CN103235094A - Rapid prediction method of freshness of Scophthalmus maximus - Google Patents

Abstract

The invention belongs to the technical field of aquatic product processing and storage, and relates to a rapid evaluation method of the freshness of Scophthalmus maximus. The equations of remaining shelf lives at a constant temperature and a fluctuation temperature which are in a range of 0-25DEG C comprise an equation (4) and an equation (5). In the equation (4) and the equation (5), SLT is the shelf life when the temperature is T, and RSLT is the remaining shelf life when the temperature is T; and T is the temperature, and RRSTn is the relative corrosive rate predication value when the temperature is Tn. The method has the advantages of rapidness, simplicity and small deviation.

Description

A kind of turbot freshness method for quick predicting

Technical field

The invention belongs to processing of aquatic products and storage technique field, be specifically related to preserve the fast appraisement method of turbot freshness, especially the 0-25 ℃ of method for quick predicting of preserving the turbot freshness down.

Background technology

Along with people to the improving constantly of requirements such as aquatic products security, keeping quality and shelf life, how effective monitoring and ensure that food quality, safety and freshness become the focus of concern.

The aquatic product quality safety guarantee is detected by traditional terminal gradually and changes into from the preventive measures such as monitoring, control and record of producing to the key parameter of consuming whole process, and is most important to reducing the freshness loss that is caused by corruption in the supply chain.Wherein temperature greatly influences spoilage organisms population and the activity rate thereof in the bright product of aquatic products and the slight processed goods, it is the deciding factor that influences the fresh fish shelf life under the GMP, temperature-time the resume of record food whole process when the temperature-time integraph can effectively be grown, shelf life forecasting model is a kind of very useful instrument to key parameters such as evaluate temperature to the influence of product quality.In recent years, be used for that statement influences the key parameter of food and the multiple kinetic model of remaining shelf life relation is developed, as the A Erniusi model, the square root model, exponential model etc., but every kind of model all has different applicable objects and scope, even the resume that the product experience is identical, there is not a kind of model can cover all types of food yet, therefore need be at product own characteristic and corruption, from statistics angle acquisition mass data with set up mathematical model, and its performance and applicability estimated, could guarantee the representative and practicality of institute's development model.

The difference of aquatic products inherence and external factor, cause product that himself distinctive corrupt flora, corrupt scope and corrupt feature etc. are arranged, especially to the spoilage organisms of new product or innovative product and corrupt scope toward contact unknown dawn, carry out correlative study and can be to understand corruption and make up reliable shelf life forecasting model the basis is provided.Need between temperature and sense organ, biochemistry and microorganism change, set up relatedly simultaneously, probe into the correlativity between temperature and shelf life.(Rate of Spoilage, RS), namely measure by the inverse of shelf life to the influence of shelf life corrupt speed commonly used for temperature.The RRS model is to develop according to the shelf life under the different temperatures, comprise A Erniusi model, square root model, exponential model etc., and verify with shelf life under constant temperature and the alternating temperature, for further research and development collection packing, storage and circulation etc. provide support for the intelligent early warning system of one.Same original equation also is not suitable for all fish, therefore, need find a suitable method to judge and predicts the turbot freshness.

Summary of the invention

The present invention aims to provide a kind of method of fast prediction turbot freshness.

Technical scheme is: a kind of method of fast prediction turbot freshness, and the remaining shelf life equation is under the different temperatures:

${\mathrm{RSL}}_T=\frac{\mathrm{SLat}{T}_{\mathrm{ref}}-\underset{i=1}{\overset{n}{\mathrm{\Σ}}}[{\mathrm{ST}}_{T_n}\×{\mathrm{RRS}}_{T_n}]}{{[1-\frac{1}{T_{\min }}\×T]}^2}---\left(1\right)$

RSL in the formula _TFor temperature=T (℃) time remaining shelf life, unit is day (d), ST _TnBe temperature T _nThe time the time of having preserved, unit is day (d); T be temperature (℃),

Be temperature T _nUnder corrupt relatively speed (RRS) predicted value, SL at T _RefBe reference temperature T _Ref(℃) under shelf life, T _MinBe theoretical minimum temperature.

Actual circulation and the storage in temperature often fluctuate up and down indefinite, time-temperature history is divided into into very short temperature-time interval, time resume under the different temperatures are changed into time under the reference temperature (0 ℃), obtain with reference temperature under shelf life poor, and then can derive remaining shelf life equation RSL under the different temperatures _T

Wherein, corrupt relatively speed RRS model account form such as equation (2), be the corrupt speed of temperature when being T ℃ divided by the corrupt speed of reference temperature, the shelf life when namely shelf life is T ℃ divided by temperature during reference temperature; Corrupt relatively Rate Models account form is:

The RS reciprocal of shelf life SL is corrupt speed, and computation schema is shown in equation (3): $\sqrt{\mathrm{RS}}=k\×(T-T_{\min })---\left(3\right)$

In equation (1) and (2), k is the empirical constant relevant with reactive system material person's character, T _RefBe the reference temperature of setting (being generally 0 ℃), and utilize equation (1) and the theoretical minimum temperature T of (2) derivation temperature _Min

Preferably, in the equation (2), SL at T _Ref=32.6(days), T _Ref=0 ℃, T _Min=-6.3 ℃.

That is, preferred turbot temperature=T (℃) time the forecasting shelf life equation shown in equation (4), SL _TShelf life during for temperature T:

${\mathrm{SL}}_T=\frac{32.6}{{[1-\frac{1}{-6.3}\×T]}^2}---\left(4\right)$

The remaining shelf life predictive equation shown in equation (5), RSL _TRemaining shelf life during for temperature T:

${\mathrm{RSL}}_T=\frac{32.6-\underset{i=1}{\overset{n}{\mathrm{\Σ}}}[{\mathrm{ST}}_{T_n}\×{\mathrm{RRS}}_{T_n}]}{{[1-\frac{1}{-6.3}\×T]}^2}---\left(5\right)$

RSL in the formula _TRemaining shelf life during for temperature T, unit are day (d), ST _TnBe temperature T _nThe time storage time, unit is day (d), T be temperature (℃),

Be temperature T _nUnder the RRS predicted value.

Preferably, said method is applicable to 0～25 ℃ of turbot freshness prediction of storage down.

Concrete grammar of the present invention is as follows:

Sense organ, chemistry and microbiology index of fish freshness to 0,3,7,10 and 25 ° of C constant temperature and fluctuating temperature storage turbot are tested, and comprehensive three kinds of index of fish freshness are judged shelf life.

Reserve temperature and shelf life correlativity: with the shelf life of 0,7,25 ℃ of storage turbot, adopt the corrupt relatively rate equation of square root to come the correlativity of accounting temperature and shelf life, and then be fitted to shelf life forecasting model under the interior constant temperature of 0～25 ° of scope of turbot.In conjunction with the time resume under the fluctuating temperature and storage turbot quality loss accumulative total effect, can obtain in 0～25 ℃ of scope through the remaining shelf life after the fluctuating temperature.

Reserve temperature and shelf life correlativity equation model goodness are estimated: the optimum means of goodness of fit evaluation evaluation are comparisons of predicted value and measured value, adopt residual sum of squares (RSS), the degree of deviation, accuracy, root-mean-square error to estimate.

Constant and fluctuating temperature shelf life verification of model: choose that the turbot shelf life comes the comparison prediction value under constant (3 ℃, 10 ℃) and the fluctuating temperature, represent with relative error.

The advantage of the inventive method is:

(1) fast, do not need the initial bacterium number of testing product, overcome the hysteresis quality of microorganism detection, solved the problem that can't reach fast prediction in actual applications 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, the freshness prediction only needs to provide the temperature-time resume, and according to the shelf life forecasting model that the growth of microorganism principle of dynamics makes up, need provide specific spoilage organisms kind, quantity and product temperature time resume.

Description of drawings

The corrupt relatively speed of Fig. 1 turbot square root and temperature dependence curves

Embodiment

Embodiment 1 turbot freshness and shelf life evaluation

(1) freshness evaluation under the constant and fluctuating temperature of turbot

Get three batches of turbot (every batch of 100-200 bar) from aquatic products wholesale market, Tongchuan, Shanghai, live fish is transported to the laboratory, and the frozen water shock causes death.Carry out low temperature (0～10 ℃) storage, namely put into the high precision low temperature incubator and carry out low temperature (3,7,10 ℃), room temperature (25 ℃) and alternating temperature (℃ (8h) → 8, ℃ (36h) → 25, ℃ (44h) → 15, A:0 ℃ of (93h) → 5 ℃ (97.5h), change 1 hour record 1 time with temperature-time registering instrument monitoring temperature.Take out the sample fish every appropriate time and carry out the evaluations of sense organ, total volatile basic nitrogen (TVBN), trimethylamine (TMA) and the bacteriology index of quality, result such as table 1.

(2) index of fish freshness is measured under the turbot reserve temperature

1. sensory evaluation: form evaluation group by 6 trained sensory evaluation persons, the aroma and flavor after the smell of living fish and fish are cooked is estimated.Namely adopt 3 point-scores, 0 is best quality, and 1 is the bright fragrance disappearance of fresh fish, and 0～1 is the high-quality phase, but 2 is the sense organ acceptance limit.When 1/2 valuation officer estimates 2 fens when above, be sense organ refusal point.

2. total volatile basic nitrogen: get 10.00g and smash to pieces and oppress in triangular flask, add 20ml distilled water water, 20ml10% trichloroacetic acid, stir evenly, jolting is filtered behind the dipping 30min, measure by the semimicro nitriding, each sample do at least 2 parallel.

3. trimethylamine: get 50g and smash to pieces and oppress in triangular flask, add the 100mL7.5% trichloroacetic acid, stir, filter, measure with picric acid method.

4. colony counting: take by weighing and smash flesh of fish 10.0g, add 90ml0.1% peptone stroke-physiological saline solution, the vibration back is got 3 suitable dilutions of gradient and is coated with 10 times of gradient dilutions at a high speed, and 2 of each dilution coatings are counted after the cultivation.

Total plate count (TVC): be coated with at agar medium, cultivate counting after 48 hours for 25 ℃; Psychrophile number: be coated with at agar medium, cultivate counting after 120～168 hours for 5 ℃; Produce H ₂S bacterium number: be coated with at the iron agar plate, cultivate the black bacterium colony that formed in 72 hours to 25 ℃ and count; Pseudomonad number: be coated with at the pseudomonad selective medium, cultivate counting after 48 hours for 25 ℃.

(3) shelf life and the index of quality

Indexs such as comprehensive sense organ, total volatile basic nitrogen, trimethylamine and bacterium number are judged the product shelf life.

When total volatile basic nitrogen, trimethylamine and bacterium number exceed standard, judge that product exceeds shelf life.

Result: turbot sense organ, physics and chemistry and microbiology freshness feature

Turbot storage initial stage sensory evaluation is good, and TVBN and TMA are respectively 7.60 and 0.04mg/100g, TVC, produces H ₂S bacterium number and pseudomonad number be respectively 3.67,2.58 and 3.06lg(CFU/g).TVBN and TMA are respectively 29.12～34.38mg/100g and 11.06～11.30mg/100g during 0～10 ℃ of storage shelf life terminal point, TVC, product H ₂S bacterium number and pseudomonad number are respectively 6.39～7.68CFU/g, 5.49～6.80CFU/g and 5.47～7.26CFU/g.TVC, product H during 25 ℃ of storage turbot shelf life terminal points ₂S bacterium number and pseudomonad number are respectively 7.06lg(CFU/g), 6.89lg(CFU/g) and 7.03lg(CFU/g), TVBN and TMA number are respectively 35.05mg/100g and 9.11mg/100g.TVC, product H during variable temperature storage turbot shelf life terminal point ₂S bacterium number and pseudomonad number are respectively 6.97lg(CFU/g), 5.75lg(CFU/g) and 6.35lg(CFU/g), TVBN and TMA number are respectively 29.68 and 8.86mg/100g.Under 0～25 ℃, turbot storage result such as table 1.

Table 10～25 ℃ storage turbot chemistry, microorganism quality

Annotate: n is replicated experimental units, and data are " mean+SD " of sample in the table, and A is alternating temperature.

Embodiment 2 match temperature and shelf life correlativity equation

(1) structure of the corrupt relatively Rate Models of turbot

Temperature to the influence of shelf life corrupt speed commonly used (Rate of spoilage, RS), i.e. inverse (the days of shelf life ^-1) measure, the corrupt speed when RRS is defined as temperature T ℃ is divided by the corrupt speed of reference temperature, i.e. shelf life (SL at T during reference temperature _Ref) shelf life (SL at T) during divided by T ℃.Originally the corrupt Rate Models of square root is used for estimating microbial growth, also for assessment of the influence (equation 3) of temperature to the fresh fish shelf life, and then can derive the corrupt relatively Rate Models of square root (equation 2).

$\sqrt{\mathrm{RS}}=k\×(T-T_{\min })---\left(3\right)$

In equation (2), (3), k is the empirical constant relevant with reactive system material person's character.With reference temperature T _RefBe set at 0, theoretical minimum temperature (T derives _Min).

Actual circulation and the storage in temperature often fluctuate up and down indefinite, time-temperature history is divided into into very short temperature-time interval, time resume under the different temperatures are changed into time under the reference temperature (0 ℃), obtain with reference temperature under shelf life poor, and then can derive remaining shelf life equation (equation 1) under the different temperatures.

${\mathrm{RSL}}_T=\frac{\mathrm{SLat}{T}_{\mathrm{ref}}-\underset{i=1}{\overset{n}{\mathrm{\Σ}}}[{\mathrm{ST}}_{T_n}\×{\mathrm{RRS}}_{T_n}]}{{[1-\frac{1}{T_{\min }}\×T]}^2}---\left(1\right)$

RSL in the formula _TRemaining shelf life during for temperature T, unit are day (d), ST _TnBe temperature T _nThe time the storage time that experiences, unit is day (d); T be temperature (℃), RRST _nBe temperature T _nUnder the RRS predicted value.

Experimental data is with SPSS (Release16.0) statistical software, carries out one-way analysis of variance and the analysis of least significant difference method and carries out curve fitting and estimate.

In embodiment 1,0,7,25 ℃ of shelf life that the storage turbot obtains draws corrupt speed (RS) and relative corrupt speed (RRS) and square root numerical value thereof, the results are shown in Table 2.According to the corrupt relatively rate equation of square root, be fitted to temperature and RRS correlativity equation (as Fig. 1), i.e. √ (RRS)=0.16T+1, R ²=0.999, reference temperature is set at 0, can draw the theoretical minimum temperature T of model parameter according to equation (2) _Min=-6.3 ℃.

Table 20～25 ℃ corrupt relatively the speed of storage turbot

(2) the corrupt relatively Rate Models performance evaluation of turbot

Adopt residual sum of squares (RSS) (residual sum of squares, RSS), the degree of deviation (bias factor, BF), accuracy (accuracy factor, AF), (root mean square error RMS) estimates the performance of corrupt relatively Rate Models the goodness of fit (goodness of fit) of model root-mean-square error.Shown in the table 3, AF=1.11 shows that mean difference is 11% between predicted value and the measured value, it has been generally acknowledged that between AF＞1.30 o'clock actual measurement and predicted value to be considered to have than large deviation; Deviation 0.75-1.25 is considered to reliably, and the degree of deviation is 1.11, illustrates that the predicted value degree of deviation is that 11%(is in 0.75-1.25), therefore think that this model goodness of fit is good, deviation is little.

The corrupt relatively Rate Models goodness of fit of table 3 square root is estimated

(3) foundation of turbot shelf life forecasting model

Model parameter T _Min=-6.3 ℃, reference temperature T _RefShelf life (32.6 days) when (being made as 0 ℃) and 0 ℃ is brought equation (2) into, derives the shelf life SL of turbot under the constant temperature _TForecast model (equation 4).In like manner, bring correlation parameter into and can derive remaining shelf life RSL _TForecast model (equation 5).

${\mathrm{SL}}_T=\frac{32.6}{{[1-\frac{1}{-6.3}\×T]}^2}---\left(4\right)$

${\mathrm{RSL}}_T=\frac{32.6-\underset{i=1}{\overset{n}{\mathrm{\Σ}}}[{\mathrm{ST}}_{T_n}\×{\mathrm{RRS}}_{T_n}]}{{[1-\frac{1}{-6.3}\×T]}^2}---\left(5\right)$

(4) checking of turbot freshness forecast model

Shown in the table 4, utilize equation (4) and (5), the predicted value of the shelf life under 3 ℃, 10 ℃ constant temperature and alternating temperature (A) 14.9,4.8 and 7.5 days respectively, predicted value and measured value relative error are respectively-12.8～14.2%, show that this model can better predict shelf life under constant temperature and the alternating temperature.

Turbot shelf life forecasting model checking under table 4 constant temperature and the alternating temperature

Annotate: SL _(obs)Be shelf life measured value, SL _(prd)Be the forecasting shelf life value, relative error=(SL _(prd)-SL _(obs)) * 100%/SL _(obs)

Claims (8)

1. the method for a fast prediction turbot freshness is characterized in that, the remaining shelf life predictive mode when temperature is T is shown in equation (1):

${\mathrm{RSL}}_T=\frac{\mathrm{SLat}{T}_{\mathrm{ref}}-\underset{i=1}{\overset{n}{\mathrm{\Σ}}}[{\mathrm{ST}}_{T_n}\×{\mathrm{RRS}}_{T_n}]}{{[1-\frac{1}{T_{\min }}\×T]}^2}---\left(1\right)$

RSL in the equation (1) _TRemaining shelf life during for temperature=T, unit are day ST _TnBe temperature T _nUnder the time of having preserved, unit is day; T is temperature,

Be temperature T _nUnder corrupt relatively speed RRS predicted value, SL at T _RefBe reference temperature T _RefUnder shelf life, T _MinBe theoretical minimum temperature;

Corrupt relatively speed RRS model computation schema in the equation (1) such as equation (2), the shelf life when shelf life is T ℃ divided by temperature when being reference temperature; The corrupt relatively Rate Models account form of square root is:

T _RefBe the reference temperature of setting.

2. the method for the described fast prediction turbot of claim 1 freshness is characterized in that, equation (1) and (2) described reference temperature T _RefIt is 0 ℃.

3. the method for the described fast prediction turbot of claim 1 freshness is characterized in that, equation (1) and (2) described T _MinBe-6.3 ℃.

4. the method for the described fast prediction turbot of claim 1 freshness is characterized in that, equation (1) and (2) described reference temperature T _RefIt is 0 ℃.

5. the method for the described fast prediction turbot of claim 1 freshness is characterized in that, the SL at T in the equation (1) _Ref=32.6 days, T _RefIt is 0 ℃.

6. the method for the described fast prediction turbot of claim 1 freshness is characterized in that,

When temperature T, shelf life SL _TPredictive equation is shown in equation (4):

${\mathrm{SL}}_T=\frac{32.6}{{[1-\frac{1}{-6.3}\×T]}^2}---\left(4\right)$

Remaining shelf life RSL _TPredictive equation is shown in equation (5):

${\mathrm{RSL}}_T=\frac{32.6-\underset{i=1}{\overset{n}{\mathrm{\Σ}}}[{\mathrm{ST}}_{T_n}\×{\mathrm{RRS}}_{T_n}]}{{[1-\frac{1}{-6.3}\×T]}^2}---\left(5\right)$

ST in the equation _TnBe temperature T _nUnder the time of having preserved, unit is day; T is temperature,

Be temperature T _nUnder corrupt relatively speed RRS predicted value.

7. the method for the described fast prediction turbot of claim 1 freshness is characterized in that, the reserve temperature of described turbot is 0～25 ℃.

8. the method for the described fast prediction turbot of claim 1 freshness is characterized in that, earlier sense organ, chemistry and the microorganism index of fish freshness to the turbot of preserving under constant temperature or the fluctuating temperature under 0～25 ℃ of different temperatures detects, and judges shelf life; Adopt the corrupt relatively rate equation accounting temperature of square root and shelf life correlativity again, and then shelf life forecasting model under the interior constant temperature of 0～25 ° of scope of match turbot; And in conjunction with the time resume under the fluctuating temperature and storage turbot quality loss accumulative total effect, obtain in 0～25 ℃ of scope through the remaining shelf life after the fluctuating temperature.

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