CN106650291A - Model for predicting shelf life of salmon - Google Patents
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- CN106650291A CN106650291A CN201710000400.6A CN201710000400A CN106650291A CN 106650291 A CN106650291 A CN 106650291A CN 201710000400 A CN201710000400 A CN 201710000400A CN 106650291 A CN106650291 A CN 106650291A
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Abstract
Disclosed is a model for predicting shelf life of salmon. The model is characterized in that quality index of salmon preserved at different temperature is researched, time based change rules of color difference (brightness L and red chromaticity a*), total volatile basic nitrogen (TVB-N), freshness index K-value, thiobarbituric acid (TBA), microorganism quality and sensory quality of salmon are measured to determine a salmon quality change dynamic model, and a salmon shelf life prediction module is built according to salmon quality indexes of TVB-N, the freshness index K-value, the TBA and the total number of bacterial colony reflecting microorganism changes. By the model, the rest shelf life of salmon preserved at the temperature within the range from 0 DEG C to 20 DEG C can be predicted quickly and effectively, so that directions are provided for storage, transportation and selling processes, working staff can make corresponding handling on salmon with different freshness degree in time, and cost is saved.
Description
Technical field
The present invention relates to a kind of method of prediction salmon fish shelf life, is especially suitable for salmon fish in storing, sales process
The prediction of shelf life.
Background technology
Salmon fish is also Sa Menyu, belongs to anadromous migration Fish, is one of famous and precious Fish in the world.It is widely distributed in peace
The high latitude areas such as foreign the north.Salmon fish squama spinule is few, and yellowish pink orange red, fine and tender taste is delicious, smooth in taste, deep to be liked by people
Love.Simultaneously the cod-liver oil by made by it is even more nutrient excellent product.
Containing abundant unsaturated fatty acid in salmon fish, can effectively reduction blood fat and cholesterolemia, preventing and treating cardiovascular disease
Disease, weekly two meal, just can reduce by 1/3rd by dead probability is attacked by heart disease.It is blue or green that salmon fish is also called shrimp containing one kind
The material of element, is a kind of antioxidant of very strength.Omega-3 fatty acid contained by it is even more brain, retina and nervous system
Essential material, has functions that cerebral function improvement, prevents senile dementia and prevention visual deterioration.In addition salmon fish can also
Effectively prevent generation, the development of the chronic diseases such as diabetes, with very high nutritive value, enjoy " treasure in water "
The good reputation of " most distinguished in fish "；
Salmon fish both can directly be eaten raw, and energy cooking dishes, be one of Fish raw material more common in western-style food.Salmon fish is because being rich in
It is beneficial to the omega-fatty acid of health（Including the olefin(e) acid EPA of 20 carbon 5, the olefin(e) acid DHA of 22 carbon 6 and the olefin(e) acid DPA of 22 carbon 5）, and meat
Matter is delicious, and deep to be liked by consumer, the market demand constantly expands.The characteristics of high-protein high-fat, causes salmon fish in transporting procedures
In easily occur putrid and deteriorated, shelf life is shorter.Shelf life refers to the food preserved under certain condition, still ensures that preferably reason
Change, microbiologic propertiess, sense organ is acceptable and is capable of a period of time of safe edible.Therefore, be badly in need of set up one kind can be not
The method of synthermal lower fast prediction salmon fish quality comparison to provide different temperatures under salmon fish freshness and remaining shelf life
Etc. information, carrying out real-time monitoring to the putrid and deteriorated situation of salmon fish becomes the urgent needss of aquatic products logistics industry.
Total volatile basic nitrogen（TVB-N）, total plate count, index of fish freshness K values and thiobarbituricacidα-（TBA）It is as Fish
The important indicator of quality comparison, can reflect fish freshness and degree of spoilage in different aspect, also there is regulation in GB
Edible scope.Total volatile basic nitrogen（TVB-N）Represent that the protein in food decomposes in the presence of microorganism and enzyme to be produced
The total amount of raw volatile nitrogen species.The value of TVB-N is higher, shows that the Protein in Food speed that is decomposed is faster, degree of spoilage
Also it is higher.Total plate count can preferably react the degree of food quality microbial contamination.Over time with reserve temperature
Increase, the growth trend of total plate count is also significantly raised.International food microorganism committee regulation, the total plate count of fresh Fish
Must not be higher than 6logCFU/g.Salmon fish fat content is higher, and TBA values can well reflect that fat oxidation becomes sour degree.The flesh of fish
Gradually there are oxidation and hydrolysis in interior unsaturated fatty acid, produce rudimentary aldoketoneses material and thiobarbituric acid reaction
Stable red compound is generated, color is deeper, show that flesh of fish degree of spoilage is higher.In flesh of fish ATP catabolic processes, produce
The total concentration of HxR and Hx, in the summation ratio of ATP related compounds concentration, is K values.It is most to reflect the weight that fish freshness changes
Want index.ATP is decomposed into inosine, hypoxanthine, adenosine triphosphate, the phosphorus of adenosine two in the presence of enzyme and microorganism
The materials such as acid, adenylic acid and inosinic acid, the less explanation fish freshness of K values is higher.
At present, kinetic model is widely used in the research of Food Shelf-life model.The goods that Tang Xiaoyang is worked out
Frame phase model is applied to 4-15 DEG C of aerobic and preserves chilled pork.Domestic less to the research of salmon fish shelf life, fourth is graceful to be established
273rd, the salmon fish shelf life model under the conditions of 277,283 K, the temperature conditionss of design are less, and lack the temperature with non-modeling
Condition is verified to model.
The content of the invention
Therefore, the present invention plans to build vertical 0 DEG C, 5 DEG C, 15 DEG C, 20 DEG C of salmon fish shelf lifes by the analysis to each physical and chemical index
Forecast model, and with 10 DEG C under the conditions of each index model is verified, and with zero order kineticses model and first order kineticss
Model Integrated comparative is more suitable for the salmon fish forecasting shelf life in the temperature range.So as to the quality to logistics progress salmon fish
Change carries out real-time monitoring, so as to lower loss, reduces economic loss.
A kind of model of prediction salmon fish shelf life, it is characterised in that：To storage at different temperatures the sense organ of salmon fish,
Aberration L-value and a* values, total plate count, index of fish freshness K values, total volatile basic nitrogen TVB-N, microorganism and reflection fat oxidation degree
The index such as TBA change, set up salmon fish quality comparison with regard to time and the kinetic model of temperature, and build with the index of quality
Found total plate count, K values, TVB-N and TBA and establish salmon fish shelf life forecasting model；Step is as follows：
1）By the fresh salmon fish just bought back, surface icing, laboratory is transported to after vanning rapidly, decaptitate the peeling of boning that truncates, and cuts
It is fitted in valve bag at random into after fritter, every piece of 100g；
2）Fish block is randomly divided into into 5 groups to be stored under 0,5,10,15,20 DEG C of temperature conditionss；It is separately sampled by intervals
Determine its aberration（L、a*）, TBA, K value, TVB-N, the physical and chemical index such as microorganism and carry out subjective appreciation；
3）Linear fit is carried out using zero level and First order dynamic model, to storage salmon fish TVB-N, K at different temperatures
Value, TBA values and total plate count carry out regression analyses, relative analyses regression coefficient R2Determine the kinetic simulation of salmon fish quality comparison
Type；
4）Using zero level and one-level Arrhenius equation analysis temperature T and speed response constant k, try to achieve salmon fish total plate count,
The pre-exponential factor A of K values, TVB-N values and TBA change0With reaction activity Ea；
5）Set up respectively at 0 ~ 20 DEG C based on index of quality total plate count, K values, TVB-N with reference to quality reaction Kinetics Model
The shelf life forecasting model of value and TBA values is respectively：
Based on total plate count shelf life forecasting model：
Based on K value shelf life forecasting models：
Shelf life forecasting model based on TVB-N：
Shelf life forecasting model based on TBA：
SL in formula（TVC）, SL（K）, SL（TVB-N）, SL（TBA）--- salmon fish total plate count, K values, the remaining goods of TVB-N, TBA model
The frame phase；BTVC, BK, BT-VBN, BTBA--- storage t d, total plate count, K values, the measured value of TVB-N, TBA； BTVC0, BK0,
BT-VBN, BTBA0--- total plate count, K values, the initial measured value of TVB-N, TBA；R --- gas constant, 8.314510 J/
mol·K；T --- thermodynamic temperature constant, K；
6）Total plate count, K values when the comprehensive tuna items index of quality and sensory evaluation determine tuna shelf life terminal,
TVB-N values and TBA values；
7）The checking and selection of shelf life forecasting model.
Design sample time interval of the present invention is：At 0 DEG C, 5 DEG C, 1 day 1 time at a temperature of 10 DEG C；At 15 DEG C, 20 DEG C of temperature
Lower 6 hours 1 time.
To speed response constant k under different temperatures carry out curve fitting acquisition salmon fish total plate count, K values, TVB-N and
The activation energy of TBA changes（EA）Respectively：70.37th, 6.656,339.606,1.147kJ/mol and 77.22,57.39,
62.07th, 56.41 kJ/mol, pre-exponential factor A0Respectively 3.69 × 1013、19.32、2.92×1065, 1.667 and 2.72 ×
1013、1.06×1010、6.63×1010、1.09×1010。
With reference to GB, sensory evaluation, the aberration of salmon fish are considered, determine TVB-N during salmon fish shelf life terminal
Be worth for 30mg/100g, total plate count be 6 logCFU/g, TBA be 0.10mg/100g and K values be 60%.
By salmon fish at 10 DEG C（283K）Under the conditions of each index of quality measured value and corresponding shelf life model predictive value
It is compared, selects optimal kinetic model and verify the Dynamic Prediction model accuracy, calculates its relative error.Jing
Overtesting checking, determines that salmon fish shelf life forecasting model at 0 ~ 20 DEG C is：
SL in formula（TVB-N）--- the remaining shelf life of salmon fish TVB-N； BT-VBN--- when preserving the t days, the measure of TVB-N
Value； BT-VBN---, the initial measured value of TVB-N；R --- gas constant, 8.314510 J/molK；T --- thermodynamics temperature
Degree constant, K.
0 ~ 20 DEG C be chilled salmon fish after the logistics transportation, be positioned over supermarket shelves go home institute to consumer's purchase may
The temperature range of experience, the present invention can predict the remaining shelf life in this section of temperature range.
The present invention combines the sense organ of salmon fish, aberration（L、a*）, K values, TVB-N, many indexes such as microorganism and TBA values
Determine total plate count numerical value, K values, TVB-N values and TBA values when judging salmon fish shelf life terminal.
The present invention establishes the method based on TVB-N, total plate count, TBA and K values to predict chilled salmon fish shelf life
The freshness and remaining shelf life of chilled salmon fish can be accurately predicted rapidly, be to provide guidance in storing and sales process, with
Just in time respective handling is carried out to the salmon fish of different freshnesss, it is cost-effective.
Description of the drawings
Fig. 1 is the change of salmon fish sense organ under different reserve temperatures；
Fig. 2 is the change of salmon fish aberration lightness l value under different reserve temperatures；
Fig. 3 is the change of salmon fish aberration redness a* under different reserve temperatures；
Fig. 4 is the change of salmon fish TVB-N under different reserve temperatures；
Fig. 5 is the change of salmon fish total plate count under different reserve temperatures；
Fig. 6 is the change of salmon fish K values under different reserve temperatures；
Fig. 7 is the change of salmon fish TBA values under different reserve temperatures.
Specific embodiment
Operating process to make present invention realization is easy to understand with creation characteristic, with reference to specific embodiment,
The present invention is expanded on further.
1 experimental raw and capital equipment
1.1 raw materials and pretreatment
Fresh salmon fish is buied from Shanghai City reed tidal harbour harbour fishery market, surface icing is transported to rapidly laboratory after vanning
It is pending.
Pretreatment：Salmon fish is cut into after 100g fritters and is fitted in valve bag at random, is divided into 5 groups, holding conditions are respectively 0,
5、10、15、20 ℃.Temperature is higher, and flesh quality change is faster.Therefore the design sample cycle is 0,1 at a temperature of 5,10 DEG C
It is 1 time；15,6 hours 1 time at a temperature of 20 DEG C.Each group index set 2 it is parallel.
1.2 experimental technique
1.2.1 sense organ is determined
6 veteran sense organ composition of personnel sense organ groups are selected, main judging quota is color and luster, abnormal smells from the patient, muscular tissue and group
Knit elasticity.Grading system is divided into fine, preferable, general, poor and poor, and score value is respectively 10,8,6,4,2 points, and comprehensive grading is 5
Think that remaining shelf life is 0 below point.
The measure of the organoleptic indicator of table 1
| Index | Very well（10 points） | Preferably（8 points） | Typically（6 points） | It is poor（4 points） | Difference（2 points） |
| Appearance luster | Bright, clean mark | Bright, texture is distinguishable | Color and luster is slightly dim, and texture is slightly distinguishable | Color and luster is dimer, and texture is obscured | Color and luster is dim, and texture is not distinguishable |
| Abnormal smells from the patient | Peculiar taste with sweet and sour flavor | Taste with sweet and sour flavor is stronger | Omit fishlike smell | Larger bad smell | Strong bad smell |
| Muscular tissue | Consolidation is complete | Completely, compared with consolidation | It is locally soft but not loose | Soft, local is loose, slightly viscosity | Loosely, viscosity is larger |
| Tissue elasticity | Tough reality is flexible, after pressing Recover immediately | Tough reality is flexible, recovers after pressing Comparatively fast | It is more resilient, recover after pressing It is slower | Slightly elasticity, recovers after pressing It is very slow | It is nonelastic, it is recessed after pressing and does not disappear Lose |
1.3.2 color difference measurement
Using ZE-2000 colour difference meters（Japanese Nikon company）Determine, the index is averaged for parallel three times.
1.3.3 total plate count is determined
Operate with reference to the method for GB GB4789.2-2010.
1.3.4 the measure of TVB-N
Using FOSS full-automatic Kjeldahl determination devices（Kjeltec8400, FOX analytical tool company）Determine.Method refers to SC/T
3032-2007 is operated.
1.3.5 K values are determined
Using high performance liquid chromatograph device（LC-2010C HT, Shimadzu Corporation）Determine.
HPLC conditions：Chromatographic column Inertsil ODP-SP（4.6 mm × 250 mm, 5 μm）, the phosphate buffers of pH 6.50
Balance eluting；The μ L of sample injection volume 10, the mL/min of flow velocity 1,30 DEG C of column temperature, the nm of Detection wavelength 254.
1.3.6 fat oxidation（TBA）Measure
Meat 5g is taken in centrifuge tube, adds 20% trichloroacetic acid 25ml, outer icing is managed after homogenizing and stands 60min at salmon fish back,
Then 10min is centrifuged with 8000r/min, the solution after centrifugation is filtered and is settled to 50ml.This solution of 5ml and 5ml are drawn after shaking up
0.02mol/L thiobarbituricacidα-s mix in test tube, after boiling water bath 20min take out, flowing water cooling 5min after adopt UV-
3000 PC type ultraviolet-uisible spectrophotometers measure absorbance at 532nm.
1.3.7 the foundation of shelf life forecasting model
In the quality comparison of aquatic products, the application that prediction is simulated using first order reaction kinetics model is relatively broad.
B=B0ekt（1）
In formula：T --- food storage time, d；B0--- determine the primary quantity of the index of quality；B --- determine during storage t d
The amount of the index of quality；K --- food quality rate of change constant.
Will（1）Both sides are taken the logarithm in formula, can be obtained：
lnB=kt+lnB0（2）
Relation between reaction temperature T and reaction rate constant kArrheniusEquation is as follows：
（3）
In formula, k --- reaction rate constant；A0--- pre-exponential factor, empirical；EA--- the activation energy of reaction, J/mol；
R --- gas constant, 8.314510 J/molK；T --- thermodynamic temperature constant, K.
Will（3）Both sides are taken the logarithm in formula, can be obtained：
（4）
By（4）Formula understands, linear between lnk and 1/T, as long as drawing reaction rate A0And activation energyA, utilize（2）With
（3）Just can be derived that the predictive equation of shelf life：
（5）
In formula, SL --- represent shelf life（shelf life）.
2 results and discussion
The change of 2.1 index of quality
Fresh salmon fish mouthfeel consolidation, delicious flavour, the flesh of fish is bright in colour, there is apparent fatty marble striped.With storage
The prolongation of Tibetan time, structure of fish muscle gradually softens under the influence of protease and microbial reproduction, or even produces bad smell, sense organ
Quality is gradually deteriorated.As shown in figure 1, each group sensory evaluation of salmon fish is presented as time went on downward trend.And preserve
Temperature is higher, and sense organ downward trend is more obvious.Sensory evaluation scores only 5.33 during salmon fish 32h at 20 DEG C, shelf life is close to
0.Structure of fish muscle now is loose, slightly bad smell.Sensory evaluation scores during salmon fish 10d at 0 DEG C are 5.67, now
Flesh of fish surfaces of tacky, is faint in color, and bad smell is not strong.Show that low temperature can significantly extend the goods of salmon fish from the angle of sense organ
The frame phase.
Aberration can react the situation of change of flesh of fish color, be the important indicator for evaluating salmon fish quality.Salmon fish it is bright
Angle value and redness value changes are as shown in Figure 2.With the prolongation of storage time, the brightness value and red scale value of the flesh of fish integrally become in decline
Gesture.Lightness l value declines and shows salmon fish in storage period, oppresses gradually dimmed, and quality gradually degenerates.Astaxanthin class etc. in the flesh of fish
Carotenoid is oxidized, so as to cause to oppress red scale value decline.
Total volatile basic nitrogen（TVB-N）Represent that the protein in food is waved produced by decomposing in the presence of microorganism and enzyme
The total amount of the property sent out nitrogen substance.The value of TVB-N is higher, and showing Protein in Food to be decomposed, speed is faster, and degree of spoilage is also got over
It is high.Salmon fish TVB-N variation tendency such as Fig. 3 under different holding conditions, prolongation over time, the TVB-N values of each group are in
Ascendant trend, wherein 15 DEG C, 20 DEG C of ascendant trends it is most obvious.This is because the rising of temperature, accelerates the decomposition of protein, enter
And accelerate the corruption of the flesh of fish.The edible TVB-N threshold values of fresh fish should be within 30mg/100g.At 0,5,10,15,20 DEG C
Under holding conditions, exceed regulation edible scope in 12d, 8d, 6d, 31h, 27h respectively.The change of TVB-N values show with
The very high dependency of sense organ.
Total plate count can preferably react the degree of food quality microbial contamination.As seen from Figure 4, with when
Between and reserve temperature increase, the growth trend of total plate count is also significantly raised.Specified according to international food microorganism committee,
The total plate count of fresh Fish must not be higher than 6logCFU/g.The salmon fish total plate count just bought back is 3.24logCFU/g.0,
5th, under 10 DEG C of holding conditions, this restriction threshold value is reached in 11d, 8d, 6d respectively.Under 15 DEG C and 20 DEG C of holding conditions, bacterium colony
Total ascendant trend becomes apparent from, and reaches acceptable thresholds in 35h, 32h respectively.Shelf life now terminates.
In flesh of fish ATP catabolic processes, the total concentration of HxR and Hx is produced in the summation ratio of ATP related compounds concentration, be
K values.It is most to reflect the important indicator that fish freshness changes.K values are considered as one-level freshness less than 0.2[13], three in experiment
The initial K values of literary fish under 0,5,10 DEG C of holding conditions, exceed one-level freshness scope in 3d, 2d, 1d respectively 0.176.Work as K
Just corruption, inedibility are started when value is more than 0.6.As can be seen from Figure 5, salmon fish shows different degrees of under each reserve temperature
Raise.Under conditions of reserve temperature is relatively low, K value ascendant trends are also slower.From under 0-20 DEG C of holding conditions, the K values of salmon fish
Exceed edible scope in 12d, 8d, 5d, 40h, 25h respectively.
Salmon fish fat content is higher, and TBA values can well reflect that fat oxidation becomes sour degree.Unsaturation in the flesh of fish
Gradually there are oxidation and hydrolysis in fatty acid, produce rudimentary aldoketoneses material stable with thiobarbituric acid reaction generation
Red compound, color is deeper, shows that flesh of fish degree of spoilage is higher.The salmon fish TBA initial values just bought back are in 0.013mg/
100g.As seen from Figure 6, under the conditions of each reserve temperature, the TBA values of salmon fish are with trend of the time lengthening in increase.Due to
Temperature is raised causes the fat oxidation speed of salmon fish to be accelerated, therefore the higher TBA ascendant trends of temperature are more notable, with experimental result
Performance is consistent.When at present salmon fish shelf life is 0, its TBA value does not have limit value, cumulated volume experiment total plate count, TVB-N, K
Evaluating, the unacceptable values of TBA are in 0.10mg/100g for value and the unacceptable degree of sense organ.TBA values have determined shelf life model
Effect reliability is proved in surimi product, therefore the index also has feasibility in salmon fish shelf life forecasting model.
2.2 kinetic models are set up
According to experimental design, useArrheniusEquation（4）, using Origin9.0 respectively under the conditions of 0,5,15,20 DEG C three
The total plate count of literary fish, freshness K value, TVB-N values, the TBA index of quality carry out linear regression fit（It is warm due in fit procedure
Degree can as denominator, Gu Celsius temperature is converted to into Fahrenheit temperature represent, i.e., 273,278,288,293K）, obtain different indexs
Regression equation under different reserve temperatures, is shown in Table 2.
The regression equation of each index of the salmon fish different temperatures of table 2
Coefficient R2It is bigger, show that equation model precision is higher, prediction effect is better.According to formula（4）, to contain reaction rate
Lnk reserve temperature inverse 1/T is fitted, the corresponding Linear Fit Chart of 4 indexs can be obtained.
According to fitting Fig. 7, convolution（4）Calculate the activation for obtaining salmon fish total plate count, K values, TVB-N and TBA change
Energy（EA）Respectively：77.22nd, 57.39,62.07,56.41 kJ/mol, pre-exponential factor A0Respectively 2.72 × 1013、1.06×
1010、6.63×1010、1.09×1010.Bring formula into（5）, can obtain：
Total plate count shelf life forecasting model：
（6）
K value shelf life forecasting models：
（7）
TVB-N shelf life forecasting models：
（8）
TBA shelf life forecasting models：
（9）
SL in formula（TVC）, SL（K）, SL（TVB-N）, SL（TBA）--- salmon fish total plate count, K values, the remaining goods of TVB-N, TBA model
The frame phase.BTVC, BK, BT-VBN, BTBA--- storage t d, total plate count, K values, the measured value of TVB-N, TBA； BTVC0, BK0,
BT-VBN, BTBA0--- total plate count, K values, the initial measured value of TVB-N, TBA.
Calculate the shelf life model with regard to salmon fish at a temperature of 273-293 K, can to logistics transportation in it is three literary
Fish freshness is predicted.By reserve temperature, the initial value of salmon fish index of fish freshness and storage time, can deduce in the storage
Hide the quality status that the salmon fish after certain hour is preserved under temperature conditionss.
The checking and evaluation of 2.3 shelf life forecasting models
By salmon fish at 10 DEG C（283K）Under the conditions of each index of quality measured value carry out with the predictive value of corresponding shelf life model
Relatively, to verify the Dynamic Prediction model accuracy.When total plate count, K values, TVB-N values, when TBA values exceed allowed band,
Think that shelf life terminates.Draw the result of table 3.
The forecasting shelf life error of salmon fish under the 283K holding conditions of table 3
As known from Table 3, by shelf life model draw First order dynamic model predictive value and 10 DEG C（283K）Under the conditions of reality
Relative error is within 5 % between measured value, and the relative error of zero level is larger.Therefore, the first order kineticss salmon fish of foundation
Remaining shelf life forecast model accuracy is higher.0-20 DEG C can accurately be monitored（273-293 K）Within the scope of salmon fish product
Qualitative change.Slightly has gap between the forecasting shelf life value of each index of fish freshness, the shelf life life-span of K values prediction is closer to experiment
Value, and the forecasting shelf life value of total plate count and measured value relative error are larger.Therefore, most it is defined by the forecasting shelf life of TVB-N
Really, using as main judge condition.
3 conclusions
（1）Total plate count, K values, TVB-N values, the prolongation over time of TBA values of the salmon fish under 0-20 DEG C of different reserve temperatures
And constantly rise, and the higher ascendant trend of temperature is bigger.This conclusion obtained with aberration, sense organ has very strong dependency.
（2）0th, under the conditions of 5,15,20 DEG C total plate count, K values, TVB-N values and TBA value changes trend meet one-level chemistry
Reaction Kinetics Model.UtilizeArrheniusThe shelf life equation phase that equation is obtained to the linear fit of reaction rate and temperature
Relation number is equal>0.9, fitting precision is higher, can accurately carry out salmon fish forecasting shelf life.
（3）Contrasted by 10 DEG C of each index measured value and shelf life prediction equation value, relative error is within 5%.
The shelf life model that this experiment is set up is monitored under the conditions of more accurately can storing and transporting at 0-20 DEG C to salmon fish, so as to effective
Avoid wasting, it is cost-effective.
Claims (6)
1. it is a kind of prediction salmon fish shelf life model, it is characterised in that：To preserving the sense organ of salmon fish, color at different temperatures
Difference L-value and a* values, total plate count, index of fish freshness K values, total volatile basic nitrogen TVB-N, microorganism and reflect fat oxidation degree
The change of the indexs such as TBA, sets up salmon fish quality comparison with regard to time and the kinetic model of temperature, and with index of quality foundation
Total plate count, K values, TVB-N and TBA establish salmon fish shelf life forecasting model；Step is as follows：
1）By the fresh salmon fish just bought back, surface icing, laboratory is transported to after vanning rapidly, decaptitate the peeling of boning that truncates, and cuts
It is fitted in valve bag at random into after fritter, every piece of 100g；
2）Fish block is randomly divided into into 5 groups to be stored under 0,5,10,15,20 DEG C of temperature conditionss；It is separately sampled by intervals
Determine its aberration（L、a*）, TBA, K value, TVB-N, the physical and chemical index such as microorganism and carry out subjective appreciation；
3）Linear fit is carried out using zero level and First order dynamic model, to storage salmon fish TVB-N, K at different temperatures
Value, TBA values and total plate count carry out regression analyses, relative analyses regression coefficient R2Determine the kinetic simulation of salmon fish quality comparison
Type；
4）Using zero level and one-level Arrhenius equation analysis temperature T and speed response constant k, try to achieve salmon fish total plate count,
The pre-exponential factor A of K values, TVB-N values and TBA change0With reaction activity Ea；
5）Set up respectively at 0 ~ 20 DEG C based on index of quality total plate count, K values, TVB-N with reference to quality reaction Kinetics Model
The shelf life forecasting model of value and TBA values：
Based on total plate count shelf life forecasting model：
Based on K value shelf life forecasting models：
Shelf life forecasting model based on TVB-N：
Shelf life forecasting model based on TBA：
SL in formula（TVC）, SL（K）, SL（TVB-N）, SL（TBA）--- salmon fish total plate count, K values, the remaining goods of TVB-N, TBA model
The frame phase；BTVC, BK, BT-VBN, BTBA--- storage t d, total plate count, K values, the measured value of TVB-N, TBA； BTVC0, BK0,
BT-VBN, BTBA0--- total plate count, K values, the initial measured value of TVB-N, TBA；R --- gas constant, 8.314510 J/
mol·K；T --- thermodynamic temperature constant, K；
6）Total plate count, K values when the comprehensive tuna items index of quality and sensory evaluation determine tuna shelf life terminal,
TVB-N values and TBA values；
7）The checking and selection of shelf life forecasting model.
2. the model of a kind of prediction salmon fish shelf life as claimed in claim 1, it is characterised in that：Design sample time interval
For：At 0 DEG C, 5 DEG C, 1 day 1 time at a temperature of 10 DEG C；At 15 DEG C, 6 hours 1 time at a temperature of 20 DEG C.
3. the model of a kind of prediction salmon fish shelf life as claimed in claim 1, it is characterised in that：To speed under different temperatures
Reaction constant k carries out curve fitting and obtains the activation energy of salmon fish total plate count, K values, TVB-N and TBA change（EA）Respectively：
70.37th, 6.656,339.606,1.147kJ/mol and 77.22,57.39,62.07,56.41 kJ/mol, pre-exponential factor A0
Respectively 3.69 × 1013、19.32、2.92×1065, 1.667 and 2.72 × 1013、1.06×1010、6.63×1010、1.09
×1010。
4. the model of a kind of prediction salmon fish shelf life as claimed in claim 1, it is characterised in that：With reference to GB, comprehensively examine
Consider sensory evaluation, the aberration of salmon fish, TVB-N values when determining salmon fish shelf life terminal are 30mg/100g, total plate count is
It is 60% that 6 logCFU/g, TBA are 0.10mg/100g and K values.
5. the model of a kind of prediction salmon fish shelf life as claimed in claim 1, it is characterised in that：By salmon fish at 10 DEG C
（283K）Under the conditions of each index of quality measured value be compared with the predictive value of corresponding shelf life model, select optimal dynamic
Mechanical model simultaneously verifies the Dynamic Prediction model accuracy, calculates its relative error.
6. the model of a kind of prediction salmon fish shelf life as claimed in claim 1, it is characterised in that：Through verification experimental verification, really
Determining salmon fish shelf life forecasting model at 0 ~ 20 DEG C is：
SL in formula（TVB-N）--- the remaining shelf life of salmon fish TVB-N； BT-VBN--- when preserving the t days, the measure of TVB-N
Value； BT-VBN---, the initial measured value of TVB-N；R --- gas constant, 8.314510 J/molK；T --- thermodynamics temperature
Degree constant, K.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101363831A (en) * | 2008-08-07 | 2009-02-11 | 上海海洋大学 | Prediction method for shelf life of fresh water fish products |
| CN101539560A (en) * | 2009-04-24 | 2009-09-23 | 上海海洋大学 | Method for predicting the shelf period of fresh pomfret |
| CN104200068A (en) * | 2014-08-12 | 2014-12-10 | 上海海洋大学 | Method for establishing river carp shelf life prediction model by using TBA |
| CN104297440A (en) * | 2014-08-12 | 2015-01-21 | 上海海洋大学 | River crucian crucian carp shelf life prediction method |
| CN104792950A (en) * | 2015-05-12 | 2015-07-22 | 上海海洋大学 | Model for predicting shelf life of tuna |
-
2017
- 2017-01-03 CN CN201710000400.6A patent/CN106650291A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101363831A (en) * | 2008-08-07 | 2009-02-11 | 上海海洋大学 | Prediction method for shelf life of fresh water fish products |
| CN101539560A (en) * | 2009-04-24 | 2009-09-23 | 上海海洋大学 | Method for predicting the shelf period of fresh pomfret |
| CN104200068A (en) * | 2014-08-12 | 2014-12-10 | 上海海洋大学 | Method for establishing river carp shelf life prediction model by using TBA |
| CN104297440A (en) * | 2014-08-12 | 2015-01-21 | 上海海洋大学 | River crucian crucian carp shelf life prediction method |
| CN104792950A (en) * | 2015-05-12 | 2015-07-22 | 上海海洋大学 | Model for predicting shelf life of tuna |
Non-Patent Citations (1)
| Title |
|---|
| 李鹏鹏 等: ""动力学模型预测真空包装罗非鱼的货架期"" * |
Cited By (14)
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