CN106701846A - Method for on-line monitoring and optimization of aspergillus niger fermentation production of sodium gluconate - Google Patents
Method for on-line monitoring and optimization of aspergillus niger fermentation production of sodium gluconate Download PDFInfo
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- CN106701846A CN106701846A CN201710039712.8A CN201710039712A CN106701846A CN 106701846 A CN106701846 A CN 106701846A CN 201710039712 A CN201710039712 A CN 201710039712A CN 106701846 A CN106701846 A CN 106701846A
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- glucose
- sodium gluconate
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- zymotic fluid
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- 238000000855 fermentation Methods 0.000 title claims abstract description 111
- 230000004151 fermentation Effects 0.000 title claims abstract description 111
- 239000000176 sodium gluconate Substances 0.000 title claims abstract description 98
- 235000012207 sodium gluconate Nutrition 0.000 title claims abstract description 98
- 229940005574 sodium gluconate Drugs 0.000 title claims abstract description 98
- AEQDJSLRWYMAQI-UHFFFAOYSA-N 2,3,9,10-tetramethoxy-6,8,13,13a-tetrahydro-5H-isoquinolino[2,1-b]isoquinoline Chemical compound C1CN2CC(C(=C(OC)C=C3)OC)=C3CC2C2=C1C=C(OC)C(OC)=C2 AEQDJSLRWYMAQI-UHFFFAOYSA-N 0.000 title claims abstract description 96
- 238000000034 method Methods 0.000 title claims abstract description 91
- 241000228245 Aspergillus niger Species 0.000 title claims abstract description 43
- 238000005457 optimization Methods 0.000 title claims abstract description 31
- 238000012544 monitoring process Methods 0.000 title abstract description 30
- 238000004519 manufacturing process Methods 0.000 title abstract description 11
- 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 claims abstract description 119
- 239000008103 glucose Substances 0.000 claims abstract description 117
- WQZGKKKJIJFFOK-VFUOTHLCSA-N beta-D-glucose Chemical compound OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-VFUOTHLCSA-N 0.000 claims abstract description 47
- 239000007788 liquid Substances 0.000 claims abstract description 15
- 239000012530 fluid Substances 0.000 claims description 53
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 33
- 239000001301 oxygen Substances 0.000 claims description 33
- 229910052760 oxygen Inorganic materials 0.000 claims description 33
- 238000006243 chemical reaction Methods 0.000 claims description 31
- 238000013461 design Methods 0.000 claims description 18
- 239000001963 growth medium Substances 0.000 claims description 17
- 239000002609 medium Substances 0.000 claims description 13
- 239000007836 KH2PO4 Substances 0.000 claims description 10
- 240000008042 Zea mays Species 0.000 claims description 10
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 claims description 10
- 235000002017 Zea mays subsp mays Nutrition 0.000 claims description 10
- 235000005822 corn Nutrition 0.000 claims description 10
- 229910000402 monopotassium phosphate Inorganic materials 0.000 claims description 10
- GNSKLFRGEWLPPA-UHFFFAOYSA-M potassium dihydrogen phosphate Chemical compound [K+].OP(O)([O-])=O GNSKLFRGEWLPPA-UHFFFAOYSA-M 0.000 claims description 10
- 239000000126 substance Substances 0.000 claims description 10
- DGAQECJNVWCQMB-PUAWFVPOSA-M Ilexoside XXIX Chemical compound C[C@@H]1CC[C@@]2(CC[C@@]3(C(=CC[C@H]4[C@]3(CC[C@@H]5[C@@]4(CC[C@@H](C5(C)C)OS(=O)(=O)[O-])C)C)[C@@H]2[C@]1(C)O)C)C(=O)O[C@H]6[C@@H]([C@H]([C@@H]([C@H](O6)CO)O)O)O.[Na+] DGAQECJNVWCQMB-PUAWFVPOSA-M 0.000 claims description 9
- 238000001514 detection method Methods 0.000 claims description 9
- 229910052708 sodium Inorganic materials 0.000 claims description 9
- 239000011734 sodium Substances 0.000 claims description 9
- 229910052564 epsomite Inorganic materials 0.000 claims description 8
- 235000009754 Vitis X bourquina Nutrition 0.000 claims description 7
- 235000012333 Vitis X labruscana Nutrition 0.000 claims description 7
- 235000014787 Vitis vinifera Nutrition 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 7
- UPMFZISCCZSDND-JJKGCWMISA-M sodium gluconate Chemical compound [Na+].OC[C@@H](O)[C@@H](O)[C@H](O)[C@@H](O)C([O-])=O UPMFZISCCZSDND-JJKGCWMISA-M 0.000 claims description 6
- 238000011218 seed culture Methods 0.000 claims description 4
- 238000000611 regression analysis Methods 0.000 claims description 3
- NAOLWIGVYRIGTP-UHFFFAOYSA-N 1,3,5-trihydroxyanthracene-9,10-dione Chemical compound C1=CC(O)=C2C(=O)C3=CC(O)=CC(O)=C3C(=O)C2=C1 NAOLWIGVYRIGTP-UHFFFAOYSA-N 0.000 claims description 2
- 240000006365 Vitis vinifera Species 0.000 claims 2
- 238000011897 real-time detection Methods 0.000 abstract description 5
- 238000004476 mid-IR spectroscopy Methods 0.000 abstract description 2
- 239000002994 raw material Substances 0.000 abstract description 2
- 239000000047 product Substances 0.000 description 28
- 239000000523 sample Substances 0.000 description 27
- 230000008859 change Effects 0.000 description 23
- 230000015572 biosynthetic process Effects 0.000 description 18
- 238000005259 measurement Methods 0.000 description 16
- RGHNJXZEOKUKBD-SQOUGZDYSA-N Gluconic acid Natural products OC[C@@H](O)[C@@H](O)[C@H](O)[C@@H](O)C(O)=O RGHNJXZEOKUKBD-SQOUGZDYSA-N 0.000 description 15
- MYMOFIZGZYHOMD-UHFFFAOYSA-N Dioxygen Chemical compound O=O MYMOFIZGZYHOMD-UHFFFAOYSA-N 0.000 description 14
- 238000012937 correction Methods 0.000 description 14
- 238000001228 spectrum Methods 0.000 description 12
- 239000000758 substrate Substances 0.000 description 11
- 239000012071 phase Substances 0.000 description 10
- 230000008569 process Effects 0.000 description 10
- RGHNJXZEOKUKBD-UHFFFAOYSA-N D-gluconic acid Natural products OCC(O)C(O)C(O)C(O)C(O)=O RGHNJXZEOKUKBD-UHFFFAOYSA-N 0.000 description 9
- HEMHJVSKTPXQMS-UHFFFAOYSA-M Sodium hydroxide Chemical compound [OH-].[Na+] HEMHJVSKTPXQMS-UHFFFAOYSA-M 0.000 description 9
- 239000000174 gluconic acid Substances 0.000 description 9
- 235000012208 gluconic acid Nutrition 0.000 description 9
- 230000006872 improvement Effects 0.000 description 9
- 238000002329 infrared spectrum Methods 0.000 description 9
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 8
- IDAGXRIGDWCIET-SDFKWCIISA-L disodium;(2s,3s,4s,5r)-2,3,4,5-tetrahydroxyhexanedioate Chemical compound [Na+].[Na+].[O-]C(=O)[C@@H](O)[C@@H](O)[C@H](O)[C@@H](O)C([O-])=O IDAGXRIGDWCIET-SDFKWCIISA-L 0.000 description 7
- 238000009826 distribution Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 6
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- 238000004611 spectroscopical analysis Methods 0.000 description 6
- 238000003786 synthesis reaction Methods 0.000 description 6
- 241000894006 Bacteria Species 0.000 description 5
- 241000219095 Vitis Species 0.000 description 5
- 239000000243 solution Substances 0.000 description 5
- 102000004190 Enzymes Human genes 0.000 description 4
- 108090000790 Enzymes Proteins 0.000 description 4
- 239000002253 acid Substances 0.000 description 4
- 238000005273 aeration Methods 0.000 description 4
- 230000001580 bacterial effect Effects 0.000 description 4
- 238000002474 experimental method Methods 0.000 description 4
- 238000010899 nucleation Methods 0.000 description 4
- 238000003756 stirring Methods 0.000 description 4
- CSNNHWWHGAXBCP-UHFFFAOYSA-L Magnesium sulfate Chemical compound [Mg+2].[O-][S+2]([O-])([O-])[O-] CSNNHWWHGAXBCP-UHFFFAOYSA-L 0.000 description 3
- OKKJLVBELUTLKV-UHFFFAOYSA-N Methanol Chemical compound OC OKKJLVBELUTLKV-UHFFFAOYSA-N 0.000 description 3
- 238000002835 absorbance Methods 0.000 description 3
- 230000002902 bimodal effect Effects 0.000 description 3
- 239000000306 component Substances 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
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- 238000009499 grossing Methods 0.000 description 3
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- 230000036632 reaction speed Effects 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- FRXSZNDVFUDTIR-UHFFFAOYSA-N 6-methoxy-1,2,3,4-tetrahydroquinoline Chemical compound N1CCCC2=CC(OC)=CC=C21 FRXSZNDVFUDTIR-UHFFFAOYSA-N 0.000 description 2
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- NBIIXXVUZAFLBC-UHFFFAOYSA-N Phosphoric acid Chemical compound OP(O)(O)=O NBIIXXVUZAFLBC-UHFFFAOYSA-N 0.000 description 2
- 230000009102 absorption Effects 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 2
- 230000033228 biological regulation Effects 0.000 description 2
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- 230000003247 decreasing effect Effects 0.000 description 2
- 238000010790 dilution Methods 0.000 description 2
- 239000012895 dilution Substances 0.000 description 2
- 238000003055 full factorial design Methods 0.000 description 2
- 239000007789 gas Substances 0.000 description 2
- 239000007791 liquid phase Substances 0.000 description 2
- 229910052943 magnesium sulfate Inorganic materials 0.000 description 2
- 239000003550 marker Substances 0.000 description 2
- 238000000691 measurement method Methods 0.000 description 2
- 239000012533 medium component Substances 0.000 description 2
- 239000000376 reactant Substances 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 150000003839 salts Chemical class 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- XDIYNQZUNSSENW-UUBOPVPUSA-N (2R,3S,4R,5R)-2,3,4,5,6-pentahydroxyhexanal Chemical compound OC[C@@H](O)[C@@H](O)[C@H](O)[C@@H](O)C=O.OC[C@@H](O)[C@@H](O)[C@H](O)[C@@H](O)C=O XDIYNQZUNSSENW-UUBOPVPUSA-N 0.000 description 1
- ODIGIKRIUKFKHP-UHFFFAOYSA-N (n-propan-2-yloxycarbonylanilino) acetate Chemical compound CC(C)OC(=O)N(OC(C)=O)C1=CC=CC=C1 ODIGIKRIUKFKHP-UHFFFAOYSA-N 0.000 description 1
- 229920001817 Agar Polymers 0.000 description 1
- 241000228212 Aspergillus Species 0.000 description 1
- 101710128063 Carbohydrate oxidase Proteins 0.000 description 1
- 240000008067 Cucumis sativus Species 0.000 description 1
- 235000010799 Cucumis sativus var sativus Nutrition 0.000 description 1
- PHOQVHQSTUBQQK-SQOUGZDYSA-N D-glucono-1,5-lactone Chemical compound OC[C@H]1OC(=O)[C@H](O)[C@@H](O)[C@@H]1O PHOQVHQSTUBQQK-SQOUGZDYSA-N 0.000 description 1
- 241000196324 Embryophyta Species 0.000 description 1
- 241000588724 Escherichia coli Species 0.000 description 1
- 244000131316 Panax pseudoginseng Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 240000004808 Saccharomyces cerevisiae Species 0.000 description 1
- 244000061458 Solanum melongena Species 0.000 description 1
- 235000002597 Solanum melongena Nutrition 0.000 description 1
- 229910000831 Steel Inorganic materials 0.000 description 1
- XSQUKJJJFZCRTK-UHFFFAOYSA-N Urea Chemical compound NC(N)=O XSQUKJJJFZCRTK-UHFFFAOYSA-N 0.000 description 1
- 241000219094 Vitaceae Species 0.000 description 1
- 239000008272 agar Substances 0.000 description 1
- 229910000147 aluminium phosphate Inorganic materials 0.000 description 1
- 238000005102 attenuated total reflection Methods 0.000 description 1
- 238000012365 batch cultivation Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- FFBHFFJDDLITSX-UHFFFAOYSA-N benzyl N-[2-hydroxy-4-(3-oxomorpholin-4-yl)phenyl]carbamate Chemical compound OC1=C(NC(=O)OCC2=CC=CC=C2)C=CC(=C1)N1CCOCC1=O FFBHFFJDDLITSX-UHFFFAOYSA-N 0.000 description 1
- 238000010364 biochemical engineering Methods 0.000 description 1
- VTYYLEPIZMXCLO-UHFFFAOYSA-L calcium carbonate Substances [Ca+2].[O-]C([O-])=O VTYYLEPIZMXCLO-UHFFFAOYSA-L 0.000 description 1
- 229910000019 calcium carbonate Inorganic materials 0.000 description 1
- 239000004202 carbamide Substances 0.000 description 1
- 125000003178 carboxy group Chemical group [H]OC(*)=O 0.000 description 1
- 230000003197 catalytic effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 229910000388 diammonium phosphate Inorganic materials 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 235000012209 glucono delta-lactone Nutrition 0.000 description 1
- 239000000182 glucono-delta-lactone Substances 0.000 description 1
- 229960003681 gluconolactone Drugs 0.000 description 1
- 235000001727 glucose Nutrition 0.000 description 1
- 230000009229 glucose formation Effects 0.000 description 1
- 235000021021 grapes Nutrition 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 239000012535 impurity Substances 0.000 description 1
- 239000004615 ingredient Substances 0.000 description 1
- 238000011081 inoculation Methods 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000001819 mass spectrum Methods 0.000 description 1
- 230000002503 metabolic effect Effects 0.000 description 1
- 238000011169 microbiological contamination Methods 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010369 molecular cloning Methods 0.000 description 1
- 235000019796 monopotassium phosphate Nutrition 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 230000009972 noncorrosive effect Effects 0.000 description 1
- 231100000252 nontoxic Toxicity 0.000 description 1
- 230000003000 nontoxic effect Effects 0.000 description 1
- 230000003204 osmotic effect Effects 0.000 description 1
- 230000036284 oxygen consumption Effects 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000002203 pretreatment Methods 0.000 description 1
- 229930010796 primary metabolite Natural products 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 239000012086 standard solution Substances 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 230000001954 sterilising effect Effects 0.000 description 1
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- 230000002194 synthesizing effect Effects 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- LENZDBCJOHFCAS-UHFFFAOYSA-N tris Chemical compound OCC(N)(CO)CO LENZDBCJOHFCAS-UHFFFAOYSA-N 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12P—FERMENTATION OR ENZYME-USING PROCESSES TO SYNTHESISE A DESIRED CHEMICAL COMPOUND OR COMPOSITION OR TO SEPARATE OPTICAL ISOMERS FROM A RACEMIC MIXTURE
- C12P7/00—Preparation of oxygen-containing organic compounds
- C12P7/40—Preparation of oxygen-containing organic compounds containing a carboxyl group including Peroxycarboxylic acids
- C12P7/58—Aldonic, ketoaldonic or saccharic acids
-
- 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
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
-
- 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
- G01N2021/3595—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using FTIR
Abstract
The invention relates to a method for on-line monitoring and optimization of aspergillus niger fermentation production of sodium gluconate. For real-timely and efficiently understanding changing dynamics of raw materials and products in an aspergillus niger fermentation process, a mid-IR spectrometer is applied for real-time detection, and through optimization modeling, the content of glucose and sodium gluconate in a fermentation liquid is analyzed. Moreover, based on the real-time detection results, the production process of sodium gluconate is also optimized.
Description
Technical field
The invention belongs to bioprocesses monitoring and optimization field, more particularly it relates to a kind of on-line monitoring
And the method that optimization fermentation of Aspergillus niger produces sodium gluconate.
Background technology
Sodium gluconate is a kind of good stability, nontoxic, non-corrosive acylate, be widely used in medicine, food,
Chemical industry, light industry etc..Sodium gluconate is industrially mainly obtained by fermentation of Aspergillus niger.Substrate glucose is passed through Portugal by aspergillus niger
Grape carbohydrate oxidase (GOD) is converted into glucono-δ-lactone, and then is hydrolyzed to gluconic acid, then is reacted with nertralizer NaOH, raw
Into sodium gluconate.The characteristics of aspergillus niger synthesizes GOD and as its carrier, and the reaction there is enzyme efficient catalytic to react.Thus
The change in concentration of substrate glucose and product sodium gluconate is rapid in fermentation process, and existing off-line measurement method is time-consuming to take
Power, it is impossible to which it changes Real Time Observation, influences the judgement to fermentation process.Quickly, real time and on line monitoring substrate and production concentration become
It is melted into improve the key of reaction rate and then raising yield.
In infrared (Mid-Infrared, MIR) spectroscopic methodology be it is a kind of it is new it is quick, pollution-free, without destruction, pre- without sample
The green analytical technology for the treatment of.Region (the 4000-400cm of its collection of illustrative plates-1), particularly in finger-print region, the function of material molecule
Group can produce distinctive absworption peak, can not only differentiate the labyrinth of material, can also carry out quantitative analysis to material.Decay
The use of total reflection (Attenuated Total Reflection, ATR) probe, can balance out strong absorptions of the MIR to water,
And solve the problems, such as that its penetration power is weaker.In order to obtain more useful informations from collection of illustrative plates, the quantitative Application of MIR is needed by change
Metrology method is learned, the most frequently used chemometrics method is PLS (Partial Least Squares, PLS).
Quantitative model is set up using the method for Chemical Measurement, and the substrate in zymotic fluid and product are predicted.And lead at this stage
If the accuracy of sampling monitoring, and model prediction offline also counteracts that the development of MIR technologies.The present invention is carried out to modeling method
Optimization, improves the accuracy of model prediction, and realizes that MIR is online, in-situ monitoring fermentation process.With reference to online MIR, instruct
And optimize the fermentation process of Aspergillus Niger sodium gluconate.But, the use of existing MIR essentially consists of offline to enter Cucumber
Row is quantitative or qualitative, is not widely used in the monitoring of fermentation process.And for the monitoring of fermentation process, be relatively generally sample offline into
Row measurement, online MIR is less, and is applied to yeast or Escherichia coli system.The present invention monitors black song to use online MIR first
Glucose and sodium gluconate in mould malaga sodium saccharate fermentation process.Its main difficulty point is the problem of forecasting accuracy,
Therefore need to grope suitable method to realize online, in situ detection.
The content of the invention
The method that fermentation of Aspergillus niger produces sodium gluconate is monitored and optimizes on-line it is an object of the invention to provide a kind of.
In the first aspect of the present invention, there is provided a kind of on-line monitoring fermentation of Aspergillus niger produces the fermentation process of sodium gluconate
Method, methods described includes：Online acquisition zymotic fluid, zymotic fluid collection of illustrative plates is obtained using mid-infrared light spectrometer, analyzes zymotic fluid
Middle glucose and gluconic acid sodium content.
In a preference, it is imported into the PLS models of optimization design by by online acquisition zymotic fluid collection of illustrative plates, is analyzed
Glucose and gluconic acid sodium content in zymotic fluid.
In another preference, the PLS models of described optimization design are set up as follows：
(1) titer of gradient design glucose and sodium gluconate, calibration set glucose, Portugal are obtained through total divisor design
Grape sodium saccharate binary-medium standard liquid and checking collection glucose, sodium gluconate binary-medium standard liquid；Calibration set and
Verify collection collection of illustrative plates import spectral analysis software in, by change modeling parameters (including modeling method, modeling region, pretreatment
Method), obtain comparatively performance indications highest model；It is the back of the body with the culture medium for removing glucose in described titer
Scape；
(2) on the basis of the model of (1), the zymotic fluid collection of illustrative plates of online acquisition is added to carry out the excellent of model in calibration set
Change, obtain the PLS models of optimization design.
In another preference, in step (2), also include：For the zymotic fluid of online acquisition, examined using chemical method
Survey, obtain the content data of glucose and sodium gluconate in fermentation broth sample；The collection of illustrative plates that will be collected is detected with chemical method
Data are associated, using Multiple Regression Analysis Method analysis model, so as to obtain comparatively performance indications highest model.
In another preference, described spectral analysis software is TQ Analyst softwares.
In another preference, the culture medium of described removal glucose includes：Corn pulp, MgSO4·7H2O、KH2PO4、
(NH4)2PO4；It is preferred that including：2 ± 0.5g/L of corn pulp, MgSO4·7H2O 0.2±0.05g/L、KH2PO4 0.17±
0.05g/L、(NH4)2PO4 0.25±0.05g/L。
In another preference, zymotic fluid is gathered at 38 ± 0.5 DEG C, obtain zymotic fluid collection of illustrative plates.
In another aspect of this invention, there is provided a kind of side of the reaction rate for improving fermentation of Aspergillus niger production sodium gluconate
Method, methods described includes：During the course of the reaction, oxygen is added with the oxygen-supply quantity higher than 8%；More preferably with the oxygen-supply quantity of 8-15%
Add oxygen；Oxygen is more preferably added with the oxygen-supply quantity of 9-11%.
In a preference, fermentation medium is：300 ± 40g/L of glucose, 2 ± 0.5g/L of corn pulp, MgSO4·
7H2O 0.2±0.05g/L、KH2PO4 0.17±0.05g/L、(NH4)2PO4 0.25±0.05g/L。
In another preference, the early stage of the method, in the seed culture medium of use, concentration of glucose is 300 ± 15g/
L；More preferably it is 300 ± 10g/L；More preferably it is further 300 ± 5g/L.
Other side of the invention, due to this disclosure, is to those skilled in the art apparent
's.
Brief description of the drawings
In Fig. 1, glucose, the calibration set of sodium gluconate binary-medium standard sample and checking collection characteristic area
Infared spectrum.
Fig. 2, the principle being distributed using scatterplot, randomly choose a series of concentration gradient of glucose and sodium gluconate, match somebody with somebody
Standard mixed liquor processed, aggregate sample concentration profile.
Fig. 3, a kind of zymotic fluid mid infrared spectrum of offline collection.
Fig. 4, a kind of mid infrared spectrum of the zymotic fluid of online acquisition.
Fig. 5, the method through total divisor design (full factorial design), the calibration set of acquisition and checking collection Portugal
Grape sugar, sodium gluconate binary-culture medium solution.
Glucose, the sodium gluconate binary-medium standard sample collection of illustrative plates of model after Fig. 6, improvement
Fig. 7, fill into the concentration profile after the zymotic fluid collection of illustrative plates of online acquisition.Wherein, diamond indicia is the dense of modeling collection
Degree distribution, square indicia collects for checking, and both is the composition of original model；Triangular marker is the new zymotic fluid figure for filling into
The concentration of spectrum.
The predicted value of Fig. 8, glucose (A) and sodium gluconate (B) compares with reference value.
Fig. 9, MIR monitor fermentation process schematic diagram on-line.
The infared spectrum of Figure 10, MIR online acquisition zymotic fluid.
Figure 11, MIR monitor fermentation of Aspergillus niger process on-line.
The comparing of Figure 12, control group and experimental group glucose change.
The comparing of Figure 13, control group and experimental group sodium gluconate change.
The change in concentration of glucose (A) and sodium gluconate (B) in Figure 14, different Initial sugar concentration fermentation process.
Specific embodiment
The present inventor is devoted to the process optimization that fermentation of Aspergillus niger produces sodium gluconate, in order in real time, efficiently understand
The change dynamic of raw material and product in fermentation of Aspergillus niger production process, present inventor has performed in-depth study, is built by optimization
Mould, mid-infrared light spectrometer is applied to carry out real-time detection, glucose and gluconic acid sodium content in analysis zymotic fluid.It is based on
Real-time detection result, the present inventor also optimizes the production process of sodium gluconate.
The present inventor sets up the forecast model of glucose, sodium gluconate using FT-MIR-ATR combination PLS methods, while prison
The concentration of substrate glucose and product sodium gluconate in fermentation process is surveyed, On-line Control Aspergillus Niger sodium gluconate is realized
Fermentation process.In the case where aeration condition is changed, substrate and production concentration change are simply and quickly predicted using model, check mould
The accuracy of type, and guides optimization fermentation process.
In currently available technology, during fermentation of Aspergillus niger production sodium gluconate, the side of efficient liquid phase is generally used
Method is measured, and this method compares elapsed time, also to carry out the pretreatment of sample, filters out thalline, dilution etc., destroys sample
Product.Therefore, the present inventor changes detection method, and mid-infrared light spectrometer is applied to carry out real-time detection, but, the present inventor
Simultaneously, it was also found that the zymotic fluid of aspergillus niger is different from the general titer prepared, composition is very complicated, and impurity is susceptible to during modeling
The influence at the peak of generation, these factors can all have influence on and quantify, so the model to be set up of infrared survey is more complicated in, need
To be groped for the influence factor in modeling process, determine, exclude, be set up the model of optimization.
In modeling process, at the beginning, the present inventor applies formulated titer, model is set up, in measurement zymotic fluid
Glucose and sodium gluconate, measurement result and actual glucose conjunction the sodium gluconate concentration difference of acquisition are big.Through analysis and
Repetition test, what the present inventor changed titer sets up mode, in titer, (is contained with the culture medium for removing glucose
Other components in culture medium in addition to glucose are background, and prediction accuracy increases.
On the basis of above-mentioned optimization, although measurement result is smaller with the difference of actual value, the present inventor is expected that by
Further optimize to cause that measurement result is more accurate.Therefore, the present inventor is on the basis of the model that original titer is set up,
Model is set up together using the collection of illustrative plates of the zymotic fluid of online acquisition, and using the model, the degree of accuracy of prediction is significantly increased again.
Therefore, decay total reflection (FT-MIR- is combined using FT-mid-IR fiber optics spectroscopy the invention provides one kind
ATR analytical technology), the method for setting up glucose and gluconic acid na concn during on-line monitoring fermentation of Aspergillus niger.As
The preferred embodiment of the present invention, described method is：(1) mixed through the standard that total divisor is designed using the collection of mid-infrared light spectrometer
Close liquid collection of illustrative plates.(2) collection of illustrative plates of mid-infrared light spectrometer online acquisition zymotic fluid, and using chemical method detection, obtain zymotic fluid sample
The content of glucose and sodium gluconate in product.(3) the above-mentioned spectrum for collecting is associated with chemical method detection data, profit
Model is set up with Multiple Regression Analysis Method.(4) by online acquisition to unknown glucose and the zymotic fluid of gluconic acid sodium content
Spectrum is imported into forecast model, predicts the content of purpose thing.In an instantiation, fermentation of Aspergillus niger process is monitored on-line
In, prediction glucose RMSEP is 3.24g/L, and relative error is 2.21%, and sodium gluconate RMSEP is 4.03g/L, relatively
Error is 2.77%, and the result shows, the method for the present invention is quick, efficiently, without destruction, green non-pollution, can accurately, quickly
Prediction purpose thing content, reflect and instruct fermentation of Aspergillus niger process.
Main prominent technological improvement of the invention and innovative point are：
(1) online mid-infrared light spectrometry is applied
Mid-infrared light spectrometry is a kind of fast and efficiently detection method.The method of conventional measurement sodium gluconate is efficient
Liquid phase.But the method needs to configure the complicated pretreatment such as mobile phase, sample filtering, dilution, and one sample of measurement needs consumption
When more than 10min.And mid-infrared light spectrometry is prepared without mobile phase, can be with direct measurement zymotic fluid, without sample pretreatment, no
Sample can be destroyed, and time of measuring only needs tens of seconds.Advantage on quick is extremely obvious.In addition middle infrared spectrum can be used for
Line monitors fermentation process, more highlights real-time and promptness.
(2) technological improvement of model
For the problem of zymotic fluid component forecasting accuracy, the present inventor is improved to model, using experimental design --
The Method Modeling of total divisor design, does background, and added in calibration set and adopt online with the culture medium in addition to glucose
The collection of illustrative plates of collection, implementation model is improved, and forecasting accuracy is improved；MIR technologies of the invention can realize online, in situ detection substrate
With product, sampled without offline, fast and convenient) to prove that this patent proposes so application first, so as to improve the new of this patent
Newness or creativeness)
It is background with the medium component for removing glucose in the method for optimization of the invention, fermentation can be filled into
Other materials influence quantitative to purpose thing in culture medium.Conventional method is with water as background.
In the method for optimization of the invention, from randomly selected scatterplot distribution standard mixed liquor collection of illustrative plates, to it is complete because
Son design (a kind of method of experimental design) standard mixed liquor collection of illustrative plates, then to total divisor design standard mixed liquor collection of illustrative plates
With reference to the collection of illustrative plates of the zymotic fluid of online acquisition.It is not limited to be modeled using conventional criteria liquid so that detection accuracy is significantly carried
Rise.
(3) improvement of zymotechnique
The present inventor has found the key in fermentation process using the fermentation process of mid-infrared light spectrometry on-line checking aspergillus niger
Factor, selects optimal oxygen-supply quantity, Initial sugar concentration, optimizes zymotechnique.
With reference to specific embodiment, the present invention is expanded on further.It should be understood that these embodiments are merely to illustrate the present invention
Rather than limitation the scope of the present invention.The experimental technique of unreceipted actual conditions in the following example, generally according to conventional strip
Part such as J. Pehanorm Brookers etc. are write, Molecular Cloning:A Laboratory guide, the third edition, Science Press, the condition described in 2002, or
According to the condition proposed by manufacturer.
Materials and methods
(1) bacterial strain
The bacterial strain that the present invention is used is aspergillus niger (Aspergillus niger), limited available from Shandong Fu Yang biotechnologies
Company.
(2) culture medium
Slant medium：Glucose (water) 60g/L, corn pulp 1g/L, MgSO4·7H2O 0.02g/L、KH2PO4
0.13g/L, urea 0.2g/L, CaCO35g/L, agar 20g/L.
Seed culture medium：Glucose (water) 250g/L, corn pulp 8g/L, MgSO4·7H2O 0.17g/L、KH2PO4
0.6g/L、(NH4)2PO4 0.25g/L。
Fermentation medium：Glucose (water) 330g/L, corn pulp 2g/L, MgSO4·7H2O 0.2g/L、KH2PO4
0.17g/L、(NH4)2PO4 0.25g/L。
(3) condition of culture
Inclined-plane culture：Picking monospore daughter colony 35 DEG C, cultivates 60-72h to eggplant bottle on flat board.
5L fermentation tank cultures：In subsequent embodiment, using 5L stirring type bioreactors, (Shanghai Guoqiang Biochemical Engineering is equipped
Co., Ltd) carry out batch cultivation and fermentation of aspergillus niger.Working volume 3L in tank, 38 DEG C of temperature, throughput is 4vvm, starting stirring
Rotating speed 500r/min.In fermentation process, 250g/L NaOH solutions regulation pH to 5.5 is added.With the aspergillus niger spore that 50mL is fresh
The sub- tank of inoculation of suspension liquid, rear 0.5L seeding tanks zymotic fluid switching fermentation tank.
(4) off-line measurement method
A. glucose
Fermentation broth sample is taken in fermentation process, centrifuging and taking supernatant is diluted to suitable concn, measured using glucose kit
Concentration of glucose in zymotic fluid.
B. sodium gluconate
The high performance liquid chromatographs of the Agilent 1100 (HPLC) of Agilent Co., Ltd measures the concentration of sodium gluconate.
Measuring condition：C18-H posts (4.6mm × 250mm, 5 μm), 26 DEG C of column temperature, mobile phase is V (10% methyl alcohol): V (2.4% phosphoric acid)
=1: 1, flow velocity 1mL/min, retention time 8min, sample size 20 μ L, detector wavelength 210nm.The data of acquisition are used as online
The reference value of measured value.
(5) spectrometer and acquisition condition
Online mid-infrared light spectrogram is by the U.S. Thermo NICOLET iS10 type Fourier that is popped one's head in equipped with diamond ATR
The spectrometer collection of conversion mid-infrared light is obtained.
It is background with the fermentation medium components removed outside glucose during collection, keeps temperature in tank constant at 38 DEG C.Ripple
Number scope is 4000-650cm-1, resolution ratio is 4cm-1, and scanning times are 16 times, and liquid nitrogen is added in time to cool down detector.By
The online acquisition collection of illustrative plates of software Result 3.0 is carried, and completes on-line prediction, send Biostar softwares to.PLS models set up by
The softwares of TQ Analyst 9.0 are completed.
Embodiment 1, infrared spectral characteristic is analyzed
Glucose, sodium gluconate binary-medium standard sample are prepared with total divisor method for designing, is gathered wherein infrared
Standard diagram.Because glucose is similar with the chemical constitution of sodium gluconate, thus the region of absworption peak is similar, respectively 950-
1177cm-1、1177-1505cm-1、1505-1650cm-1Three regions, such as Fig. 1.With glucose and gluconic acid na concn
Change, peak area is also changing.Sodium gluconate can distinguish two kinds of things compared with many carboxyl structures of glucose, Given this characteristic
Matter.1505-1650cm-1Region forms Dan Qiangfeng, is caused by the C=O double bond stretching vibrations in sodium gluconate carboxyl structure
, it is the distinctive peak of sodium gluconate, but due to being also herein the position of water peak, even if removal background, still occurs not only
Sliding noise.1177-1505cm-1Region it is bimodal be sodium gluconate COO- stretching vibrations and glucose, sodium gluconate
C-H flexural vibrations cause.950-1177cm-1What bimodal mainly both C-O stretching vibrations at place caused.Last two zones
The peak both of which in domain is contributed, then select these three regions as modeling quantification area.It can be seen that two kinds of materials
Original figure spectrum is closely similar, and the content of purpose thing is not with to absorb peak-to-peak value be simple linear relationship, it is necessary to be counted by chemistry
Amount learns to do section to carry out quantitative analysis.
The method of modeling is：According to the TuPu method that infrared spectrometer is collected, the suitable modeling region of interception, Ran Houshi
When change parameter, computation modeling is carried out using software.
Embodiment 2, PLS models are set up
The principle being distributed using scatterplot, randomly chooses a series of concentration gradient of glucose and sodium gluconate, prepares mark
Quasi- mixed liquor, concentration distribution such as Fig. 2.
It is collection background, the condition in above-mentioned " (5) spectrometer and acquisition condition " with water by this series of standards mixed liquor
It is acquisition condition, gathers its mid infrared spectrum.It is conducted into the softwares of TQ Analyst 9.0, by the suitable modeling ginseng of selection
Number is such as：Modeling method, preprocess method, modeling region etc., such as table 1, obtain performance indications highest model.The modeling of this model
Method is PLS methods.
The performance indications and coefficient correlation of table 1, each parameter drag
Note：In table in the four figures numeral of the 1st row, first digit represents modeling region from left to right：2 is two features
Peak, 3 is three characteristic peaks；Second digit represents collection of illustrative plates preprocess method：1 composes for artwork, and 2 is first derivative；3rd number
Word is smoothing method：1 is rough, and 2 is Savitzky-Golay filter；Fourth digit is baseline correction method：1 is
Do not correct, 2 is linearity correction, and 3 is curvature correction.
As can be seen from the above table, 3222 i.e. modeling region is three characteristic peaks, first derivative, Savitzky-Golay
Filter, the preprocess method performance indications highest of linear baseline correction, are 98.0, and glucose RESEP is 1.94 minimum, grapes
Sodium saccharate RESEP is 1.57 minimum, and both are 0.9999 by similarity factor, so selecting this parameter to be modeled.
Based on this model, the concentration of glucose and sodium gluconate in offline prediction zymotic fluid, as a result such as table 2.
The concentration of glucose and sodium gluconate in table 2, model prediction zymotic fluid
From upper table data：The percentage error of low concentration is larger, and high concentration is relatively small.Glucose mean error hundred
Divide than being 6.51%, predicated error root mean square RESEP is 8.73g/L.Sodium gluconate mean error percentage is 5.27%,
RESEP is 5.06g/L.The result shows also there is predicted value with the larger situation of actual value error, it is necessary to find to produce error
The reason for.
Based on above mentioned problem, the present inventor has analyzed and researched the reason for may causing this error.The aspect of analysis includes：
Vibration, bubble, thalline for causing etc. are stirred in fermentation tank, it is whether big on collection of illustrative plates collection influence；The shadow that binary liquid mixture is prepared
Ring；Using water as background, the component difference with zymotic fluid is larger.Knowable to contrast offline (Fig. 3) and online (Fig. 4) collection of illustrative plates, offline
Collection of illustrative plates is relatively smooth, and characteristic peak substantially, easily carries out quantitative analysis, and the collection of illustrative plates of online acquisition is subject to the shadow of complex situations in tank
Ring, and noise is larger, influences quantitative accuracy, the difficulty of this explanation on-line prediction is big.
By studying repeatedly and testing, inventors determined that the physicochemical property of zymotic fluid and two end number mixing standard sample is in the presence of poor
It is different, it is the main reason for causing this larger error.Therefore, the present inventor prepares standard items again, re-establishes model.
Embodiment 3, PLS model refinements (total divisor design, control collecting temperature simultaneously change collection background)
In the present embodiment, on the experiential basis of model in example 2, experimental design is carried out to hybrid standard sample
(DOE).During Aspergillus Niger value in ferment of sodium gluconate, glucose and gluconic acid na concn are general in 1-400g/L, because
This, the concentration of standard solution of glucose and sodium gluconate is designed in seven gradients of 1-400g/L, respectively 1g/L, 10g/L, 50g/
L、100g/L、200g/L、300g/L、400g/L.Calibration set Portugal is obtained through total divisor design (full factorial design)
Grape sugar, sodium gluconate binary-culture medium solution 49, such as checking collection 12, Fig. 5.
In standard aggregate sample in addition to glucose and sodium gluconate, the inorganic salts ingredients KH in culture medium is additionally added2PO4
0.17g/L, (NH4)2HPO40.25g/L, MgSO40.2g/L, these inorganic salts may influence quantitative problem, so to the greatest extent may be used
Can simulated fermentation broth environment, exclusion influence factor.It is the back of the body with the fermentation medium for removing glucose during collection standard sample collection of illustrative plates
Scape, and ensure that collecting temperature is 38 DEG C.
To remove the fermentation medium (formula of glucose：Corn pulp 2g/L, MgSO47H2O 0.2g/L, KH2PO4
0.17g/L, (NH4) 2PO4 0.25g/L) it is the background of standard aggregate sample, the standard aggregate sample collection of illustrative plates at 38 DEG C of constant temperature is gathered,
Such as Fig. 6.Find out from standard aggregate sample collection of illustrative plates, the absorbance at each peak is all gradient distribution, distribution uniform, standard sample proportioning
It is preferable with collection.It is then introduced into the softwares of TQ Analyst 9.0, by changing modeling parameters such as：It is modeling method, pre-
Processing method, modeling region etc., such as table 3, obtain performance indications highest model.
The performance indications and coefficient correlation of DOE models under table 3, each parameter
Note：In table in the four figures numeral of the 1st row, first digit represents modeling region from left to right：2 is two features
Peak, 3 is three characteristic peaks；Second digit represents collection of illustrative plates preprocess method：1 composes for artwork, and 2 is first derivative；3rd number
Word is smoothing method：1 is rough, and 2 is Savitzky-Golay filter；Fourth digit is baseline correction method：1 is
Do not correct, 2 is linearity correction, and 3 is curvature correction.
As can be seen from the above table, 3223 i.e. modeling region is three characteristic peaks, first derivative, Savitzky-Golay
Filter, the preprocess method performance indications highest of curve baseline correction, are 98.4, and glucose RESEP is 1.73, gluconic acid
Sodium RESEP be 1.69, be it is minimum, both similarity factors be 1.0000 and 0.9999 highest, so selecting this parameter to be built
Mould.
Based on this model, the concentration of glucose and sodium gluconate in offline prediction zymotic fluid, as a result such as table 4.
The concentration of glucose and sodium gluconate in table 4, DOE model prediction zymotic fluids
From upper table data：The percentage error of low concentration is larger, and high concentration is relatively small.Glucose mean error hundred
Divide than being 5.93%, predicated error root mean square RESEP is 4.95g/L.Sodium gluconate mean error percentage is 5.13%,
RESEP is 4.60g/L.The model for not carrying out DOE is slightly good.But due to the collection of illustrative plates of online acquisition be subject in yeasting it is complicated because
The influence of element, noise is larger, with the calibration set profile variation of offline collection substantially, the online collection of illustrative plates for just having added as described above with from
The comparing of line chart spectrum is, it is necessary to further improve.
The optimization of PLS models after embodiment 4, improvement
Because the PLS models after improvement are by the mid infrared spectrum of offline glucose-glucose acid sodium binary liquid mixture
Composition, although designed by experimental design-total divisor, but still there is certain error.And, environment is answered in fermentation tank
Miscellaneous, the factor such as stirring, bubble can all have influence on the collection of collection of illustrative plates.And during the fermentation, 1 molecule glucose is converted into 1 molecule
Sodium gluconate, so, both concentration relationships are almost linear relationship, for model without dispersiveness.Thus, in original
Come on the basis of model, by the standard aggregate sample collection of illustrative plates of the total divisor design with dispersiveness, add the fermentation of online acquisition
Liquid collection of illustrative plates (during modeling, collection of illustrative plates and its corresponding glucose, the standard value of gluconic acid na concn needed for modeling need to be imported, this
Standard value is measured offline by chemical method, forms a forecast model.When being predicted, it would be desirable to the collection of illustrative plates of the solution of prediction
It imported into this model, you can obtain the concentration of this solution) carry out the optimization of model.Fig. 7 is the zymotic fluid for filling into online acquisition
Concentration profile after collection of illustrative plates.Diamond indicia is the concentration distribution of modeling collection, and square indicia collects for checking, and both is original
The composition of model.Triangular marker is the concentration of the new zymotic fluid collection of illustrative plates for filling into.
It is background with the culture medium except glucose, collection of illustrative plates is collected at 38 DEG C of constant temperature.It is then introduced into TQ
In the softwares of Analyst 9.0, by changing modeling parameters such as：Modeling method, modeling region, preprocess method etc., such as table 5, obtain
Obtain performance indications highest model.
The performance indications and coefficient correlation of DOE models under table 5, each parameter
Note：In table in the four figures numeral of the 1st row, first digit represents modeling region from left to right：2 is two features
Peak, 3 is three characteristic peaks；Second digit represents collection of illustrative plates preprocess method：1 composes for artwork, and 2 is first derivative；3rd number
Word is smoothing method：1 is rough, and 2 is Savitzky-Golay filter；Fourth digit is baseline correction method：1 is
Do not correct, 2 is linearity correction, and 3 is curvature correction.
As seen from Table 5, the difference of each group coefficient of performance is not obvious, and the highest coefficient of performance 95.2 corresponds to three groups of parameters,
3221st, preferably, 3223 sodium gluconate RMSEP is preferable for 3223 glucose RMSEP, so selection 3223 is best model,
I.e. modeling region is three characteristic peaks, first derivative, Savitzky-Golay filter, the pretreatment side of linear baseline correction
Method performance indications highest, is 95.2, and glucose RESEP is 4.03, and sodium gluconate RESEP is 5.44, and both are at similarity factor
0.9997 and 0.9991.Fig. 8 is the difference of glucose and sodium gluconate predicted value and actual value, it can be seen that data point is equal
Near y=x, 0.999 similarity factor has been confirmed.
Based on this model, the concentration of glucose and sodium gluconate in offline prediction zymotic fluid, as a result such as table 6.
The concentration of glucose and sodium gluconate in table 6, Optimized model prediction zymotic fluid
From the data of table 6：The percentage error of low concentration is larger, and high concentration is relatively small.Glucose mean error hundred
Divide than being 5.64%, predicated error root mean square RESEP is 4.64g/L.More originally model 5.93%, 4.95g/L is slightly good.Glucose
Sour sodium mean error percentage is 4.12g/L for 4.65%, RESEP, compared with master mould 5.13%, 4.60g/L it is also good.But it is offline
The zymotic fluid collection of illustrative plates of collection, has differences with the model collection of illustrative plates for being adapted to online acquisition, so being existed with each model prediction below
Whether with optimization effectively the collection of illustrative plates of line collection, verify the improvement of model, such as table 7.
Table 7, each model predication value compare
, it is evident that adding the more former two model prediction abilities of Optimized model after online acquisition collection of illustrative plates accurate in table 7.School
Before just, in 10g/L or so, error is in 5-9%, and original model for the glucose and sodium gluconate RMSEP of Optimized model prediction
It is far longer than these numerical value.After correction, the glucose and sodium gluconate RMSEP of Optimized model prediction are missed within 5g/L
Difference is 2% or so.It can be seen that, the percentage error of glucose is from 2.74% to 2.27% again to 2.16%, improvement progressively.
And the percentage error of sodium gluconate from 4.14% to 3.14% again to 2.08%, the improvement of predictive ability is clearly.Can
Can contribute to have more noise peak at more peak in sodium gluconate due to the collection of illustrative plates of online acquisition, the optimization of model is reduced
Influences of these noise peaks to quantitatively causing.This shows that present Optimized model performance is higher, can more precisely carry out
On-line monitoring, reacts and instructs fermentation of Aspergillus niger process in time.
Embodiment 5, on-line monitoring fermentation process
After overcoming the multiple problem such as background collection, model optimization, mid-infrared light spectrometer on-line monitoring aspergillus niger is realized
Fermentation process, schematic diagram such as Fig. 9.It is inserted into reactor by the interface being adapted to after the sterilizing of ATR fibre-optical probes, middle infrared spectrum
Instrument can realize the collection of illustrative plates of zymotic fluid in online acquisition tank, be transferred in computer, the softwares of Result 3.0 to collection of illustrative plates at
Manage and predict the concentration of purpose thing glucose and sodium gluconate, be transferred to biostar softwares, and with other online data phases
With reference to reflection in time and guides fermentation process.
During fermentation of Aspergillus niger malaga sodium saccharate, aspergillus niger consumption of glucose changes into gluconic acid, grape
Saccharic acid generates sodium gluconate with NaOH reactions again.Mid-infrared light spectrometer can be with the infrared figure of Real-time Collection fermentation cylinder for fermentation liquid
Spectrum, such as Figure 10 realizes the change in concentration of substrate glucose and product sodium gluconate in on-line monitoring fermentation process, such as Figure 11.
The infared spectrum of the zymotic fluid of online acquisition goes out peak position and peak type closely compared with the infared spectrum of modeling, special
Levy peak obvious.Over time, feature peak-to-peak value also regular change.1505-1650cm-1Region (X-axis)
Dan Qiangfeng, is the distinctive peak of sodium gluconate, and with the increase of fermentation time (Z axis), absorbance (Y-axis) gradually increases, explanation
The concentration of sodium gluconate gradually increases.1177-1505cm-1The bimodal change at place is not obvious, is slowly increased, due to glucose
The concentration of sour sodium gradually increases, and concentration of glucose is gradually decreased, so contribution of the sodium gluconate to this peak is larger.950-
1177cm-1Two peaks in region are increased over time, absorbance gradually decreasing, i.e., glucose to the contribution in this region compared with
Greatly.
Fermentation initial stage, glucose is consumed with less speed.Fermentation about 6h, rotating speed is improved, and DO is raised, the increase of oxygen
Reaction rate is improve, and then OUR increases, the consumption of glucose increases, thus slope of a curve increase, such as Figure 11 A.Fermentation is about
18h, concentration of glucose is no longer reduced close to zero, fermentation ends.The concentration of glucose of offline sampling and measuring and on-line prediction
Concentration compares, the curve for fitting, similarity factor R2It is 0.9988, such as Figure 11 B.Sodium gluconate is by glucose conversion
Come, concentration is related to glucose, ferment the initial stage, synthesized with smaller speed, 3.5h surveys off-line measurement value and on-line checking value difference is different
It is larger, from the point of view of reflection trend, it may be possible to the exception of off-line measurement.Fermentation about 6h, the raising of rotating speed causes reaction rate
Raising, and then the speed of synthesis of glucose acid sodium also raises, and ferment about 18h, and sour na concn no longer increases, fermentation ends, with
The change of glucose is corresponding, such as Figure 11 C.The concentration of the sodium gluconate of offline sampling and measuring is compared with the concentration of on-line checking
Compared with the similarity factor R of matched curve2It is 0.9971, such as Figure 11 D.
The glucose RMSEP values of real-time estimate are 3.24g/L, and relative error is 2.21%, the RMSEP values of sodium gluconate
It is 4.03g/L, relative error is 2.77%.Because on-line monitoring is influenceed by bubble, stirring, vibration etc. are multifactor, offline
Measured value has certain error, and the concentration range more greatly 1-400g/L for predicting with on-line measurement value, so RMSEP values can slightly
It is high.By the way that after simple fitting correction, on-line monitoring value with off-line measurement value closely, and can correctly reflect purpose thing
Change in concentration situation, confirmed mid-infrared light spectrometer on-line monitoring reliability.
Embodiment 6, middle infrared spectrum instructs optimization fermentation of Aspergillus niger process 1
In the reaction of Aspergillus Niger sodium gluconate, oxygen, as one of reactant, is the required of reaction.Human hair of the present invention
Show, in the reaction the phase, dissolved oxygen is usually closer to 0, and the shortage of oxygen result in reaction and be limited.Thus contrived experiment, is passed through 5%
(v/v), the pure oxygen of 7.5%, 10% gradient concentration, observation sugar consumption rate, Product formation speed, with judge reaction limitation because
Element, so as to optimize fermentation process.5L fermentation tank cultures method is as previously described.
The addition of mid-infrared light spectrometer, can embody the rapidity of real-time monitoring.Although controlling oxygen with mass flowmenter
The amount of filling into of gas, but due to steel cylinder pressure instability, the amount of filling into is unstable sometimes for oxygen, and oxygen is dangerous combustion-supporting product, occurs asking
Topic consequence is hardly imaginable.And dissolved oxygen is still in the state close to zero, it is impossible to which reflect oxygen is passed through situation, so as to influence
To fermentation process.Online mid-infrared light spectrometry can in time detect the content of glucose and sodium gluconate, calculate consumption sugar
Speed rs, Product formation speed rp, according to this parameter, can timely reflect fermentation situation, and effectively fermentation process is carried out
Instruct and optimize.
Although each group (10% pure oxygen group；7.5% pure oxygen group；5% pure oxygen group)) initial sugar concentration it is slightly different (due to plant
Degree is inconsistent causes for the consumption sugar of sub- tank), but slope can still reflect the response situation of each group, such as Figure 12.Fermentation is just
Phase, the sugar consumption rate of each group is all relatively low, but will be obvious that the experimental group for being passed through 10% pure oxygen, slope apparently higher than other groups,
And the sugar consumption rate of other groups is closer to.After fermentation 6h, the raising of rotating speed makes dissolved oxygen slightly improve, slope increase, reaction speed
Rate is accelerated.The later stage fermentation stage can be seen that control group sugar concentration slope and be significantly less than each experimental group, and sugar consumption rate is less than experiment
Group, illustrates being passed through for oxygen, and reaction is accelerated to a certain degree.And in three experimental groups of gradient oxygen, the experiment of 10% pure oxygen
Group, oxygen is passed through at most, maximum slope, reaction rate highest.The experimental group of 7.5% pure oxygen is taken second place, the experimental group phase of 5% pure oxygen
To most slow.Illustrate that oxygen within the specific limits is passed through more, reaction rate is faster, and now enzyme is unrestricted.
Initial glucose acid sodium (sodium gluconate, SG) concentration of each group seeding tank is different, but slope is still
The situation of the Product formation of each group, such as Figure 13 can be reflected.Fermentation initial stage, the sodium gluconate synthesis rate of each group is relatively low,
Experimental group synthesis rate is similar, slightly above control group.After fermentation 6h, rotating speed is improved, dissolved oxygen is raised, slope increase, reaction rate
Accelerate.The experimental group oxygen of 10% pure oxygen is passed through at most, and, apparently higher than each group, the speed for synthesizing SG is most fast, 7.5% pure oxygen for slope
Experimental group take second place.5% experimental group and control group slope difference is smaller, observes unobvious in figure.But can still illustrate
What oxygen was passed through in certain limit is more, and reaction rate is faster, and now enzyme is unrestricted.With the Expenditure Levels phase one of glucose
Cause.
Predicated error under table 8, different aeration conditions compares
The prediction case of glucose and sodium gluconate is shown in Table 8, and the RMSEP values of wherein glucose and sodium gluconate exist
It is that caused concentration deviation is slightly larger because the concentration range predicted is larger between 2-4g/L.And relative error is all in 1%-3%
Between, R20.998 or so, can in accurate real time reaction fermentation tank residual sugar and product concentration.And then in also illustrating
It is infrared in the case where oxygen is passed through, still can more accurately reflect the change of glucose and gluconic acid na concn, almost
The influence of oxygen is not affected by, can in time reflect fermentation situation, instruct fermentation process.
Table 9, different batches fermentation situation compares
| Batch | Unit | Control group | 5% pure oxygen | 7.5% pure oxygen | 10% pure oxygen |
| Fermentation period h | h | 18 | 17.5 | 17 | 15 |
| Average sugar consumption rate | g/L·h | 16.50 | 18.85 | 19.29 | 20.53 |
| On-line monitoring sugar consumption rate | g/L·h | 16.20 | 18.78 | 18.94 | 20.29 |
| Average product synthesis rate | g/L·h | 15.62 | 16.79 | 17.24 | 19.59 |
| On-line monitoring Product formation speed | g/L·h | 15.45 | 16.81 | 17.01 | 19.37 |
| Yield | mol/mol | 0.86 | 0.84 | 0.86 | 0.91 |
| On-line monitoring yield | mol/mol | 0.86 | 0.84 | 0.86 | 0.91 |
| Bacterium is dense | g/L | 1.33 | 1.68 | 1.89 | 2.05 |
Growth, response situation (table 9) with reference to several wholesale ferment though can be seen that bacterium dense (the average bacterium of fermentation process is dense) no
Together, but now influence reaction rate is reactant oxygen, and enzyme amount is unrestricted enough, thus it is not influence reaction speed that bacterium is dense
The factor of degree.With the raising of oxygen intake, fermentation period is gradually shortened, and sugar consumption rate rs and Product formation speed rp
Increase, do not lose yield.
Sugar consumption rate, Product formation speed, the yield of middle infrared survey, can be just timely in the reaction close to reference value
Reflect the situation of these fermentations, accomplish quick, timely, accurate.Fermentation ends can judge 10% by online data
Under the conditions of logical oxygen, sugar consumption rate rs, Product formation speed rp highests can be reached, yield is uninfluenced.Change in aeration condition
In the case of, it is online in infrared predictive ability almost without influence, can accurately predict glucose and Portugal in zymotic fluid
The concentration of grape sodium saccharate.Oxygen is dangerous combustion-supporting product, it is impossible to fill into excessive, easily causes the danger such as blast, and tail oxygen already close to
28%, the limitation of the accurate measurement of tail gas mass spectrum monitoring is exceeded soon, that is to say, that oxygen uptake rate OTR has been limited, and fills into
Oxygen participates in reaction in can not being timely dissolved in zymotic fluid, but is expelled directly out fermentation tank, also result in waste.Thus, 10%
Pure oxygen intake be the now optimum oxygen amount of filling into.
Embodiment 7, middle infrared spectrum instructs optimization fermentation of Aspergillus niger process 2
The online test method that the present inventor is instructed using middle infrared spectrum, it was found that different initial sugar in seeding tank
(glucose) concentration, may have an impact to reaction.Therefore, the present embodiment design with 200 in seed culture medium, 300,400,
The different Initial sugar concentrations of 500g/L are fermented, and are closed by using MIR on-line monitoring technique Real Time Observations sugar consumption rate, product
Into the situation of speed, the optimal Initial sugar concentration of seeding tank is screened, to optimize fermentation process.
The fermentation process of Aspergillus Niger sodium gluconate is mainly the process of primary metabolite, and the cycle is relatively short, and reaction
Speed is fast, the characteristics of with efficient, quick, hypertonic, oxygen consumption high.Because the change of lag phase remaining sugar concentration is little, and easily microbiological contamination, because
This starts with MIR and carries out on-line checking from production period.Figure 14 can clearly reflect, in infrared on-line monitoring glucose,
Sodium gluconate concentration with fermentation time change, and the slope of curve can reflect the wear rate of glucose, sodium gluconate
Synthesis rate speed.Fermentation 0h, each batch rotating speed is set to 800rpm, after fermentation 4h, the first batch curve of sugar 300g/L
Slope be significantly greater than other each batches, illustrate aspergillus niger under the Initial sugar concentration of 300g/L, glucose consumption rate, Portugal
Grape sodium saccharate synthesis rate is most fast, i.e., reaction rate is most fast.Initial sugar concentration be 500g/L when, osmotic pressure is higher and cause bacterial strain
Lag phase increases, it is impossible to sodium gluconate is synthesized at once, about ferment 9h, starts consumption of glucose synthesis of glucose acid sodium.
The batch speed of first sugar 200g/L is relatively minimum.Infrared on-line monitoring fermentation of Aspergillus niger can clearly, quickly, in fact in utilization
When ground reaction substrate and product situation of change, be easy to be best understood from fermentation process, the anomaly of feedback fermentation in time.Finally
As a result, when Initial sugar concentration is 300g/L, the sugar consumption rate of production period, Product formation speed are most fast.
Under the conditions of different Initial sugar concentrations, in addition to the batch of first sugared 500g/L, the glucose and gluconic acid of different batches
Between 2~4g/L, error is in 3% or so, R for the predicated error root mean square (RMSEP) of sodium2Close to 0.999, such as the institute of table 10
Show.Show the robustness (Robustness means the stability under the influence of foeign element) of the model that preceding method is set up preferably,
Can in accurate real time reaction fermentation tank residual sugar and product concentration.
Table 10, the predicated error of each batch compare
Reaction of the fermentation process of Aspergillus Niger sodium gluconate under conditions of sugar high.Find out in table 11, at the beginning of each batch
Just the outer condition of culture of sugar is consistent, and yield, bacterium are dense to be more or less the same, and just the sugar consumption rate and Product formation speed of sugar 300g/L are remote
Higher than other Initial sugar concentrations.It is consistent with the fermentation situation monitored on-line in Figure 14.Therefore, Initial sugar concentration is too high or mistake
The low malaga sodium saccharate reaction that can suppress bacterial strain.Using the glucose and sodium gluconate of mid-infrared light spectrometer on-line prediction
Concentration, the reference value difference of the sugar consumption rate for calculating, Product formation speed, yield and off-line measurement is smaller, except just sugared
The batch of 500g/L, it may be possible to beyond the scope of model prediction；On the other hand, also prompting is that osmolality higher makes carefully
The metabolic characteristic of born of the same parents is affected.Infrared on-line monitoring fermentation of Aspergillus niger process in explanation, except can accurately predict
Outside the concentration of glucose and sodium gluconate, sugar consumption rate, Product formation speed, yield can also be accurately calculated, preferably
Instruct and optimization fermentation process.The infrared online monitoring data with of comprehensive off-line measurement value, Initial sugar concentration 300g/L is black
The first sugar condition of aspergillus growth period most suitable fermentation, Product formation speed now is 20.8g/Lh.
Table 11, each Batch fermentation situation compare
3rd, discuss
Present invention application FT-MIR-ATR combinations PLS successfully realizes the fermentation of on-line monitoring aspergillus niger
Process.Glucose and sodium gluconate are in 950-1650cm-1Region has obvious infrared absorption peak, is set up by PLS methods and marked
Quasi- liquid forecast model, the glucose RMSEC and RMSEV of model are 0.96,1.71g/L, similarity factor R2It is respectively 1.000 Hes
0.999, the RMSEC and RMSEV of sodium gluconate are 2.67,1.59g/L, R2Respectively 0.999 and 0.999, estimated performance compared with
It is good.Because titer is larger with zymotic fluid physicochemical property difference, then line chart spectrum optimization mould is added in titer spectrum model
Type.The zymotic fluid collection of illustrative plates of the model prediction online acquisition after optimization, glucose RMSEP is 3.68g/L, and relative error is
2.16%, sodium gluconate RMSEP are 4.58g/L, and relative error is 2.08%, slightly good compared with master mould, are particularly suited for online
Monitoring process.MIR is while detection substrate glucose and gluconic acid na concn, the RMSEP of glucose is 3.24g/L, relative to miss
Difference is 2.21%.And the RMSEP values of sodium gluconate be 4.03g/L, relative error is 2.77%, can quickly, it is pre- in time
The change in concentration situation of substrate and product is surveyed, the fermentation situation of aspergillus niger is effectively monitored, the generation for instructing sodium gluconate to synthesize
Thank to regulation and control.Under different aeration conditions, different Initial sugar concentrations, fermentation process, and optimization still can be exactly presented
This stage optimal yeastiness.
The all documents referred in the present invention are all incorporated as reference in this application, independent just as each document
It is incorporated as with reference to such.In addition, it is to be understood that after above-mentioned instruction content of the invention has been read, those skilled in the art can
Made various changes or modifications with to the present invention, these equivalent form of values equally fall within the model that the application appended claims are limited
Enclose.
Claims (10)
1. it is a kind of to monitor the method that fermentation of Aspergillus niger produces the fermentation process of sodium gluconate on-line, it is characterised in that the side
Method includes：Online acquisition zymotic fluid, zymotic fluid collection of illustrative plates, glucose and Portugal in analysis zymotic fluid are obtained using mid-infrared light spectrometer
Grape saccharic acid sodium content.
2. the method for claim 1, it is characterised in that imported into optimization design by by online acquisition zymotic fluid collection of illustrative plates
PLS models in, analysis zymotic fluid in glucose and gluconic acid sodium content.
3. method as claimed in claim 2, it is characterised in that the PLS models of described optimization design are set up as follows：
(1) titer of gradient design glucose and sodium gluconate, calibration set glucose, glucose are obtained through total divisor design
Sour sodium binary-medium standard liquid and checking collection glucose, sodium gluconate binary-medium standard liquid；Calibration set and checking
The collection of illustrative plates of collection is imported in spectral analysis software, by changing modeling parameters, obtains comparatively performance indications highest model；Institute
It is background with the culture medium for removing glucose in the titer stated；
(2) on the basis of the model of (1), the zymotic fluid collection of illustrative plates of addition online acquisition carries out the optimization of model in calibration set, obtains
Obtain the PLS models of optimization design.
4. method as claimed in claim 3, it is characterised in that in step (2), also include：For the zymotic fluid of online acquisition,
Detected using chemical method, obtain the content data of glucose and sodium gluconate in fermentation broth sample；The collection of illustrative plates that will be collected
It is associated with chemical method detection data, using Multiple Regression Analysis Method analysis model, so as to obtain comparatively performance refer to
Mark highest model.
5. method as claimed in claim 3, it is characterised in that described spectral analysis software is TQ Analyst softwares.
6. method as claimed in claim 3, it is characterised in that the culture medium of described removal glucose includes：Corn pulp,
MgSO4·7H2O、KH2PO4、(NH4)2PO4；It is preferred that including：2 ± 0.5g/L of corn pulp, MgSO4·7H2O 0.2±0.05g/
L、KH2PO4 0.17±0.05g/L、(NH4)2PO4 0.25±0.05g/L。
7. the method for claim 1, it is characterised in that gather zymotic fluids at 38 ± 0.5 DEG C, obtains zymotic fluid collection of illustrative plates.
8. it is a kind of to improve the method that fermentation of Aspergillus niger produces the reaction rate of sodium gluconate, it is characterised in that methods described bag
Include：During the course of the reaction, oxygen is added with the oxygen-supply quantity higher than 8%；Oxygen is more preferably added with the oxygen-supply quantity of 8-15%；More preferably
Ground adds oxygen with the oxygen-supply quantity of 9-11%.
9. method as claimed in claim 8, it is characterised in that fermentation medium is：300 ± 40g/L of glucose, corn pulp 2
±0.5g/L、MgSO4·7H2O 0.2±0.05g/L、KH2PO4 0.17±0.05g/L、(NH4)2PO4 0.25±0.05g/L。
10. method as claimed in claim 8, it is characterised in that the early stage of the method, in the seed culture medium of use, grape
Sugared concentration is 300 ± 15g/L；More preferably it is 300 ± 10g/L；More preferably it is further 300 ± 5g/L.
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