CN101949870B - Method for predicting refrigerated carp freshness quality - Google Patents

Abstract

The invention relates to a method for predicting refrigerated carp freshness quality. The method comprises the following steps of: determining the shelf life of a product by researching the sense organ quality, chemical quality and microbiologic quality of refrigerated bred carp; describing a relationship between temperature and the carp freshness quality by adopting an index relative corruption rate equation model, wherein the relative corruption rate equation is LN(RRS)=0.14*T-0.0314; RRS is a relative corruption rate; and T is temperature (DEG C); and further building a carp freshness quality predicting equation in the temperature range (0 to 15 DEG C), wherein RSL is residual shelf life (day), SL(Tn) is the time (day) at the temperature of Tn, Tn is the temperature (DEG C), and RRS (Tn) is the relative corruption rate at the temperature of Tn. The quality predicting model is applied to monitoring the environmental condition of packaged foods, the food quality and safety information of the product during transportation and storage is provided, the production efficiency and product quality are effectively improved and a quantized basis is provided for reducing complaints from shopkeepers and consumers.

Description

A kind of refrigeration carp freshness quality prediction method

Technical field

The invention belongs to processing of aquatic products and storage technique field, particularly relate to a kind of refrigeration carp freshness quality prediction method.

Background technology

Carp is under the jurisdiction of Cyprinidae, is distributed widely in all parts of the country, inhabits the water bottom in rivers, lake etc. more, is main with the food zoobenthos, is one of main kind of China's freshwater aquiculture, and it is big to have a breed face, and output is high, the characteristics that the market demand is vigorous.Many at present with the live fish circulation, but transportation cost is high, and far transportation and sales are difficult, are difficult to meet the requirement of industrialization and big circulation, therefore develop cooling chain fresh fish material flow industry, are to solve the undue effective way that relies on live fish to circulate that cultivates fresh water fishes.The cooling chain is a kind of special supply of material chain, refers to fish from each link that production, transportation, storage, sale eat up to the consumer, adopts the comprehensive measure of cooling means maintenance fresh fish quality.But often depart from the temperature conditions of recommendation in the cold chain process, temperature determines microbial activities speed and shelf life to a great extent, therefore, and to the monitoring of temperature with control most important.Especially for certain single product of a collection of or delivery unit, when temperature departed from, it is more complicated that this problem seems.Effective method is exactly the temperature conditions of in the whole process of circulation, monitoring food respectively the most, sets up between temperature and the shelf life to concern kinetic model, just can identify real quality of product and security.

The rotten speed of fish products depends on composition own, processing stage, storage condition etc.The processor judges the shelf life of product under the specific holding conditions according to the concrete condition in general knowledge, experience and food storage, circulation, the sales process.Yet in the reality, the quality of final products has nothing in common with each other, and the external condition (particularly temperature) of product also disagrees with perfect condition before the consumption, and this often causes the mistake of commodity shelf life is estimated.Accurately the remaining shelf life of prediction product can farthest reduce the risk of the improper processing of wholesome food, also farthest reduces the risk that the consumer buys rotten food, to the producer, and the circulation merchant, consumer three is beneficial to.

Summary of the invention

Technical matters to be solved by this invention provides a kind of refrigeration carp freshness quality prediction method; Through carp sense organ, chemistry and microbiology Study on Quality are cultured in refrigeration; Confirm the product shelf life, adopt the relation of index corrupt relatively rate equation model description temperature and fish products freshness, and then the structure shelf life forecasting model is also verified; Environmental baseline through the monitoring packaged food; Be provided at the information of transportation and duration of storage food quality and safety, effectively enhance productivity and product quality, for the complaint that reduces from retailer and consumer provides the foundation that quantizes.

The technical solution adopted for the present invention to solve the technical problems is: a kind of refrigeration carp freshness quality prediction method is provided, comprises the following steps:

(1) sense organ, chemistry and the microbiology quality of 5 ± 0.1 ℃, 10 ± 0.1 ℃ and 15 ± 0.1 ℃ refrigeration carps are estimated, comprehensively judged shelf life according to this;

(2) set up the relevance model of refrigerated storage temperature and shelf life: the shelf life that 0,5,10,15 ℃ of refrigeration carp is obtained; With temperature the equation of the corrupt relatively rate value of shelf life reaction is come the relevance of accounting temperature and shelf life, and then obtain the shelf life forecasting model between carp 0-15 ℃;

(3) relevance of refrigerated storage temperature and shelf life is estimated: the corrupt relatively Rate Models of exponential, adopt the degree of deviation and accuracy to estimate; The degree of deviation and accuracy obtain by geometrical mean, represent with the form of ratio; The degree of deviation is used for checking the fluctuating range up and down of predicted value, and accuracy is the difference of weighing between predicted value and the measured value;

(4) set up shelf life forecasting model: according to actual institute survey and document in 0,10,15 ℃ shelf life numerical prediction value and measured value compare, the evaluation of forecasting shelf life value and measured value is represented with relative error;

(5) set up the remaining shelf life forecast model: according to the relevance of temperature and shelf life, the quality loss accumulative total effect in conjunction with time resume under the different temperatures and refrigeration carp obtains in the 0-15 ℃ of scope through the remaining shelf life behind the random time temperature history.

The equation of the corrupt relatively rate value in the described step (2) be LN (RRS)=0.14 * T-0.0314 wherein RRS be Relative Rate of Spoilage--RRS for corrupt relatively speed, T be temperature (℃).

The degree of deviation in the described step (3) is obtained by formula

; Accuracy is obtained by formula .

The forecasting shelf life value in the described step (4) and the evaluation of measured value represent that with relative error the corrupt shelf life model of its index does Index remaining shelf life model does

Relative error does

Wherein SL be shelf life (my god), RSL be remaining shelf life (my god), SL _(Tn)For temperature is T _nThe time of being experienced down (my god), T _nFor temperature (℃), RRS _(Tn)For temperature is T _nThe time corrupt relatively speed, SL _(pre)And SL _(obs)Be respectively prediction shelf life and actual measurement shelf life.

Beneficial effect

Beneficial effect of the present invention is:

(1) quick: as not need the initial bacterium number of testing product, overcome the hysteresis quality of microorganism detection, solved the problem that in practical application, can't reach fast prediction that causes thus.Use this model and temperature-time resume can obtain any some place remaining shelf life information in the cold chain, the quantity of deteriorating items when reducing consumption to greatest extent.

(2) easy and simple to handle: as only the temperature-time resume need to be provided; And, specific spoilage organisms kind, quantity and product temperature time resume need be provided according to the shelf life forecasting model that the growth of microorganism principle of dynamics makes up.

(3) external extrapolation experimental data based on microorganism behavior under the ecological factor of setting in the liquid microbial nutrient culture media; This project is according to the experimental data of culturing shelf life; Owing to eliminated the error that nutrient culture media and microbial diversity bring, improved the data accuracy greatly.

Description of drawings

Fig. 1 is the corrupt relatively rate profile of refrigeration carp.

Fig. 2 is temperature-time resume and remaining shelf life curve map.

Embodiment

Below in conjunction with specific embodiments, further set forth the present invention.Should be understood that these embodiment only to be used to the present invention is described and be not used in the restriction scope of the present invention.Should be understood that in addition those skilled in the art can do various changes or modification to the present invention after the content of having read the present invention's instruction, these equivalent form of values fall within the application's appended claims institute restricted portion equally.

The present invention cultures carp sense organ, chemistry and microbiology Study on Quality to 0-15 ℃ of refrigeration; Confirm the product shelf life, adopt the corrupt relatively rate equation of index to describe the relation of temperature and fish products freshness, and then the structure shelf life forecasting model is also verified; Environmental baseline through the monitoring packaged food; Be provided at the information of transportation and duration of storage food quality and safety, effectively enhance productivity and product quality, for the complaint that reduces from retailer and consumer provides the foundation that quantizes.

Implementation step of the present invention is following:

(1) sense organ, chemistry and the microbiology quality of 5 ± 0.1 ℃, 10 ± 0.1 ℃ and 15 ± 0.1 ℃ refrigeration carps are estimated, comprehensive These parameters is judged shelf life.

(2) relevance of refrigerated storage temperature and shelf life is calculated: 0,5,10,15 ℃ of refrigeration carp is tested the shelf life that obtains; With temperature to the corrupt relatively rate value of shelf life reaction equation--Exponential Relative Rate ofSpoilage comes the relevance of accounting temperature and shelf life, and then obtain the shelf life forecasting model between carp 0-15 ℃.

(3) the relevance evaluation of refrigerated storage temperature and shelf life:, adopt the degree of deviation and accuracy to estimate for the corrupt relatively Rate Models of just exponential objectively; The degree of deviation and accuracy obtain by geometrical mean, represent with the form of ratio.The degree of deviation is used for checking the fluctuating range up and down of predicted value, and accuracy is the difference of weighing between predicted value and the measured value.

(4) shelf life forecasting model checking: according to experiment survey and document in 0,10,15 ℃ shelf life numerical prediction value and measured value compare, the evaluation of forecasting shelf life value and measured value is represented with relative error.

(5) according to temperature and shelf life relevance model, the quality loss accumulative total effect in conjunction with time resume under the different temperatures and refrigeration carp can obtain in the 0-15 ℃ of scope through the remaining shelf life behind the random time temperature history.

After the live carp of taking from wholesale market, road, Tongchuan, Shanghai transported to the laboratory, the frozen water shock caused death immediately.Be divided into 4 groups, 1 group of layer ice sheet fish is packed into and has in the clean Box of foamed plastics of weeper, puts into high precision low temperature incubator (Sanyo MIR553, Japan), and 3 ± 0.1 ℃ of control reserve temperatures are on the rocks in good time; Put into down respectively in the plastic tub that double-edged fine-toothed comb can draining for other 3 groups; Cover the lid of leakage hole; Put in the high precision low temperature incubator (Sanyo MIR 253,553, Japan); Control reserve temperature respectively at 5 ± 0.1 ℃, 10 ± 0.1 ℃ and 15 ± 0.1 ℃, comprehensively judge shelf life according to sense organ, chemistry and microbiological indicator.

Randomly draw 2 tail sample fishes, the sampling method of GB/T18108-2008 is adopted in living earlier fish sensory evaluation then at every turn.

In the living fish sensory evaluation of when sampling, the half of fish of dissecing in addition cooked carry out ripe fish sensory evaluation.With smell of living fish and the aroma and flavor of ripe fish is the main foundation of estimating, and makes an appraisal in conjunction with other organoleptic features.Sensory quality assessment is divided into 0～2 grade, and 0 is that initial quality is promptly the most high-quality; 1 is the light or disappearance of bright fragrance, is high-quality final point; 2 is that obvious peculiar smell of appearance and stink are that sense organ is rejected point.

TVBN measures: take by weighing and smash flesh of fish 10.00g in conical flask; Add 20ml water, 20ml 10% trichloroacetic acid, stir jolting with glass bar; Filter behind the dipping 30min; Filtrating is measured by the semimicro nitriding, each sample do at least 2 parallel, the result with contained N in every 100g sample the milligram numerical table show.

Count of bacteria: take by weighing and smash flesh of fish 25g, in the sterilization mortar, grind, add the aseptic 0.1% peptone physiological saline of 225mL and mix, be 10 ^-1Dilution.Pipette 1mL 10 ^-1Dilution is 10 in the aseptic 0.1% peptone physiological saline of 9mL ^-2Dilution; At a high speed vibration, with 10 times of dilutions, and the like 10 ^-3, 10 ^-4, 10 ^-5Dilution.Get 3 dilution 0.1mL that concentration is suitable, coat in the nutrient agar.Each dilution is parallel makees 2 double dish.

Total plate count: nutrient agar, cultivate 2～3d for 25 ℃, counting.

The psychrophile number: the nutrient agar that was coated with, cultivate 14d for 5 ℃, counting.

The pseudomonad number: the pseudomonad special culture media (CFC, Oxoid code CM 559, supplemented with SR103, Oxoid UK), cultivates 2～3d for 25 ℃ by operation instruction, counting.

8.1.4 the structure of corrupt relatively Rate Models and evaluation

0,5,10,15 ℃ of refrigeration carp is tested the shelf life that obtains; The corrupt relatively speed of fit indices (ExponentialRelative Rate of Spoilage) equation (formula 1); Wherein RRS for corrupt relatively speed (Relative Rate ofSpoilage, RRS), K ₁Be constant, A is an equation coefficient, T be temperature (℃); For just this model of using objectively, adopt the degree of deviation and accuracy to estimate; Both obtain the degree of deviation (formula 2) and accuracy (formula 3) by geometrical mean, represent with the form of ratio.The degree of deviation is used for checking the fluctuating range up and down of predicted value, and accuracy is the difference of weighing between predicted value and the measured value.

LN(RRS)＝K ₁+A×T (1)

$\mathrm{Accuracy\; Factor}\left(\mathrm{RRS}\right)={10}^{\frac{\mathrm{\Σ}\left|1g({\mathrm{RRS}}_{\mathrm{pre}}/{\mathrm{RRS}}_{\mathrm{obs}})\right|}{n}}---\left(2\right)$

$\mathrm{Bias\; Factor}\left(\mathrm{RRS}\right)={10}^{\frac{\mathrm{\Σ}1g({\mathrm{RRS}}_{\mathrm{pre}}/{\mathrm{RRS}}_{\mathrm{obs}})}{n}}---\left(3\right)$

The evaluation of forecasting shelf life value and measured value is represented with relative error.Equation 4 is the corrupt shelf life model of index, and equation 5 is an index remaining shelf life model, and equation 6 is a relative error; Wherein Tref is a reference temperature, and A is equation coefficient (with A in the equation 1), and SL is shelf life (Shelf life); RSL is remaining shelf life (Remaining shelf life); ST is the time of experience, and Tn is a temperature, and SL (pre) and SL (obs) are respectively prediction shelf life and actual measurement shelf life.

${\mathrm{SL}}_{\left(T\right)}=\frac{{\mathrm{SL}}_{\left(T_{\mathrm{ref}}\right)}}{\mathrm{Exp}[A\×(T-T_{\mathrm{ref}})]}---\left(4\right)$

${\mathrm{RST}}_{\left(T\right)}=\frac{{\mathrm{SL}}_{\left(T_{\mathrm{ref}}\right)}-\underset{i=1}{\overset{n}{\mathrm{\Σ}}}[{\mathrm{SL}}_{\left(T_n\right)}\×{\mathrm{RRS}}_{\left(T_n\right)}]}{\mathrm{Exp}[A\×\left({T-T}_{\mathrm{ref}}\right)]}---\left(5\right)$

$\mathrm{Relative\; Error}(\%)=\frac{{\mathrm{SL}}_{\left(\mathrm{pre}\right)}-{\mathrm{SL}}_{\left(\mathrm{obs}\right)}}{{\mathrm{SL}}_{\left(\mathrm{obs}\right)}}\×100\%---\left(6\right)$

Flesh of fish microbial status is relevant with catching method and envirment factor; Shelf life receives the growth and biochemical movable restriction of Gram-negative psychrophilic bacteria (pseudomonad, corrupt Shiva Salmonella, acinetobacter calcoaceticus and Mohs bacillus etc.), and initial bacterium number and terms of packing also are the key factors that influences shelf life.Take all factors into consideration the above-mentioned product shelf life factor that influences, comprehensive sensory evaluation scores, microorganism and chemical index judge, 0,5,10 and 15 ℃ of refrigeration large yellow croaker shelf life is respectively 29.7,15.9,6.1 and 3.6d (table 1).During the shelf life terminal point total plate count, pseudomonad number, psychrophile number be respectively 7.23 ± 0.29,6.67 ± 0.33,6.96 ± 0.37lg cfu/g, TVBN is 20.41 ± 1.14mg/100g.

Show 10-15 ℃ of refrigeration carp chemistry, microorganism quality and shelf life

Table?1Sensory，chemical?and?bacteriological?qualities?ofCyprinus?carpio?stored?aerobically?at?0-15℃

When RRS accuracy=1.0, show that measured value and predicted value are identical, when RRS accuracy＞1, show measured value and the predicted value property of there are differences.Though the upper limit of ESM model good authentication is not also set up, the RRS accuracy factor reaches at 1.3 o'clock, shows that measured value and predicted value exist than large deviation.0, the ESM model of 10 and 15 ℃ of structures (equation 7), R ²=0.998 (as shown in Figure 1).Table 2 is visible, can know accuracy=1.07 according to equation 2, explains that the mean difference between predicted value and the actual value is 4%; Deviation 1.0 expression predicted values do not have system mistake, and 0.75-1.25 is considered to reliably, can know that according to equation 3 degree of deviation is 1.04, and the deviation of expression predicted value is 4%.Accuracy and degree of deviation result show that the ESM model of the carp of structure is reliable.

LN(RRS)＝0.14×T-0.0314(7)

Table 2ESM model construction and evaluation

Table?2?Development?and?assessment?of?exponentialspoilage?model

Can know that by equation 7 A=0.14 can derive shelf life forecasting model (formula 8) between 0-15 ℃ that refrigerates carp.Table 3 is visible, and in 0-15 ℃ of scope, when Tref was made as 0,5 and 15 ℃, relative error was respectively-7.5～0%, 0～8.3% and-7.5～0%, shows that 0,5,8 and 10 ℃ is made as reference temperature and predicts that shelf life all is reliable.Yet carp is many with the circulation of iced storage cold chain, so establish 0 ℃ for reference temperature, can get equation 9.

Carp shelf life during with 0,10,5 ℃ of refrigeration of equation 9 predictions, the relative error of predicted value and measured value is 19.7%～23.8%, sees table 4.Above-mentioned checking result shows, the corrupt shelf life model of exponential can 0～15 ℃ of storage of fast and reliable real-time estimate carp shelf life.

The evaluation of the corrupt shelf life model reference of table 3 refrigeration carp index temperature

Tab.3?Assessment?of?reference?temperature?of?exponential?spoilage?shelf?life?modelfor?chilled?Cyprinus?carpio

${\mathrm{SL}}_{\left(T\right)}=\frac{{\mathrm{SL}}_{\left(T_{\mathrm{ref}}\right)}}{\mathrm{Exp}[0.14\×(T-T_{\mathrm{ref}})]}---\left(8\right)$

${\mathrm{SL}}_{\left(T\right)}=\frac{29.7}{\mathrm{Exp}[0.14\×T]}---\left(9\right)$

The checking of table 4 carp shelf life forecasting model

Table?4?Validation?of?shelf?life?model?of?Cyprinus?carpio

Temperature-time that the fish body is experienced in reality cooling chain is in the fluctuation, causes the difference of different phase fish body freshness.After setting time-temperature history of 0 ℃ (1d), 5 ℃ (1.5d), 3 ℃ (2d), 7 ℃ (2.5d) and 12 ℃ (1d); Utilize the interior carp remaining shelf life forecast model (equation 10) of equation 7 and 0-15 ℃ of scope; Can derive the time resume that experienced and the remaining shelf life under the different temperatures (0,5,10,15 ℃), as shown in Figure 2.Table 5 shows through behind the above-mentioned resume, and the remaining shelf life under 0,5,8,10 ℃ of condition is respectively 0.5,0.2,0.1 and 0d.In like manner can calculate in the 0-15 ℃ of scope through random time temperature history (ST ₁, ST ₂, ST ₃... ST _n) after remaining shelf life.

${\mathrm{RSL}}_{\left(T\right)}=\{29.7-\underset{i=1}{\overset{n}{\mathrm{\Σ}}}[{\mathrm{SL}}_{\left(T_n\right)}\×{\mathrm{RRS}}_{\left(T_n\right)}\left]\right\}/\mathrm{Exp}[0.14\×T]$ Equation 10

Application during table 5 carp shelf life forecasting model fluctuating temperature

Table?5?Application?of?shelf?life?model?of?chilled?Cyprinus?carpiounder?fluctuating?temperatures

The environmental baseline that fish perched, especially temperature and water pollution condition have material impact to the initial bacterium number and the bacteria types of fish body, and initial pollution level is low more, and be long more to the time (shelf life) that occurs before obviously corrupt.Secondary pollution and fish after the fishery harvesting after death changes (after death stiff etc.), also is the key factor that influences fish products matter, and for example the pollution bacterium colony in ice or the equipment also is prone to pollute fish products, thereby the operation of health can reduce the microbial spoilage of fish.Simultaneously, the difference of method of counting of microorganism (temperature, nutrient culture media, salinity etc.) and limit standard causes also there are differences in the judgement to product quality.The tradition agar plate count methods that adopt are cultivated counting for 30 ℃ more, and some researcher uses other conditions such as reducing cultivation temperature (20 ℃ or 15 ℃), other nutrient culture media, the NaCl of variable concentrations and dissimilar culture plates.It is that the result is 10 when using the aerobic plate count method to analyze that the microorganism that ICMSF (1986) recommends is limited the quantity of ⁷CFU/g, and limiting the quantity of that other people recommend is 3 * 10 ⁶CFU/g, some researchists think that the corrupt ability of total plate count and fish does not have necessary relation, because specific spoilage organisms (SSO) is the part of whole bacteriums.The total plate count of in the time of 20 ℃, being found can explain that the health in the process of curing fish keeps situation, but quality and prediction shelf life are still suspectable.

In the industrialized country, the common iced storage of fresh fish, shelf life adopts corrupt relatively speed to express when different temperatures is preserved.The shelf life that obtains according to experiment under the RRS model different temperatures is developed, and mainly contains 3 types, and square root RRS model makes up according to the minimum temperature of psychrophile, mainly be applicable to temperate zone and cold belt waters aquatic products, but the research demonstration has bigger deviation; The corrupt model of Arrhenius is used to make up hairtail, ripe mussel different temperatures storage forecast model down according to making up according to apparent activation energy (Ea) under the fish different temperatures, and it is still to be tested whether to be fit to refrigerate the carp shelf life forecasting model.The corrupt relatively Rate Models of index is usually used in the influence of predicted temperature to warm band and the corrupt speed of tropic fishes, and the degree of deviation and the accuracy of the refrigeration carp RRS model of structure are 1.04 and 1.04 respectively, explains that model is reliable being suitable for.Resulting quantitative information provides solid foundation for the exploitation of monitoring commodity shelf life device in storage, circulation, the retail process in this field of prediction microbiology.Therefore we infer prediction microbiology will become accurate estimation commodity shelf life and calculate remaining shelf life effective means developed.

The present invention studies sense organ, physics and chemistry and the microorganism quality of 0～15 ℃ of refrigeration carp, confirms the product shelf life, describes the relation of temperature and fish products freshness with the corrupt relatively rate equation of index, and then makes up shelf life forecasting model and also verify.The result shows; 0,5,10 and 15 ℃ of refrigeration carp shelf lifes are respectively 29.7,15.9,6.1 and 3.6d; Total plate count, pseudomonad number, psychrophile number are respectively 7.23 ± 0.29,6.67 ± 0.33,6.96 ± 0.37lg cfu/g, and TVBN is 20.41 ± 1.14mg/100g.The shelf life forecasting model of 0～15 ℃ of refrigeration carp is SL _(T)=29.7/ [Exp (0.14 * T].The shelf life measured value that is housed in 0,10,15 ℃ with carp is verified the model of setting up, and relative error is 19.7%～23.8%.

Claims (1)

1. a refrigeration carp freshness quality prediction method comprises the following steps:

(1) sense organ, chemistry and the microbiology quality of 0 ℃, 5 ± 0.1 ℃, 10 ± 0.1 ℃ and 15 ± 0.1 ℃ refrigeration carps are estimated, comprehensively judged shelf life according to this;

(2) set up the relevance model of refrigerated storage temperature and shelf life: the shelf life that the experiment of 0,5,10,15 ℃ of refrigeration carp is obtained; With temperature the equation of the corrupt relatively rate value of shelf life reaction is come the relevance of accounting temperature and shelf life, and then obtain refrigerated storage temperature and the relevance model of shelf life between carp 0-15 ℃;

(3) relevance of refrigerated storage temperature and shelf life is estimated: the corrupt relatively Rate Models of exponential, adopt the degree of deviation and accuracy to estimate; The degree of deviation and accuracy obtain by geometrical mean, represent with the form of ratio; The degree of deviation is used for checking the fluctuating range up and down of predicted value, and accuracy is the difference of weighing between predicted value and the measured value;

(4) set up shelf life forecasting model: 0,10,15 ℃ shelf life numerical prediction value and measured value are compared, and the evaluation of forecasting shelf life value and measured value is represented with relative error;

(5) set up the remaining shelf life forecast model: according to the relevance of temperature and shelf life, the quality loss accumulative total effect in conjunction with time resume under the different temperatures and refrigeration carp obtains in the 0-15 ℃ of scope through the remaining shelf life behind the random time temperature history;

The equation of the corrupt relatively rate value in the described step (2) be LN (RRS)=0.14 * T-0.0314 wherein RRS be Relative Rate of Spoilage--RRS for corrupt relatively speed, T is a temperature ℃; The degree of deviation in the described step (3) is by formula $\mathrm{AccuracyFactor}\left(\mathrm{RRS}\right)={10}^{\frac{\mathrm{\Σ}\left|\mathrm{Lg}({\mathrm{RRS}}_{\mathrm{Pre}}/{\mathrm{RRS}}_{\mathrm{Obs}})\right|}{n}}$ Obtain; Accuracy is by formula $\mathrm{BiasFactor}\left(\mathrm{RRS}\right)={10}^{\frac{\mathrm{\Σ\; Lg}({\mathrm{RRS}}_{\mathrm{Pre}}/{\mathrm{RRS}}_{\mathrm{Obs}})}{n}}$ Obtain, wherein RRS _PreBe the corrupt relatively speed of prediction, RRS _ObsBe the corrupt relatively speed of actual measurement; The forecasting shelf life value in the described step (4) and the evaluation of measured value represent that with relative error the corrupt shelf life model of its index does

Remaining shelf life forecast model in the described step (5) does ${\mathrm{RSL}}_{\left(T\right)}=\{29.7-\underset{i=1}{\overset{n}{\mathrm{\Σ}}}[{\mathrm{SL}}_{\left(T_n\right)}\×{\mathrm{RRS}}_{\left(T_n\right)}\left]\right\}/\mathrm{Exp}[0.14\×T];$ Relative error does $\mathrm{RelativeError}(\%)=\frac{{\mathrm{SL}}_{\left(\mathrm{Pre}\right)}-{\mathrm{SL}}_{\left(\mathrm{Obs}\right)}}{{\mathrm{SL}}_{\left(\mathrm{Obs}\right)}}\×100\%;$ Wherein SL is the shelf life sky, and RSL is the remaining shelf life sky, SL _(Tn)For temperature is T _nThe time sky that is experienced down, T _nBe temperature ℃, RRS _(Tn)For temperature is T _nThe time corrupt relatively speed, SL _(pre)And SL _(obs)Be respectively prediction shelf life and actual measurement shelf life.

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