CN102058012A - Oyster sapidity peptide controllable enzymolysis process based on optimization of nerve network system - Google Patents
Oyster sapidity peptide controllable enzymolysis process based on optimization of nerve network system Download PDFInfo
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Abstract
The invention relates to the technical field of foods, in particular to an oyster sapidity peptide controllable enzymolysis process based on optimization of a nerve network system. In the oyster sapidity peptide controllable enzymolysis process based on optimization of the nerve network system, a hidden layer is processed in a BP nerve network learning process in the processes of positive propagation of input data and reverse propagation of faults, and finally the simulation of a complicated uncertain enzymolysis problem is realized. The method realizes nonlinear mapping relations between enzymolysis factors and peptide contents and between enzymolysis factors and sense scores on the basis of no need of accurate mathematic models by simulating a brain judgment system of a human and accurately simulating processing data in real time. With accurate prediction ability, the invention reduces some constructions of actual processes and some defects and limitations on man-made sense evaluation so as to directly achieve a quick and correct prediction simulation effect. In the invention, the enzymolysis process is more scientific, thereby the process production efficiency and the product quality can be improved and the production cost can be reduced.
Description
Technical field
The present invention relates to food technology field, particularly a kind of oyster based on nerve network system optimization is gustin Controlled-enzymatic Hydrolysis technology.
Background technology
Oyster and oyster goods thereof with its peculiar flavour and nutritive value extremely the consumer like that the oyster taste after is extremely delicious especially by fermentation, ancients just are processed into oyster fermented seasonings such as oyster sauce, dried oyster since ancient times.The modern scientific research result shows that the gustin that is that produces in the good local flavor of oyster fermented seasonings such as oyster sauce, dried oyster and the oyster sweat has closely related.Because traditional zymotic process machinery degree is low, length consuming time, production efficiency is low, and present modern enzyme engineering technology is progressively replacing traditional zymotechnique.Be the gustin output capacity but how to improve to greatest extent, and realize that being the gustin Controlled-enzymatic Hydrolysis is the key technical problem that needs to be resolved hurrily in the deep processing of oyster gourmet powder.Oyster is gustin enzymolysis process result of study and shows, multiple factors such as enzyme concentration, material-water ratio, hydrolysis temperature and enzymolysis time and are non-linearly the generation that influence is gustin and local flavor thereof all largely.But many traditional enzymolysis process condition optimizings are all taked the optimum process scheme that orthogonal experimental method obtains at present.Though orthogonal experiment possesses the characteristics of " evenly disperse, neat comparable ", because protease in the complexity of enzyme kinetics and non-linear, causes the not necessarily best factor condition of best factor condition of orthogonal experiment acquisition.Artificial neural network is because its distinctive non-linear adaptive information processing capability, has self-learning function, association's memory function and seeks advantage such as optimization solution ability at a high speed and be used widely at aspects such as automation field, treatment combination optimization, image processing.Can infer that artificial neural network has broad application prospects equally in enzyme engineering technology.But the research of neutral net in enzyme engineering technology at present still is in initial stage, and being in the gustin enzymolysis process optimization application oyster does not still have any report.
Summary of the invention
The objective of the invention is in order to remedy the deficiency that above-mentioned prior art exists, provide a kind of and be gustin Controlled-enzymatic Hydrolysis technology method based on nerve network system optimization oyster, make more science of enzymolysis process, improve explained hereafter efficient and product quality, reduce production costs.
For achieving the above object, the technical scheme taked of the present invention is that this oyster based on nerve network system optimization is gustin Controlled-enzymatic Hydrolysis technology and comprises 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.
Described oyster enzymolysis process is meant the homogenate of oyster meat, adjusts material-water ratio, adds protease, enzyme reaction under certain condition, and boiling water bath heating 10 minutes, cooling under 4000 rev/mins of conditions centrifugal 20 minutes, is got the enzymolysis supernatant and is got 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.
Described homogenate is meant that oyster meat through after the running water cleaning and draining surface moisture, carries out historrhexis, homogenate, obtains 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.
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.
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.
Described subjective appreciation is meant that (the pairing freshness of 0.2～1.6g/L) monosodium glutamate solution is as standard with variable concentrations, 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.
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 (neuron number is 4), be output layer (neuron number is 1) 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).
The training of described step (3) structural model is meant that adopting the trainlm function is the network training function, and setting the training objective error is 0.0001, carries 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 realizes that at last error signal reaches within the scope of permission at 0.0001 o'clock, just stops training.
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.
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.
The oyster that the present invention is based on nerve network system optimization is gustin Controlled-enzymatic Hydrolysis technology, BP neural network learning process has finally realized the simulation of complicated uncertainty enzymolysis problem by the processing of process hidden layer in the process of the backpropagation of the forward-propagating of importing data and error.This method anthropomorphic dummy's brain is judged system, with high-precision real-time Simulation deal with data, is not needing accurately the basis of Mathematical Modeling to realize that the Nonlinear Mapping between enzymolysis factor and peptide content and the sense organ score value concerns; And the ability that calculates to a nicety, avoided reducing enforcement and some drawbacks and the limitation in artificial subjective appreciation of some actual process, directly reach and predict simulated effect fast and accurately.The enzymolysis process that employing the present invention obtains is science more, therefore can improve explained hereafter efficient and product quality, reduces production costs.
Description of drawings
Fig. 1 is neural network structure figure of the present invention
Fig. 2 is the flow chart of neutral net of the present invention and genetic algorithm combination
The specific embodiment
Below in conjunction with embodiment the oyster that the present invention is based on nerve network system optimization is gustin Controlled-enzymatic Hydrolysis process and makes detailed description.
(1) obtains the neural network learning sample
Oyster meat prepares the oyster enzymolysis liquid according to following technological process, measures the peptide nitrogen content and the total nitrogen content of oyster enzymolysis liquid then, and calculates the peptide ratio of oyster enzymolysis liquid, simultaneously the oyster enzymolysis liquid is carried out subjective appreciation.Obtain the one-to-one relationship of enzymolysis process parameter and evaluation index, as the neural network learning sample.
Oyster enzymolysis process flow process: oyster meat (fresh or freezing) → clean → drain → homogenate → adjustment material-water ratio → interpolation protease → enzyme reaction under certain condition (temperature, time) → boiling water bath heating 10min → cooling → centrifugal (4000 rev/mins, 20min) → get enzymolysis supernatant → oyster enzymolysis liquid.
Key points for operation are as follows:
1) homogenate.Oyster meat carries out historrhexis, homogenate with after the running water cleaning, draining surface moisture with tissue mashing machine or refiner, obtains oyster meat homogenate stoste.
2) adjust material-water ratio.According to different material-water ratios (1: 2,1: 3,1: 4,1: 5 equal proportion), adjust oyster meat homogenate concentration with pure running water.
3) add protease.With the weight percent meter of oyster meat homogenate stoste,, in the oyster slurries, add protease according to different enzyme additions (0.3%, 0.6%, 0.9%, 1.2% etc.).
4) enzyme reaction temperature.Add the oyster slurries behind the protease, be positioned in the thermostat water bath,, adjust enzyme reaction temperature according to different enzyme reaction temperatures (45 ℃, 50 ℃, 55 ℃, 60 ℃ etc.) condition.
5) the enzyme reaction time.Pick up counting constantly from adding protease, the oyster slurries are carried out enzymolysis according to different enzyme reaction time requirements (4 hours, 5 hours, 6 hours, 7 hours etc.).
6) oyster enzymolysis liquid.After the enzyme digestion reaction that the oyster slurries carry out according to different technology conditions finishes, put into fully heating 10min of boiling water bath, cooling, centrifugal is got its supernatant and is promptly obtained the oyster enzymolysis liquid.
(2) set up neural network model
With material than 4 technological parameters such as water, protease addition, enzyme reaction temperature and enzyme reaction times as input value, as output signal, adopt MATLAB7.8 version software with the peptide ratio of oyster enzymolysis liquid and sense organ scoring, set up the BP neutral net.
According to shown in Figure 1, the BP neutral net is designed to 3 layer networks: an input layer, a hidden layer and an output layer.Input layer is set four neurons: material is than water, protease addition, enzyme reaction temperature and enzyme reaction time.Hidden layer is set at 13 neurons, and the peptide ratio of oyster enzymolysis liquid and sense organ scoring are set at output layer.Transfer function between input layer and the hidden layer is tangent sigmoid function (tansig), and the transfer function between hidden layer and the output layer is linear function (purelin), and the network training function adopts the trainlm function.
(3) neural metwork training
According to shown in Figure 2, with 80% data of training sample total amount neutral net is trained, set the peptide ratio desired value and the target precision of an oyster enzymolysis liquid, allow the BP neural network model bring into operation then.By adjusting training step number, e-learning speed and target precision, constantly neutral net to be trained, the error between network peptide ratio output valve and desired value is reduced in 5%.Neutral net obtains optimum neural network model parameter through continuous learning training: maximum training step number is 100, and e-learning speed is 0.1, and the network performance objective error is 0.0001.
(4) the BP neutral net is carried out hereditary computing, obtain best enzymolysis process condition
According to shown in Figure 2, neural network model with foundation 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 relevant parameters such as generating initial population, crossover probability and variation probability simultaneously.
Generate 3 parameters such as initial population, crossover probability and variation probability and be set at 24,0.3 and 0.1 respectively, begin by 24 individual initial population of forming, respectively with oyster enzymolysis liquid peptide ratio maximum and sensory evaluation scores peak as output signal, by computings such as selection, intersection, variations, produce the colony of a new generation, search for best enzymolysis process condition through computing repeatedly.
With oyster enzymolysis liquid peptide ratio maximum as output signal, through after the above hereditary computing, the best enzymolysis process condition that obtains is: 58.6 ℃ of material-water ratios 1: 2.8, protease addition 1.03%, enzyme reaction temperature, 5.4 hours enzyme reaction time, the peptide scale prediction value of oyster enzymolysis liquid is 80.81%.According to above-mentioned technology oyster meat is carried out demonstration test, experimental result shows that the peptide ratio of oyster enzymolysis liquid reaches 78.35%, and the sensory evaluation scores value reaches 6.58, relative error all remains on ± 5% in, predicted value and actual value do not have notable difference.In addition, compare with the optimised process that orthogonal experiment obtains, the oyster enzymolysis liquid that the optimised process that obtains with neutral net obtains, its peptide ratio and sensory evaluation scores all obviously are better than orthogonal experiment (peptide ratio and sensory evaluation scores value are respectively 75.34% and 5.51).
With the sensory evaluation scores peak as output signal, through after the above hereditary computing, the best enzymolysis process condition that obtains is: 53.8 ℃ of material-water ratios 1: 2.1, protease addition 0.95%, enzyme reaction temperature, 6.0 hours enzyme reaction time, the sense organ scoring predicted value of oyster enzymolysis liquid is 6.67 minutes.According to above-mentioned technology oyster meat is carried out demonstration test, experimental result shows that the sensory evaluation scores value of oyster enzymolysis liquid reaches 6.39, and predicted value and actual value do not have notable difference.
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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Application publication date: 20110518 |