CN101339186A - Method for on-line detection for solid-state biomass bioconversion procedure - Google Patents
Method for on-line detection for solid-state biomass bioconversion procedure Download PDFInfo
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Abstract
The invention relates to a method for on line detecting biological transmutation process of solid-state biomass, the steps of the method are as follows: choosing not more than 50 portions of samples of representative biomass in the biological transmutation process as standard samples for acquiring standard maps; obtaining the parameters on target process by other means; establishing a quantitative model through multiple regression to quantify other unknown processes. The method provided by the invention can realize on-line detection of parameters in biological transmutation process only by establishing a forecasting model and can automatically detect and control the biological transmutation process, without sample preparation or reagent consumption. The method is used for forecasting related coefficients in solid-state fermentation, including moisture content, biomass, grease output and cellulase, the forecast related coefficients respectively reach 0.994, 0.999, 0.984 and 0.994.
Description
Technical field
The present invention relates to the instrumental analysis field, the method for particularly a kind of non-destructive, on-line determination solid-state biomass bioconversion procedure mesostroma and product content.
Background technology
In the conversion process of living beings, bio-transformation is owing to its friendly to environment becomes the research focus.The bio-transformation of living beings mainly comprises microbial fermentation (mainly being solid state fermentation) and enzymolysis.The substrate of traditional solid state fermentation and modern solid-state fermentation is basically based on agricultural byproducts, and fermentation substrate commonly used comprises maize straw, wheat stalk, rice straw, wheat bran, rice husk, corncob, corn, millet, soybean, bagasse, cassava etc.From source analysis, stalk biomass is the main substrate of solid state fermentation in the nature of things, the bulk product produced of solid state fermentation especially, and as alcohol fuel, biodiesel etc.The difference of nutrient culture media has determined the mensuration difference of the numerous parameters of sweat, the mensuration of many parameters can not be applied mechanically liquid fermentation method, as biomass, when liquid fermentation, only need centrifugal process just thalline can be separated from fermentation liquor, and the mensuration of biomass just quite bother in the solid state fermentation.The biomass of solid state fermentation is measured the general indirect assay method that adopts, such as Glucosamine method (Lu Xiuling etc., 2000, the Tianjin Light Industry College journal, the 4th phase, the 57-62 page or leaf), and the method for lignocellulose component analysis (Standard Biomass Analytical Procedures, National Renewable Energy Laboratory, http://www.nrel.gov/biomass/analytical_procedures.html) need use a large amount of chemical reagent, and process is very long, and inefficiency is difficult to realize on-line determination.It is a more extensive process that solid state fermentation is compared with liquid state fermentation, and this is because this process often is difficult to find suitable process monitoring mode, thereby is difficult to also accomplish that On-line Control can only operate by rule of thumb.The enzymolysis of living beings transforms owing to being subjected to paying close attention to widely for craving for of biomass fuel.The monitoring of its enzymolysis process has the demand identical with solid state fermentation owing to existing solid and liquid to be difficult to carry out fast detecting with common analytical approach in its process simultaneously.
Near infrared spectrum mainly is because the anharmonicity of molecular vibration makes molecular vibration produce when the high level transition from ground state, what write down mainly is the frequency multiplication and the sum of fundamental frequencies absorption of hydrogeneous radicals X-H (X=C, N, O) vibration, different groups are (as methyl, methylene, phenyl ring etc.) or near infrared absorption wavelength and the intensity of same group in the different chemical environment significant difference is all arranged, near infrared spectrum has abundant structures and composition information, and the composition and the character that are suitable for very much carbon-hydrogen organic are measured.Though the non-constant width of the scope of NIR spectrum, and often overlapping, it but has the ability of the very strong similar raw material of difference biology, such as the different cultivars of coffee, and the milk powder of separate sources and Chinese herbal medicine.In addition, near infrared optical fiber diffuse reflection probe measurement, the Long-distance Control that can be used for small amount of sample makes the whole operation process convenient simultaneously.But in solid state fermentation field and living beings enzymolysis process, still do not have any about near infrared relevant patent documentation report.
Summary of the invention
Purpose of the present invention is difficult to quick on-line determination complex solid-state matrix components problem in order to solve prior art exactly, the abundant information that makes full use of in the near infrared spectrum to be comprised provides a kind of method of online, quick, simple to operate, the insoluble biomass bioconversion procedure parameter of nondestructively measuring.The method of a kind of fast measuring biomass bioconversion procedure parameter of the present invention is characterized in that it comprises the steps:
(1) select representational biomass bioconversion procedure, timing sampling in conversion process, the sample of acquisition is as the standard model of setting up the multiple regression forecasting model;
(2) diffuse reflection spectrum of each living beings bio-transformation sample in use ft-nir spectrometer and the light transmitting fiber probe bioassay standard sample sets;
(3) adopt the procedure parameter of each living beings bio-transformation sample in corresponding chemistry or the physical analysis means bioassay standard sample sets, as with sample sets near infrared spectrum chemical score one to one;
(4) adopt multivariate calibration methods to set up and optimization multiple regression forecasting model, cross validation mean square deviation (RMSEVC) with the calibration set sample is index optimization preprocessing procedures and model parameter, the prediction mean square deviation (RMSEP) of unknown sample is investigated the prediction accuracy of model, choose the as far as possible little combination of RMSECV and RMSEP.The computing formula of RMSECV and RMSEP is as follows:
In the formula: C
iBe the value that standard chemical process records,
Be the NIR predicted value, n is the calibration set sample number, and m is a checking collection sample number.
(5) in biotransformation, gather the near infrared collection of illustrative plates at any time, spectrum is imported forecast model, thereby pass through the procedure parameter of the online definite biotransformation of model.
The present invention compares with background technology, and the beneficial effect that has is:
(1) function is strong, when can realize in the living beings conversion process multiple parameter, and on-line determination.
(2) easy to use, have good transplantability, when the living beings conversion process changes, can realize applicability very easily to new technology by the sample size that increases regressive prediction model.
(3) have good economic benefit, traditional measurement means will expend lot of manpower and material resources and can not realize on-line measurement at aspects such as sampling, mensuration, data analyses, and this method has realized on-line determination easily.
Embodiment
The water cut of nutrient culture media in the embodiment 1 usefulness near infrared spectroscopy fast measuring solid ferment process
The flow process of the embodiment of the invention is as follows:
The preparation of first step modeling sample collection, adopt steam exploded wheat straw and wheat bran main medium as solid state fermentation, regulate nutrient culture media between 45-85%, the inoculation Trichoderma viride is carrying out solid state fermentation behind the medium sterilization in the gas-phase double-dynamic solid fermentation reactor under 30 ℃, after cultivating certain hour, as the calibration set sample of near infrared multiple regression forecasting model, take out fermented sample.The quantity of sample 50 parts for well, the representational quality of sample sets has a significant impact stability, the adaptability of forecast model.Require the standard deviation of water cut in the sample bigger for well.
The mensuration of the near infrared spectrum of the second step solid state fermentation sample.The Nexus type ft-nir spectrometer that present embodiment adopts U.S. Nicolet company to produce, instrument parameter is set to: the scanning spectrum district is 4000-10000cm
-1, scanning times is 64 times, resolution is 4cm
-1The collection of sample near infrared spectrum is carried out in the diffuse reflection mode by the light transmitting fiber solid probe.The setting that each scanning is adopted will be consistent, and fibre-optical probe is not moved or rotated to scan period simultaneously.
The mensuration of the water cut of the 3rd step solid state fermentation sample.Get above-mentioned sample, accurately weigh, sample is dried to constant weight at 105 ℃.Accurately weigh, calculate the water cut of sample, standard model concentrates the water cut of each sample and the near infrared spectrum of being gathered corresponding one by one.
The foundation and the optimization of the 4th step mathematical prediction model.The multivariate calibration methods of setting up the mathematical prediction model employing can be offset minimum binary algorithm (PLS), principal component regression method (PCR), contrary least square method (ILS), artificial neural network method, support vector machine and multiple linear regression method (MLR) etc.The TQAnalyst6.2 version quantitative analysis software that present embodiment is used U.S. Nicolet company carries out the correction and the optimization of multivariate regression model.Standard model is concentrated the corresponding one by one input TQAnalyst6.2 of the chemical score quantitative analysis software of the water cut of the near infrared spectrum of each sample and solid state fermentation sample.Automatic searching that utilization is provided with in software and optimizational function are sought the top condition of setting up model, by the more various coefficient of determination (R that may make up down forecast model
2), choose R
2Big as far as possible combination.Adopt internal chiasma to confirm mathematical prediction model is verified.Internal chiasma confirms to be meant that rejecting modeling sample successively concentrates (or a plurality of) sample, come the content of the disallowable sample of modeling and forecasting with remaining sample, the difference of more disallowable sample predicted value and chemical score, judge the forecasting accuracy of institute's established model thus, investigate with cross validation mean square deviation (RMSEVC), RMSEVC is more little, and the model prediction accuracy is high more.Investigate the prediction accuracy of model at last by prediction mean square deviation (RMSEP).
The top condition that solid state fermentation sample moisture content multiple regression forecasting model is set up in the present embodiment acquisition is: best major component dimension is 10, and optimized spectrum district scope is 8630-6880cm
-1, the optimal spectrum preprocess method is: without any pre-service.The coefficient of multiple correlation of model (RSQ) is 0.994, and cross validation standard deviation (RMSECV) is 0.00776.
The 5th step application model is measured unknown sample and has been set up after the mathematical prediction model, just can measure the change of moisture content in the unknown solid ferment process.Repeat the near infrared spectrum of second step collection unknown sample, spectrum is imported forecast model, computing machine provides the water cut of unknown sample immediately.The water cut of 10 solid state fermentation samples of model determination of setting up with present embodiment adopts oven drying method to measure its water cut as a comparison simultaneously, and predicted value and measured value are with t check check back unrestricted difference under 0.05 level.
The biomass of nutrient culture media in the embodiment 2 usefulness near infrared spectroscopy fast measuring solid ferment process
The flow process of the embodiment of the invention is as follows:
The preparation of first step modeling sample collection, adopt quick-fried maize straw of vapour and wheat bran main medium as solid state fermentation, the water cut of regulating nutrient culture media is about 75%, the inoculation penicillium decumbens carries out solid state fermentation at 30 ℃ behind the medium sterilization, after cultivating certain hour, take out fermented sample, as the calibration set sample of near infrared multiple regression forecasting model.The quantity of sample at 50 parts for well.
The mensuration of the near infrared spectrum of the second step solid state fermentation sample.The Nexus type ft-nir spectrometer that present embodiment adopts U.S. Nicolet company to produce, instrument parameter is set to: the scanning spectrum district is 4000-10000cm
-1, scanning times is 64 times, resolution is 4cm
-1The collection of sample near infrared spectrum is carried out in the diffuse reflection mode by the light transmitting fiber solid probe.
The mensuration of the biomass of the 3rd step solid state fermentation sample.Get the fermented sample 0.50g of oven dry, grinding the back soaked 24 hours with 10ml12NHCl, add 40ml distilled water in the sample 121 ℃ of following hydrolysis 2 hours, constant volume is to 50.0ml behind the sample filtering, gets 10.0ml and gets 1.0ml with the NaOH constant volume that neutralizes behind the pH7.0 to 25.0ml, add 1ml diacetone reagent 90 ℃ of insulations 1 hour, the cooling back adds 6ml ethanol and 1mlEhrlich reagent is incubated 10 minutes at 65 ℃, and the content of Glucosamine is determined in the 530nm colorimetric in the cooling back according to typical curve.Then these data reductions are become mg Glucosamine/g sample that wets.Because according to these data, the content of the Glucosamine that the unit's of adding thalline biomass is contained just can obtain the biomass content in the matrix, has just solved the mensuration problem of biomass so solved the problem of Glucosamine in some sense.Standard model concentrates the biomass of each sample and the near infrared spectrum of being gathered corresponding one by one.
The foundation and the optimization of the 4th step mathematical prediction model.The TQAnalyst6.2 version quantitative analysis software that present embodiment is used U.S. Nicolet company carries out the correction and the optimization of multivariate regression model, can adopt other business-like quantitative analysis softwares of the same type to finish equally.
The top condition that solid state fermentation sample biomass multiple regression forecasting model is set up in the present embodiment acquisition is: best major component dimension is 9, and optimized spectrum district scope is 8630-6880cm
-1, the optimal spectrum preprocess method is: first order derivative is handled.The coefficient of multiple correlation of model (RSQ) is 0.999, and cross validation standard deviation (RMSECV) is 0.0331mg/g.
The 5th step application model is measured unknown sample and has been set up after the mathematical prediction model, just can measure the biomass of unknown solid state fermentation sample.Repeat the near infrared spectrum of second step collection unknown sample, spectrum is imported forecast model, computing machine provides the biomass of unknown sample immediately.The biomass of 10 solid state fermentation samples of model determination of setting up with present embodiment adopts its biomass of acidolysis colorimetric method for determining as a comparison simultaneously.Predicted value and measured value are checked back unrestricted difference under 0.05 level with the t check.
The cellulase of nutrient culture media in the embodiment 3 usefulness near infrared spectroscopy fast measuring solid ferment process
The flow process of the embodiment of the invention is as follows:
The preparation of first step modeling sample collection, adopt the main medium of the quick-fried maize straw of vapour as solid state fermentation, the water cut of regulating nutrient culture media is about 75%, the inoculation Trichoderma viride carries out solid state fermentation at 30 ℃ behind the medium sterilization, after cultivating certain hour, take out fermented sample, as the calibration set sample of near infrared multiple regression forecasting model.The quantity of sample at 50 parts for well.
The mensuration of the near infrared spectrum of the second step solid state fermentation sample.The Nexus type ft-nir spectrometer that present embodiment adopts U.S. Nicolet company to produce, instrument parameter is set to: the scanning spectrum district is 4000-10000cm
-1, scanning times is 64 times, resolution is 4cm
-1The collection of sample near infrared spectrum is carried out in the diffuse reflection mode by the light transmitting fiber solid probe.
The mensuration of the cellulase of the 3rd step solid state fermentation sample.Get a certain amount of solid state fermentation sample, the distilled water that adds 20 times of accurately weighing at room temperature soaks 4 hours extraction cellulases.It is centrifugal to extract the end back, and supernatant uses the DNS method to measure the filter paper enzyme activity of cellulase after inhaling and making certain multiple.Standard model concentrates the cellulase of each sample and the near infrared spectrum of being gathered corresponding one by one.
The foundation and the optimization of the 4th step mathematical prediction model.The TQAnalyst6.2 version quantitative analysis software that present embodiment is used U.S. Nicolet company carries out the correction and the optimization of multivariate regression model, can adopt other business-like quantitative analysis softwares of the same type to finish equally.
The top condition that the plain enzyme multiple regression forecasting of solid state fermentation sample fiber model is set up in the present embodiment acquisition is: best major component dimension is 10, and optimized spectrum district scope is 8630-6880cm
-1, the optimal spectrum preprocess method is: first order derivative is handled and is added Norris derivative Filtering Processing.The coefficient of multiple correlation of model (RSQ) is 0.984, and cross validation standard deviation (RMSECV) is 1.69FPA/g.
The 5th step application model is measured unknown sample and has been set up after the mathematical prediction model, just can measure the cellulase content of unknown solid state fermentation sample.Repeat the near infrared spectrum of second step collection unknown sample, spectrum is imported forecast model, computing machine provides the cellulase content of unknown sample immediately.The cellulase of 10 solid state fermentation samples of model determination of setting up with present embodiment adopts the DNS method to measure its cellulase as a comparison simultaneously.Predicted value and measured value are checked back unrestricted difference under 0.05 level with the t check.
Cellulosic content in the embodiment 4 usefulness near infrared spectroscopy fast measuring enzymolysis process
The flow process of the embodiment of the invention is as follows:
The preparation of first step modeling sample collection, adopt the enzymolysis substrate of the quick-fried maize straw of vapour as cellulase, the pH that regulates material is 4.8, solid-to-liquid ratio is 1: 9, ratio according to the quick-fried maize straw of 15IU FPA/g vapour adds cellulase, and 50 ℃ are carried out enzymolysis, and enzymolysis is behind certain hour, take out enzymolysis sample, as the calibration set sample of near infrared multiple regression forecasting model.The quantity of sample at 50 parts for well.
The mensuration of the near infrared spectrum of the second step enzymolysis sample.The Nexus type ft-nir spectrometer that present embodiment adopts U.S. Nicolet company to produce, instrument parameter is set to: the scanning spectrum district is 4000-10000cm
-1, scanning times is 64 times, resolution is 4cm
-1The collection of sample near infrared spectrum is carried out in the diffuse reflection mode by the light transmitting fiber solid probe.
The cellulosic mensuration of the 3rd step solid state fermentation sample.Get a certain amount of enzymolysis sample, accurately weigh, oven dry is back with the content of cellulose in the improved filter bag method working sample.Standard model concentrates the cellulase of each sample and the near infrared spectrum of being gathered corresponding one by one.
The foundation and the optimization of the 4th step mathematical prediction model.The TQAnalyst6.2 version quantitative analysis software that present embodiment is used U.S. Nicolet company carries out the correction and the optimization of multivariate regression model, can adopt other business-like quantitative analysis softwares of the same type to finish equally.
The top condition that the plain multiple regression forecasting model of solid state fermentation sample fiber is set up in the present embodiment acquisition is: best major component dimension is 9, and optimized spectrum district scope is 8630-4280cm
-1, the optimal spectrum preprocess method is: first order derivative is handled and is added Norris derivative Filtering Processing.The coefficient of multiple correlation of model (RSQ) is 0.994, and cross validation standard deviation (RMSECV) is 1.09%.
The 5th step application model is measured unknown sample and has been set up after the mathematical prediction model, just can measure the content of cellulose of unknown enzymolysis sample.Repeat the near infrared spectrum of second step collection unknown sample, spectrum is imported forecast model, computing machine provides the content of cellulose of unknown sample immediately.The cellulose of 10 enzymolysis samples of model determination of setting up with present embodiment adopts improved filter bag method to measure its cellulose as a comparison simultaneously.Predicted value and measured value are checked back unrestricted difference under 0.05 level with the t check.
Embodiment 5 usefulness near infrared spectroscopy on-line monitoring microbial grease accumulative process
The flow process of the embodiment of the invention is as follows:
The collection of the near infrared spectrum of first step enzymolysis sample.The Nexus type ft-nir spectrometer that present embodiment adopts U.S. Nicolet company to produce, instrument parameter is set to: the scanning spectrum district is 4000-10000cm
-1, scanning times is 64 times, resolution is 4cm
-1The bed of material that inserts solid-state fermentation reactor of directly will popping one's head in during the collection of sample near infrared spectrum is undertaken by the light transmitting fiber solid probe.
The mensuration of the microbial grease of the second step solid state fermentation sample.Get a certain amount of fermented sample, accurately weigh, the grease in the fermented sample is proposed, quantitatively with sherwood oil.Standard model concentrates the fat content of each sample and the near infrared spectrum of being gathered corresponding one by one.
The foundation and the optimization of the 3rd step mathematical prediction model.The TQAnalyst6.2 version quantitative analysis software that present embodiment is used U.S. Nicolet company carries out the correction and the optimization of multivariate regression model, can adopt other business-like quantitative analysis softwares of the same type to finish equally.
The top condition that solid state fermentation sample grease multiple regression forecasting model is set up in the present embodiment acquisition is: best major component dimension is 10, and optimized spectrum district scope is 8630-4080cm
-1, the optimal spectrum preprocess method is: first order derivative is handled and is added Norris derivative Filtering Processing.The coefficient of multiple correlation of model (RSQ) is 0.974, and cross validation standard deviation (RMSECV) is 0.008lg/g.
The 5th step application model is measured unknown sample and has been set up after the mathematical prediction model, just can measure the microbial grease content in the new sweat.Repeat the first step and gather the near infrared spectrum of unknown sample, spectrum is imported forecast model, computing machine provides the microbial grease content of unknown sample immediately.Predicated error when predicting with above-mentioned sample between measured value and predicted value (RMSEP) is 0.5618%, and the related coefficient between predicted value and chemical assay value is 0.9631.
Claims (6)
1, a kind of method of online detection solid-state biomass bioconversion procedure, its basic step is as follows:
(1) sample in the representational biomass bioconversion procedure of selection is as the standard model collection of setting up the multiple regression forecasting model;
(2) diffuse reflection spectrum of each living beings bio-transformation sample in use ft-nir spectrometer and the light transmitting fiber solid probe bioassay standard sample sets;
(3) adopt the procedure parameter value of each living beings bio-transformation sample in corresponding chemistry or the physical method bioassay standard sample sets, as with sample sets near infrared spectrum chemical score one to one;
(4) each standard model near infrared spectrum data that second step was obtained is through pre-service, adopt multivariate calibration methods to set up and optimization multiple regression forecasting model, cross validation mean square deviation (RMSEVC) with the calibration set sample is index optimization preprocessing procedures and model parameter, the prediction mean square deviation (RMSEP) of unknown sample is investigated the prediction accuracy of model, choose the as far as possible little combination of RMSECV and RMSEP, the computing formula of RMSECV and RMSEP is as follows
In the formula: Ci is the value that standard chemical process records,
Be the NIR predicted value, n is the calibration set sample number, and m is a checking collection sample number:
(5) in biotransformation, gather the near infrared collection of illustrative plates at any time, spectrum is imported forecast model, thereby pass through the procedure parameter of the online definite biotransformation of model.
2, the method for a kind of online detection solid-state biomass bioconversion procedure according to claim 1, it is characterized in that described living beings comprise is unprocessed or quick-fried through pulverizing, extruding, vapour, diluted acid, hot water, alkali lye, ray, organic solvent are handled maize straw, wheat stalk, rice straw, wheat bran, rice husk, corncob, corn, millet, soybean, bagasse, cassava or combination wherein.
3, the method of a kind of online detection solid-state biomass bioconversion procedure according to claim 1, it is characterized in that, described procedure parameter comprises the content of the nutritional labeling in the fermentation substrate, as glucose, amino acid, protein, greases etc. can be for microorganism as carbon source, the organic principle of nitrogenous source, the water cut that comprises fermentation substrate, comprise the pH value in the fermentation substrate, comprise the biomass in the fermentation substrate, the composition of indirect determination biomass comprises Glucosamine, ergosterol, protein, nucleic acid and ATP etc., the various organic products that comprise sweat are as enzyme, protein, amino acid, organic acid, microbiotic and vitamin etc.
4, the method for a kind of online detection solid-state biomass bioconversion procedure according to claim 1 is characterized in that, described pre-service comprises smoothly, centralization, normalization, single order or second derivative are handled.
5, the method for a kind of online detection solid-state biomass bioconversion procedure according to claim 1, it is characterized in that described multiple regression procedure comprises offset minimum binary algorithm, principal component regression method, contrary least square method, multiple linear regression method, artificial neural network method and support vector machine etc.
6, the method for a kind of online detection solid-state biomass bioconversion procedure according to claim 1 is characterized in that, described biomass bioconversion procedure comprises solid state fermentation and lignocellulosic material enzymolysis.
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