CN113640245A - Method for detecting beta-carotene, astaxanthin and starch in haematococcus pluvialis under nitrogen stress - Google Patents
Method for detecting beta-carotene, astaxanthin and starch in haematococcus pluvialis under nitrogen stress Download PDFInfo
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- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3581—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using far infrared light; using Terahertz radiation
- G01N21/3586—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using far infrared light; using Terahertz radiation by Terahertz time domain spectroscopy [THz-TDS]
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Abstract
The invention discloses a method for detecting beta-carotene, astaxanthin and starch in haematococcus pluvialis under nitrogen stress, which comprises the following steps: s1, obtaining terahertz and far infrared band spectrograms under the haematococcus pluvialis stress environment by using a Fourier infrared spectrometer; s2, obtaining spectrums of the beta-carotene, the astaxanthin and the starch standard substance by using a Fourier infrared spectrometer, and comparing the spectrums with each other in the step S1 to obtain light of haematococcus pluvialis in a stress environment; s3, collecting content change information of different substances in haematococcus pluvialis under nitrogen stress environment in multiple rounds to prepare a plurality of standard sample books; s4, respectively detecting a spectrogram of haematococcus pluvialis under nitrogen stress and a spectrogram of a metabolite of haematococcus pluvialis under nitrogen stress by using a standard sample; and S5, establishing a correlation model based on the absorbance values at the characteristic peak positions and the corresponding substance components. According to the invention, the problem that the pretreatment by physical and chemical methods is destructive at present is solved, and the problem that the physical and chemical methods are difficult to realize the synchronous real-time monitoring of various metabolites is solved.
Description
Technical Field
The invention relates to the technical field of far infrared spectrums of terahertz wave bands, in particular to a method for detecting beta-carotene, astaxanthin and starch in haematococcus pluvialis under nitrogen stress.
Background
Haematococcus pluvialis (Haematococcus pluvialis) is a freshwater unicellular green alga of Chlorophyta, Chlorophyceae, Volvocales, Haematococcus, and has the highest natural astaxanthin content in nature. The cell can rapidly accumulate ketocarotenoid astaxanthin (3, 3-dihydroxy-beta, beta-carotene-4, 4-dione) in adverse environments such as nitrogen deficiency and high illumination. The natural astaxanthin has strong antioxidation, and can protect microalgae cells from photooxidation damage by eliminating active oxygen. Almost all carotenoids have antioxidant properties from conjugated double bonds, but the antioxidant activity of natural astaxanthin is superior to other carotenoids. Natural astaxanthin is currently approved by regulatory agencies in the united states, china, japan and some european countries for use in aquaculture feed, as well as for use in dietary supplements, cosmetic ingredients, and the like. For example, astaxanthin as a feed additive in the aquaculture industry not only increases the color of aquatic animals, but also increases the quality of fish eggs; astaxanthin can be added into cosmetics to effectively delay skin aging; in clinical medicine, astaxanthin can effectively promote cancer cell apoptosis and inhibit inflammation caused by cardiovascular diseases, and has good prevention effect on eye diseases caused by diabetes. Compared with natural astaxanthin, the artificial astaxanthin has obvious defects in the aspects of structure, function, application, safety and the like, and the production of the natural astaxanthin is prone to be extracted from biological sources. Therefore, the use of Haematococcus pluvialis as an organism with the highest content of natural astaxanthin for extracting astaxanthin has become a hot spot of astaxanthin research at home and abroad.
In addition to the change in cell morphology, such as green flagellated cells turning into red macrocysts, a large number of metabolic components are also changed during the process of astaxanthin accumulation by Haematococcus pluvialis. As shown in fig. 1, the metabolic process of astaxanthin synthesis by haematococcus pluvialis involves the synthesis and decomposition of many substances, such as beta-carotene, chlorophyll, lipids, proteins, starch, etc. Wherein, starch is the main form of plant carbohydrate storage, chlorophyll is the main participant of microalgae photosynthesis, has the capacity of transmitting and converting light energy, and the content change can directly reflect the photosynthetic efficiency and physiological activities of microalgae. Beta-carotene, as a precursor for the synthesis of astaxanthin, is also closely related to the synthesis of astaxanthin in Haematococcus pluvialis. Therefore, the research on the metabolic components in the astaxanthin accumulation process of haematococcus pluvialis, such as beta-carotene, starch and the like, has important significance for the control of the synthetic mechanism of natural astaxanthin and industrial large-scale production.
Far infrared light has strong penetrating power and radiation power, has obvious temperature control effect and resonance effect, and is easy to be absorbed by an object and converted into internal energy of the object. After the far infrared light is absorbed by human body, the water molecules in the body can produce resonance, so that the water molecules are activated, the intermolecular binding force of the water molecules is enhanced, and therefore, biological macromolecules such as protein and the like are activated, and the biological cells are at the highest vibration energy level.
Terahertz waves are a general term for electromagnetic radiation of a specific waveband, the frequency range of the terahertz waves is 0.1-10Hz, and the terahertz waves have the characteristics of instantaneity, coherence, low energy type, perspective and water absorption. Usually, the vibration absorption frequency of chemical bonds in biomolecules is mainly in an infrared band, but weak interaction among molecules, such as ammonia bonds, rotation of molecules, phonon vibration of crystals and the like, corresponds to a terahertz band, so that the terahertz technology can be used for researching the structure, configuration and other problems of biomolecules.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method for detecting beta-carotene, astaxanthin and starch in haematococcus pluvialis under nitrogen stress, which solves the problem that the existing method is destructive through physical and chemical pretreatment, and simultaneously solves the problem that the physical and chemical methods are difficult to realize synchronous real-time monitoring of various metabolites. To achieve the above objects and other advantages in accordance with the present invention, there is provided a method for detecting beta-carotene, astaxanthin, starch in haematococcus pluvialis under nitrogen stress, comprising the steps of:
s1, obtaining terahertz and far infrared band spectrograms of haematococcus pluvialis in a stress environment by using a Fourier infrared spectrometer;
s2, obtaining spectrums of the beta-carotene, the astaxanthin and the starch standard substance by utilizing a Fourier infrared spectrometer, comparing the spectrums of the haematococcus pluvialis in the stressed environment obtained in the step S1, and finding out absorption peak frequencies corresponding to the two standard substances and the haematococcus pluvialis in the stressed environment;
s3, collecting content change information of different substances in haematococcus pluvialis bodies under nitrogen stress environment (0, 2, 4, 6, 8, 10 and 12 days) in multiple rounds to prepare a plurality of standard sample books;
s4, detecting the spectrogram of haematococcus pluvialis under nitrogen stress and the spectrogram of metabolic products beta-carotene, astaxanthin and starch of the haematococcus pluvialis under nitrogen stress by using the standard sample in the step S3 respectively;
s5, establishing a correlation model between the absorbance values of 17.32THz of beta-carotene, 8.69THz of astaxanthin and 16.22THz of starch and corresponding substance components based on the characteristic peak position, and establishing a metabolite prediction model based on the absorbance values of the characteristic peak interval by combining a partial least square method.
Preferably, in the step S2, when detecting the standard beta-carotene, astaxanthin and starch, 15mg of astaxanthin, 20mg of starch and 15mg of beta-carotene are weighed and tabletted for 2 minutes at 4.5T.
Preferably, in the fourier infrared spectrum detection in step S1, the light source is far infrared radiation of a high-pressure arc mercury lamp, the background and the number of times of scanning the sample are both 64 times, and the resolution is 4cm-1Sample scanning wavenumber range from 30cm-1To 680cm-1。
Preferably, the beta-carotene, astaxanthin, starch in said step S2 correspond to haematococcus pluvialis absorption peak frequencies of 17.32THz,8.69THz, and 16.22 THz.
Preferably, the obtained spectral data further comprises: the obtained spectrum is removed with corresponding thickness in the processing process, and then smoothing, baseline removal and second-order derivation processing are carried out, and the processing can correct baseline fluctuation and improve the signal-to-noise ratio.
Preferably, in the step S4, a Fourier infrared spectrometer is adopted to measure the haematococcus pluvialis metabolite standard, and the spectral range of the system is 30cm & lt-1 & gt-4000 cm & lt-1 & gt. Therefore, the terahertz/far infrared spectrum region effectively covers 30-680 cm & lt-1 & gt, and the signal-to-noise ratio (SNR) is superior to 10000: 1.
preferably, the accuracy of the prediction model obtained by using the metabolite at the characteristic peak position in the step S5 is 0.938, 0.985, 0.924; the absorbance value of a characteristic peak interval obtained by using a partial least square method to predict the model and the prediction models of metabolites beta-carotene, astaxanthin and starch, wherein the model precision is 0.991, 0.995 and 0.998 respectively.
Compared with the prior art, the invention has the beneficial effects that: the invention realizes a method for detecting beta-carotene, astaxanthin and starch in haematococcus pluvialis under nitrogen stress based on a far-infrared spectrum fingerprint technology of a terahertz waveband, and finds that the traditional detection method needs physical or chemical pretreatment, is destructive, is difficult to realize synchronous real-time monitoring of various metabolites, and can realize simultaneous detection of various metabolites by using the terahertz technology detection method. The data of the content of the metabolic products of haematococcus pluvialis can be obtained from taking a sample to spectral extraction and matching with software analysis for almost 20 minutes, and the complicated procedures are simplified.
Drawings
FIG. 1 is a diagram of a far infrared terahertz waveband spectrum of haematococcus pluvialis under nitrogen stress at different times according to the method for detecting beta-carotene, astaxanthin and starch in haematococcus pluvialis under nitrogen stress;
FIG. 2 is a chart of the spectrum of the far infrared terahertz waveband of Haematococcus pluvialis for detecting beta-carotene, astaxanthin and starch in the Haematococcus pluvialis under nitrogen stress according to the method of the present invention;
FIG. 3 is a chart of the spectrum of astaxanthin versus Haematococcus pluvialis far infrared terahertz band for the method of detecting beta-carotene, astaxanthin, starch in Haematococcus pluvialis under nitrogen stress according to the present invention;
FIG. 4 is a chart of a method for detecting beta-carotene, astaxanthin and starch in haematococcus pluvialis under nitrogen stress, wherein starch is compared with a far infrared terahertz waveband spectrum of haematococcus pluvialis.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method for detecting the terahertz spectrogram of haematococcus pluvialis under nitrogen stress and the metabolite standard products beta-carotene, astaxanthin and starch in the haematococcus pluvialis under nitrogen stress adopts a Brookvertex 70v Fourier infrared spectrometer, and the room temperature is kept at the external condition of 20 ℃. And (3) taking a uniform semitransparent algae membrane obtained by drying the haematococcus pluvialis under nitrogen stress and a round sample of a metabolic product standard substance of the haematococcus pluvialis under nitrogen stress for measurement. The infrared spectrometer adopts far infrared band with high-pressure arc mercury lamp as light source and resolution of 4cm-1Wave number selected from the range of 30cm-1To 680cm-1And selecting 64 times of collection, collecting for multiple times, performing trend removing and smoothing treatment by using matlab, and taking background reference by using an empty liquid pool before spectrum collection. Meanwhile, 15mg of astaxanthin, 20mg of starch and 15mg of beta-carotene are weighed by an analytical balance and put into an infrared tabletting grinding tool, tabletting is carried out for 2mins by using 4.5T pressure, after the tablet is taken, noise is removed by using air as a background, and spectrum collection of the standard products, astaxanthin, starch and beta-carotene is carried out by using the parameters of an infrared spectrometer as the basis. Because the infrared spectrometer adopted in the experiment is a vacuumizing light path, the empty light path is used for collecting the background single light beamThe spectrum, the background single beam spectrum which is deducted in this case, is mainly deducted from the influence of various factors of the optical path instrument. And obtaining a haematococcus pluvialis spectrum, comparing the beta-carotene spectrum with the haematococcus pluvialis spectrum to obtain a spectrum frequency of 17.32THz, wherein the spectrum frequency corresponding to the astaxanthin and the haematococcus pluvialis is 16.22THz, and the spectrum frequency corresponding to the starch and the haematococcus pluvialis is 8.69 THz.
Referring to fig. 1 to 4, a method for detecting beta-carotene, astaxanthin and starch in haematococcus pluvialis under nitrogen stress comprises the following steps: s1, obtaining terahertz and far infrared band spectrograms of haematococcus pluvialis in a stress environment by using a Fourier infrared spectrometer;
s2, obtaining spectrums of the beta-carotene, the astaxanthin and the starch standard substance by utilizing a Fourier infrared spectrometer, comparing the spectrums of the haematococcus pluvialis in the stressed environment obtained in the step S1, and finding out absorption peak frequencies corresponding to the two standard substances and the haematococcus pluvialis in the stressed environment;
s3, collecting content change information of different substances in haematococcus pluvialis bodies under nitrogen stress environment (0, 2, 4, 6, 8, 10 and 12 days) in multiple rounds to prepare a plurality of standard sample books;
s4, detecting the spectrogram of haematococcus pluvialis under nitrogen stress and the spectrogram of metabolic products beta-carotene, astaxanthin and starch of the haematococcus pluvialis under nitrogen stress by using the standard sample in the step S3 respectively;
s5, establishing a correlation model between the absorbance values of 17.32THz of beta-carotene, 8.69THz of astaxanthin and 16.22THz of starch and corresponding substance components based on the characteristic peak position, and establishing a metabolite prediction model based on the absorbance values of the characteristic peak interval by combining a partial least square method.
Further, in the step S2, when detecting the standard β -carotene, astaxanthin, and starch, 15mg of astaxanthin, 20mg of starch, and 15mg of β -carotene were weighed and tabletted at 4.5T for 2 minutes.
Further, in the fourier infrared spectrum detection in step S1, the light source is far infrared radiation of a high-pressure arc mercury lamp, the background and the number of times of scanning the sample are both 64 times, and the resolution is4cm-1Sample scanning wavenumber range from 30cm-1To 680cm-1。
Further, in the step S2, the β -carotene, astaxanthin, and starch correspond to haematococcus pluvialis absorption peak frequencies of 17.32THz,8.69THz, and 16.22 THz.
Further, the obtained spectrum data further comprises: the obtained spectrum is removed with corresponding thickness in the processing process, and then smoothing, baseline removal and second-order derivation processing are carried out, and the processing can correct baseline fluctuation and improve the signal-to-noise ratio.
Further, in the step S4, a Fourier infrared spectrometer is adopted to measure the haematococcus pluvialis metabolite standard substance, and the spectral range of the system is 30cm & lt-1 & gt-4000 cm & lt-1 & gt. Therefore, the terahertz/far infrared spectrum region effectively covers 30-680 cm & lt-1 & gt, and the signal-to-noise ratio (SNR) is superior to 10000: 1.
further, the accuracy of the prediction model obtained by using the metabolite at the characteristic peak position in the step S5 is 0.938, 0.985, 0.924; the absorbance value of a characteristic peak interval obtained by using a partial least square method to predict the model and the prediction models of metabolites beta-carotene, astaxanthin and starch, wherein the model precision is 0.991, 0.995 and 0.998 respectively.
Example 1
(1) Terahertz sample preparation
Preparing a pure sample: weighing standard astaxanthin 15mg, starch 20mg and beta-carotene 15mg, and respectively placing in a mold with diameter of 13mm for pressing. Two circular samples were prepared for each standard for terahertz standard spectroscopic testing. The spectrum collection of the terahertz sample is easily influenced by moisture, so that the microalgae sample under a certain concentration needs to be dried into a film sample to eliminate the influence of the moisture.
Preparing a microalgae sample: the algae solution was centrifuged repeatedly and washed with ultrapure water to obtain 100mg of algae paste, and distilled water was added thereto to 1 ml. And (3) putting each 1ml of algae liquid sample into a smooth polystyrene mold with the diameter of 20mm for drying, arranging a drying box for drying for 3 hours at the temperature of 30-40 ℃, finally preparing a uniform semi-permeable algae membrane, and measuring the thickness of each algae membrane sample by using a micrometer. The thickness of the film is in the range of 40 + -5 μm, which allows better spectra to be obtained.
(2) Terahertz spectrum collection
And performing terahertz measurement on the algae membrane and the standard sample by using a Fourier transform spectrometer to obtain an absorption spectrum in a range of 3.0-20.0 THz. The haematococcus pluvialis is subjected to stress experiments for 12 days continuously, data are collected every other day, and three rounds of experiments are carried out. 4 phycomembrane samples were prepared at each time point of the round, and 12 THz absorption spectra at different positions of each sample were averaged as data for the sample, i.e. 4 sample data were taken at each time point. Samples were taken on days 0, 2, 4, 6, 8, 10, and 12, for a total of 84 samples in three runs.
After data is obtained, due to the influence of various factors, the data needs to be preprocessed to obtain the finally usable data, the corresponding thickness of the obtained spectrum is removed in the processing process, and then smoothing, baseline removal and second-order derivation processing are carried out, so that baseline fluctuation can be corrected and the signal-to-noise ratio can be improved.
And (3) processing the spectral information, namely using Origin software to make a metabolite prediction model of the characteristic peak from a base line and establishing a metabolite prediction model of a characteristic peak interval by adopting a small-to-small two-multiplication method.
Predictive model for metabolites of characteristic peaks: through analysis of the haematococcus pluvialis absorption spectrum, the astaxanthin, the starch and the beta-carotene have better independence respectively at 8.69THz, 16.22THz and 17.32THz, and other components have smaller influence. According to beer's law, under general conditions, the absorbance value of the corresponding peak position of the algae spectrum and the concentration of the substance are linearly increased. A correlation model between these three peak absorbances and the amounts of the components measured by conventional chemical methods was therefore established. The results of prediction of the astaxanthin model in Haematococcus pluvialis are shown in FIG. 4, the results of prediction of β -carotene are shown in FIG. 4, and the results of prediction of starch are shown in FIG. 4. Wherein the correlation coefficient Rpre of the beta-carotene prediction model is 0.938, and the RMSE is 0.043; the prediction correlation coefficient Rpre of the astaxanthin is 0.985, and the RMSE is 0.042; the correlation coefficient Rpre for the starch model was 0.924 and the RMSE was 0.045. The results show that the predicted correlation coefficients of the three metabolites are all greater than 0.92 based on the characteristic peak absorbance values, indicating that the absorbance values of the above peak positions of beta-carotene (17.32THz), astaxanthin (8.69THz) and starch (16.22THz) have a better correlation with the component contents.
A metabolite prediction model of a characteristic peak interval is established by adopting a small-to-small multiplication method: in order to further improve the detection precision, an interval partial least square regression prediction model is established. And taking the three characteristic peaks determined in the linear regression model as central points, and taking the minimum value point which is closest to the left and right of each central point as a boundary. The selected beta-carotene interval is 17.11-17.57 THz, the astaxanthin interval is 8.49-8.90 THz, and the starch interval is 16.07-16.47 THz. The PLS model established from the spectral interval and the prediction results are shown in table 4, the correlation coefficients Rpre for the three substance models are all greater than 0.99, and the RMSEP values are 0.061, 0.031, and 0.040, respectively. Wherein cross validation of the beta-carotene, astaxanthin and starch models showed higher statistical accuracy. The RPD values are all larger than 4, which shows that the prediction accuracy of the model is higher.
The model precision obtained by using the characteristic peak position metabolite prediction model is 0.938, 0.985 and 0.924; the absorbance value of a characteristic peak interval obtained by using a partial least square method to predict the model and the prediction models of metabolites beta-carotene, astaxanthin and starch, wherein the model precision is 0.991, 0.995 and 0.998 respectively.
The number of devices and the scale of the processes described herein are intended to simplify the description of the invention, and applications, modifications and variations of the invention will be apparent to those skilled in the art. While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (7)
1. A method for detecting beta-carotene, astaxanthin and starch in haematococcus pluvialis under nitrogen stress is characterized by comprising the following steps:
s1, obtaining terahertz and far infrared band spectrograms of haematococcus pluvialis in a stress environment by using a Fourier infrared spectrometer;
s2, obtaining spectrums of the beta-carotene, the astaxanthin and the starch standard substance by utilizing a Fourier infrared spectrometer, comparing the spectrums of the haematococcus pluvialis in the stressed environment obtained in the step S1, and finding out absorption peak frequencies corresponding to the two standard substances and the haematococcus pluvialis in the stressed environment;
s3, collecting content change information of different substances in haematococcus pluvialis bodies under nitrogen stress environment (0, 2, 4, 6, 8, 10 and 12 days) in multiple rounds to prepare a plurality of standard sample books;
s4, detecting the spectrogram of haematococcus pluvialis under nitrogen stress and the spectrogram of metabolic products beta-carotene, astaxanthin and starch of the haematococcus pluvialis under nitrogen stress by using the standard sample in the step S3 respectively;
s5, establishing a correlation model between the absorbance values of 17.32THz of beta-carotene, 8.69THz of astaxanthin and 16.22THz of starch and corresponding substance components based on the characteristic peak position, and establishing a metabolite prediction model based on the absorbance values of the characteristic peak interval by combining a partial least square method.
2. The method for detecting beta-carotene, astaxanthin and starch in haematococcus pluvialis under nitrogen stress according to claim 1, wherein in the step S2, 15mg of astaxanthin, 20mg of starch and 15mg of beta-carotene are weighed and tabletted for 2 minutes at 4.5T in the detection of the beta-carotene, astaxanthin and starch standards.
3. The method for detecting beta-carotene, astaxanthin and starch in haematococcus pluvialis under nitrogen stress according to claim 1, wherein in the Fourier infrared spectrum detection in the step S1, a light source is used for far infrared irradiation of a high-pressure arc mercury lamp, the background and the sample are scanned 64 times, and the resolution is 4cm-1Sample scanning wavenumber range from 30cm-1To 680cm-1。
4. The method for detecting β -carotene, astaxanthin and starch in Haematococcus pluvialis under nitrogen stress according to claim 1, wherein the β -carotene, astaxanthin and starch in step S2 correspond to Haematococcus pluvialis absorption peak frequencies of 17.32THz,8.69THz and 16.22 THz.
5. The method of claim 1, wherein the spectral data obtained by the method of detecting β -carotene, astaxanthin, starch in Haematococcus pluvialis under nitrogen stress further comprises: the obtained spectrum is removed with corresponding thickness in the processing process, and then smoothing, baseline removal and second-order derivation processing are carried out, and the processing can correct baseline fluctuation and improve the signal-to-noise ratio.
6. The method for detecting beta-carotene, astaxanthin and starch in haematococcus pluvialis under nitrogen stress according to claim 1, wherein in the step S4, a Fourier infrared spectrometer is adopted to measure a haematococcus pluvialis metabolite standard substance, and the spectral range of the system is 30cm < -1 > to 4000cm < -1 >. Therefore, the terahertz/far infrared spectrum region effectively covers 30-680 cm & lt-1 & gt, and the signal-to-noise ratio (SNR) is superior to 10000: 1.
7. the method for detecting beta-carotene, astaxanthin and starch in haematococcus pluvialis under nitrogen stress according to claim 1, wherein the accuracy of the prediction model obtained by using the characteristic peak position metabolites in the step S5 is 0.938, 0.985 and 0.924; the absorbance value of a characteristic peak interval obtained by using a partial least square method to predict the model and the prediction models of metabolites beta-carotene, astaxanthin and starch, wherein the model precision is 0.991, 0.995 and 0.998 respectively.
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| CN106018320A (en) * | 2015-10-26 | 2016-10-12 | 沈阳农业大学 | Carotenoid detection method based on near infrared spectroscopy analysis |
| CN111474133A (en) * | 2020-03-31 | 2020-07-31 | 上海理工大学 | Method for detecting two hormones of progesterone and estrone in placenta extract |
| CN112179867A (en) * | 2020-09-21 | 2021-01-05 | 上海理工大学 | Method for measuring grease in microalgae cells based on terahertz spectrum technology |
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| CN106018320A (en) * | 2015-10-26 | 2016-10-12 | 沈阳农业大学 | Carotenoid detection method based on near infrared spectroscopy analysis |
| CN111474133A (en) * | 2020-03-31 | 2020-07-31 | 上海理工大学 | Method for detecting two hormones of progesterone and estrone in placenta extract |
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| CN114414519B (en) * | 2022-01-24 | 2023-07-25 | 上海理工大学 | Method for detecting type and concentration of heavy metal in water body |
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