CN104634771A - Monitoring method for change of nitrogen stress of characteristic peaks of microalgal oil with time - Google Patents
Monitoring method for change of nitrogen stress of characteristic peaks of microalgal oil with time Download PDFInfo
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- CN104634771A CN104634771A CN201510043140.1A CN201510043140A CN104634771A CN 104634771 A CN104634771 A CN 104634771A CN 201510043140 A CN201510043140 A CN 201510043140A CN 104634771 A CN104634771 A CN 104634771A
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Abstract
The invention discloses a monitoring method for the change of nitrogen stress of the characteristic peaks of microalgal oil with the time. The monitoring method comprises the following steps: (1) adopting a Raman spectrometer and acquiring Raman-spectrum original information of chlorella pyrenoidosa under the same nitrogen-nutrition environment at different periods; (2) preprocessing the obtained Raman-spectrum original information, obtaining a preprocessed spectrogram and extracting Raman intensity values corresponding to the microalgal characteristic peaks in the spectrogram; (3) adopting the Raman intensity values as input, adopting different periods of the same nitrogen-nutrition environment as output, and establishing a judgment model based on a multiple regression algorithm; and (4) taking live algae liquid, obtaining a corresponding Raman intensity value by processing in the step (1) and the step (2), inputting the judgment model and obtaining a result of the change of the nitrogen stress of the algae liquid to be monitored with the time. The monitoring method disclosed by the invention has the advantage that the problems that dyeing or complex chemical treatment needs to be carried out on the sample, the operation is relatively tedious and consumes time and labor in the existing detection method are solved.
Description
Technical field
The present invention relates to micro algae growth environmental monitoring technology field, particularly relate to the time dependent monitoring method of a kind of N stress based on microalgae grease characteristic peak.
Background technology
The bioactive molecule of micro-algae to be a class can be by carbon dioxide conversion potential bio-fuel, food, feed and high value also can carry out photosynthetic eukaryotic microorganisms.Micro-algae has ecology and biological value, is a kind of important biomass resource.
Chlorella is a class monoplast green alga, belongs to Chlorophyta, Chlorophyceae (Chlorophyceae), Chlorococcale, Ruan Nang algae section, Chlorella, is distributed widely in nature, most species in freshwater.The known chlorella of current global range has about 15 kinds, and has the mutation of nearly more than hundred kinds.Chlorella cells shape is generally spherical or elliposoidal, diameter 2-12 μm.There are some researches show, chlorella is containing rich in protein, lipid, polysaccharide, dietary fibre, vitamin, trace element and active metabolite.In recent years, China has started the exploitation paying attention to chlorella.In sum, chlorella has important economy and scientific research value, has broad application prospects.
Raman spectrum is a kind of scattering spectrum, it is a kind of spectrographic technique of research molecular vibration, its principle is different from infrared spectrum with mechanism, and infrared spectrum has very strong Detection capability to polar group, and non-polar group such as C=C, C-C etc. then have very strong Raman active.But the structural information that they provide is similar, all about the various molecular vibrational frequency of intramolecule and the situation about vibrational energy level, so the difference on sample chemical composition and molecular structure can be reflected from molecular level, realize " fingerprint verification " of some chemical bond and functional group in molecule.The Raman scattering of water is very faint in addition produces undesired signal hardly, makes the Non-Destructive Testing of the living body biological of Raman in research aqueous solution has the incomparable advantage of other molecular spectrums.
Summary of the invention
The invention provides the time dependent monitoring method of a kind of N stress based on microalgae grease characteristic peak, solving existing detection method needs to dye to sample or the chemical treatment of complexity, operates problem that is relatively loaded down with trivial details, consuming time, effort.Raman spectrum strength value easily passes generation decomposition etc. in time impact by micro-algae Different growth phases, different exposure time and pigment is overcome by carrying out pre-service to the Raman signal collected.
The time dependent monitoring method of N stress based on microalgae grease characteristic peak, comprises the following steps:
(1) adopt Raman spectrometer, obtain the Raman spectrum raw information of chlorella pyrenoidosa sample different times under same nitrogen nutrition environment;
(2) pre-service is carried out to the Raman spectrum raw information obtained in step (1) and obtain pre-service spectrogram, extract the raman scattering intensity value that in spectrogram, multiple micro-algae characteristic peak is corresponding;
(3) using the raman scattering intensity value in step (2) as input, the different times of same nitrogen nutrition environment, as output, sets up the discrimination model based on multivariate regression algorithm;
(4) live body algae liquid to be monitored is got, obtain the raman scattering intensity value at the characteristic peak place of this live body algae liquid to be monitored by the process of step (1) and step (2) and discrimination model described in input, obtain algae liquid nitrogen to be monitored and coerce time dependent result.
In the present invention, Raman spectrometer specifically selects Reinshaw microscopic confocal Raman spectrometer, when carrying out information acquisition to sample, all carries out under constant temperature (about 25 DEG C) condition.
In step (1), by the algae drop that makes on microslide, flatten (avoiding producing bubble) with cover glass, then to be fixed on below micro-Raman spectroscopy object lens on objective table, utilize the laser beam that laser intensity is 1mv, and focused on the surface of sample by the object lens of 50X, time shutter 1s, obtain described Raman spectrum raw information.Meanwhile, considering that object of classification is the micro-algae of live body, because the problems such as sample drift or sampled point easily move easily appear in the micro-algae sample of live body when gathering, agar need be adopted to be fixed to the algae liquid on microslide.
In step (2), described pre-service be carry out successively smoothing processing, baseline correction and normalized.
Because original Raman is larger by fluorescence interference, the generation of fluorescence can cover the signal of Raman, therefore first adopt method that is level and smooth and baseline correction to remove the interference of fluorescence, highlight signal, and smoothly and baseline correction process all based on the software WIRE3.3 that Raman spectrometer is subsidiary.Normalized, mainly passing to eliminate micro-algae Different growth phases, different exposure time and pigment the impact producing decomposition etc. in time, adopting software unscrambler 9.7 to realize.
In described step (3), described multivariate regression algorithm is partial least-squares regression method algorithm, principal component regression algorithm, the Stepwise Regression Algorithm, artificial neural network algorithm, algorithm of support vector machine or LDA model, and preferred multivariate regression algorithm is LDA model.The basic thought of LDA discriminatory analysis is that the pattern sample of higher-dimension is projected to best discriminant technique vector space, to reach the effect extracting classified information and compressive features space dimensionality, after projection, Assured Mode sample has maximum between class distance and minimum inter-object distance in new subspace, and namely pattern has best separability within this space.Therefore, it is a kind of effective Feature Extraction Method.
In the present invention, described live body algae fluid samples is chlorella pyrenoidosa.Because this algae kind fat content is higher, the impact easily by environment nitrogen accumulates certain lubricant component, observes under the Individual Size of algae is adapted at microscopic Raman simultaneously.
In described step (3), described micro-algae characteristic peak refers to 1440cm
-1, 1301cm
-1and 1270cm
-1the grease peak at place.
Compared with prior art, beneficial effect of the present invention is:
Present invention achieves the time dependent monitoring method of N stress based on microalgae grease characteristic peak, do not need to prepare any solution and chemical assay, enormously simplify operation steps, shorten detection time, it also avoid because operating personnel operate the consequences such as measurement result that unskilled or subjective factor brings is inaccurate.Raman spectrum strength value easily passes generation decomposition etc. in time impact by micro-algae Different growth phases, different exposure time and pigment is overcome by being normalized Raman signal.
Accompanying drawing explanation
Fig. 1 is the original Raman spectrogram of chlorella pyrenoidosa sample different times under N stress.
Fig. 2 is the Raman spectrogram of different times under N stress after chlorella pyrenoidosa sample preprocessing.
Embodiment
The present invention is explained further below in conjunction with specific embodiment.
Get the chlorella pyrenoidosa sample under N stress, adopt Reinshaw microscopic confocal Raman spectrometer (inVia – Reflex 532/XYZ), obtain the Raman spectrum raw information of live body algae fluid samples.The algae drop being about to make, on microslide, flattens (avoid produce bubble) with cover glass, and adopts agar to be fixed to algae liquid, to be then fixed on below micro-Raman spectroscopy object lens on objective table.Wherein the time shutter is set to 1s, and laser intensity is 1mv, and cumulative number once.Whole experimentation all carries out under constant temperature (about 25 DEG C) condition.The original Raman line of (1 day, 4 days, 7 days) chlorella pyrenoidosa sample under adopting above-mentioned method to gather N stress respectively, as shown in Figure 1.
Because original Raman spectrogram is comparatively large by fluorescence interference, the generation of fluorescence can cover the signal of Raman, therefore first adopts method that is level and smooth and baseline correction to remove the interference of fluorescence, highlights signal.These two kinds of pretreated processes all realize in software WIRE3.3, then adopt normalized easily to pass to overcome raman spectrum strength value the impact producing decomposition etc. in time by micro-algae Different growth phases, different exposure time and pigment, realized by software unscrambler 9.7.Fig. 2 is the Raman spectrogram of chlorella pyrenoidosa sample after pre-service.
Linear discriminant analysis (LDA) is a kind of supervised subspace learning technology, is the conventional statistics instrument of feature extraction and classification, is now widely used in computer vision, pattern-recognition and machine learning.LDA seeks a kind of linear transformation, makes to maximize with covariance in class between class after the conversion, and finds differentiation conversion (matrix) distance by maximizing between class distance and minimizing in class.The Raman spectrum of different times chlorella pyrenoidosa under this patent collection N stress, by extracting raman scattering intensity value corresponding to grease characteristic peak as input, sets up the time dependent discrimination model of N stress in conjunction with LDA.
Above-mentioned pre-service is carried out to 60 algae fluid samples, then adopts LDA to set up the discrimination model of different times, wherein by after N stress the 1st day, within the 4th day and the 7th day, be demarcated as " 1 ", " 2 " and " 3 " respectively.Each 45 samples of the above-mentioned different times of random selecting are used for modeling, and 15 samples are used for prediction.Pretreated Raman spectrum is extracted it at 1440cm
-1, 1301cm
-1and 1270cm
-1the raman scattering intensity value that the grease peak located is corresponding, using them as input variable, need the growth period judged as output, the differentiation rate obtaining model is 100%.
Each 5 forecast samples of different times, for each forecast sample, Reinshaw microscopic confocal Raman spectrometer is adopted to obtain the Raman spectrum raw information of each sample, and to Raman spectrum raw information successively smoothing process, baseline correction and normalized, obtain corresponding pre-service spectrogram, then extract 1440cm
-1, 1301cm
-1and 1270cm
-1the raman scattering intensity value at grease peak place, is inputted LDA model, and the precision obtaining forecast sample is 100%.
Claims (8)
1., based on the time dependent monitoring method of N stress of microalgae grease characteristic peak, it is characterized in that, comprise the following steps:
(1) adopt Raman spectrometer, obtain the Raman spectrum raw information of chlorella pyrenoidosa sample different times under same nitrogen nutrition environment;
(2) pre-service is carried out to the Raman spectrum raw information obtained in step (1) and obtain pre-service spectrogram, extract the raman scattering intensity value that in spectrogram, multiple micro-algae characteristic peak is corresponding;
(3) using the raman scattering intensity value in step (2) as input, the different times of same nitrogen nutrition environment, as output, sets up the discrimination model based on multivariate regression algorithm;
(4) live body algae liquid to be monitored is got, obtain the raman scattering intensity value at the characteristic peak place of this live body algae liquid to be monitored by the process of step (1) and step (2) and discrimination model described in input, obtain algae liquid nitrogen to be monitored and coerce time dependent result.
2. as claimed in claim 1 based on the time dependent monitoring method of N stress of microalgae grease characteristic peak, it is characterized in that, in step (1), by the algae drop that makes on microslide, flatten with cover glass, to be then fixed on below micro-Raman spectroscopy object lens on objective table, utilize the laser beam that laser intensity is 1mv, and focused on the surface of sample by the object lens of 50X, time shutter 1s, obtain described Raman spectrum raw information.
3. as claimed in claim 2 based on the time dependent monitoring method of N stress of microalgae grease characteristic peak, it is characterized in that, adopt agar to be fixed to the algae liquid on microslide.
4., as claimed in claim 1 based on the time dependent monitoring method of N stress of microalgae grease characteristic peak, it is characterized in that, in step (2), described pre-service be carry out successively smoothing processing, baseline correction and normalized.
5. as claimed in claim 1 based on the time dependent monitoring method of N stress of microalgae grease characteristic peak, it is characterized in that, in described step (3), described multivariate regression algorithm is partial least-squares regression method algorithm, principal component regression algorithm, the Stepwise Regression Algorithm, artificial neural network algorithm, algorithm of support vector machine or LDA model.
6. as claimed in claim 5 based on the time dependent monitoring method of N stress of microalgae grease characteristic peak, it is characterized in that, described multivariate regression algorithm adopts LDA model.
7., as claimed in claim 1 based on the time dependent monitoring method of N stress of microalgae grease characteristic peak, it is characterized in that, described live body algae fluid samples is chlorella pyrenoidosa.
8., as claimed in claim 7 based on the time dependent monitoring method of N stress of microalgae grease characteristic peak, it is characterized in that, in step (2), described micro-algae characteristic peak refers to 1440cm
-1, 1301cm
-1and 1270cm
-1the grease peak at place.
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| CN107132208A (en) * | 2017-04-10 | 2017-09-05 | 苏州贝康医疗器械有限公司 | A kind of cell culture fluid quality determining method based on raman spectroscopy measurement |
| 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 |
| CN109752351A (en) * | 2017-11-03 | 2019-05-14 | 中国科学院大连化学物理研究所 | A kind of adjusting method of the microalgae nitrogen nutrition coercing cultivation process based on feedback control |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107132208A (en) * | 2017-04-10 | 2017-09-05 | 苏州贝康医疗器械有限公司 | A kind of cell culture fluid quality determining method based on raman spectroscopy measurement |
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| CN107132208B (en) * | 2017-04-10 | 2018-11-30 | 苏州贝康医疗器械有限公司 | A kind of cell culture fluid quality determining method based on raman spectroscopy measurement |
| US11561182B2 (en) | 2017-04-10 | 2023-01-24 | Suzhou Basecare Medical Device Co., Ltd. | Method for detecting quality of cell culture fluid based on Raman spectral measurement |
| CN109752351A (en) * | 2017-11-03 | 2019-05-14 | 中国科学院大连化学物理研究所 | A kind of adjusting method of the microalgae nitrogen nutrition coercing cultivation process based on feedback control |
| CN109752351B (en) * | 2017-11-03 | 2021-03-30 | 中国科学院大连化学物理研究所 | Feedback control-based regulation method for microalgae nitrogen nutrition stress culture process |
| 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 |
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