CN112086137B - Method for quantitatively analyzing sorbose content in fermentation liquor - 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
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- 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
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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Abstract
The invention provides a method for quantitatively analyzing sorbose content in fermentation liquor, and relates to the technical field of quantitative analysis of sorbose content. The method for quantitatively analyzing the sorbose content in the fermentation liquor comprises the following specific steps: s1, firstly, establishing an orthogonal signal spectrum preprocessing algorithm, wherein the algorithm process is as follows: firstly, standardizing an original correction set spectrum matrix X and an information matrix Y to be detected; s2, calculating M ═ I-X ' Y (Y ' XX ' Y)－1Y' X, I is a unit matrix; s3, calculating that Z is equal to X; s4, singular value decomposition is carried out on ZZ', and the first p characteristic values g needing orthogonal processing are extractediAnd corresponding feature vector Ci(ii) a S5, calculating a weight vector wi＝MX’Cigi(ii) a S6, calculating a score vector ti＝Cigi. The near infrared spectrum of the sorbose content in the fermentation liquor is preprocessed by an orthogonal signal correction method, and then a correction model is established by a local weight regression method, so that the sorbose content in the fermentation liquor can be completely determined and analyzed, the working efficiency is greatly improved, and the method is worthy of wide popularization.
Description
Technical Field
The invention relates to the technical field of quantitative analysis of sorbose content, in particular to a method for quantitatively analyzing sorbose content in fermentation liquor.
Background
Vitamins are essential elements for human life activities, mainly participate in various chemical reactions of organisms in the form of coenzyme or prosthetic group, play an important role in medical treatment, such as vitamin B group is used for treating various inflammations such as neuritis, keratitis and the like, vitamin D is an important drug for treating Dong's rachitis and the like, the vitamins are also applied to animal husbandry and feed industry, chemical synthesis methods are mainly adopted for producing the vitamins, and then some microorganisms are found to complete some important steps in vitamin synthesis; on this basis, the semi-synthesis method combining chemical synthesis and biotransformation has been widely used in vitamin production, and vitamins which can be produced by fermentation or semi-synthesis at present include vitamins C, B2, B12, D, and β -carotene, vitamin C, a water-soluble vitamin, which is abundant in fresh vegetables and fruits, can be oxidized from D-sorbitol to L-sorbose using some strains of Gluconobacter suboxydans, Gluconobacter nigricans, and Acetobacter (commonly referred to as one-step fermentation method), and D-sorbitol used as a substrate is produced by D-glucose mediated reduction. L-sorbose is chemically oxidized to produce L-ascorbic acid, and L-keto-L-gulonic acid is first produced and then treated with acid to convert L-sorbose into vitamin C.
An important intermediate product in the production process of vitamin C is sorbose fermentation broth, which is the fermentation broth of sorbose serving as a main component generated after sorbitol is fermented by a strain, and the method is very important for accurately monitoring the change of each component in the fermentation and whether the fermentation is completely carried out in the fermentation process; in addition, the two methods are relatively complex in operation process and long in time consumption, so that a method for quickly, simply and conveniently detecting the sorbose fermentation liquor in real time on line is necessary to develop, and therefore, a new method for quantitatively analyzing the sorbose content in the fermentation liquor is developed.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a method for quantitatively analyzing the sorbose content in fermentation liquor, which solves the problems that the detection method mainly applied to the sorbose fermentation liquor at present is a titration method, a high performance liquid chromatography and the like; wherein the titration method uses toxic and harmful chemical reagents, and the two methods have the problems of complicated operation process and long time consumption.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: a method for quantitatively analyzing the sorbose content in fermentation liquor comprises the following specific steps:
s1, firstly, establishing an orthogonal signal spectrum preprocessing algorithm, wherein the algorithm process is as follows: firstly, standardizing an original correction set spectrum matrix X and an information matrix Y to be detected;
s2, calculating M ═ I-X ' Y (Y ' XX ' Y)-1Y' X, I is a unit matrix;
s3, calculating that Z is equal to X;
s4, singular value decomposition is carried out on ZZ', and the first p characteristic values g needing orthogonal processing are extractediAnd corresponding feature vector Ci；
S5, calculating a weight vector wi＝MX’Cigi；
S6, calculating a score vector ti＝Cigi；
S7, calculating a load vector pi＝X’ti(ti’ti)；
S8, subtracting the orthogonal signal in X: x0x＝X-∑tip’i；
S9, for the prediction vector XNavThe corrected spectrum is found from the weight w and the load p: t ═ X'Navw，XNav＝XNav-t′pThen, a local weight regression modeling method is established, and the loss function of the local weight regression modeling method is as follows:
where x is the independent variable and y is the dependent variable, finding the appropriate parameter θ to minimize the loss function, where ω isiThe expression of (a) is as follows:
in the above formula, x is the newly predicted sample feature data and is a vector, the parameter τ controls the rate of weight change, and the weight has a property, (1) if | xiX | ≈ 0, then ωi1 is approximately distributed; (2) if | xiX | ≈ infinity, then ωi≈0；
S10, introducing the established preprocessing and modeling algorithm into a near-infrared spectrometer, performing spectrum scanning and spectrum preprocessing through the near-infrared spectrometer, establishing a correction model by using a local weight regression method, and finally selecting an unknown sample for prediction to verify the prediction capability of the correction model.
Preferably, the near infrared spectrometer can adopt a GSA103 type near infrared spectrometer.
(III) advantageous effects
The invention provides a method for quantitatively analyzing sorbose content in fermentation liquor. The method has the following beneficial effects:
1. the method for quantitatively analyzing the sorbose content in the fermentation liquor does not need to use toxic and harmful chemical reagents through an orthogonal signal correction method, and is simple in the whole operation process, short in time consumption and high in detection efficiency.
2. According to the method for quantitatively analyzing the sorbose content in the fermentation liquor, the near infrared spectrum of the sorbose content in the fermentation liquor is preprocessed by using an orthogonal signal correction method, and then a correction model is established by using a local weight regression method, so that the sorbose content in the fermentation liquor can be completely measured and analyzed, the working efficiency is greatly improved, and the method is worthy of wide popularization.
Drawings
FIG. 1 is a schematic diagram of a spectrum structure of a sample of the present invention in which an orthogonal signal correction algorithm is used to preprocess an original spectrum;
FIG. 2 is a diagram of a calibration model for sorbose content in fermentation broth according to the present invention.
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 first embodiment is as follows:
as shown in fig. 1-2, an embodiment of the present invention provides a method for quantitatively analyzing sorbose content in a fermentation broth, including the following specific steps:
s1, firstly, establishing an orthogonal signal spectrum preprocessing algorithm, wherein the algorithm process is as follows: firstly, standardizing an original correction set spectrum matrix X and an information matrix Y to be detected;
s2, calculating M ═ I-X ' Y (Y ' XX ' Y)-1Y' X, I is a unit matrix;
s3, calculating that Z is equal to X;
s4, singular value decomposition is carried out on ZZ', and the first p characteristic values g needing orthogonal processing are extractediAnd corresponding feature vector Ci；
S5, calculating a weight vector wi＝MX’Cigi；
S6, calculating a score vector ti＝Cigi；
S7, calculating a load vector pi＝X’ti(ti’ti)；
S8, subtracting the orthogonal signal in X: x0x＝X-∑tip’i；
S9, for the prediction vector XNavThe corrected spectrum is found from the weight w and the load p: t ═ X'Navw，XNav＝XNav-t′pThen, a local weight regression modeling method is established, and the loss function of the local weight regression modeling method is as follows:
where x is the independent variable and y is the dependent variable, finding the appropriate parameter θ to minimize the loss function, where ω isiThe expression of (a) is as follows:
in the above formula, x is the newly predicted sample feature data and is a vector, the parameter τ controls the rate of weight change, and the weight has a property, (1) if | xiX | ≈ 0, then ωi1 is approximately distributed; (2) if | xiX | ≈ infinity, then ωi≈0；
S10, introducing the established preprocessing and modeling algorithm into a near-infrared spectrometer, performing spectrum scanning and spectrum preprocessing through the near-infrared spectrometer, establishing a correction model by using a local weight regression method, and finally selecting an unknown sample for prediction to verify the prediction capability of the correction model.
The near-infrared spectrometer can adopt a GSA103 type near-infrared spectrometer, and the GSA103 type near-infrared spectrometer can be used for quickly and effectively completing spectral scanning and spectral preprocessing on the sorbose content in the fermentation liquor, so that a data basis is provided for the subsequent establishment of a correction model.
Example two:
in this embodiment, a set of experiments is performed based on the first embodiment, and the experimental steps are as follows:
1. collecting samples: taking samples of fermentation time of 8 hours from 12 hours to 20 hours in the middle of the fermentation process, taking one sample every 10 minutes for the first 30 samples, taking one sample every 5 minutes for the second 30 samples, taking 60 samples in total, containing the samples in glass test tubes, and placing the samples in a refrigerator for refrigeration after scanning the spectrum. Spectrum collection: and (4) putting the fermentation liquor into a sample cup, and performing spectrum collection by using a GSA103 near-infrared spectrometer. Randomly selecting 50 samples from the spectra of 60 samples for modeling, taking the rest 10 samples as external verification samples to not participate in modeling, and verifying the accuracy of the model;
2. acquiring an original spectrum of a sample, and preprocessing the original spectrum by adopting an orthogonal signal correction algorithm to obtain a preprocessed spectrum shown in figure 1;
3. a correction model of the sorbose content in the fermentation liquor is established by adopting a local weight regression method, the model is shown as figure 2, and the correlation of the model is quite significant as 0.978305, which indicates that the change of the absorbance of the near infrared spectrum can well reflect the change of the sorbose content in the fermentation liquor;
4. in order to verify the prediction capability of the model, 10 samples are selected as verification samples, then the correction model in the step 3 is called to carry out verification analysis on the verification samples, and the verification results are shown in table 1; as can be seen from the table, the predicted mean deviation of the correction model is 1.21%;
table 1 verification results
Through the analysis, the conclusion is drawn that the near infrared spectrum of the sorbose content in the fermentation liquor is preprocessed through an orthogonal signal correction method, and then a correction model is established through a local weight regression method, so that the sorbose content in the fermentation liquor can be completely measured and analyzed.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (2)
1. A method for quantitatively analyzing the sorbose content in fermentation liquor is characterized by comprising the following steps: the method comprises the following specific steps:
s1, firstly, establishing an orthogonal signal spectrum preprocessing algorithm, wherein the algorithm process is as follows: firstly, standardizing an original correction set spectrum matrix X and an information matrix Y to be detected;
s2, calculating M ═ I-X ' Y (Y ' XX ' Y)-1Y' X, I is a unit matrix;
s3, calculating that Z is equal to X;
s4, singular value decomposition is carried out on ZZ', and the first p characteristic values g needing orthogonal processing are extractediAnd corresponding feature vector Ci；
S5, calculating a weight vector wi＝MX’Cigi；
S6, calculating a score vector ti＝Cigi；
S7, calculating a load vector pi＝X’ti(ti’ti)；
S8. subtracting the orthogonal signal in X：X0x＝X-∑tipi’；
S9, for the prediction vector XNavThe corrected spectrum is found from the weight w and the load p: t ═ X'Navw，XNav＝XNav-t' p, then establishing a local weight regression modeling method with a loss function of:
where x is the independent variable and y is the dependent variable, finding the appropriate parameter θ to minimize the loss function, where ω isiThe expression of (a) is as follows:
in the above formula, x is the new predicted sample feature data and is a vector, the parameter τ controls the rate of weight change, and the weight has a property, (1) if | xiX | ≈ 0, then ωi1 is approximately distributed; (2) if | xiX | ≈ infinity, then ωi≈0；
S10, introducing the established preprocessing and modeling algorithm into a near-infrared spectrometer, performing spectrum scanning and spectrum preprocessing through the near-infrared spectrometer, establishing a correction model by using a local weight regression method, and finally selecting an unknown sample for prediction to verify the prediction capability of the correction model.
2. The method for quantitatively analyzing the sorbose content in the fermentation broth according to claim 1, wherein the method comprises the following steps: the near infrared spectrometer can adopt a GSA103 type near infrared spectrometer.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102230888A (en) * | 2011-06-16 | 2011-11-02 | 浙江大学 | Method for detecting content of plasticizing agent |
| CN102288572A (en) * | 2011-05-09 | 2011-12-21 | 河南中医学院 | Method for quickly detecting content of index ingredient of traditional Chinese medicinal material by utilizing near infrared spectrum technique |
| CN106645018A (en) * | 2015-08-11 | 2017-05-10 | 南京理工大学 | Method for establishing near infrared spectrum predication mathematical model for content of glucose in human eye aqueous humor |
| CN107179370A (en) * | 2017-07-27 | 2017-09-19 | 石药集团维生药业（石家庄）有限公司 | A kind of method of gluconic acid content in use high performance liquid chromatography detection vitamin c fermenting liquid |
| CN111195131A (en) * | 2018-11-20 | 2020-05-26 | 三星电子株式会社 | Apparatus for measuring spectrum and apparatus and method for estimating analyte concentration |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1583802A (en) * | 2004-06-02 | 2005-02-23 | 中国农业大学 | Method for extracting beta-1,3-dextran |
| CN101231274B (en) * | 2008-01-28 | 2011-06-01 | 河南中医学院 | Method for rapid measuring allantoin content in yam using near infrared spectrum |
| US7807971B2 (en) * | 2008-11-19 | 2010-10-05 | The Boeing Company | Measurement of moisture in composite materials with near-IR and mid-IR spectroscopy |
| US8268624B2 (en) * | 2010-04-29 | 2012-09-18 | Thermal-Lube, Inc. | System and method for determining acid content in lubricants |
| CN102636454B (en) * | 2012-05-15 | 2015-08-12 | 武汉轻工大学 | The method of low carbon number content of fatty acid near infrared spectrum quick test edible oil |
| CN104950060B (en) * | 2015-04-01 | 2016-10-26 | 广西科技大学 | Based on chromatograph-spectrogrph combination and the analysis method of the paeonol content of subspace angle criterion |
| CN105866065B (en) * | 2016-05-09 | 2018-10-30 | 北京理工大学 | Methenamine content analysis method in a kind of methenamine-acetum |
| CN106198447A (en) * | 2016-07-13 | 2016-12-07 | 中国科学院合肥物质科学研究院 | Chemical Mixed Fertilizer main component harmless quantitative detection method based on near-infrared spectrum technique |
| CN107367467B (en) * | 2017-09-22 | 2020-07-28 | 武汉轻工大学 | Quantitative analysis method for substance content |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102288572A (en) * | 2011-05-09 | 2011-12-21 | 河南中医学院 | Method for quickly detecting content of index ingredient of traditional Chinese medicinal material by utilizing near infrared spectrum technique |
| CN102230888A (en) * | 2011-06-16 | 2011-11-02 | 浙江大学 | Method for detecting content of plasticizing agent |
| CN106645018A (en) * | 2015-08-11 | 2017-05-10 | 南京理工大学 | Method for establishing near infrared spectrum predication mathematical model for content of glucose in human eye aqueous humor |
| CN107179370A (en) * | 2017-07-27 | 2017-09-19 | 石药集团维生药业（石家庄）有限公司 | A kind of method of gluconic acid content in use high performance liquid chromatography detection vitamin c fermenting liquid |
| CN111195131A (en) * | 2018-11-20 | 2020-05-26 | 三星电子株式会社 | Apparatus for measuring spectrum and apparatus and method for estimating analyte concentration |
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