Method for indirectly detecting total amount of amino acids in yellow rice wine
阅读说明：本技术 一种间接检测黄酒中氨基酸总量的方法 (Method for indirectly detecting total amount of amino acids in yellow rice wine ) 是由 刘太昂 刘振昌 刘太行 刘远 刘婷婷 朱鲁阳 吴治富 周晶晶 周央 朱峰 于 2021-10-12 设计创作，主要内容包括：黄酒中含量多的成分是水、乙醇、糖类、酸类物质。黄酒中氨基酸含量偏低,但黄酒中氨基酸种类众多,如天冬氨酸、谷氨酸、丝氨酸、组氨酸、谷氨酸、丙氨酸、精氨酸、赖氨酸、脯氨酸、半胱氨酸等等。这些氨基酸含量虽然微小,但其含量的改变,对黄酒风味也有着直接的影响。但由于这些氨基酸种类众多,含量微小,检测的前处理、检测方法、手段都十分复杂,工作量巨大等原因,需要尝试对其进行间接预测。本专利针对黄酒中的氨基酸含量检测复杂、费用高、需要大量的化学试剂等缺点,尝试设计出一种间接检测黄酒中氨基酸总量的方法,利用黄酒中常规的化学成分含量结合化学计量学方法建立氨基酸总量大量预报模型,来实现间接检测黄酒中氨基酸总量。(The yellow wine contains water, ethanol, saccharides, and acids. Yellow wine has a low content of amino acids, but the yellow wine has a wide variety of amino acids, such as aspartic acid, glutamic acid, serine, histidine, glutamic acid, alanine, arginine, lysine, proline, cysteine, etc. Although the content of these amino acids is small, the change of the content of these amino acids has a direct influence on the flavor of yellow wine. However, due to the fact that the amino acids are numerous in types and small in content, pretreatment, detection methods and means for detection are very complicated, and workload is huge, indirect prediction of the amino acids needs to be tried. Aiming at the defects of complex detection, high cost, large quantity of chemical reagents and the like of the amino acid content in the yellow wine, the patent tries to design a method for indirectly detecting the total amount of the amino acid in the yellow wine, and the indirect detection of the total amount of the amino acid in the yellow wine is realized by establishing a large-amount forecasting model of the total amount of the amino acid by combining the conventional chemical component content in the yellow wine with a chemometric method.)
1. A method for indirectly detecting the total amount of amino acids in yellow rice wine comprises the following steps:
1) collecting a plurality of yellow wine samples of different brands, respectively detecting the content of total acid, total sugar and reducing sugar, the alcoholic strength and the pH value, wherein the data form basic data for indirectly detecting the total amount of amino acid; 2) carrying out combined transformation on the basic data to form intermediate data for indirectly detecting the total amount of the amino acid; 3) based on the intermediate data, a quantitative prediction model of the total amount of amino acids in the yellow rice wine is established by adopting a support vector machine algorithm; 4) collecting new yellow wine samples, respectively detecting basic data such as total acid, total sugar and reducing sugar content, alcoholic strength and pH value, substituting the basic data into a combined transformation equation to calculate intermediate data, substituting the intermediate data into a support vector machine quantitative forecasting model, and indirectly detecting the total amino acid amount of the new yellow wine samples.
2. The method for indirectly detecting the total amount of amino acids in yellow wine according to claim 1, wherein the mapping in step 2) is transformed, and the equation is as follows:
Y1= -0.208[ total acids]-1.806E-3[ Total sugar]-1.766E-3[ reducing sugar]-0.165[ alcohol degree]+3.493[pH]-9.489
Y2= 4.608E-2[ total acid]+7.976E-3[ Total sugars]+8.214E-3[ reducing sugar]+5.793E-2[ alcohol content]+2.433[pH]-10.642
Y3= 0.271[ total acid ]]+1.666E-3[ Total sugar]+2.117E-3[ reducing sugars]+0.568 alcohol degree]+2.596[pH]-16.810
Y4= -0.550[ total acids]+1.630E-3[ Total sugars]+1.569E-3[ reducing sugars]+0.436[ alcohol degree]+0.170[pH]-0.823。
Technical Field
The invention relates to the technical field of yellow wine amino acid content detection, in particular to a method for indirectly detecting the total amount of amino acids in yellow wine.
Background
Yellow wine is one of the oldest wines in the world, originates from China, and is unique to China, and is called three ancient wines in the world together with beer and wine. Yellow wine is a product developed by the industrial policy of wine development in China, is a wine variety with long history and cultural connotation, and is also a wine product which is hopefully to move to the world and occupies a place in the future. The yellow wine contains water, ethanol, saccharides, and acids. Yellow wine has a low content of amino acids, but the yellow wine has a wide variety of amino acids, such as aspartic acid, glutamic acid, serine, histidine, glutamic acid, alanine, arginine, lysine, proline, cysteine, etc. Although the content of these amino acids is small, the change of the content has a direct influence on the flavor of the yellow wine, which is a result that the flavor of the yellow wine is not a single substance action, but a result that a plurality of different trace components are subtly balanced in quantity. In early studies, conventional detection of yellow wine and detection of components with larger content were performed, while detection of flavor substances, particularly trace components, was performed less frequently, and detection of amino acids with lower content and more variety was performed less frequently. The main reasons are influenced by equipment instruments, such as conventional gas phase, liquid phase, gas chromatography and mass spectrometry, which require expensive instruments. But more importantly, the amino acids have various types and small content, and the pretreatment, detection method and means of detection are very complicated, the workload is huge, and the like. Due to the fact that a great deal of research is carried out on the content of important components such as water, ethanol and total sugar in the yellow wine, a great deal of experience is accumulated, the content of the components in the yellow wine is relatively simple to detect and low in cost, and whether the content of conventional chemical components in the yellow wine can be used for indirectly forecasting the total amount of amino acid or not can be tried. Aiming at the defects of complex detection, high cost, large quantity of chemical reagents and the like of the amino acid content in the yellow wine, the patent tries to design a method for indirectly detecting the total amount of the amino acid in the yellow wine, and the indirect detection of the total amount of the amino acid in the yellow wine is realized by establishing a large-amount forecasting model of the total amount of the amino acid by combining the conventional chemical component content in the yellow wine with a chemometric method.
Disclosure of Invention
The invention aims to provide a simple, convenient, quick, low-cost and pollution-free detection method for overcoming the defects that the traditional detection method for detecting amino acid in yellow wine needs complex pre-absorption treatment, has high manual work intensity and poor reproducibility, uses a large amount of organic solvent and the like. The indirect detection method for the total amount of amino acids in the yellow wine is established by utilizing data of mature detection of main components in the yellow wine, such as total acid, total sugar, reducing sugar, alcoholic strength, pH value and the like.
The purpose of the invention can be realized by the following technical scheme:
a method for indirectly detecting total amount of amino acids in yellow wine is provided. The method comprises the following steps:
1) a plurality of yellow wine samples of different brands are collected, the total acid, total sugar and reducing sugar content, the alcoholic strength and the pH value are respectively detected, and the data form basic data for indirectly detecting the total amount of the amino acid.
2) Carrying out combined transformation on the basic data to form intermediate data for indirectly detecting the total amount of the amino acid;
3) based on the intermediate data, a quantitative prediction model of the total amount of amino acids in the yellow rice wine is established by adopting a support vector machine algorithm;
4) collecting new yellow wine samples, respectively detecting basic data such as total acid, total sugar and reducing sugar content, alcoholic strength and pH value, substituting the basic data into a combined transformation equation to calculate intermediate data, substituting the intermediate data into a support vector machine quantitative forecasting model, and indirectly detecting the total amino acid amount of the new yellow wine samples.
Compared with the prior art, the invention has the following advantages:
firstly, low cost: although professional experiments are needed in testing basic data such as total acid, total sugar, reducing sugar content, alcoholic strength and pH value, the total amount of amino acid can be calculated after detecting the content of various amino acids. The whole process reduces a large number of experiments and saves a lot of cost.
Secondly, reducing pollution: if the contents of dozens of amino acids in the yellow wine are detected by a chemical method, a large amount of chemical reagents are needed, and the environment is polluted. A large amount of chemical reagents are saved and pollution is reduced by a method for indirectly detecting the total amount of amino acids in the yellow wine.
Drawings
FIG. 1 shows the modeling result of the indirect prediction model of the total amount of amino acids in yellow wine, and the modeling result is high in accuracy.
FIG. 2 is a leave-one-out internal cross validation result of an indirect prediction model of total amino acid content in yellow wine, and the result accuracy is high.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Example (b):
a method for indirectly detecting total amount of amino acids in yellow wine is provided. The method comprises the following steps:
(1) 18 yellow wine samples of different brands are collected, the total acid, total sugar and reducing sugar content, the alcoholic strength and the pH value are respectively detected, and the data form basic data for indirectly detecting the total amount of the amino acid. The base data is an 18 x 5 data matrix. A partial basic data representation is shown in table 1.
Table 1 partial basic data
Total amino acids
Total acid
Total sugar
Reducing sugar
Alcohol content
pH
2192.16
5.672
10.85
6.388
10
3.79
1031.17
8.355
92.705
92.376
7.85
3.54
1758.42
8.202
49.337
48.241
8.9
3.87
733.25
7.512
84.304
84.304
7.46
3.45
1513.742
5.519
2.9
2.65
9.27
3.37
1610.608
5.672
289.444
286.264
5.9
3.49
1578.126
7.588
5.15
5
10.98
3.73
2119.12
10.424
4.05
3.975
6.33
3.74
1086.38
7.972
4.1
3.82
10.08
3.7
1190.79
7.742
4.1
3.95
10.05
3.68
(2) And for the basic data of the 18 x 5 data matrix, converting the basic data by adopting a combined conversion equation to obtain intermediate data. The intermediate data is an 18 x 4 data matrix. The combined transformation equation is as follows:
Y1= -0.208[ total acids]-1.806E-3[ Total sugar]-1.766E-3[ reducing sugar]-0.165[ alcohol degree]+3.493[pH]-9.489
Y2= 4.608E-2[ total acid]+7.976E-3[ Total sugars]+8.214E-3[ reducing sugar]+5.793E-2[ alcohol content]+2.433[pH]-10.642
Y3= 0.271[ total acid ]]+1.666E-3[ Total sugar]+2.117E-3[ reducing sugars]+0.568 alcohol degree]+2.596[pH]-16.810
Y4= -0.550[ total acids]+1.630E-3[ Total sugars]+1.569E-3[ reducing sugars]+0.436[ alcohol degree]+0.170[pH]-0.823
A partial intermediate data representation is shown in table 2.
Table 2 partial intermediate data
Y1 Y2 Y3 Y4 0.8884
-0.4406
0.2819
1.0885
-0.487
0.3092
-0.5429
-1.0976
0.6804
0.4576
0.7034
-0.6397
-0.5322
-0.1045
-1.2578
-0.8458
-0.4054
-1.6061
-1.2861
0.7642
-0.4793
3.1128
-1.7696
0.1441
0.1313
-0.4984
1.19
0.4405
0.0994
-0.624
0.7006
-0.1715
0.0821
-0.6839
0.5695
-0.0613
(3) And (4) according to the intermediate data, establishing an indirect prediction model of the total amount of amino acids in the yellow wine by adopting a support vector machine algorithm. For parameters of a support vector machine algorithm, a radial basis function is selected, a penalty factor is 140, and an insensitive function is 0.03.
(4) And collecting 2 new yellow wine samples, respectively detecting basic data such as total acid, total sugar and reducing sugar content, alcoholic strength and pH value, substituting the basic data into a combined transformation equation to calculate intermediate data, substituting the intermediate data into a support vector machine quantitative forecasting model, and indirectly detecting the total amino acid amount of the 2 new yellow wine samples.
Example 1: modeling result of indirect prediction model of total amino acid amount in 18 yellow wine samples
And establishing amino acid total quantity indirect prediction modeling on the basis of the intermediate data of the 18 yellow rice wines by utilizing a support vector machine algorithm. The correlation coefficient between the calculated total amino acid amount of the modeling result and the experimentally detected value is 0.94. The results are shown in FIG. 1.
Example 2: the results of leave-one-out test of the indirect prediction model of the total amount of amino acids in 18 yellow wine samples are shown in fig. 2.
And establishing amino acid total quantity indirect prediction modeling on the basis of the intermediate data of the 18 yellow rice wines by utilizing a support vector machine algorithm. The correlation coefficient of the total amino acid calculation value of the leave-one-out internal cross validation result and the experimental detection value is 0.82.
Example 3: prediction result of total amino acid amount of 2 new yellow wine samples
2 new yellow wine samples were collected, and the basic data and the intermediate data set amino acid total indirect prediction results are shown in table 3.
TABLE 3 Indirect prediction of results
Total acid
Total sugar
Reducing sugar
Alcohol content
pH
Y1 Y2 Y3 Y4 Total amount of amino acids (predicted result)
10.424
4.05
3.975
6.33
3.74
0.3491
-0.6301
-0.6611
-3.1466
2378.944
5.519
25.365
25.589
9.91
3.76
0.7702
-0.2525
0.1762
1.1821
890.391