CN102297930A - Method for identifying and predicting freshness of meat - Google Patents
Method for identifying and predicting freshness of meat Download PDFInfo
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- CN102297930A CN102297930A CN2011102036926A CN201110203692A CN102297930A CN 102297930 A CN102297930 A CN 102297930A CN 2011102036926 A CN2011102036926 A CN 2011102036926A CN 201110203692 A CN201110203692 A CN 201110203692A CN 102297930 A CN102297930 A CN 102297930A
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Abstract
The invention discloses a method for identifying and predicting the freshness of meat, comprising the following steps: putting a meat sample in a closed container, adsorbing the gas of the sample through a built-in pump in a sensor channel of an electronic nose, letting the gas and the sensor be subject to reaction to obtain response signals; then carrying out sense evaluation, determination of volatile basic and content determination of microbe on the detected sample; carrying out feature selection and extraction on the response signals of the electronic nose; and establishing a mathematical model of the relationship among the response signal of the electronic nose and the storage time of the meat, scores of the sense evaluation, the volatile basic content and the content of microbe with neural network. According to the invention, the freshness of meat can be identified and predicted with the electronic nose effectively without pretreatment, the analysis result is objective and accurate, the operation is simple, the cost is low, and the process is rapid without damage to the meat. The invention has important economic value to the field of meat processing, marketing, and detection.
Description
Technical field
The present invention relates to a kind of identification and the method for predicting the meat freshness.
Background technology
In recent years, along with living standard is improved, people are also more and more higher to the taste quality requirement of meat, and the freshness of meat is the principal element of decision meat taste quality.For the evaluation of meat freshness, existing both at home and abroad have sensory review, total volatile basic nitrogen content detection and the content of microorganisms that generally adopts detects.Sensory review's method is often evaluated the influence of expert's factors such as experience, psychology and physiology, different teacher of the evaluating is because influences such as its hobby, mood, sex and sense organ sensitivity, may be difficult to obtain consistent evaluation result, so the accuracy of evaluation result often is difficult to guarantee; Total volatile basic nitrogen and content of microorganisms detect and all belong to the experimental technique that damage is arranged, complex operation step, and detection time is long, and at the corrupt initial stage of meat, is difficult to detect its variation.In recent years, also have some scholars to begin to attempt carrying out the detection of smell with vapor-phase chromatography (GC), gas chromatography and mass spectromentry coupling technique (GC-MS) and freshness analyzer, though the corrupt initial stage is also had excellent sensitivity, but these detection methods all diminish, and the testing cost costliness of chromatogram mass-spectrometric technique, sense cycle is also long, though and the freshness detector is very fast detection time, but higher to testing environment and experimenter's competency profiling, disturbed easily.
Summary of the invention
The objective of the invention is to overcome the deficiencies in the prior art, the method for a kind of identification with prediction meat freshness is provided.Analysis result of the present invention is objective and accurate, and is simple to operate, with low cost, the identification of quick nondestructive and the method for predicting the meat freshness.Identification provided by the invention and the method for predicting the meat freshness, be with an Electronic Nose device based on metal oxide sensor, it has comprised 10 sensors, each sensor all has different responses to the different odor material, finally the data of catching in conjunction with 10 sensors provide the judgement of an integral body to institute's detected object.
Identification is as follows with the step of the method for prediction meat freshness:
1) be quality that the meat sample of M is placed in the closed container that volume is V, the airtight time is T, the vessel top air body reaches after the balance, head space gas is absorbed in the sensor array passage sensor array and the head space gas signal that meets with a response that reacts by the Electronic Nose internal pump; The sensor response signal is conductivity G and the conductivity G of sensor through pure air time the after sensor touches head space gas
0Ratio, i.e. G
/G
0Sampling time is 60 s; Per 1 s of each sensor gathers a secondary data in the sensor array, and during sampling, sample gas is adsorbed in the sensor passage by the speed of Electronic Nose internal pump with 400 ml/min, through being discharged by outlet behind the sensor array;
2) after the detection by electronic nose, the sample that detected is carried out sensory evaluation, total volatile basic nitrogen mensuration and content of microorganisms at once and detect;
3) after experiment finishes, on computers data are handled; Extract the 50th s Electronic Nose response signal as raw data, utilize principal component analysis (PCA) and progressively discriminatory analysis raw data matrix is carried out feature selecting and feature extraction, thereby reduce redundant data, reach dimensionality reduction and do in order to improve data-handling efficiency;
4) with raw data and above-mentioned steps 3) data after handling are as eigenwert, adopt neural network to set up the mathematical model that concerns between storage time, sense organ score, total volatile basic nitrogen content and the content of microorganisms of Electronic Nose response signal and meat sample, thereby reach the purpose of only utilizing Electronic Nose just can effectively discern and predict the meat freshness.
Described step 1) is: head space gas evaporates into the closed container head space by the meat sample gas and forms; The Electronic Nose sensor array that uses is made up of 10 metal oxide sensors, per 1 s of each sensor gathers a secondary data, during sampling, sample gas is adsorbed in the sensor passage by the speed of Electronic Nose internal pump with 400 ml/min, through being discharged by outlet behind the sensor array; Sample quality M becomes fixed proportion to concern M with the volume V of closed container with airtight time T: V: T=25: 500: 5; The detection by electronic nose of sample is finished under 25 ℃ of room temperatures.
Described step 2) be: sensory evaluation is marked from color and luster, elasticity, viscosity and smell four aspects by prior 8 professionals through training, and standards of grading are with reference to standard GB/T 5009.44-and 2003.Each index is the freshest to be 5 minutes, inferior fresh be 3 minutes, beginning obvious corruption is 1 minute, sensory evaluation is consistent with the detection by electronic nose time detection time; Total volatile basic nitrogen is measured and is adopted the semimicro nitriding, and method step is with reference to standard GB/T 5009.44-and 2003, detection time and detection by electronic nose are synchronous; Microbe culture adopts nutrient agar, and method step is with reference to standard GB/T 4789.2-and 2008, detection time and detection by electronic nose are synchronous.
Described step 3) is: raw data is the response signal of 10 sensor the 50th s, i.e. one 10 dimensional vector, utilize respectively principal component analysis (PCA) and progressively discriminatory analysis this 10 dimensional vector is carried out feature selecting and feature extraction.When using principal component analysis (PCA) to carry out feature selecting and feature extraction, only extract preceding 4 major components, obtain one 4 dimensional vector; When using 4 kinds of stepwise discriminant analysis methods to carry out feature selecting and feature extraction, these 4 kinds of methods are screened raw data according to Wilks ' lambda, unaccounted variance, mahalanobis distance and F value respectively, remove reciprocation between sensor, only keep necessary sensing data.
Described step 4) is: for obtaining preferable neural network model, respectively with the input layer of the data after handling in Electronic Nose raw data and the described step 3) as neural network model, set up the mathematical model that concerns between storage time, sense organ score, total volatile basic nitrogen content and the content of microorganisms of Electronic Nose response signal and meat sample, for investigating the modeling effect of above-mentioned different input layer data, calculate the standard error Se of storage time, sense organ score, total volatile basic nitrogen content and the content of microorganisms of every kind of model prediction meat sample, it is defined as follows:
Se wherein: standard error; N: sample size;
: actual value;
: predicted value; Df: degree of freedom.
The present invention can effectively differentiate the meat of different qualities, its sensitivity, reliability and repeatability all improve a lot, only need utilize Electronic Nose just can discern and predict the meat freshness fast, need not pre-treatment, analysis result is objective and accurate, simple to operate, with low cost, quick nondestructive has great economic worth for processing, sale and the detection range of meat.
Description of drawings
The Electronic Nose structural representation that uses among Fig. 1 the present invention;
Sensor response signal in Fig. 2 example of the present invention;
Storage fate prediction curve in Fig. 3 example of the present invention;
Sense organ score prediction curve in Fig. 4 example of the present invention;
Total volatile basic nitrogen content prediction curve in Fig. 5 example of the present invention;
Content of microorganisms prediction curve in Fig. 6 example of the present invention.
Embodiment
Identification comprises the steps: with the method for prediction meat freshness
1) be quality that the meat sample of M is placed in the closed container that volume is V, the airtight time is T, the vessel top air body reaches after the balance, head space gas is absorbed in the sensor array passage sensor array and the head space gas signal that meets with a response that reacts by the Electronic Nose internal pump; The sensor response signal is conductivity G and the conductivity G of sensor through pure air time the after sensor touches head space gas
0Ratio, i.e. G
/G
0Sampling time is 60 s; Per 1 s of each sensor gathers a secondary data in the sensor array, and during sampling, sample gas is adsorbed in the sensor passage by the speed of Electronic Nose internal pump with 400 ml/min, through being discharged by outlet behind the sensor array;
2) after the detection by electronic nose, the sample that detected is carried out sensory evaluation, total volatile basic nitrogen mensuration and content of microorganisms at once and detect;
3) after experiment finishes, on computers data are handled; Extract the 50th s Electronic Nose response signal as raw data, utilize principal component analysis (PCA) and progressively discriminatory analysis raw data matrix is carried out feature selecting and feature extraction, thereby reduce redundant data, reach dimensionality reduction and do in order to improve data-handling efficiency;
4) with raw data and above-mentioned steps 3) data after handling are as eigenwert, adopt neural network to set up the mathematical model that concerns between storage time, sense organ score, total volatile basic nitrogen content and the content of microorganisms of Electronic Nose response signal and meat sample, thereby reach the purpose of only utilizing Electronic Nose just can effectively discern and predict the meat freshness.
Described step 1) is: head space gas evaporates into the closed container head space by the meat sample gas and forms; The Electronic Nose sensor array that uses is made up of 10 metal oxide sensors, per 1 s of each sensor gathers a secondary data, during sampling, sample gas is adsorbed in the sensor passage by the speed of Electronic Nose internal pump with 400 ml/min, through being discharged by outlet behind the sensor array; Sample quality M becomes fixed proportion to concern M with the volume V of closed container with airtight time T: V: T=25: 500: 5; The detection by electronic nose of sample is finished under 25 ℃ of room temperatures.
Described step 2) be: sensory evaluation is marked from color and luster, elasticity, viscosity and smell four aspects by prior 8 professionals through training, and standards of grading are with reference to standard GB/T 5009.44-and 2003.Each index is the freshest to be 5 minutes, inferior fresh be 3 minutes, beginning obvious corruption is 1 minute, sensory evaluation is consistent with the detection by electronic nose time detection time; Total volatile basic nitrogen is measured and is adopted the semimicro nitriding, and method step is with reference to standard GB/T 5009.44-and 2003, detection time and detection by electronic nose are synchronous; Microbe culture adopts nutrient agar, and method step is with reference to standard GB/T 4789.2-and 2008, detection time and detection by electronic nose are synchronous.
Described step 3) is: raw data is the response signal of 10 sensor the 50th s, i.e. one 10 dimensional vector, utilize respectively principal component analysis (PCA) and progressively discriminatory analysis this 10 dimensional vector is carried out feature selecting and feature extraction.When using principal component analysis (PCA) to carry out feature selecting and feature extraction, only extract preceding 4 major components, obtain one 4 dimensional vector; When using 4 kinds of stepwise discriminant analysis methods to carry out feature selecting and feature extraction, these 4 kinds of methods are screened raw data according to Wilks ' lambda, unaccounted variance, mahalanobis distance and F value respectively, remove reciprocation between sensor, only keep necessary sensing data.
Described step 4) is: for obtaining preferable neural network model, respectively with the input layer of the data after handling in Electronic Nose raw data and the described step 3) as neural network model, set up the mathematical model that concerns between storage time, sense organ score, total volatile basic nitrogen content and the content of microorganisms of Electronic Nose response signal and meat sample, for investigating the modeling effect of above-mentioned different input layer data, calculate the standard error Se of storage time, sense organ score, total volatile basic nitrogen content and the content of microorganisms of every kind of model prediction meat sample, it is defined as follows:
Se wherein: standard error; N: sample size;
: actual value;
: predicted value; Df: degree of freedom.
Because different types of meat volatilization gas composition has difference, when practical application is of the present invention, at different types of meat, at first need to set up the mathematical model of every kind of meat, model is in case after setting up, need not the physics and chemistry test and only need pass through detection by electronic nose, just can dope these 4 kinds of physical and chemical indexs of storage time, sense organ score, total volatile basic nitrogen content and content of microorganisms of meat, thereby judge the freshness of meat fast.
Embodiment
The present invention is applicable to that the freshness that various livestock and poultry meat such as beef, the flesh of fish, chicken, shrimp, pork and seawater produce meat detects.Following embodiment is convenient to understand better the present invention, but does not limit the present invention.
The present invention mainly is the data processing and the modeling method of Electronic Nose.Used Electronic Nose based on the metal oxide sensor array is available from German Airsense instrument company among the following embodiment, and model is PEN 2, and its sensor array is made up of 10 sensors, and is as shown in table 1 below.
Table 1 sensor array and performance characteristics thereof
The function of these sensors is that the effect of different scent molecules on its surface is converted into the physical signalling that can measure.Electronic Nose structure and workflow are as shown in Figure 1.During sampling, sample gas is adsorbed to the sensor passage from inflow point by the speed of an internal pump with 400 ml/min, through being discharged by outlet behind the sensor array.Reference gas (zero gas) is through the clean air behind the activated carbon filtration, speed with 600 ml/min pumps into by another pump, wherein with the data rate stream of 400 ml/min through sensor array, sensor array is cleaned, make the response signal of sensor revert to zero.
Test sample is a beef in the example of the present invention, fresh beef is buied and stored 0,3,5,7,10,12 and 14 day in 2 ℃ of refrigerators, detects 20 samples every day, every duplicate samples 25 g.Measure wanting test sample to carry out Electronic Nose earlier, earlier sample is placed in the 500 ml beakers, rest on 25 ℃ of following 5 min of room temperature with the tinfoil diaphragm seal, with sampling pump the head space gas in the container is imported in the sensor array reaction chamber then, sensor and gas take place to send out and should obtain corresponding response signal.This signal is changed into numeral by capture card and is input to computing machine.The Electronic Nose sensor is gathered a signal and preserve per 1 second, finish the detection of a sample after, each sensor of Electronic Nose cleans automatically, to carry out detection next time, the time of cleaning was 70 seconds.Fig. 2 is the response curve during 10 sensor test fresh beef of Electronic Nose in the example of the present invention, horizontal ordinate is the sampling time, and ordinate is that sensor touches conductivity G and the conductivity G of sensor through pure air time the behind the head space gas for the sensor response signal
0Ratio, i.e. G
/G
0.
Then the sample that detected being carried out sensory evaluation, total volatile basic nitrogen mensuration and content of microorganisms detects.
Sensory evaluation scores is with standard GB/T 5009.44-and 2003 is criterion, is marked from color and luster, elasticity, viscosity and smell four aspects by prior 8 professionals through training, and standards of grading are as shown in table 2.
Table 2 sensory evaluation scores standard scale
Each index is the freshest to be 5 minutes, inferior fresh be 3 minutes, beginning obvious corruption is 1 minute.Sensory evaluation scores is consistent with the detection by electronic nose time detection time, also is storage the 0th, 3,5,7,10,12,14 day, totally 7 times.
Total volatile basic nitrogen is measured and is adopted the semimicro nitriding, and method step is with reference to standard GB/T 5009.44-and 2003, detection time and detection by electronic nose are synchronous.Microbe culture adopts nutrient agar, and method step is with reference to standard GB/T 4789.2-and 2008.Detection time and detection by electronic nose are synchronous.
With computing machine the data of gained are handled: extract the 50th s Electronic Nose signal as raw data, i.e. one 10 dimensional vector, utilize respectively principal component analysis (PCA) and progressively discriminatory analysis respectively this 10 dimensional vector is carried out feature selecting and feature extraction.When using principal component analysis (PCA) to carry out feature selecting and feature extraction, only extract preceding 4 major components, obtain one 4 dimensional vector, the accumulative total variance contribution ratio is 99.9%; When using 4 kinds of stepwise discriminant analysis methods to carry out feature selecting and feature extraction, these 4 kinds of methods are screened original 10 sensing datas according to Wilks ' lambda, unaccounted variance, mahalanobis distance and F value respectively, remove reciprocation between sensor, only keep necessary sensing data.
With the data after raw data and the above-mentioned processing respectively as the sensor response signal, adopt generalized regression nerve networks to set up projected relationship between storage time, sense organ score, total volatile basic nitrogen content and the content of microorganisms of sensor response signal and meat sample, for investigating the modeling effect of different pieces of information disposal route, calculate the standard error Se of every kind of method prediction, it is defined as follows:
Se wherein: standard error; N: sample size;
: actual value;
: predicted value; Df: degree of freedom.
Fig. 3-6 is respectively the prediction curve that neural network model is stored fate, sense organ score, total volatile basic nitrogen content and content of microorganisms in the example test of the present invention to beef.Horizontal ordinate is every index actual measured value, and ordinate is a predicted value.Departing from the degree of Y=X curve according to the data point of each sample comes the predicted value of each index of judgement sample to depart from the amplitude of actual measured value.
For storage time, when with raw data as input vector, it is best to predict the outcome, Se is 1.36(days);
For the sense organ score, when with raw data as input vector, it is best to predict the outcome, Se is 1.31;
For total volatile basic nitrogen content, when with raw data as input vector, it is best to predict the outcome, Se is 4.64(mg
/100g);
For content of microorganisms, when with raw data as input vector, it is best to predict the outcome, Se is 1.612(10
6CFU
/G).
Therefore data point substantially all is to be centered around near the Y=X, illustrates that prediction effect is all better.
Claims (5)
1. the method for an identification and prediction meat freshness is characterized in that its step is as follows:
1) be quality that the meat sample of M is placed in the closed container that volume is V, the airtight time is T, the vessel top air body reaches after the balance, head space gas is absorbed in the sensor array passage sensor array and the head space gas signal that meets with a response that reacts by the Electronic Nose internal pump; The sensor response signal is conductivity G and the conductivity G of sensor through pure air time the after sensor touches head space gas
0Ratio, i.e. G
/G
0Sampling time is 60s; Per 1 s of each sensor gathers a secondary data in the sensor array, and during sampling, sample gas is adsorbed in the sensor passage by the speed of Electronic Nose internal pump with 400ml/min, through being discharged by outlet behind the sensor array;
2) after the detection by electronic nose, the sample that detected is carried out sensory evaluation, total volatile basic nitrogen mensuration and content of microorganisms at once and detect;
3) extract the 50th s Electronic Nose response signal as raw data, utilize principal component analysis (PCA) and progressively discriminatory analysis raw data matrix is carried out feature selecting and feature extraction, thereby reduce redundant data, reach dimensionality reduction and do in order to the raising data-handling efficiency;
4) with raw data and above-mentioned steps 3) data after handling are as eigenwert, adopt neural network to set up the mathematical model that concerns between storage time, sense organ score, total volatile basic nitrogen content and the content of microorganisms of Electronic Nose response signal and meat sample, thereby reach the purpose of only utilizing Electronic Nose just can effectively discern and predict the meat freshness.
2. a kind of identification according to claim 1 and the method for predicting the meat freshness, it is characterized in that described step 1) is: head space gas evaporates into the closed container head space by the meat sample gas and forms; The electronic sensor array that uses is made up of 10 metal oxide sensors; Sample quality M becomes fixed proportion to concern M with the volume V of closed container with airtight time T: V: T=25: 500: 5; The detection by electronic nose of sample is finished under 25 ℃ of room temperatures.
3. a kind of identification according to claim 1 and the method for predicting the meat freshness, it is characterized in that described step 2) be: sensory evaluation is marked from color and luster, elasticity, viscosity and smell four aspects by prior 8 professionals through training, standards of grading are with reference to standard GB/T 5009.44-and 2003, each index is the freshest to be 5 minutes, inferior fresh be 3 minutes, beginning obvious corruption is 1 minute, and sensory evaluation is consistent with the detection by electronic nose time detection time; Total volatile basic nitrogen is measured and is adopted the semimicro nitriding, and method step is with reference to standard GB/T 5009.44-and 2003, detection time and detection by electronic nose are synchronous; Microbe culture adopts nutrient agar, and method step is with reference to standard GB/T 4789.2-and 2008, detection time and detection by electronic nose are synchronous.
4. a kind of identification according to claim 1 and the method for predicting the meat freshness, it is characterized in that described step 3) is: raw data is the response signal of 10 sensor the 50th s, i.e. one 10 dimensional vector, utilize respectively principal component analysis (PCA) and progressively discriminatory analysis this 10 dimensional vector is carried out feature selecting and feature extraction, when using principal component analysis (PCA) to carry out feature selecting and feature extraction, only extract preceding 4 major components, obtain one 4 dimensional vector; When using 4 kinds of stepwise discriminant analysis methods to carry out feature selecting and feature extraction, these 4 kinds of methods are screened raw data according to Wilks ' lambda, unaccounted variance, mahalanobis distance and F value respectively, remove reciprocation between sensor, only keep necessary sensing data.
5. a kind of identification according to claim 1 and the method for predicting the meat freshness, it is characterized in that described step 4) is: for obtaining preferable neural network model, respectively with the input layer of the data after handling in Electronic Nose raw data and the described step 3) as neural network model, set up the storage time of Electronic Nose response signal and meat sample, the sense organ score, the mathematical model that concerns between total volatile basic nitrogen content and the content of microorganisms, for investigating the modeling effect of above-mentioned different input layer data, calculate the storage time of every kind of model prediction meat sample, the sense organ score, the standard error Se of total volatile basic nitrogen content and content of microorganisms, it is defined as follows:
Se wherein: standard error; N: sample size;
: actual value;
: predicted value; Df: degree of freedom.
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