustules is established, the confocal Raman light including different algaes
Spectrum, Raman signatures information of algae etc., in case the quick identification of algae；
By the confocal Raman spectra standard database of the different algae frustules, it is based on discriminate least-squares algorithm,
Establish algae discrimination model；
The result and algae discriminate Least square analysis model counted based on aforementioned frustule, with the institute in a certain cluster
Having spectroscopic data is forecast set, identifies to the cluster frustule, realizes the counting and automatic identification of frustule.
The frustule counting and algae discriminant analysis that Raman mapping and multivariate data excavate specifically are scanned based on line
Scheme is as shown in Figure 1, Fig. 3 indicates the foundation of algae discrimination model and the identification process figure of algae to be measured.
In conclusion a kind of frustule for scanning Raman mapping and multivariate data excavation based on line provided by the invention
It counts and algae discriminant analysis method is, it can be achieved that the quick discrimination of the counting of cell and algae is analyzed in algae solution, it is easy to accomplish algae
The accurate counting of cell and the simultaneously and rapidly identification of algae.
Finally, it is stated that preferred embodiment above is only used to illustrate the technical scheme of the present invention and not to limit it, although logical
It crosses above preferred embodiment the present invention is described in detail, however, those skilled in the art should understand that, can be
Various changes are made to it in form and in details, without departing from claims of the present invention limited range.
Claims (3)
1. the frustule based on line scanning Raman mapping counts and algae method of discrimination, which is characterized in that including walking as follows
It is rapid:
1) it prepares frustule detection tally: etching sensing chamber on a silicon substrate, the sensing chamber is 1mm*1mm*0.1mm's
The sensing chamber is divided into 3*3 small sensing chamber with lines in sensing chamber bottom by cuboid slot, and two in sensing chamber are right
Side etches sample inlet channel and sample export channel respectively again；
2) algae solution sample: being carried out sufficiently being diluted to frustule not overlapping by the pretreatment of frustule sample with ultrapure water,
Frustule detects covered on tally, and the sample after dilution is added to the sensing chamber of tally from sample inlet channel
In, extra sample is then flowed out from sample export channel, and frustule detection tally is placed on horizontal station, is stood
It is adsorbed on sensing chamber bottom, nonvoluntary travelling as much as possible to frustule；
3) frustule is imaged: selection 532nm laser, 50 times/100 times telephoto lens, the confocal drawing of large area based on line scanning
Graceful imaging joint carries out quick Overlap-scanning to the sample in frustule tally, wherein the splicing of sample room large area imaging,
Automated imaging is moved with the feature peak height of frustule, peak area, peak height or peak area ratio and characteristic peak frequency to obtain scanning based on line
Large area splicing frustule Raman image figure；
4) frustule based on frustule Raman image counts: the Raman image of the large area splicing frustule based on line scanning
Figure carries out clustering to the frustule Raman signatures information in four angle imaging regions of sensing chamber respectively, realizes frustule
It counts respectively, finds out four region frustule number mean values, 9 times of this mean value are the quantity of frustule in the sensing chamber, in turn
Realize that frustule counts, the frustule Raman signatures information includes feature peak height, peak area or ratio, peak frequency displacement；
5) identification of algae:
Firstly, the confocal Raman spectra standard database of different algae frustules is established, the confocal Raman light including different algaes
Spectrum, the Raman signatures information of algae；By the confocal Raman spectra standard database of the different algae frustules, it is based on discriminate
Least-squares algorithm, support vector classification or artificial neural network establish algae discriminant analysis model；
Based on step 4) frustule count as a result, by it is a certain cluster sample characteristic information be averaged, as this kind of samples
This center, from the confocal Raman spectra standard database of the different algae frustules scan for comparing in turn to algae into
Row identification；Alternatively, using all spectroscopic datas in a certain cluster as forecast set, the algae discriminant analysis mould based on aforementioned foundation
Type identifies the cluster frustule.
2. the frustule according to claim 1 based on line scanning Raman mapping counts and algae method of discrimination, special
Sign is, step 3) is apparent in order to make frustule imaging, is filtered to frustule image and the pretreatment of baseline correction,
The final Raman image figure for obtaining the large area splicing frustule scanned based on line.
3. the frustule according to claim 2 based on line scanning Raman mapping counts and algae method of discrimination, special
Sign is, the preprocessing process of filtering and baseline correction described in step 3) is carried out according to two methods, and a kind of method is with Raman
Based on image, handled using two-dimensional filtering and baseline correction algorithm；Another method is with the single drawing in imaging
It based on graceful spectrum, is handled using one-dimensional filtering and baseline correction algorithm, is filtered into wavelet filtering, smothing filtering；Baseline
Correction is selected as wavelet transformation, asymmetric least square, smoothing spline fitting or is based on genetic groups baseline correction algorithm.
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| CN107941783A (en) * | 2017-12-14 | 2018-04-20 | 中国科学院重庆绿色智能技术研究院 | A kind of water environment based on the scattering of frustule Characteristic Raman disturbs appraisal procedure |
| CN108627494B (en) * | 2018-05-09 | 2020-11-10 | 吉林大学 | System for rapid two-dimensional Raman spectrum scanning imaging |
| CN108802065A (en) * | 2018-05-30 | 2018-11-13 | 大连理工大学 | The test device and method of algae and bacterial content in a kind of fast slowdown monitoring water |
| CN109949284A (en) * | 2019-03-12 | 2019-06-28 | 天津瑟威兰斯科技有限公司 | Deep learning convolution neural network-based algae cell analysis method and system |
| CN109991208B (en) * | 2019-05-05 | 2021-06-15 | 中国科学院重庆绿色智能技术研究院 | A method for studying the mechanism of competitive allelopathic action of blue-green algae populations based on surface-enhanced Raman scattering spectroscopy |
| CN118711177B (en) * | 2024-08-27 | 2024-11-08 | 浙江大学 | A cell counting method based on identification of individual differences of microalgae cells |
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