KR101544586B1 - Analyzing method of methane production of raw material using near infrared system - Google Patents

Abstract

The present invention relates to a method for analyzing the amount of methane generated in a feedstuff using a near-infrared system. More particularly, the present invention relates to a method for analyzing and predicting the methane generation rate and methane index by applying a measurement value of methane generation amount of a feedstuff using a rumen model continuous culture system to a near-infrared system. By using the method of analyzing the amount of methane generated from the feed materials using the near-infrared ray system according to the present invention, it is possible to overcome the limitations of the animal living body experiment, and the burden of time and cost required is small and continuous cultivation similar to the actual digestive system of the rumen System to analyze and predict accurate and objective methane emissions and methane indexes. Further, since the amount of methane generated and the methane index for the feed materials can be analyzed early in the near-infrared spectrum using only the feed samples using the present invention, it is possible to maintain the digestibility of the rumen animal and the degradation rate of the feed, It can be used to develop feed.

Description

Technical Field [0001] The present invention relates to a method for analyzing a methane production amount of a feedstock using a near-

The present invention relates to a method for analyzing the amount of methane generated in a feedstuff using a near-infrared system. More particularly, the present invention relates to a method for analyzing and predicting the methane generation rate and methane index by applying a measurement value of methane generation amount of a feedstuff using a rumen model continuous culture system to a near-infrared system.

Numerous microorganisms live in the rumen of cattle, the cattle digest feeds instead of cows, and cows have evolved in a very unusual way to reuse the products from their degradation process. In this process, when organic matter is decomposed, hydrogen and carbon dioxide are also generated. When hydrogen accumulates in the rumen, the rumen health deteriorates because it interferes with the metabolism of microorganisms. Therefore, methane-producing bacteria use hydrogen and carbon dioxide to produce methane by discharging hydrogen out of the rumen. In other words, methane production is one of the important ways to keep the microbial ecosystem in cattle rumen healthy.

As a result of the efforts of many researchers around the world, the methane production mechanism of cow rumen has been revealed, and therefore methane reducer researches have been conducted to reduce methane from various ruminants including cows. However, it has been confirmed that these materials are effective in reducing the amount of methane produced in experiments using culture bottles. However, it has been found that the reduction of digestibility, the prohibition of the use of antibiotics, or the residual toxicity There is no mature technology that can be put to practical use due to problems.

  As a method for measuring the amount of methane generated by conventional feed materials, a method using a culture method has been mainly used. The method of using the above-described culture bottle is disadvantageous in that it takes a long time and a lot of cost because it is a method of measuring the amount of generated methane by culturing the microorganism in the feed raw material for 24 hours. Conventional near-infrared spectroscopy has been used mainly for analyzing feed ingredients, but it has been used directly for the development of a so-called "green feed" that effectively reduces the methane generated from the rumen of cattle and maintains the degradation rate of the feed, It does not provide a solution.

NIR spectroscopy is as follows.

Near infrared rays are a part of an infrared ray adjacent to a visible ray region and generally refer to electromagnetic waves in the range of 780 to 2,500 nm. Near-infrared spectroscopy is a study of near-infrared light (or energy) absorbed by a molecule and is a field of molecular spectroscopy related to the interaction between electromagnetic waves and matter. Generally, infrared and near-infrared rays excite molecular vibrations, and molecules absorbing near-infrared and infrared rays oscillate at specific frequencies and amplitudes like vibrating dipoles. At this time, when the energy causing the molecular vibrations coincides with the energy of the light, the molecule absorbs the light, and as the new energy level transitions to the high state, the oscillation frequency is not changed but the amplitude is increased. However, if the energy causing the molecular vibration does not match the energy of the light, the light will be reflected.

In general, functional groups (O-H, C-H, N-H, S-H groups, etc.) of molecules constituting an organic material have a unique absorption band in the infrared region (reference oscillation band). That is, the material that absorbs infrared rays contains components composed of O-H, C-H, N-H, and S-H groups. By using this characteristic, it is possible to measure a component contained in the organic matter by irradiating infrared rays to an organic matter and measuring the degree of absorption (or reflection) of the infrared ray. However, when infrared rays are irradiated onto an organic material, the absorption is too strong to quantify, so that the wavelength of the near infrared region is mainly used.

Generally, in the near-infrared wavelength range, it shows overtone absorption or combination absorption wavelength as compared with the reference absorption wavelength induced in the infrared wavelength range. The harmonic absorption is approximately twice or three times the reference absorption frequency 1/2 or 1/3 times as large as the wavelength (or wavelength). For example, if the reference absorption of the C-H stretch occurs in the infrared region of 3,380 nm, the first harmonic occurs at 1,690 nm and the second harmonic occurs at the near infrared region of 1,126 nm.

In the case of polyatomic functional groups such as proteins and starches constituting organic matter, the absorption is further complicated. The number of reference absorption bands increases to 3n - 6 when the number of constituent members is n, and thus the absorption of harmonic absorption becomes more complicated. Further, when two or more absorptions occur at the same time, binding absorptions occur in the near-infrared region. Thus, the absorption spectrum specific to the substance appears in the near-infrared region due to absorption of the harmonic absorption and absorption of the reference absorption band. For example, if the reference absorption wavelength of CH stretch is 2,960 nm and the reference absorption wavelength of CH bend occurs in the infrared region of 6,849 nm, the absorption absorption wavelength appears in the near infrared region of 2,262 nm. Therefore, when the near infrared rays are irradiated to the organic matter and the amount of energy absorbed at each frequency is quantified, it is possible to measure the content of the organic matter by a chemometric method.

 Journal of Korean Society of Grassland and Forage Science Volume 24, Issue 1 (2004. 3) pp. 81-90 1013-9354 KCI

Under these technical backgrounds, the present inventors have made intensive efforts to develop a method for analyzing the amount of methane generated in a feedstuff using a near-infrared ray system.

The present invention has been made in order to solve the problems of the prior art. In order to derive a methane index which can be used as an index for evaluating the digestibility and nutritional value of feed materials in ruminants, The present invention provides a method of analyzing and estimating methane generation amount and methane index by applying a measurement value of methane generation amount to a near infrared ray system.

In order to solve the above technical problem,

According to an aspect of the present invention, there is provided a method for preparing a fermentation product, comprising: (a) feeding a feedstock to a continuous culture fermentation tank composed of a gastric juice, a buffer solution, and a rumen microbial culture collected from a rumen; (b) obtaining a near infrared spectrum by irradiating the feedstock with near-infrared light; (c) generating a methane generation prediction equation using the methane emission amount measured in the step (a) and the near-infrared spectrum obtained in the step (b); (d) converting the methane generation prediction formula generated in the step (c) into a database; And (e) estimating a methane emission amount by applying the methane generation prediction formula to a near-infrared spectrum for an arbitrary feedstock, and a method for analyzing a methane emission amount of a feedstuff using the near-infrared system.

In one embodiment, the feedstuff may be selected from the group consisting of wheat, corn, soybean meal, large scalp, palm leaves, seedlings, and palms.

In one embodiment, the irradiated near-infrared rays may have a wavelength of 0.80 to 2.50 μm.

In one embodiment, the near infrared spectrum may be obtained in the step (b), and then a mathematical preprocessing step according to a differential method or a multiplicative scatter correction may be further included.

In one embodiment, the methane generation prediction formula generated in the step (c) may include a standard prediction error, a standard prediction correction error, and a correlation coefficient for the amount of methane generated in the continuous culture fermentation tank using an arbitrary feed material. And a step of verifying by using the method.

By using the method of analyzing the amount of methane generated from the feed materials using the near-infrared ray system according to the present invention, it is possible to overcome the limitations of the animal living body experiment, and the burden of time and cost required is small and continuous cultivation similar to the actual digestive system of the rumen System to analyze and predict accurate and objective methane emissions and methane indexes. Further, since the amount of methane generated and the methane index for the feed materials can be analyzed early in the near-infrared spectrum using only the feed samples using the present invention, it is possible to maintain the digestibility of the rumen animal and the degradation rate of the feed, It can be used to develop feed.

FIG. 1 shows the pH values of major feed materials measured in a rumen model continuous culture system.
FIG. 2 shows the total amount of gas produced by the main feed materials measured in the rumen model continuous culture system.
FIG. 3 shows the amount of methane gas generated by the main feed materials measured in the continuous culture system of the rumen model.
FIG. 4 is a graph showing a result of prediction of methane emission of a feedstuff using a methane generation prediction formula according to an embodiment of the present invention.

Hereinafter, the present invention will be described in more detail.

According to an aspect of the present invention, there is provided a method for preparing a fermentation product, comprising: (a) feeding a feedstock to a continuous culture fermentation tank composed of a gastric juice, a buffer solution, and a rumen microbial culture collected from a rumen; (b) obtaining a near infrared spectrum by irradiating the feedstock with near-infrared light; (c) generating a methane generation prediction equation using the methane emission amount measured in the step (a) and the near-infrared spectrum obtained in the step (b); (d) converting the methane generation prediction formula generated in the step (c) into a database; And (e) estimating a methane emission amount by applying the methane generation prediction formula to a near-infrared spectrum for an arbitrary feedstock, and a method for analyzing a methane emission amount of a feedstuff using the near-infrared system.

The near-infrared spectrum shows a complex shape by overlapping absorption spectra of several components. The most commonly used method is to differentiate the absorption bands in the overlapped spectrum and to find out the spectra of each component accurately. Typically, primary or secondary differentials are used most often, and tertiary or quaternary differentials may be used but may cause over-fitting.

In the first derivative, (Bavg-Aavg) and (Cavg-Bavg) are calculated to obtain the moving average of the entire spectrum and the slope of each compartment is calculated. In the second derivative, (Cavg-Bavg) - (Bavg-Aavg) is calculated to obtain the moving average of the entire spectrum and the slope of each compartment is calculated. In the second derivative, the absorption peak of the original spectrum appears as a negative peak. It is possible to separate the absorption peaks overlapping each other while dividing the peaks of the original spectrum by the differential treatment. Therefore, if the differential processing is performed, the spectrum characteristic can be easily extracted and the inclination to be considered in the original spectrum can be removed. It is possible to offset the baseline of the spectra due to the difference in particle size in the same sample by the second derivative.

Next, a multivariate analysis method will be described. The near infrared spectra show that the harmonic and bond absorption of the reference absorption of the infrared region are synthesized. Therefore, the spectral shape is not so complicated in the simple substance, but in actual grains and the like, the absorption spectrum is combined with each component to show a highly complicated shape.

In near - infrared spectroscopy, the complex spectrum is analyzed by multivariate analysis, and a calibration model is created to predict parameters such as moisture, protein, or starch 's gelatinization properties, processing parameters and the like. There are many methods for multivariate analysis such as multiple regression analysis, discriminant analysis, canonical correlation analysis, principal component analysis, atomic analysis, and cluster analysis, but the following three mainly used (multiple regression analysis, principal component regression analysis and PLS regression analysis) .

First, MLR (Multiple Linear Regression) is the most widely used multivariate analysis method. It selects several wavelengths from the spectrum and creates a prediction model as follows.

Where Y _i : component value; a ₀ : an integer term; a _i : regression coefficient; X _i : absorbance for each wavelength (log (1 / R)), second derivative, etc.; e _i : residual

a ₀ and a _i are obtained by the least squares method. When the number of wavelengths (m) and the number of samples (n) included in the regression satisfy only n = m + 1, the number of the intermediate correlation r is 1.0 regardless of any data. Therefore, it is necessary to make n sufficiently large in order to create a predictive model with a high degree of accuracy with as little wavelength as possible. Also, when the explanatory variable having a high correlation with each other is included in the regression equation, there arises a problem of multi-collinearity in which the degree of prediction remarkably decreases. Therefore, the following variable selection methods have been developed to create a predictive model with a higher degree of accuracy with fewer explanatory variables.

Total Variable Method: All explanatory variables are combined and analyzed, and the combination with which the highest regression coefficient is obtained is selected.

Variable Increment Method: Select one explanatory variable that represents the highest correlation coefficient with the objective variable, and then select the explanatory variable that represents the highest regression variable by repeating the operation of combining the following variables.

Variable Reduction Method: A multiple regression equation is constructed using the full explanatory variable, and then the explanatory variables with the least degree of decreasing the regression coefficient are removed.

Variable increment method: The regression equation is obtained by incrementing the explanatory variable one by one and excluded from the multiple regression equation if it does not help to increase the regression coefficient.

Variable assignment method: Empirically, variables with significant importance are selected for use in multiple regression analysis.

Next, Principal Component Regression (PCR) is a method developed to solve the inherent drawbacks of the regression analysis, that is, the number of samples (n) must be large, and the problem of multi-collinearity, We use the principal component (z) extracted from the variable (x) as the explanatory variable to obtain the multiple regression equation. The predictive model of principal component regression analysis is written as follows. The method of obtaining the regression coefficient is the same as the multiple regression analysis.

Where Y _i : component value; a ₀ : an integer term; a _q : regression coefficient; Z _q : q th principal component; e _i : residual

The principal components have the following characteristics when compared with the original explanatory variables. In other words, since the information can be concentrated on the principal component, which is much smaller than the original explanatory variable, the number of samples required for regression analysis can be greatly reduced. In addition, since the main components are orthogonal to each other and have no correlation with each other, there is no fear of multi-collinearity. Also, since information and noise are concentrated on each major component regardless of whether the eigenvalue is large or small, the risk of overfitting is small and a stable prediction model can be obtained even if the number of principal components is increased.

On the other hand, in the PLS (Partial Least Square) regression analysis, error is assumed to be both the explanatory variable and the objective variable, as opposed to assuming the error for each explanatory variable in the normal regression analysis. In the PLS regression analysis, like the principal component regression analysis, the potential parameters are extracted and used as explanatory variables. The difference between the explanatory variables and the objective variables is that the explanatory variables and the objective variables are used together. In the PLS method, since the regression equation is calculated by using all the information of the variable, it is possible to obtain a higher prediction accuracy than the principal component regression analysis. In addition, like the principal component analysis, there is an advantage that the problem of multi-collinearity and the number of samples and variables can be solved at the same time.

In one embodiment, the feedstuff may be selected from the group consisting of wheat, corn, soybean meal, large scalp, palm leaves, seedlings, and palms.

In one embodiment, the irradiated near-infrared rays may have a wavelength of 0.80 to 2.50 μm.

In one embodiment, the near infrared spectrum may be obtained in the step (b), and then a mathematical preprocessing step according to a differential method or a multiplicative scatter correction may be further included.

In general, the near-infrared spectrum is excellent in reproducibility, but it may involve slight changes such as baseline changes depending on the kind, condition, and measurement conditions of the sample. Therefore, in one embodiment, the mathematical preprocessing step The calibration result can be improved.

Polydispersion correction is a method of eliminating scattering effects by linearizing individual spectra with the ideal spectrum of the sample. Standard normalization, unlike polydispersion correction, normalizes the standard deviation of the absorbance over the entire spectrum, without using the ideal spectrum, to eliminate scattering of the spectrum. For the development of each model, the pre-processing spectrum using polydispersity correction and standard normalization and the non-preprocessed spectrum were used, respectively.

In one embodiment, the methane generation prediction formula generated in the step (c) may include a standard prediction error, a standard prediction correction error, and a correlation coefficient for the amount of methane generated in the continuous culture fermentation tank using an arbitrary feed material. And a step of verifying by using the method.

The above step is to evaluate the predictive ability of the selected methane prediction formula and the prediction accuracy of the predictive formula generated using the independent feed materials or unknown feed materials not used in the methane prediction formula (NIRs calibration formula) Standard predictive error, standard predictive correction error, and correlation coefficient for the amount of methane generated can be used for evaluation, but the present invention is not limited thereto.

Experimental Method

1. Construction of rumen simulation continuous culture system

(1) Culture medium: The obtained gastric juice was filtered with 8 layers of gauze and then wrapped with glass wool. Filtered gastric juice while maintaining (bubbling) to carbon dioxide (CO ₂₎ gas was maintained at anaerobic conditions. The previously prepared buffer was mixed with the gastric juice (250 mL of gastric juice, 600 mL of buffer) while keeping it in the carbon dioxide gas.

(2) Substrate: 10 g of substrate was added to the fermenter.

(3) Buffer solution tank: Buffer solution was put into the buffer solution tank while maintaining the anaerobic state. The Tedlar bag filled with carbon dioxide gas was buffered and the buffered tube was connected to the buffer inlet of the lid of the fermenter.

(4) Gas collection bag: The tube connected to the gas collection bag is connected to the gas collection port in the lid of the fermentation tank (the connection tube is used for the gas tube).

(5) pH and temperature Electrode: The electrode was inserted so as to sufficiently reach the culture solution. At this time, care should be taken not to touch the stirring rod.

(6) Feeding tube: The feed tube was inserted into the feed inlet of the fermenter lid to the extent sufficient to lock it into the culture liquid.

(7) The lid of the fermentation tank was connected with the fermentation tank and the anaerobic condition was maintained by using vacuum grease and Teflon tape. Then, carbon dioxide gas was injected into the fermenter through the gas trap in the lid of the fermenter for about 10 minutes (the inside of the fermenter was changed to the anaerobic state, and the carbon dioxide gas was continuously flown until the start of the culture). The connecting parts of all devices were sealed with vacuum grease and Teflon tape, and the peristaltic pump was operated and the buffer was injected into the fermenter (1000 ~ 1200 ml / day).

(8) The anaerobic culture was fed into the fermenter until it overflowed through the feeding port, stopped feeding the carbon dioxide gas, and blocked all the inlets. The circulating water tank and the fermentation tank were connected and the temperature was set (37 to 40 ° C).

(9) The rpm of the stirring rod was set at 10 to 20 rpm, and the culture was started. Methane production was measured as the average of the methane generation measurements for 3 days of each sample after 7 days of adaptation period and 7 days of first adaptation period of rumen adaptation period. Soybean meal, wheat, soybean meal, coconut husk, protein bloom, palm and corn were used as main feed materials for measuring methane production.

2. Generation of Methane Generation Prediction Equation Using Near Infrared Systems (NIRs)

(1) Secure NIRs DB

In order to prepare and verify the calibration equation, the methane emission measurement method and the NIRs spectrum analysis method of the ruminal model continuous culture system of each sample were combined and the total number of samples to be analyzed was 55. The spectral data output from the near-infrared ray measuring apparatus is input to a computer in real time in a database as described below.

① Analyze the feedstock analysis. Measure the near-infrared spectrum of each sample.

② Input the result of methane emission of each feedstock into the measured spectrum.

③ Prepare the NIRs DB by extracting the generated Cal file after completing steps ① and ② above.

The FOSS XDS model was used for the near infrared spectroscopy (Model: FOSS XDS NIRs instrument, Scan range: 400 ~ 2500nm, Resolution: 0.5nm, Detector type: Si & Pb detector, Method: Rock measurement method, Operation S / W: ISIScan, Calibration S / W: WinISI)

(2) Generation of NIRs calibration formula

A calibration equation using the mathematical statistical algorithm between the methane emission amount analysis result and the NIRs spectrum from the secured NIRs DB was generated as follows.

1) The first-order differentiated spectrum was used for the calibration of the NIRs DB and the spectrum of 400 ~ 2500 nm was used for the calibration.

② Using PCA (Principal Component Analysis) algorithm using the preprocessed spectrum in step 1), a group is formed and a pca file is generated and used as a group's outlier determination tool

③ The NIR calibration equation (eqa file) was generated by applying PLS (Partial Least Squares) regression algorithm to the pre-processed spectrum and the feedstock analysis results in step ① above.

(3) NIRs calibration formula (methane generation prediction formula) verification

In order to verify the NIRs calibration formula (methane generation prediction formula), the validity of the NIRs calibration formula (methane generation prediction formula) was verified through standard prediction error, standard prediction correction error and correlation coefficient based on the analysis result using unknown feed material samples As shown below.

① The RSQ (R-square) and standard error of calibration (SEC) of the generated calibration equation (eqa file) were used to determine the degree of application of the calibration equation.

② Confirmation of the standard of compatibility (RSQ> 0.7) confirms the SEC, confirms the allowable degree of the current analysis value (tolerance standard), and determines the degree of conformity. If the available level is available, Verify the error in application.

③ If unknown feed materials are verified and the standard error of predicted correction (SEPc) applicable at the experimental design level is shown, it is judged that the initial calibration equation is available, To take advantage of the NIRs predicted value.

Experiment result

1. pH value of major raw materials measured in continuous culture system of rumen model

As shown in FIG. 1, when the pH level of each feed material was measured using the continuous culture apparatus, the average value of the pH values of the feed materials was in the order of soybean meal>wheat> vegetable oil> palm oil> protein p> corn = Respectively. The highest standard deviation was observed in corn, and the variation of soybean meal, wheat, soybean meal, protein, and palm boar was small.

2. Total gas production by major feedstuffs measured by ruminal model continuous culture system

As shown in FIG. 2, the total amount of gas produced by the feed materials in the continuous cultivation apparatus was in the order of protein p>chickpea>corn>wheat>coconut> soybean cake> pharm. There was a high standard deviation in the soybean meal and palm leaves, and the least variation in the soybean meal was small. The fermentation pattern of the raw feed can be estimated in the fermentation tank of the continuous culture apparatus through the total gas generation amount.

3. Methane gas production by main feedstuffs measured by ruminal model continuous culture system

As shown in FIG. 3, the amounts of methane gas produced by the feed materials in the continuous culture apparatus were in the order of wheat>corn> soybean meal> vegetable oil> palm oil> palm oil> protein p. There was a high standard deviation in the palm, and the least variation in soybean meal was small. The tendency of the total methane generation rate was similar to the total gas generation rate, but the amount of methane gas production of soybean meal and protein was opposite to that of total gas generation.

4. Methane emission estimation results using NIRs calibration formula (methane generation prediction formula)

As shown in Table 1 and FIG. 4, the methane generation prediction equation was generated by combining the methane emission amount of each feed material measured in the continuous culture system of the rumen model with the near-infrared system, and the methane production amount As a result of the prediction, the correlation coefficient was 0.842, which is statistically significant.

Estimation of methane emission using near-infrared system Verification samples () 69 Average 13.576 Standard prediction error 2.664 Standard Prediction Correction Error 4.563 Standard Deviation 6.154 Correlation coefficient 0.842

Therefore, as mentioned above, by using the method of analyzing the amount of methane generated from the feed materials using the near-infrared ray system according to the present invention, it is possible to overcome the limitations of the animal living body experiment, Accurate and objective methane generation and methane index can be analyzed and predicted by introducing a continuous culture system similar to the digestive system of rumen. Further, since the amount of methane generated and the methane index for the feed materials can be analyzed early in the near-infrared spectrum using only the feed samples using the present invention, it is possible to maintain the digestibility of the rumen animal and the degradation rate of the feed, It can be used to develop feed.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, It will be obvious that it is not. It is therefore intended that the scope of the invention be defined by the claims appended hereto and their equivalents.

Claims (6)

(a) The amount of methane generated in the feedstock is measured by a device for measuring the amount of methane generated by capturing the methane gas generated after the feedstock is fed into the continuous culture fermentation tank composed of the gastric juice, buffer solution and rumen microbial culture collected from the rumen step;
(b) extracting near-infrared spectral data of the feed material by a near-infrared spectroscopic analyzer for extracting near-infrared spectrum data by irradiating the feed material with near-infrared light;
(c) generating a methane generation prediction equation using the methane emission amount measured in the step (a) and the near-infrared spectrum data extracted in the step (b);
(d) a step of converting the methane generation prediction formula generated in the step (c) into a database; And
and (e) the near-infrared spectrum data of any feed material extracted by the near-infrared spectroscopic analyzer is applied to the methane generation prediction formula to predict the methane generation amount.
The method according to claim 1,
Wherein the near infrared rays to be irradiated have a wavelength of 0.80 to 2.50 占 퐉.
The method according to claim 1,
Wherein the spectrum data extracted from the NIR spectroscopic analyzer in the step (b) further includes a mathematical preprocessing step according to a differential method or a multiple scatter correction.
The method according to claim 1,
The methane generation prediction formula generated in the step (c) may include a standard prediction error, a standard prediction correction error, or a correlation coefficient with the methane production amount measured in the continuous culture fermentation tank for any feed materials not used in the step (b) ≪ / RTI > wherein the method further comprises utilizing and verifying the methane emissions.
A near-infrared spectroscopic analyzer for extracting near-infrared spectrum data by irradiating near-infrared rays to an input feed material; And
And an arithmetic unit for calculating a methane emission amount of the feed material by comparing the preliminarily memorized methane generation prediction formula with the near-infrared spectrum extracted by the near-infrared spectroscopic analyzer.
6. The method of claim 5,
The methane generation prediction formula is,
The amount of methane generated in the feedstuffs measured by the apparatus for measuring the amount of methane generated by capturing the methane gas generated after the feedstock is input into the continuous culture fermentation tank composed of the gastric juice, buffer solution, and rumen microbial culture collected from the rumen, and the near- An apparatus for analyzing the amount of methane in a feed that is produced using near infrared spectrum data extracted by an analyzer.

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