CN109147865A - Salmonella risk evaluating system in chicken based on Fast Detection Technique - Google Patents

Abstract

The purpose of the present invention is to provide salmonella risk evaluating systems in the chicken based on Fast Detection Technique, and the method for using artificial contamination first collects salmonella and campylobacter growth data；Then it is fitted using amendment Gompertz model, Baranyi model and Logistic model, selects optimal first-level model by comparing Akaike criterion and Sawa's Bayesian criterion to correct Gompertz model；The growth parameter(s) obtained based on first-level model, second-level model is constructed, by carrying out model verifying using deviation factors, the accurate factor, mean square error, root-mean-square error, median relative error, average absolute relative error, prediction standard error and the coefficient of determination；Obtained second-level model reliability with higher and predictive ability.The beneficial effects of the invention are as follows method atopic and high sensitivity, operation are succinct, quick and convenient, at low cost.

Description

Salmonella risk evaluating system in chicken based on Fast Detection Technique

Technical field

The invention belongs to technical field of food detection, it is related to salmonella risk in the chicken based on Fast Detection Technique and comments Estimate system.

Background technique

As China " in history most sternly " " the law of food safety " is in the formal implementation on October 1st, 2015, China's food safety Situation is gradually become better, and the Consciousness of food security of the people further increases." the law of food safety " Article 3 regulation: " food peace Full work effect prevention based on, risk management, whole-process control, society control altogether, establish science, stringent supervision and management system." this It is the ideals and principles that new method embodies.The essence of Xin Faxin is that risk management, and preamble work should be and put prevention first, Ying Jian Found quick early warning system.

It is complex to the detection program of the mushrooms such as salmonella in chicken and campylobacter at present, need large scale equipment branch It holds, detection time is longer, is not suitable for the detection of high-volume food chicken.

Summary of the invention

The purpose of the present invention is to provide salmonella risk evaluating system, this hairs in the chicken based on Fast Detection Technique Bright beneficial effect be established method atopic and high sensitivity, operation it is succinct, quick and convenient, at low cost.

The technical scheme adopted by the invention is that following the steps below:

Step 1: the method for using artificial contamination first collects salmonella and campylobacter at 15 DEG C, 25 DEG C and 37 DEG C Growth data；

Step 2: and then be fitted using amendment Gompertz model, Baranyi model and Logistic model, pass through Compare Akaike criterion and Sawa's Bayesian criterion selects optimal first-level model to correct Gompertz model；

Step 3: the growth parameter(s) obtained based on first-level model constructs second-level model, by utilizing deviation factors (Bf), standard The true factor (Af), mean square error (MSE), root-mean-square error (RMSE), median relative error (MRE), average relative error are exhausted To value (MARE), prediction standard error (%SEP) and the coefficient of determination (R²) carry out model verifying；

Step 4: obtained second-level model reliability with higher and predictive ability；

Step 5: salmonella and contamination by Campylobacter in monitoring chicken, with growth prediction model to salmonella Growth with campylobacter carries out quantitative analysis, and Quantitative Risk Assessment is unfolded in the prediction model based on monitoring data and building.

Detailed description of the invention

Fig. 1 is the fit solution figure of growing state and first-level model of the salmonella under condition of different temperatures in chicken；

Fig. 2 is the fit solution figure of growing state and first-level model of the campylobacter under condition of different temperatures in chicken.

Specific embodiment

The present invention is described in detail With reference to embodiment.

Salmonella risk evaluating system in chicken based on Fast Detection Technique, comprising the following steps:

Step 1: the method for using artificial contamination first collects salmonella and campylobacter at 15 DEG C, 25 DEG C and 37 DEG C Growth data；

Step 2: and then be fitted using amendment Gompertz model, Baranyi model and Logistic model, pass through Compare Akaike criterion and Sawa's Bayesian criterion selects optimal first-level model to correct Gompertz model；

Step 3: the growth parameter(s) obtained based on first-level model constructs second-level model, by utilizing deviation factors (Bf), standard The true factor (Af), mean square error (MSE), root-mean-square error (RMSE), median relative error (MRE), average relative error are exhausted To value (MARE), prediction standard error (%SEP) and the coefficient of determination (R²) carry out model verifying；

Step 4: obtained second-level model reliability with higher and predictive ability；

Step 5: salmonella and contamination by Campylobacter in monitoring chicken, with growth prediction model to salmonella Growth with campylobacter carries out quantitative analysis, and Quantitative Risk Assessment is unfolded in the prediction model based on monitoring data and building.

The present invention completes the Quantitative Risk Assessment of salmonella in chicken；Because lacking the dose response mould of campylobacter Type can not temporarily carry out the Quantitative Risk Assessment of campylobacter in chicken.Using establishing rapid detection method, sampling monitoring chicken In meat production in links salmonella pollution condition；For the uncertain factor in better simulated environment system, According to influence Salmonella growth various factors the characteristics of and data distribution, select suitable probability-distribution function, adopt Relevant parameter is simulated with Monte Carlo simulation；The cross contamination situation in production process is considered simultaneously, based on constructed Microbiology Growth Prediction Model, in chicken salmonella carry out risk assessment, analyze chicken from processing factory to dining table A possibility that constituting a threat in unpredictable situation on different crowd in entire food chain because of pollution salmonella and influence journey Degree, formulates reasonable food safety management strategy.

1. the method for the present invention experiment condition:

(1) LAMP condition: when design of primers, salmonella uses invA gene, and campylobacter jejuni uses ORC-F gene Carry out design of primers.

(2) amplification system: 10 × ThermoPo l Buffer, 2.5 μ L, 2.5mmol/L dNTP 5 μ L, 100mmol/L MgSO4 2μL,1.6μmol/L FIP 1μL,1.6μmol/L BIP 1μL,0.2μmol/L F3 1μL,0.2μmol/L B3 1 2 μ L, 8U/ μ L Bst DN A polymerase of μ L, 1mmo l/L glycine betaine (Betaine) 1 μ L, 2.5 μ L. of DNA profiling

(3) sensitivity: the sensitivity of salmonella and campylobacter jejuni is respectively 420CFU/ pipe and 200CFU/ pipe.

(4) result judgement is simple, does not need special equipment, and naked eyes are observable yin and yang attribute experimental result:

2. establishing the growth kinetics model of salmonella and campylobacter in chicken meat product, and model is carried out reliable The verifying of property and predictive ability；

(1) building of one step growth model

Compared by screening, the life using modified Gompertz model to salmonella in chicken and campylobacter Length is fitted: modified Gompertz model is as follows:

N_t=N₀+(N_max-N₀)*exp{-exp[(2.718μ_max/(N_max-N₀))*(λ-t)+1]}

Fig. 1, Fig. 2 respectively show growing state under condition of different temperatures of salmonella and campylobacter in chicken and The fit solution of first-level model.Table 1 be in chicken salmonella and campylobacter in the growth parameter(s) of first-level model.

Table 1

(2) building of diauxy model

Salmonella:

Ln (LT)=- 0.031T+3.5788

Campylobacter:

Ln (LT)=- 0.0451T+3.6851

(3) verifying of growth prediction model

Utilize Bf, Af, MSE, RMSE, MRE, MARE, %SEP and R²Model verifying is carried out, the experimental results showed that, this project In constructed model reliability with higher and predictive ability, can be used for microbiological risk assessment in the chicken of next step The growing state of salmonella and campylobacter calculates.Specific certificate parameter value such as the following table 2:

The certificate parameter value of 2 salmonella of table and campylobacter growth prediction model

3. in sampling monitoring chicken production in links salmonella pollution condition, and utilize@Risk software pair Salmonella and campylobacter in chicken meat product have carried out risk assessment

This research samples in batches from Qingdao, Weifang, Tai'an, Jining, Linyi Deng Di chicken processing factory, living body before slaughter 118 parts are respectively sampled with chicken after refrigeration, amounts to 236 parts of samples.Sample is quick using the LAMP established after increasing bacterium, separation Detection method, carries out the detection of salmonella and campylobacter, and is verified according to the method for GB 4789.4.Testing result Such as the following table 3:

The pollution condition of salmonella and campylobacter in 3 chicken meat sample of table

The risk assessment of salmonella mainly includes following four step in chicken --- and harm identification, harm describe, are sudden and violent Dew assessment and risk are described as follows table 4.

Risk assessment process of the salmonella from factory to dining table in 4 chicken of table

Dose-response model refers to β-Poisson model that CAC recommends, i.e.,

Wherein, α=0.3126, β=2885.

In risk assessment processes, using probability distribution different in@Risk software (RiskPert, RiskPossion, RiskDiscrete, RiskBinomial, RiskUniform etc.) on to influence Salmonella growth parameter (including pollution Rate, pollution level, temperature, time, cross contamination etc.) data are simulated, and it is each in the actual production process preferably to assess Size of a link to venture influence degree caused by salmonella.When scenario simulation, Latin is passed through using Monte Carlo simulation The hypercube methods of sampling carries out 10000 scenario simulations.To guarantee that identical random number sequence can be repeated, in simulation It is preceding that generating random number seed is fixed as 1.

By Monte Carlo simulation, according to the monitoring data in this research, it is known that salmonella-polluted rate is in chicken 9.37%-37.9%, average out to 21.67%.

The contaminated bacteria of sample is dense in 0.0048log CFU/g-4.76log CFU/g, and average value is 2.28log CFU/g, For chicken after cross contamination, the dense range of bacterium is extended to 0.0048log CFU/g-7.03log CFU/g, and average value rises to 2.36log CFU/g.Cross contamination increases risk brought by salmonella.

Risk evaluation result is shown, because salmonella and campylobacter bring year calculated risk are in our province chicken 2.25*10^-4, the illness ratio of every 100,000 people is 22.5, and annual expected number of patients is respectively 22175 people.Campylobacter is because lacking Few dose-response model, can not temporarily carry out Quantitative microbial risk assessment work.

The above is only not to make limit in any form to the present invention to better embodiment of the invention System, any simple modification that embodiment of above is made according to the technical essence of the invention, equivalent variations and modification, Belong in the range of technical solution of the present invention.

Claims (1)

1. salmonella risk evaluating system in the chicken based on Fast Detection Technique, it is characterised in that according to the following steps into Row:

Step 1: the method for using artificial contamination first is collected salmonella and campylobacter and is grown at 15 DEG C, 25 DEG C and 37 DEG C Data；

Step 2: and then be fitted using amendment Gompertz model, Baranyi model and Logistic model, by comparing Akaike criterion and Sawa's Bayesian criterion select optimal first-level model to correct Gompertz model；

Step 3: the growth parameter(s) obtained based on first-level model, construct second-level model, by using deviation factors, the accurate factor, Mean square error, root-mean-square error, median relative error, average absolute relative error, prediction standard error and the coefficient of determination Carry out model verifying；

Step 4: obtained second-level model reliability with higher and predictive ability；

Step 5: salmonella and contamination by Campylobacter in monitoring chicken, with growth prediction model to salmonella and curved The growth of curved bar bacterium carries out quantitative analysis, and Quantitative Risk Assessment is unfolded in the prediction model based on monitoring data and building.