CN112086137A - Method for quantitatively analyzing sorbose content in fermentation liquor - Google Patents
Method for quantitatively analyzing sorbose content in fermentation liquor Download PDFInfo
- Publication number
- CN112086137A CN112086137A CN202010831592.7A CN202010831592A CN112086137A CN 112086137 A CN112086137 A CN 112086137A CN 202010831592 A CN202010831592 A CN 202010831592A CN 112086137 A CN112086137 A CN 112086137A
- Authority
- CN
- China
- Prior art keywords
- calculating
- vector
- spectrum
- fermentation liquor
- sorbose
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000000855 fermentation Methods 0.000 title claims abstract description 36
- 230000004151 fermentation Effects 0.000 title claims abstract description 36
- LKDRXBCSQODPBY-AMVSKUEXSA-N L-(-)-Sorbose Chemical compound OCC1(O)OC[C@H](O)[C@@H](O)[C@@H]1O LKDRXBCSQODPBY-AMVSKUEXSA-N 0.000 title claims abstract description 32
- 238000001228 spectrum Methods 0.000 claims abstract description 26
- 238000012937 correction Methods 0.000 claims abstract description 23
- 239000011159 matrix material Substances 0.000 claims abstract description 12
- 238000007781 pre-processing Methods 0.000 claims abstract description 12
- 230000008569 process Effects 0.000 claims abstract description 7
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 4
- 238000012545 processing Methods 0.000 claims abstract description 4
- 230000008859 change Effects 0.000 claims description 6
- 230000001419 dependent effect Effects 0.000 claims description 3
- 238000002329 infrared spectrum Methods 0.000 abstract description 4
- 238000004445 quantitative analysis Methods 0.000 abstract description 2
- CIWBSHSKHKDKBQ-JLAZNSOCSA-N Ascorbic acid Chemical compound OC[C@H](O)[C@H]1OC(=O)C(O)=C1O CIWBSHSKHKDKBQ-JLAZNSOCSA-N 0.000 description 8
- 229930003231 vitamin Natural products 0.000 description 7
- 239000011782 vitamin Substances 0.000 description 7
- 235000013343 vitamin Nutrition 0.000 description 7
- 229940088594 vitamin Drugs 0.000 description 7
- 238000012795 verification Methods 0.000 description 6
- 238000003786 synthesis reaction Methods 0.000 description 4
- 235000019154 vitamin C Nutrition 0.000 description 4
- 239000011718 vitamin C Substances 0.000 description 4
- FBPFZTCFMRRESA-FSIIMWSLSA-N D-Glucitol Natural products OC[C@H](O)[C@H](O)[C@@H](O)[C@H](O)CO FBPFZTCFMRRESA-FSIIMWSLSA-N 0.000 description 3
- ZZZCUOFIHGPKAK-UHFFFAOYSA-N D-erythro-ascorbic acid Natural products OCC1OC(=O)C(O)=C1O ZZZCUOFIHGPKAK-UHFFFAOYSA-N 0.000 description 3
- FBPFZTCFMRRESA-JGWLITMVSA-N D-glucitol Chemical compound OC[C@H](O)[C@@H](O)[C@H](O)[C@H](O)CO FBPFZTCFMRRESA-JGWLITMVSA-N 0.000 description 3
- 229930003268 Vitamin C Natural products 0.000 description 3
- 229960002920 sorbitol Drugs 0.000 description 3
- 150000003722 vitamin derivatives Chemical class 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 239000003153 chemical reaction reagent Substances 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000003647 oxidation Effects 0.000 description 2
- 238000007254 oxidation reaction Methods 0.000 description 2
- 230000003595 spectral effect Effects 0.000 description 2
- 238000004448 titration Methods 0.000 description 2
- 231100000331 toxic Toxicity 0.000 description 2
- 230000002588 toxic effect Effects 0.000 description 2
- 241000589220 Acetobacter Species 0.000 description 1
- 241000589236 Gluconobacter Species 0.000 description 1
- 241000589232 Gluconobacter oxydans Species 0.000 description 1
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 description 1
- 206010061218 Inflammation Diseases 0.000 description 1
- 239000002211 L-ascorbic acid Substances 0.000 description 1
- 235000000069 L-ascorbic acid Nutrition 0.000 description 1
- 206010029240 Neuritis Diseases 0.000 description 1
- 229930003270 Vitamin B Natural products 0.000 description 1
- 229930003316 Vitamin D Natural products 0.000 description 1
- QYSXJUFSXHHAJI-XFEUOLMDSA-N Vitamin D3 Natural products C1(/[C@@H]2CC[C@@H]([C@]2(CCC1)C)[C@H](C)CCCC(C)C)=C/C=C1\C[C@@H](O)CCC1=C QYSXJUFSXHHAJI-XFEUOLMDSA-N 0.000 description 1
- 238000002835 absorbance Methods 0.000 description 1
- 239000002253 acid Substances 0.000 description 1
- OENHQHLEOONYIE-UKMVMLAPSA-N all-trans beta-carotene Natural products CC=1CCCC(C)(C)C=1/C=C/C(/C)=C/C=C/C(/C)=C/C=C/C=C(C)C=CC=C(C)C=CC1=C(C)CCCC1(C)C OENHQHLEOONYIE-UKMVMLAPSA-N 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 229960005070 ascorbic acid Drugs 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- TUPZEYHYWIEDIH-WAIFQNFQSA-N beta-carotene Natural products CC(=C/C=C/C=C(C)/C=C/C=C(C)/C=C/C1=C(C)CCCC1(C)C)C=CC=C(/C)C=CC2=CCCCC2(C)C TUPZEYHYWIEDIH-WAIFQNFQSA-N 0.000 description 1
- 235000013734 beta-carotene Nutrition 0.000 description 1
- 239000011648 beta-carotene Substances 0.000 description 1
- 229960002747 betacarotene Drugs 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000036983 biotransformation Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000005515 coenzyme Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 238000004128 high performance liquid chromatography Methods 0.000 description 1
- 230000004054 inflammatory process Effects 0.000 description 1
- 239000013067 intermediate product Substances 0.000 description 1
- 206010023332 keratitis Diseases 0.000 description 1
- 230000001404 mediated effect Effects 0.000 description 1
- 244000005700 microbiome Species 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000005057 refrigeration Methods 0.000 description 1
- 208000007442 rickets Diseases 0.000 description 1
- 239000000600 sorbitol Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000000758 substrate Substances 0.000 description 1
- 238000001308 synthesis method Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 235000013311 vegetables Nutrition 0.000 description 1
- 235000019156 vitamin B Nutrition 0.000 description 1
- 239000011720 vitamin B Substances 0.000 description 1
- 235000019166 vitamin D Nutrition 0.000 description 1
- 239000011710 vitamin D Substances 0.000 description 1
- 150000003710 vitamin D derivatives Chemical class 0.000 description 1
- 229940046008 vitamin d Drugs 0.000 description 1
- OENHQHLEOONYIE-JLTXGRSLSA-N β-Carotene Chemical compound CC=1CCCC(C)(C)C=1\C=C\C(\C)=C\C=C\C(\C)=C\C=C\C=C(/C)\C=C\C=C(/C)\C=C\C1=C(C)CCCC1(C)C OENHQHLEOONYIE-JLTXGRSLSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/20—Identification of molecular entities, parts thereof or of chemical compositions
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/70—Machine learning, data mining or chemometrics
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Theoretical Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Biochemistry (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Crystallography & Structural Chemistry (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- Computational Mathematics (AREA)
- Immunology (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pathology (AREA)
- Pure & Applied Mathematics (AREA)
- Analytical Chemistry (AREA)
- Medical Informatics (AREA)
- General Engineering & Computer Science (AREA)
- Algebra (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
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 pairCorresponding 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 the basis, the semi-synthesis method combining chemical synthesis and biotransformation is widely used in vitamin production, vitamins which can be produced by fermentation or semi-synthesis at present include vitamins C, B2, B12, D, beta-carotene and the like, vitamin C, a water-soluble vitamin, is abundant in fresh vegetables and fruits, oxidation from D-sorbitol to L-sorbose can be performed by using certain strains of Gluconobacter suboxydans, Gluconobacter nigricans and Acetobacter (generally called one-step fermentation method), and D-sorbitol used as a substrate is prepared by D-glucose mediated reduction. L-ascorbic acid is produced by chemical oxidation of L-sorbose, and is converted into vitamin C by first producing L-keto-L-gulonic acid and then treating with acid.
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' p. Then, a local weight regression modeling method is established, and the loss function of the local weight regression modeling method is as follows:
in the formula, x is an independent variable, y is a dependent variable, and an appropriate parameter θ is found so that the loss function is minimized. Wherein 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 the sorbose content in fermentation liquor. The method has the following beneficial effects:
1. according to the method for quantitatively analyzing the sorbose content in the fermentation liquor, a toxic and harmful chemical reagent is not needed by an orthogonal signal correction method, the whole operation process is simple, the consumed time is short, and the detection efficiency is high.
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' p. Then, a local weight regression modeling method is established, and the loss function of the local weight regression modeling method is as follows:
in the formula, x is an independent variable, y is a dependent variable, and an appropriate parameter θ is found so that the loss function is minimized. Wherein 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≈1；
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 type 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 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-∑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' p. Then, a local weight regression modeling method is established, and the loss function of the local weight regression modeling method is as follows:
in the formula, x is an independent variable, y is a dependent variable, and an appropriate parameter θ is found so that the loss function is minimized. Wherein 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.
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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010831592.7A CN112086137B (en) | 2020-08-18 | 2020-08-18 | Method for quantitatively analyzing sorbose content in fermentation liquor |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010831592.7A CN112086137B (en) | 2020-08-18 | 2020-08-18 | Method for quantitatively analyzing sorbose content in fermentation liquor |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112086137A true CN112086137A (en) | 2020-12-15 |
| CN112086137B CN112086137B (en) | 2022-06-10 |
Family
ID=73729133
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010831592.7A Active CN112086137B (en) | 2020-08-18 | 2020-08-18 | Method for quantitatively analyzing sorbose content in fermentation liquor |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112086137B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112881697A (en) * | 2021-01-13 | 2021-06-01 | 北京中检葆泰生物技术有限公司 | Method for stably detecting aflatoxin content |
Citations (14)
| 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 |
| CN101231274A (en) * | 2008-01-28 | 2008-07-30 | 河南中医学院 | Method for rapid measuring allantoin content in yam using near infrared spectrum |
| US20100123079A1 (en) * | 2008-11-19 | 2010-05-20 | The Boeing Company | Measurement of Moisture in Composite Materials with Near-IR and MID-IR Spectroscopy |
| CN102230888A (en) * | 2011-06-16 | 2011-11-02 | 浙江大学 | Method for detecting content of plasticizing agent |
| US20110269236A1 (en) * | 2010-04-29 | 2011-11-03 | Van De Voort F R | System and Method for Determining Acid Content in Generally Hydrophic Products |
| 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 |
| CN102636454A (en) * | 2012-05-15 | 2012-08-15 | 武汉工业学院 | Method for quickly measuring content of low carbon number fatty acid in edible oil by near infrared spectrum |
| CN104950060A (en) * | 2015-04-01 | 2015-09-30 | 广西科技大学 | Analysis method for content of paeonol based on chromatograph-spectrograph combination and sub-space included angle criteria |
| CN105866065A (en) * | 2016-05-09 | 2016-08-17 | 北京理工大学 | Method of analyzing content of urotropine in urotropine-acetic acid solution |
| 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 |
| 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 |
| CN107367467A (en) * | 2017-09-22 | 2017-11-21 | 武汉轻工大学 | A kind of content of material quantitative analysis method |
| 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 |
-
2020
- 2020-08-18 CN CN202010831592.7A patent/CN112086137B/en active Active
Patent Citations (14)
| 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 |
| CN101231274A (en) * | 2008-01-28 | 2008-07-30 | 河南中医学院 | Method for rapid measuring allantoin content in yam using near infrared spectrum |
| US20100123079A1 (en) * | 2008-11-19 | 2010-05-20 | The Boeing Company | Measurement of Moisture in Composite Materials with Near-IR and MID-IR Spectroscopy |
| US20110269236A1 (en) * | 2010-04-29 | 2011-11-03 | Van De Voort F R | System and Method for Determining Acid Content in Generally Hydrophic Products |
| 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 |
| CN102636454A (en) * | 2012-05-15 | 2012-08-15 | 武汉工业学院 | Method for quickly measuring content of low carbon number fatty acid in edible oil by near infrared spectrum |
| CN104950060A (en) * | 2015-04-01 | 2015-09-30 | 广西科技大学 | Analysis method for content of paeonol based on chromatograph-spectrograph combination and sub-space included angle criteria |
| 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 |
| CN105866065A (en) * | 2016-05-09 | 2016-08-17 | 北京理工大学 | Method of analyzing content of urotropine in urotropine-acetic acid solution |
| 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 |
| 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 |
| CN107367467A (en) * | 2017-09-22 | 2017-11-21 | 武汉轻工大学 | A kind of content of material quantitative analysis method |
| 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 |
Non-Patent Citations (5)
| Title |
|---|
| DI WU等: "Content Determination of Proteins in Milk Powder Using Short-Wave Near-Infrared Spectroscopy", 《PROCEEDINGS OF THE 2008 CONGRESS ON IMAGE AND SIGNAL PROCESSING》 * |
| 刘燕德: "水果糖度和酸度的近红外光谱无损检测研究", 《中国博士学位论文全文数据库 工程科技Ⅰ辑》 * |
| 赵杰文 等: "基于OSC/PLS的茶叶中EGCG含量的近红外光谱法测定", 《食品与生物技术学报》 * |
| 邱江 等: "ATR／FT-IR监测山梨醇发酵中山梨醇和山梨糖浓度变化", 《华东理工大学学报》 * |
| 邹振民 等: "一种水溶液中果糖含量的近红外光谱分析法", 《食品安全质量检测学报》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112881697A (en) * | 2021-01-13 | 2021-06-01 | 北京中检葆泰生物技术有限公司 | Method for stably detecting aflatoxin content |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112086137B (en) | 2022-06-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105548138B (en) | The detection method of brewing fermentation liquid yeast activity on-line measuring device based on Raman spectrum | |
| Jiang et al. | Quantitative analysis of yeast fermentation process using Raman spectroscopy: Comparison of CARS and VCPA for variable selection | |
| Li et al. | Monitoring black tea fermentation using a colorimetric sensor array‐based artificial olfaction system | |
| CN111141836B (en) | Method and device for nondestructive detection of early internal diseases of pome based on fusion of sound vibration multi-domain spectrum and near infrared spectrum information | |
| McLeod et al. | A comparison of variate pre-selection methods for use in partial least squares regression: a case study on NIR spectroscopy applied to monitoring beer fermentation | |
| US9360421B2 (en) | Use of nuclear magnetic resonance and near infrared to analyze biological samples | |
| CN109001180A (en) | A kind of Raman spectrum combination artificial intelligence high throughput single cell analysis identification method | |
| Cavaglia et al. | ATR-MIR spectroscopy and multivariate analysis in alcoholic fermentation monitoring and lactic acid bacteria spoilage detection | |
| CN112086137B (en) | Method for quantitatively analyzing sorbose content in fermentation liquor | |
| Ergin et al. | Fourier transform infrared spectral evaluation for the differentiation of clinically relevant Trichophyton species | |
| CN115436508A (en) | Method for predicting fruit juice quality based on fruit raw material indexes | |
| CN116952896A (en) | Near infrared rapid detection method for total nitrogen in saccharified wort of brewery | |
| Jin et al. | Quantitative inversion model of protein and fat content in milk based on hyperspectral techniques | |
| CN205333518U (en) | Making wine zymotic fluid yeast vigor on -line measuring device based on raman spectroscopy | |
| CN115963074A (en) | Rapid detection method and system for spore and hypha ratio of microbial material | |
| Wang et al. | Novel colorimetric sensor array for Chinese rice wine evaluation based on color reactions of flavor compounds | |
| Yang et al. | Identification of Peanut Kernels Infected with Multiple Aspergillus flavus Fungi Using Line-Scan Raman Hyperspectral Imaging | |
| CN107561058B (en) | Method for evaluating metabolic similarity of flavor substances of industrial Lager yeast strains | |
| CN113295625A (en) | Machine vision dye concentration spectrum detection method based on extreme gradient promotion | |
| Karakach et al. | Fluorescence‐based real time monitoring and diagnostics of recombinant Pichia pastoris cultivations in a bioreactor | |
| CN112067577A (en) | Method, device and equipment for identifying overproof cream pigment based on support vector machine | |
| Liu et al. | Determination of Protein Content of Wheat Using Partial Least Squares Regression Based on Near-Infrared Spectroscopy Preprocessing | |
| CN105651727A (en) | Method for discriminating shelf life of apple through near infrared spectroscopy based on JADE and ELM | |
| CN111474134A (en) | Method for controlling butyric acid fermentation by using online near infrared | |
| CN109554439A (en) | A kind of Pichia pastoris fermentation process soft measuring method based on ONLINE-RLSSVM |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |