The fermentation process of Luzhou-flavor liquor is almost uncontrollable, and at the same time, in his production process, the fermentation method is utilized. Therefore, eel conditions are an important reference indicator for liquor fermentation. In the study of the conditions for the emergence of Luzhou-flavor liquor, the quantitative evaluation of the conditions of liquor discharge was achieved by the cloud model and the improved D-S (Dempater-Shafer) evidence theory. There are five main factors that have a profound influence on the sputum conditions, namely temperature, moisture, acidity, starch content and reducing sugar. The membership degree cloud model is established by Matlab simulation for the out-of-the-box condition, so that each parameter has its corresponding membership function. The collected sample parameters are used to realize the membership degree of each evaluation standard through the cloud model, and the sample data are analyzed by using the improved D-S evidence theory. Through experimental analysis, the outburst condition of liquor can reach a more accurate quantitative analysis, so as to guide the fermentation process of Luzhou-flavor liquor, and then improve its yield and quality.
During production of Luzhou-flavor liquor, the fermentation of Luzhou-flavor liquor is an anaerobic, solid, and closed process. Solid fermentation is used throughout the fermentation, and it is not easy to check the parameters in the middle of the fermentation [
The main parameters of Luzhou-flavor liquor were analyzed by Matlab to establish the membership degree cloud model of each outflow condition, and the cloud model membership degree of sample parameters was obtained, and the improved D-S evidence theory was used for information fusion. Finally, the conditions for the liquor production were evaluated.
Each bacterium and microorganism have its optimum temperature for growth and reproduction. Luzhou-flavored kanji is slowly fermented by solid low temperature. The temperature of the exit will influence the quality of the fermentation. The most suitable exit temperature is between 24˚C and 28˚C. Excessive temperature in the early stage is too high, resulting in faster acid production, resulting in higher acidity of the sputum; too low fermentation process is not ideal, which has a greater impact on the production and quality of white wine.
Water is not only the basic condition for microbial survival and metabolism. But also an important carrier for mass transfer and heat transfer during fermentation. The most suitable moisture for the cockroach is 60% - 62%. The specific heat and specific gravity of water, combined with its unique permeability, fluidity and solubility, therefore, the level of moisture has a greater impact on the sputum. If the moisture is too high, it will cause excessive gelatinization, which will lead to undue heat temperature. If the water is too small, it will cause the aerobic bacteria to multiply and affect the flavor of the wine.
Suitable acid in the fermentation process can hydrolyze starch, fiber and the like into sugar (glucose), which is beneficial to gelatinization, saccharification and fermentation, and the yield and quality of the wine are improved. Acid is not just a precursor of ester, but because yeast has certain acid resistance, suitable acid can inhibit the growth and reproduction of some harmful bacteria without affecting the fermentation ability of yeast. The suitable acidity of the sputum is 4.0 - 4.8 mol/L. Too low acidity will lead to the proliferation of miscellaneous bacteria, wasting sugar and starch; too high will inhibit the activity of yeast and affect the yield of wine.
In the fermentation, the starch decomposes into fermentable sugar, and the yeast uses these sugars to ferment to produce alcohol. The suitable concentration of the starch is 11% - 13%. Too high will lead to insufficient fermentation, too much residue, not only waste, but also cause harmful acid bacteria to multiply and cause rancidity; when too low, the starch content cannot meet The need for microorganisms during a single fermentation process results in reduced production efficiency and affects the quality of the wine [
During the fermentation process, the starch will be decomposed into reduction, and the sugar yeast will use these sugars to ferment to produce alcohol. The reducing sugar content is 0.5% - 1%, and the reducing sugar content is too high, indicating that the saccharification enzyme saccharification is better, but the yeast is The fermentation process is relatively poor, and the fermentation in the pond is not sufficient; the low content will affect the low alcohol content in the next fermentation process.
Uncertainty is the concept of relative precision, including ambiguity and randomness. Fuzziness mainly refers to the uncertainty of subjective understanding, while randomness reflects the uncertainty of natural law [
According to data of the outbound conditions, the exact value of the membership degree of different grades can be obtained. The sum of the membership degrees corresponding to the constant monitoring quantity is 1. When the sum of the exact values is lower than 1, the difference is attributed to the uncertainty item. The normalization condition that satisfies the basic probability distribution of DS evidence [
| Temperature/˚C | Moisture /% | Acidity/ mol・L−1 | Starch/% | Reducing sugar/% | |
|---|---|---|---|---|---|
| Too low (not suitable) | <20 | <58 | <3.4 | <11 | <0.5 |
| Lower (better) | 20 - 22 | 58 - 60 | 3.4 - 4.0 | 10 - 12 | 0.5 - 0.7 |
| suitable | 22 - 25 | 60 - 62 | 4.0 - 4.5 | 12 - 14 | 0.7 - 0.9 |
| Higher (better) | 25 - 28 | 62 - 63 | 4.5 - 5.0 | 14 - 16 | 0.9 - 1.2 |
| Too high (not suitable) | >28 | >63 | >5.0 | >16 | >1.2 |
Data collected from a Luzhou-flavored winery are shown in
The sample data satisfying the normalization condition of the D-S evidence theory in
D-S evidence theory is the most commonly used fusion technique and was proposed by Dempater and Shafer in the 1960s. The evidence theory first defines U as the recognition framework, m is the basic credibility assignment on U, and m satisfies:
{ m ( ∅ ) = 0 ∑ ( A ⊆ Θ ) m ( A ) = 1 (1)
In the formula (1), A is a subset of U, and ∅ is an empty set. m ( A ) is BPA (basic probability distribution function) indicating the degree of accurate trust of evidence against A [
m 1 , m 2 are two BPAs on the U (basic probability distribution function), and A 1 , A 2 , ⋯ , A k , B 1 , B 2 , ⋯ , B r the focal points to be identified, then:
m ( A ) = { Q 1 − K A ≠ ∅ 0 A = ∅ (2)
In formula (2)
Q = ∑ A i ∩ B j = A m 1 ( A i ) m 2 (Bj)
K = ∑ A i ∩ B j = ∅ m 1 ( A i ) m 2 (Bj)
K is the coefficient of conflict, and the larger K, the greater the conflict. A value of 1 indicates a complete conflict, indicating that the rule is not available [
| Temperature/˚C | Moisture/% | Acidity/mol・L−1 | Starch/% | Reducing sugar/% | |
|---|---|---|---|---|---|
| Sample 0 | 24.8 | 61.5 | 4.2 | 13.5 | 0.72 |
| Sample 1 | 25.6 | 63.32 | 3.82 | 12.26 | 0.64 |
| Sample 2 | 27.5 | 62.23 | 3.43 | 11.67 | 0.74 |
| Sample 3 | 23.6 | 58 | 4.58 | 16.39 | 1.7 |
| Sample 4 | 23.1 | 59.92 | 3.74 | 15.11 | 1.83 |
| Detection volume | suitable m(A1)% | More suitable m(A2)% | Not suitable m(A3)% | uncertain m(X)% | |
|---|---|---|---|---|---|
| Sample 0 | Temperature/˚C | 38.31 | 25.52 | 0 | 36.17 |
| Moisture/% | 29.92 | 24.51 | 0 | 45.57 | |
| Acidity/mol・L−1 | 99.99 | 0.01 | 0 | 0 | |
| starch/% | 56.45 | 6.4 | 0 | 37.15 | |
| Reducing sugar% | 23.08 | 35.14 | 0 | 41.78 | |
| Sample 1 | Temperature/˚C | 17.02 | 58.02 | 0 | 24.96 |
| Moisture /% | 0 | 0 | 99.99 | 0.01 | |
| Acidity/mol・L−1 | 1.42 | 98.4 | 0 | 0.18 | |
| starch/% | 35.43 | 22.25 | 0 | 42.32 | |
| Reducing sugar% | 0.12 | 99.88 | 0 | 0 | |
| Sample 2 | Temperature/˚C | 0 | 19.42 | 33.8 | 46.78 |
| Moisture /% | 0.24 | 99.76 | 0 | 0 | |
| Acidity/mol・L−1 | 0 | 10.08 | 89.92 | 0 | |
| starch/% | 4.96 | 78.93 | 0 | 16.11 | |
| Reducing sugar% | 35.56 | 20.73 | 0 | 43.71 | |
| Sample 3 | Temperature/˚C | 85.99 | 0.54 | 0 | 13.47 |
| Moisture /% | 0 | 3.16 | 78.08 | 18.76 | |
| Acidity/mol・L−1 | 3.32 | 84.12 | 0 | 12.56 | |
| starch/% | 0 | 0.01 | 99.99 | 0 | |
| Reducing sugar% | 0 | 0.01 | 99.99 | 0 | |
| Sample 4 | Temperature/˚C | 46.46 | 5.8 | 0 | 47.74 |
| Moisture /% | 32.92 | 36.02 | 0 | 31.06 | |
| Acidity/mol・L−1 | 0 | 87.15 | 0.18 | 12.67 | |
| starch/% | 0 | 85.72 | 2.48 | 11.8 | |
| Reducing sugar% | 0 | 0.01 | 99.99 | 0 | |
m ¯ ( A k ) = 1 n ∑ i = 1 n m i ( A k ) (3)
d i = ‖ m i − m ¯ ‖ = ∑ k = 1 n ( m i ( A k ) − m ¯ ( A k ) ) 2 (4)
When the reliability level of the following formula is 5, the formula of the weighting coefficient can be determined according to the value of the evidence according to the evidence credibility of each evidence:
w i = { 5 ; d min ≤ d i < d min + 0.2 Δ d 4 ; d min + 0.2 Δ d ≤ d i < d min + 0.4 Δ d 3 ; d min + 0.4 Δ d ≤ d i < d min + 0.6 Δ d 2 ; d min + 0.6 Δ d ≤ d i < d min + 0.8 Δ d 1 ; d min + 0.8 Δ d ≤ d i ≤ d max (5)
where Δ d = d max − d min , the partition coefficient c i can be further obtained:
c i = w i ∑ i = 1 n w i (6)
m ˜ ( A k ) = ∑ i = 1 n c i m i ( A k ) (7)
m = m ˜ ⊕ m ˜ ⊕ ⋯ ⊕ m ˜ ︷ n − 1 (8)
where: n − 1 is the number of times the operator ⊕ performs D-S operations [
After increasing the D-S evidence theory, the weight coefficient will change as the reliability level changes. The smaller the average evidence deviation is, the higher the confidence level is, the larger the weight is. Information fusion results of the samples under different reliability levels are shown in
| Reliability level | m(A1)% | m(A2)% | m(A3)% | m(X)% | |
|---|---|---|---|---|---|
| Sample 0 | 1 | 49.55 | 18.32 | 0.00 | 32.13 |
| 3 | 68.38 | 14.47 | 0.00 | 17.15 | |
| 5 | 87.74 | 9.28 | 0.00 | 2.98 | |
| 7 | 93.41 | 6.36 | 0.00 | 0.23 | |
| 9 | 97.23 | 2.64 | 0.00 | 0.13 | |
| Sample 1 | 1 | 10.80 | 55.71 | 20.00 | 13.49 |
| 3 | 7.46 | 72.23 | 16.56 | 3.75 | |
| 5 | 3.24 | 85.67 | 10.28 | 0.81 | |
| 7 | 1.61 | 92.11 | 6.24 | 0.04 | |
| 9 | 0.12 | 98.34 | 1.52 | 0.02 | |
| Sample 2 | 1 | 8.15 | 45.79 | 24.74 | 21.32 |
| 3 | 5.27 | 65.76 | 17.65 | 11.32 | |
| 5 | 3.25 | 82.36 | 12.46 | 1.93 | |
| 7 | 1.51 | 92.43 | 5.65 | 0.41 | |
| 9 | 0.72 | 95.32 | 3.73 | 0.23 | |
| Sample 3 | 1 | 17.86 | 17.57 | 55.61 | 8.96 |
| 3 | 13.32 | 12.16 | 71.69 | 2.83 | |
| 5 | 9.31 | 7.26 | 83.24 | 0.19 | |
| 7 | 3.67 | 1.88 | 94.38 | 0.07 | |
| 9 | 1.18 | 0.52 | 98.25 | 0.05 | |
| Sample 4 | 1 | 15.88 | 42.94 | 20.53 | 20.65 |
| 3 | 10.24 | 61.32 | 17.82 | 10.62 | |
| 5 | 6.52 | 80.33 | 11.14 | 2.01 | |
| 7 | 3.13 | 89.75 | 6.65 | 0.47 | |
| 9 | 1.78 | 93.64 | 4.58 | 0 |
Comparing the fusion results of
Aiming at the situation after the wine cellars, the detection system consisting of fuzzy theory and DS evidence theory makes extensive use of the wine cellar detection information to realize the effective fusion of the sensors, and scientifically predicts the situation of the wine cellars. The next round of utilization of the wine cellar and the feasibility of fermentation ensures that the next round of fermentation is safe and appropriate. In the future work, the situation will be created after the wine cellar is released, and the relationship will be analyzed in order to more accurately study the impact of the conditions on the entire fermentation process.
The authors declare no conflicts of interest regarding the publication of this paper.
Luo, H.J., Li, G. and Yao, Y. (2019) Analysis of the Con- ditions of Luzhou-Flavor Liquor Based on Information Fusion. Open Access Library Journal, 6: e5754. https://doi.org/10.4236/oalib.1105754