CN102058012A - Oyster sapidity peptide controllable enzymolysis process based on optimization of nerve network system - Google Patents

Abstract

The invention relates to the field of food technology, in particular to a controllable enzymatic hydrolysis process of oyster taste peptides optimized based on a neural network system. The present invention is based on the controllable enzymatic hydrolysis process of oyster flavor peptides optimized by the neural network system. The BP neural network learning process is processed by the hidden layer in the process of forward propagation of input data and reverse propagation of errors, and finally realizes complex Simulation of the nondeterministic enzymatic hydrolysis problem. This method simulates the human brain judgment system, uses high-precision real-time simulation to process data, and realizes the nonlinear mapping relationship between enzymatic hydrolysis factors, peptide content and sensory scores without the need for precise mathematical models; and has accurate It avoids reducing the implementation of some actual processes and some drawbacks and limitations in human sensory evaluation, and directly achieves fast and accurate prediction simulation results. The enzymolysis process obtained by adopting the invention is more scientific, so the process production efficiency and product quality can be improved, and the production cost can be reduced.

Description

A controllable enzymatic hydrolysis process of oyster taste peptide based on neural network system optimization

technical field

The invention relates to the field of food technology, in particular to a controllable enzymatic hydrolysis process of oyster taste peptides optimized based on a neural network system.

Background technique

Oysters and oyster products are loved by consumers for their unique flavor and nutritional value, especially the fermented oysters are extremely delicious. Since ancient times, the ancients have processed oysters into fermented condiments such as oyster sauce and oyster sauce. Modern scientific research results show that the excellent flavor of oyster fermented condiments such as oyster sauce and dried oyster is closely related to the taste peptides produced during the fermentation process of oysters. Due to the low degree of mechanization, long time consumption and low production efficiency of the traditional fermentation process, the current modern enzyme engineering technology is gradually replacing the traditional fermentation process. However, how to maximize the yield of taste peptides and realize the controllable enzymatic hydrolysis of taste peptides is a key technical problem to be solved in the deep processing of oyster seasoning. The research results of the enzymatic hydrolysis process of oyster flavor peptides showed that the amount of enzyme added, the ratio of material to water, the enzymatic hydrolysis temperature and the enzymatic hydrolysis time and other factors were all to a great extent and non-linearly affected the production of flavor peptides and their flavor. But at present, many traditional enzymatic hydrolysis process conditions are optimized by adopting the best process plan obtained by orthogonal experiment method. Although the orthogonal experiment has the characteristics of "uniform dispersion, neat and comparable", due to the complexity and nonlinearity of protease kinetics, the optimal factor conditions obtained by the orthogonal experiment are not necessarily the optimal factor conditions. Due to its unique nonlinear adaptive information processing ability, artificial neural network has the advantages of self-learning function, associative storage function and high-speed search for optimal solution, and has been widely used in the field of automatic control, processing combination optimization, image processing, etc. It can be speculated that the artificial neural network also has broad application prospects in enzyme engineering technology. However, the research of neural network in enzyme engineering technology is still in the initial stage, and there is no report on the application of oyster flavor peptide enzymatic hydrolysis process optimization.

Contents of the invention

The purpose of the present invention is to make up for the deficiencies in the above-mentioned prior art, and provide a controllable enzymolysis process technology method based on a neural network system to optimize the oyster flavor peptide, so that the enzymolysis process is more scientific, and the process production efficiency and product quality are improved. ,reduce manufacturing cost.

In order to achieve the purpose of the above invention, the technical solution adopted by the present invention is that the controllable enzymolysis process of oyster taste peptide based on neural network system optimization includes the following steps:

(1) Using oyster meat as raw material, four factors including material ratio water, protease addition amount, enzyme reaction temperature and enzyme reaction time were used as enzymatic hydrolysis process parameters, and peptide ratio and sensory score were used as the evaluation of taste peptides in oyster enzymatic hydrolyzate Index, using orthogonal experiments and random enzymatic hydrolysis experiments to obtain the one-to-one correspondence between enzymatic hydrolysis process parameters and evaluation indicators;

(2) Taking four process parameters as input values, such as material ratio water, protease addition amount, enzyme reaction temperature and enzyme reaction time, and taking the peptide ratio and sensory score of oyster enzymatic hydrolyzate as output signals, the oyster flavor peptide can be established. Enzyme-controlled BP neural network structure model training samples;

(3) Train and simulate the BP neural network structure model, and finally realize a good mapping relationship between the enzymatic hydrolysis process parameters and the enzymatic hydrolysis index;

(4) On the basis of the BP neural network, the function obtained by the neural network is used as the fitness function of the genetic algorithm, and the maximum value of the peptide ratio of the enzymatic hydrolysis solution and the highest sensory score are used as the targets to further obtain the enzymatic hydrolysis process by genetic algorithm the best combination of parameters.

The oyster enzymatic hydrolysis process refers to the homogenization of oyster meat, adjusting the ratio of feed to water, adding protease, enzymatic reaction under certain conditions, heating in a boiling water bath for 10 minutes, cooling, centrifuging at 4000 rpm for 20 minutes, and taking the enzyme The oyster enzymatic hydrolyzate was obtained by decomposing the supernatant, and the four factors including the ratio of material to water, the amount of protease added, the temperature and time of enzymatic reaction were the variable parameters of the oyster enzymatic hydrolysis process.

The homogenization refers to that the oyster meat is washed with tap water and drained of surface water, then the tissue is broken and homogenized to obtain the oyster meat homogenate stock solution;

The ratio of adjusted material to water refers to the ratio of oyster meat homogenate stock solution and added water;

The added amount of protease refers to the percentage by weight of protease added per 100 grams of oyster meat homogenate stoste;

The enzyme reaction time refers to the time for the oyster meat to undergo the enzyme reaction from the moment when the protease is added after pretreatment such as homogenization and adjustment of the feed-to-water ratio.

The peptide ratio of the oyster enzymatic hydrolyzate refers to the determination of the peptide nitrogen and total nitrogen contents in the oyster enzymatic hydrolyzate respectively, and the percentage of the peptide nitrogen content in the total nitrogen content is the peptide ratio.

The sensory score of the oyster hydrolyzate refers to the score obtained by sensory evaluation of the oyster hydrolyzate by a sensory evaluation method.

Described sensory evaluation refers to the freshness corresponding to the sodium glutamate solution of different concentrations (0.2～1.6g/L) as a standard, and the freshness of the sodium glutamate solution concentration is 1.6g/L is the highest value (8 points), the freshness of sodium glutamate solution with a concentration of 0.2g/L is the lowest value (1 point), and the corresponding scores for other concentrations range from 1 to 8. The sensory assessors have all received basic taste training and are composed of at least 15 people. The oyster enzymatic hydrolyzate was subjected to sensory evaluation by the above methods, and the raw data of the evaluation were inspected and processed by the Dixon method, and finally the average value was taken.

The structural model construction of described step (2) BP neural network refers to application MATLAB7.8 version software, is input quantity with 4 factors such as material-water ratio, protease addition, enzyme reaction time and enzyme reaction temperature when setting up neural network model (the number of neurons is 4), and the peptide ratio or sensory score is used as the output layer respectively (the number of neurons is 1), and a three-layer BP neural network with four inputs and one output is constructed, and the difference between the input layer and the hidden layer The transfer function between them is a tangent S-shaped function (tansig), and the transfer function between the hidden layer and the output layer is a linear function (purelin).

The step (3) structural model training refers to adopting the trainlm function as the network training function, setting the training target error as 0.0001, and performing simulation training. Make the BP neural network modify the weights of each neuron connection one by one through the continuous learning process of forward propagation and back propagation. This process is iterated continuously, and finally the training is stopped when the error signal reaches 0.0001 within the allowable range.

The step (4) genetic algorithm refers to taking the established neural network model as the fitness function of the genetic algorithm, taking the level range of each enzymatic hydrolysis parameter as a constraint condition, and taking the maximum value of the enzymatic hydrolysis liquid peptide ratio and the highest sensory score as the optimal value The goal is to establish an optimization model, determine the coding method and decoding method of the feasible solution, and set the relevant parameters of the genetic algorithm at the same time.

The setting of related parameters of the genetic algorithm refers to setting parameters such as initial population generation, crossover probability and mutation probability. Generate the initial population, that is, the number of people contained in the population, generally 20 to 160, if it is too small, it will affect the search range, so that the optimal solution cannot be obtained, if it is too large, the search time will be long and the efficiency will be low; crossover probability and mutation probability , between 0 and 1, the larger the two, the stronger the detection ability of the algorithm, and the easier it is to detect new hyperplanes, but the average fitness of individuals fluctuates greatly, on the contrary, the smaller the algorithm is, the stronger the development ability is. The better individual is not easy to be destroyed, and the average fitness of the individual is balanced.

Run the set parameters and programs in MATLAB software, start the optimal search process with an initial population composed of multiple individuals, and perform operations such as selection, crossover, and mutation on this population to generate a new generation of population , continue to search at multiple points, after several trial calculations and reasonable parameter settings, a stable optimal factor combination is finally obtained by the genetic algorithm.

The present invention is based on the controllable enzymatic hydrolysis process of oyster flavor peptides optimized by the neural network system. The BP neural network learning process is processed by the hidden layer in the process of forward propagation of input data and reverse propagation of errors, and finally realizes complex Simulation of the nondeterministic enzymatic hydrolysis problem. This method simulates the human brain judgment system, uses high-precision real-time simulation to process data, and realizes the nonlinear mapping relationship between enzymatic hydrolysis factors, peptide content and sensory scores without the need for precise mathematical models; and has accurate It avoids reducing the implementation of some actual processes and some drawbacks and limitations in human sensory evaluation, and directly achieves fast and accurate prediction simulation results. The enzymolysis process obtained by adopting the invention is more scientific, so the process production efficiency and product quality can be improved, and the production cost can be reduced.

Description of drawings

Fig. 1 is the neural network structural diagram of the present invention

Fig. 2 is the flow chart that neural network of the present invention and genetic algorithm combine

Detailed ways

The controllable enzymatic hydrolysis process of oyster taste peptide based on neural network system optimization of the present invention will be described in detail below in conjunction with the examples.

(1) Obtain neural network learning samples

Oyster meat was prepared according to the following process, and then the peptide nitrogen content and total nitrogen content of the oyster hydrolyzate were determined, and the peptide ratio of the oyster hydrolyzate was calculated, and the sensory evaluation of the oyster hydrolyzate was carried out at the same time. Obtain the one-to-one correspondence between enzymatic hydrolysis process parameters and evaluation indicators, and use them as neural network learning samples.

Oyster enzymatic hydrolysis process: oyster meat (fresh or frozen)→cleaning→draining→homogenization→adjusting material-water ratio→adding protease→enzyme reaction under certain conditions (temperature, time)→heating in boiling water bath for 10min→cooling→centrifugation (4000 rpm, 20min) → take the enzymatic hydrolysis supernatant → oyster enzymatic hydrolyzate.

The main points of operation are as follows:

1) homogenate. After the oyster meat is washed with tap water and the surface water is drained, the tissue is crushed and homogenized by a tissue masher or a homogenizer to obtain the oyster meat homogenate stock solution.

2) Adjust the ratio of material to water. According to different material-water ratios (1:2, 1:3, 1:4, 1:5, etc.), the concentration of oyster meat homogenate was adjusted with pure tap water.

3) Add protease. Add protease to oyster slurry according to different enzyme addition amounts (0.3%, 0.6%, 0.9%, 1.2%, etc.) based on the weight percentage of oyster meat homogenate stock solution.

4) Enzyme reaction temperature. The oyster slurry after adding protease is placed in a constant temperature water bath, and the enzyme reaction temperature is adjusted according to different enzyme reaction temperatures (45°C, 50°C, 55°C, 60°C, etc.).

5) Enzyme reaction time. Start timing from the moment of adding protease, and enzymatically hydrolyze the oyster slurry according to different enzyme reaction time requirements (4 hours, 5 hours, 6 hours, 7 hours, etc.).

6) Oyster enzymatic hydrolyzate. After the enzymatic hydrolysis reaction of the oyster slurry is carried out according to different process conditions, it is placed in a boiling water bath and fully heated for 10 minutes, cooled and centrifuged, and the supernatant is taken to obtain the oyster enzymatic hydrolysis solution.

(2) Establish a neural network model

Taking four process parameters as input values, such as material ratio water, protease addition amount, enzyme reaction temperature and enzyme reaction time, and taking the peptide ratio and sensory score of oyster enzymatic hydrolyzate as output signals, the BP was established by using MATLAB version 7.8 software. Neural Networks.

As shown in Figure 1, the BP neural network is designed as a 3-layer network: an input layer, a hidden layer and an output layer. The input layer sets four neurons: material ratio water, protease addition amount, enzyme reaction temperature and enzyme reaction time. The hidden layer was set as 13 neurons, and the peptide ratio and sensory score of oyster hydrolyzate were set as the output layer. The transfer function between the input layer and the hidden layer is a tangent S-shaped function (tansig), the transfer function between the hidden layer and the output layer is a linear function (purelin), and the network training function uses the trainlm function.

(3) Neural network training

As shown in Figure 2, use 80% of the total training sample data to train the neural network, set an expected value of the peptide ratio and target precision of the oyster hydrolyzate, and then let the BP neural network model start running. By adjusting the number of training steps, network learning rate and target precision, the neural network is continuously trained until the error between the network peptide ratio output value and the expected value is reduced to within 5%. After continuous learning and training, the neural network obtains the optimal neural network model parameters: the maximum number of training steps is 100, the network learning rate is 0.1, and the network performance target error is 0.0001.

(4) Perform genetic calculation on BP neural network to obtain the best enzymatic hydrolysis process conditions

As shown in Figure 2, the established neural network model is used as the fitness function of the genetic algorithm, the level range of each enzymatic hydrolysis parameter is used as the constraint condition, and the maximum value of the enzymatic hydrolyzed peptide ratio and the highest sensory score are the optimization goals to establish an optimization model , to determine the encoding method and decoding method of the feasible solution, and set related parameters such as initial population generation, crossover probability and mutation probability.

The three parameters of initial population generation, crossover probability and mutation probability were set to 24, 0.3 and 0.1, respectively. An initial population consisting of 24 individuals was started, and the maximum ratio of oyster enzymatic hydrolyzed peptide and the highest sensory score were used as the parameters respectively. The output signal, through operations such as selection, crossover, and mutation, generates a new generation of groups, and searches for the best enzymatic hydrolysis process conditions through repeated operations.

Taking the maximum peptide ratio of oyster enzymatic hydrolyzate as the output signal, after the above genetic calculations, the optimal enzymolysis process conditions were: material-water ratio 1:2.8, protease addition 1.03%, enzyme reaction temperature 58.6°C, enzyme reaction After 5.4 hours, the predicted value of peptide ratio in oyster hydrolyzate was 80.81%. According to the above-mentioned process, the verification test of oyster meat was carried out. The experimental results showed that the peptide ratio of oyster enzymatic hydrolyzate reached 78.35%, and the sensory score value reached 6.58. The relative error was kept within ±5%, and there was no significant difference between the predicted value and the actual value. In addition, compared with the optimal process obtained by the orthogonal experiment, the peptide ratio and sensory score of the oyster hydrolyzate obtained by the optimal process obtained by the neural network were significantly better than those obtained by the orthogonal experiment (peptide ratio and sensory score values were respectively 75.34% and 5.51).

Taking the highest value of sensory score as the output signal, after the above genetic calculations, the optimal enzymatic hydrolysis process conditions obtained are: material-water ratio 1:2.1, protease addition 0.95%, enzyme reaction temperature 53.8°C, enzyme reaction time 6.0 hours, The predicted sensory score of oyster hydrolyzate was 6.67 points. According to the above process, the verification test of oyster meat was carried out. The experimental results showed that the sensory score value of the oyster enzymatic hydrolyzate reached 6.39, and there was no significant difference between the predicted value and the actual value.

Claims (10)

1. the oyster based on nerve network system optimization is gustin Controlled-enzymatic Hydrolysis technology, it is characterized in that comprising the steps:

(1) with the oyster meat is raw material, is the enzymolysis process conditional parameter with material than 4 factors such as water, protease addition, enzyme reaction temperature and enzyme reaction times, be the gustin evaluation index with peptide ratio and sensory evaluation scores as the oyster enzymolysis liquid, adopt the orthogonal experiment and the one-to-one relationship of enzymolysis experiment acquisition enzymolysis process parameter and evaluation index at random;

(2) to expect than 4 technological parameters such as water, protease addition, enzyme reaction temperature and enzyme reaction times as input value, peptide ratio and sense organ with the oyster enzymolysis liquid are marked as output signal, set up the BP neural network structure model training sample that oyster is the gustin Controlled-enzymatic Hydrolysis;

(3), finally realize the good mapping relations of enzymolysis process parameter to the enzymolysis index to the training of BP neural network structure model, emulation;

(4) on the basis of BP neutral net, with the function of neutral net gained fitness function, be that target adopts genetic algorithm further to try to achieve the best of breed of enzymolysis process parameter with enzymolysis liquid peptide ratio maximum and sensory evaluation scores peak respectively as genetic algorithm.

2. be gustin Controlled-enzymatic Hydrolysis technology according to the described oyster of claim 1 based on nerve network system optimization, it is characterized in that: described oyster enzymolysis process is meant the homogenate of oyster meat, adjust material-water ratio, add protease, enzyme reaction under certain condition, boiling water bath heating 10 minutes, cooling, under 4000 rev/mins of conditions centrifugal 20 minutes, get the enzymolysis supernatant and get the oyster enzymolysis liquid; Wherein 4 factors such as material-water ratio, protease addition, enzyme reaction temperature and time are the variable parameters of oyster enzymolysis process.

3. be gustin Controlled-enzymatic Hydrolysis technology according to the described oyster of claim 2 based on nerve network system optimization, it is characterized in that: described homogenate is meant that oyster meat is through after the running water cleaning and draining surface moisture, carry out historrhexis, homogenate, obtain oyster meat homogenate stoste;

Described adjustment material-water ratio is meant oyster meat homogenate stoste and the umber ratio of adding water;

Described protease addition is meant the weight percent of per 100 gram oyster meat homogenate stostes interpolation protease;

The described enzyme reaction time is meant that oyster meat is after preliminary treatment such as homogenate, adjustment material-water ratio, from adding the time that protease begins to carry out enzyme reaction constantly.

4. be gustin Controlled-enzymatic Hydrolysis technology according to the described oyster of claim 1 based on nerve network system optimization, it is characterized in that: the peptide ratio of described oyster enzymolysis liquid is meant to be measured respectively peptide nitrogen and total nitrogen content in the oyster enzymolysis liquid, and the percentage that peptide nitrogen content accounts for total nitrogen content is the peptide ratio.

5. be gustin Controlled-enzymatic Hydrolysis technology according to the described oyster based on nerve network system optimization of claim 1, it is characterized in that: the sensory evaluation scores of described oyster enzymolysis liquid is meant by the subjective appreciation method carries out the score value that sensory evaluation obtains to the oyster enzymolysis liquid.

6. be gustin Controlled-enzymatic Hydrolysis technology according to the described oyster of claim 5 based on nerve network system optimization, it is characterized in that: described subjective appreciation is meant with the pairing freshness of the monosodium glutamate solution of 0.2～1.6g/L variable concentrations as standard, monosodium glutamate solution concentration is that the freshness of 1.6g/L is peak 8 minutes, monosodium glutamate solution concentration is that the freshness of 0.2g/L is minimum 1 minute, and the corresponding score value scope of other each concentration is 1～8; Subjective appreciation person all passes through basic flavour training, and at least by forming more than 15 people; The oyster enzymolysis liquid carries out sensory evaluation scores by above method, and the initial data of scoring adopts the processing of testing of Dixon method, finally averages.

7. be gustin Controlled-enzymatic Hydrolysis technology according to the described oyster of claim 1 based on nerve network system optimization, it is characterized in that: the structural model of described step (2) BP neutral net makes up and is meant application MATLAB7.8 version software, when setting up neural network model with material-water ratio, the protease addition, 4 factors such as enzyme reaction time and enzyme reaction temperature are input quantity, be output layer with peptide ratio or sensory evaluation scores value respectively, make up three layers of BP neutral net of the single output of four inputs, and be tangent sigmoid function tansig with the transfer function between input layer and the hidden layer, the transfer function between hidden layer and the output layer is linear function purelin.

8. be gustin Controlled-enzymatic Hydrolysis technology according to the described oyster of claim 1 based on nerve network system optimization, it is characterized in that: the training of described step (3) structural model is meant that adopting the trainlm function is the network training function, setting the training objective error is 0.0001, carry out simulation training, make the continuous learning process of BP neutral net by forward-propagating and backpropagation, revise the weights that each neuron connects one by one, the continuous iteration of this process, realize that at last error signal reaches within the scope of permission at 0.0001 o'clock, just stops training.

9. be gustin Controlled-enzymatic Hydrolysis technology according to the described oyster of claim 1 based on nerve network system optimization, it is characterized in that: described step (4) genetic algorithm is meant that the neural network model to set up is the fitness function of genetic algorithm, with each enzymolysis parameter horizontal extent is constraints, with enzymolysis liquid peptide ratio maximum and sensory evaluation scores peak is that optimization aim is set up the optimization model, determine the coding method and the coding/decoding method of feasible solution, set the genetic algorithm relevant parameter simultaneously.

10. be gustin Controlled-enzymatic Hydrolysis technology according to the described oyster based on nerve network system optimization of claim 9, it is characterized in that: described setting genetic algorithm relevant parameter is meant sets parameters such as generating initial population, crossover probability and variation probability; Generate initial population, promptly the quantity of contained number in the colony generally gets 20～160, and it crosses the young pathbreaker influences the hunting zone, thereby can not get optimal solution, and excessive then search time is long, and efficient is low; Crossover probability and variation probability, get between 0～1, both are big more, then the algorithm detectivity is strong more, detect new hyperplane easily more, but individual average fitness fluctuation is bigger, the development ability of opposite more little then algorithm is strong more, it is destroyed to make that more excellent individuality is difficult for, the average fitness balance of individuality;

The parameter that configures and program be placed in the MATLAB software move, an initial population of being made up of a plurality of individualities begins optimal search procedure, and computing such as selection that this colony is carried out, intersection, variation, produce the colony of a new generation, continue the search of multiple spot, through after the tentative calculation repeatedly and under the setting of Reasonable Parameters, finally draw a stable optimum factor combination by genetic algorithm.

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