CN111398213A - Method for judging eligibility of fermented grain model - Google Patents
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- CN111398213A CN111398213A CN202010270942.7A CN202010270942A CN111398213A CN 111398213 A CN111398213 A CN 111398213A CN 202010270942 A CN202010270942 A CN 202010270942A CN 111398213 A CN111398213 A CN 111398213A
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- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000013178 mathematical model Methods 0.000 claims abstract description 39
- 229920002472 Starch Polymers 0.000 claims abstract description 29
- 239000008107 starch Substances 0.000 claims abstract description 29
- 235000019698 starch Nutrition 0.000 claims abstract description 23
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 18
- 238000012549 training Methods 0.000 claims abstract description 15
- 238000009614 chemical analysis method Methods 0.000 claims abstract description 14
- 230000003595 spectral effect Effects 0.000 claims abstract description 14
- 230000005484 gravity Effects 0.000 claims description 3
- 238000001514 detection method Methods 0.000 abstract description 11
- 238000005516 engineering process Methods 0.000 abstract description 8
- 238000002329 infrared spectrum Methods 0.000 abstract description 7
- 238000012360 testing method Methods 0.000 description 9
- 238000004458 analytical method Methods 0.000 description 6
- 238000000855 fermentation Methods 0.000 description 4
- 230000004151 fermentation Effects 0.000 description 4
- 239000011159 matrix material Substances 0.000 description 4
- 239000000126 substance Substances 0.000 description 4
- 238000001228 spectrum Methods 0.000 description 3
- 238000012795 verification Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012628 principal component regression Methods 0.000 description 2
- 238000012797 qualification Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 244000005700 microbiome Species 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- 238000009659 non-destructive testing Methods 0.000 description 1
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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Abstract
The invention discloses a method for judging the eligibility of a fermented grain model, which comprises the steps of preparing two fermented grain samples which are respectively defined as a training set A and a prediction set B; respectively collecting the spectral data of the two fermented grain samples by using a near-infrared spectrometer, and measuring the water content, the acidity and the real content of starch in the two fermented grain samples by using a chemical analysis method after the collection is finished; aiming at the training set A, taking the real content data as a calibration value, and establishing a mathematical model between the calibration value and the spectral data by using a partial least square method; predicting the spectral data of the prediction set B by using a mathematical model to obtain the predicted content of water, acidity and starch in the prediction set B; and comparing the predicted content of the prediction set B with the real content value obtained by a chemical analysis method, and judging whether the mathematical model is qualified. The invention provides a method for judging the eligibility of a fermented grain model, which provides powerful support for the data accuracy of a near infrared spectrum detection technology and promotes the application and implementation of near infrared spectrum detection.
Description
Technical Field
The invention relates to the technical field of near infrared spectrum detection, in particular to a method for judging the eligibility of a fermented grain model.
Background
The white spirit fermentation process is a traditional method, the fermentation period is long, and the fermentation process is uncontrollable, so that the chemical component analysis of fermented grains plays a very important role in the brewing industry, the conventional chemical analysis method is time-consuming, labor-consuming, complex and complex to operate, the obtained analysis result is delayed in time, and the formula of the fermented grains cannot be guided to be adjusted.
The near infrared light refers to electromagnetic waves between visible light and mid-infrared light, the wavelength is about 780-H, N-H, O-H, C ═ O and other group vibration frequencies in substance molecules, the frequency combination and frequency multiplication of the group vibration frequencies are absorbed in a near infrared region, therefore, the near infrared technology is suitable for analyzing components which are directly or indirectly related to the groups, substances such as total starch, moisture, acidity and the like in fermented grains all contain the groups, the indexes can be quantitatively analyzed by using the near infrared detection technology, the indexes such as the starch, the moisture, the acidity and the like in the fermented grains are quickly analyzed by using the near infrared technology to guide a formula, and a good substance environment foundation is created for the activity of the fermented microorganisms, the near infrared analysis has the following characteristics that (1) the testing speed is high, the near infrared detection technology is suitable for large-scale repeated testing, (2) the operation is simple, (3) no chemical reaction exists in the testing process, no chemical reagent is needed, no pollution exists, (4) the nondestructive testing, the samples can be repeatedly used for online testing, the online detection of production lines and the like, the key of the near infrared detection technology lies in the establishment of the models, the accuracy of the establishment of the models, the establishment of the optimal analytical method is determined by the least square regression algorithm, the effective regression method, the verification of the least square regression method is divided into a main regression method, whether the verification method, whether the method of establishing whether the method of establishing the infrared spectrum is established, and the method of.
Disclosure of Invention
The invention aims to provide a method for judging the eligibility of a fermented grain model, which is used for solving the problem that the accuracy and the eligibility of the fermented grain model cannot be verified by establishing a detection model in a near infrared spectrum detection technology in the prior art.
The invention solves the problems through the following technical scheme:
a method for judging the eligibility of a fermented grain model comprises the following steps:
step S100: preparing two fermented grain samples which are respectively defined as a training set A and a prediction set B;
step S200: respectively collecting the spectral data of the two fermented grain samples by using a near-infrared spectrometer, and measuring the water content, the acidity and the real content of starch in the two fermented grain samples by using a chemical analysis method after the collection is finished;
step S300: aiming at the training set A, taking the real content data as a calibration value, and establishing a mathematical model between the calibration value and the spectral data by using a partial least square method;
step S400: predicting the spectral data of the prediction set B by using a mathematical model to obtain the predicted content of water, acidity and starch in the prediction set B;
step S500: and comparing the predicted content of the prediction set B with the real content value obtained by a chemical analysis method, and judging whether the mathematical model is qualified.
The mathematical model in step S300 includes a spectrum-moisture mathematical model, a spectrum-acidity mathematical model, and a spectrum-starch mathematical model.
And in the step S400, the spectrum data of the prediction set B is predicted by using a mathematical model to obtain the predicted contents of water, acidity and starch in the fermented grain sample B. And predicting the water content of the prediction set B by adopting a spectrum-water mathematical model to obtain a water content prediction value, predicting the acidity content of the prediction set B by adopting a spectrum-acidity mathematical model to obtain an acidity content prediction value, and predicting the starch content of the prediction set B by adopting a spectrum-starch mathematical model to obtain a starch content prediction value.
In the step S500, difference operation is respectively carried out on the moisture, the acidity, the starch predicted value and the true value of each fermented grain sample in the prediction set B, threshold judgment is carried out on the difference, when the specific gravity smaller than the preset threshold exceeds the preset value, the mathematical model is qualified, otherwise, the mathematical model is unqualified.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the invention provides a method for judging the eligibility of a fermented grain model, provides a verification method for the data accuracy and eligibility of a near infrared spectrum detection technology, and provides a powerful support for promoting the application and implementation of near infrared spectrum detection.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples, but the embodiments of the present invention are not limited thereto.
Example (b):
with reference to the attached drawing 1, a method for judging the eligibility of a fermented grain model comprises the following steps:
step S100: preparing two fermented grain samples which are respectively defined as a training set A and a prediction set B; 100 fermented grain samples are selected from a laboratory of a brewing workshop, are averagely divided into two parts and are respectively defined as a training set A and a prediction set B, wherein the training set A is used for establishing a model of the fermented grain samples, and the prediction set B is used for verifying the qualification of the model.
The process for preparing the fermented grain sample comprises the following steps: and flatly pouring the fermented grain sample into a disc-shaped tool vessel and compacting downwards to enable the surface of the fermented grain to be flat and the thickness of the fermented grain to be more than 3 cm. The method has the advantages that the fermented grain sample is kept to have a certain thickness, the test surface is as flat as possible, and errors of the environment or the sample to test data are reduced. When selecting the number of training sample wine, the more the number is, the wider the model coverage range is, the higher the precision is, but the more the resource consumed in the test is, on the premise of ensuring the model precision, the less the number of wine samples can effectively save the resource. Similarly, the greater the quantity, the greater the reliability, but the more resources consumed in the test in selecting the quantity of the prediction sample. In the invention, 100 fermented grain samples are selected and equally divided into two parts, the training set A comprises 50 fermented grain samples, and the prediction set B comprises 50 fermented grain samples. The method has the advantages that the accuracy and the reliability of the model are guaranteed, and meanwhile, resources are saved as much as possible.
Step S200: respectively collecting the spectral data of the two fermented grain samples by using a near-infrared spectrometer, and measuring the water content, the acidity and the real content of starch in the two fermented grain samples by using a chemical analysis method after the collection is finished; the contents of moisture, acidity and starch in fermented grains are three factors which most directly affect the fermentation quality of the white spirit, the chemical analysis method is time-consuming, labor-consuming, complex and complex in operation, the data obtained by analysis is real and reliable, and the model accuracy can be greatly improved by combining the data to perform analysis modeling.
Because the chemical analysis method can destroy the structure of the sample, the collection of the spectral data is carried out before the chemical analysis method, and the specific collection mode is as follows: the near-infrared spectrometer is flatly placed on the fermented grain sample, the average value is taken as the spectral data of the fermented grain sample after multiple measurements, the data are transmitted to the cloud, and downloading and calling of subsequent modeling are facilitated.
Step S300: aiming at the training set A, taking the real content data as a calibration value, and establishing a mathematical model between the calibration value and the spectral data by using a partial least square method; the method comprises the steps of obtaining the real content of moisture, acidity and starch in a fermented grain sample A by a chemical analysis method as calibration values, and respectively establishing a spectrum-moisture mathematical model, a spectrum-acidity mathematical model and a spectrum-starch mathematical model by a partial least square method.
The partial least square method is used as a modeling algorithm which is most widely applied, combines the advantages of a multivariate linear regression (M L R) method and a Principal Component Regression (PCR) method, not only can process a spectrum matrix, but also can process a content concentration matrix in the same way, so that noise information in the spectrum matrix and the concentration matrix can be eliminated, and a better prediction effect can be obtained.
Step S400: predicting the spectral data of the prediction set B by using a mathematical model to obtain the predicted content of water, acidity and starch in the prediction set B; and predicting the water content of the prediction set B by adopting a spectrum-water mathematical model to obtain a water content prediction value, predicting the acidity content of the prediction set B by adopting a spectrum-acidity mathematical model to obtain an acidity content prediction value, and predicting the starch content of the prediction set B by adopting a spectrum-starch mathematical model to obtain a starch content prediction value.
Step S500: and comparing the predicted content of the prediction set B with the real content value obtained by a chemical analysis method, and judging whether the mathematical model is qualified.
And (3) carrying out one-to-one correspondence on the moisture, acidity and starch predicted value of each fermented grain sample in the prediction set B and the true value, carrying out difference value operation, carrying out threshold value judgment on the difference value, and when the specific gravity smaller than a certain threshold value reaches a set proportion, indicating that the mathematical model is qualified, otherwise, indicating that the mathematical model is unqualified.
Such as: in the components of the fermented grains, the threshold value of the water concentration is 1, the threshold value of the acidity concentration is 0.3, and the threshold value of the starch concentration is 1. The method for judging whether the moisture prediction is qualified comprises the following steps: and (3) carrying out one-to-one correspondence on the water predicted value of each fermented grain sample in the prediction set B and the real value obtained by the chemical analysis method, carrying out one-to-one difference value calculation after the correspondence is finished, and carrying out threshold judgment on the difference value, wherein if the difference value is greater than a threshold value 1, the model of the fermented grain sample is unqualified, and if the difference value is less than 1, the model of the fermented grain sample is qualified, 50 fermented grain samples are in the prediction set B, and if the qualification rate is more than 90%, more than 45 fermented grain sample models are qualified, namely the spectrum-water mathematical model constructed by the training set A is qualified. And (5) similarly, testing whether the acidity prediction is qualified and whether the starch prediction is qualified. If the acidity prediction is qualified, the spectrum-acidity mathematical model constructed by the training set A is qualified; if the starch prediction is qualified, the spectrum-starch mathematical model constructed by the training set A is qualified.
Although the present invention has been described herein with reference to the illustrated embodiments thereof, which are intended to be preferred embodiments of the present invention, it is to be understood that the invention is not limited thereto, and that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure.
Claims (3)
1. A method for judging the eligibility of a fermented grain model is characterized by comprising the following steps:
step S100: preparing two fermented grain samples which are respectively defined as a training set A and a prediction set B;
step S200: respectively collecting the spectral data of the two fermented grain samples by using a near-infrared spectrometer, and measuring the water content, the acidity and the real content of starch in the two fermented grain samples by using a chemical analysis method after the collection is finished;
step S300: aiming at the training set A, taking the real content data as a calibration value, and establishing a mathematical model between the calibration value and the spectral data by using a partial least square method;
step S400: predicting the spectral data of the prediction set B by using a mathematical model to obtain the predicted content of water, acidity and starch in the prediction set B;
step S500: and comparing the predicted content of the prediction set B with the real content value obtained by a chemical analysis method, and judging whether the mathematical model is qualified.
2. The method for determining the eligibility of the fermented grain model according to claim 1, wherein the mathematical model in the step S300 comprises a spectrum-moisture mathematical model, a spectrum-acidity mathematical model, and a spectrum-starch mathematical model.
3. The method for judging the eligibility of the fermented grain model according to claim 1, wherein in the step S500, the difference value operation is respectively performed on the moisture, the acidity, the predicted value of starch and the true value of each fermented grain sample in the prediction set B, the difference value is judged by a threshold value, when the specific gravity smaller than the preset threshold value exceeds the preset value, the mathematical model is qualified, otherwise, the mathematical model is unqualified.
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| CN111965140A (en) * | 2020-08-24 | 2020-11-20 | 四川长虹电器股份有限公司 | Wavelength point recombination method based on characteristic peak |
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| CN112924413A (en) * | 2021-01-27 | 2021-06-08 | 四川长虹电器股份有限公司 | Method for predicting vinasse components |
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| CN113250042A (en) * | 2021-05-06 | 2021-08-13 | 黑龙江工程学院 | Detection method for rolling quality of high-speed railway roadbed |
| CN113250042B (en) * | 2021-05-06 | 2023-02-17 | 黑龙江工程学院 | Detection method for rolling quality of high-speed railway roadbed |
| CN113724803A (en) * | 2021-09-02 | 2021-11-30 | 四川启睿克科技有限公司 | Method for effectively improving prediction accuracy of vinasse sample |
| CN113724803B (en) * | 2021-09-02 | 2024-03-12 | 四川启睿克科技有限公司 | Method for effectively improving prediction accuracy of vinasse sample |
| CN115914063A (en) * | 2023-02-17 | 2023-04-04 | 四川景诺电子有限公司 | Intelligent terminal automatic testing method and system based on 5G communication |
| CN116223441A (en) * | 2023-05-08 | 2023-06-06 | 四川威斯派克科技有限公司 | Method and system for detecting gelatinization degree of fermented grains by utilizing near infrared spectrum analysis |
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