CN103389324A - Prawn freshness detection method based on smell analysis technology - Google Patents
Prawn freshness detection method based on smell analysis technology Download PDFInfo
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Abstract
The invention relates to a prawn freshness detection method based on a smell analysis technology. The detection method comprises (1) a step of sample learning, wherein prawn samples with different freshness degrees are selected and disposed in a cavity, sample gases in the cavity are fed into an air chamber of an electronic nose system, detected data are transmitted into a signal-to-noise ratio spectrum analysis system, a resonance signal-to-noise ratio curve of the detected data is obtained by adjusting noise intensity, and according to the resonance signal-to-noise ratio curve, the characteristic peak maximum SNRMax is obtained and a prawn freshness detection model is constructed; and (2) a step of disposing a prawn to be detected in the cavity, feeding the gas in the cavity into the air chamber of the electronic nose system, and obtaining a storage time according to the current characteristic peak maximum SNRMax and the prawn freshness detection model. When the storage time is larger than 2, the TVBN value of the prawn already exceeds the national standards and the prawn cannot be eat. The detection method has strong operability, good accuracy and high sensitivity.
Description
Technical field
The present invention relates to a kind of prawn Noninvasive Measuring Method of Freshness.
Background technology
Prawn, formal name used at school " prawn ", also claim " Chinese prawn ".Have another name called " prawn ", " angle shrimp ", " white shrimp ", " phoenix shrimp ".Its meat is plump, and delicious flavour, be rich in protein, and is very popular.Prawn contains abundant multiple nitrogen base acids, and the normal replication effect to human chitinase and nucleic acid configuration and DNA such as micro-Fe, Zn, Cu, Cr, Mn, have material impact.Separately predict the ancient amount of Se element higher, for general material can't be obtained, selenium is a kind of trace element useful to human body, the selenium average of normal human serum is 22.9g%, its function day is aobvious important, and very large on the body immunity impact, AIDS is immunodeficiency syndrome, the patient is lower by 54% than selenium level in the human normal plasma, and in erythrocyte, the selenium amount low 24%; He is 15.8g% as colorectal cancer patients serum selenium average, and patients with gastric cancer is 15 μ g%, and hepatitis is 14.5 μ g%, and liver cirrhosis patient is 13.6 μ g%.Because selenium can prevent hepatonecrosis, have and promote people's bulk-growth, delay senility, remove heavy metal toxicity and the effect such as anticancer, and in prawn, Se content is more, merits attention.
Food is in storage, processing and transportation, and due to self effect with external environment, microorganism etc., aquatic food is putrid and deteriorated, produces bad flavor, causes quality to descend, and impact is edible.Therefore, the food quality evaluation seems quite important.The food quality management link need to drop into certain manpower, and especially evaluation result mainly relies on subjectivity evaluation, has that complicacy, monitoring difficulty are large, level requires high.
Shrimp contains abundant histidine, and this is to make its delicious principal ingredient.But shrimp is in case dead, and histidine namely is decomposed into harmful histamine material by bacterium.Also have, often contain pathogenic bacteria and noxious material in the stomach of shrimp, after death very easily putrid and deteriorated.Particularly along with the prolongation of shrimp death time, the toxin of shrimp cylinder accumulation is more and more, has eaten and just there will be intoxicating phenomenon.
Summary of the invention
The deficiency that operability is strong, accuracy is poor, sensitivity is lower that detects in order to overcome existing prawn freshness, the invention provides a kind of workable, accuracy good, the higher Noninvasive Measuring Method of Freshness of the prawn based on fire detection of sensitivity.
The technical solution adopted for the present invention to solve the technical problems is:
A kind of Noninvasive Measuring Method of Freshness of prawn based on fire detection, described detection method comprises the following steps:
1) sample learning process, select the prawn sample of different grade of freshness, described prawn sample is placed in cavity, sample gas in cavity is passed into the air chamber of electric nasus system, sensor in described air chamber obtains detecting data, to detect data and be input to the signal to noise ratio (S/N ratio) spectral analysis system, regulate the resonance signal to noise ratio (S/N ratio) curve that noise intensity obtains detecting data, obtain characteristic peak maximal value SNRMax according to described resonance signal to noise ratio (S/N ratio) curve, build the prawn freshness detection model of described characteristic peak maximal value SNRMax and storage time Time:
Time=(SNRMax+68.25713）/3.52485 （1）
2), prawn to be detected is placed on cavity, gas in cavity is passed into the air chamber of electric nasus system, to detect data and be input to the signal to noise ratio (S/N ratio) spectral analysis system, regulate the resonance signal to noise ratio (S/N ratio) curve that noise intensity obtains detecting data, obtain characteristic peak maximal value SNRMax according to described resonance signal to noise ratio (S/N ratio) curve, according to current characteristic peak maximal value SNRMax, obtain storage time according to prawn freshness detection model, as storage time Time〉2, the TVBN value of expression prawn has exceeded national standard and can't eat again.
Further, in described step 1), sensor obtains detecting data and whether belongs to the abnormity point differentiation, and setting the data value that detects data is the amount X of random variation, has
X～N(μ,σ
2)
μ and σ are respectively average and the standard deviation of Electronic Nose single channel data, determine whether satisfied:
P(|x-μ|＞3σ)≤2-2Φ(3)
Wherein, P represents probability function, the probability of Φ (3) expression normal distribution when x=3, if meet, the current detection data are abnormity point, with its deletion, otherwise judge that current data is normal data.
Beneficial effect of the present invention is mainly manifested in: workable, accuracy is good, sensitivity is higher.
Description of drawings
Fig. 1 is the structural representation of detection by electronic nose system.
Fig. 2 is total volatile basic nitrogen measurement result schematic diagram in the prawn preserving process.
Fig. 3 is the sensor array response curve schematic diagram of detection system.
Fig. 4 is the PCA analysis of two-dimensional figure of the prawn Electronic Nose response of different preservation times.
Embodiment
The invention will be further described below in conjunction with accompanying drawing.
With reference to Fig. 1～Fig. 4, a kind of Noninvasive Measuring Method of Freshness of prawn based on fire detection, described detection method comprises the following steps:
1) sample learning process, select the prawn sample of different grade of freshness, described prawn sample is placed in cavity, sample gas in cavity is passed into the air chamber of electric nasus system, sensor in described air chamber obtains detecting data, to detect data and be input to the signal to noise ratio (S/N ratio) spectral analysis system, regulate the resonance signal to noise ratio (S/N ratio) curve that noise intensity obtains detecting data, obtain characteristic peak maximal value SNRMax according to described resonance signal to noise ratio (S/N ratio) curve, build the prawn freshness detection model of described characteristic peak maximal value SNRMax and storage time Time:
Time=(SNRMax+68.25713）/3.52485 （1）
2), prawn to be detected is placed on cavity, gas in cavity is passed into the air chamber of electric nasus system, to detect data and be input to the signal to noise ratio (S/N ratio) spectral analysis system, regulate the resonance signal to noise ratio (S/N ratio) curve that noise intensity obtains detecting data, obtain characteristic peak maximal value SNRMax according to described resonance signal to noise ratio (S/N ratio) curve, according to current characteristic peak maximal value SNRMax, obtain storage time according to prawn freshness detection model, as storage time Time〉2, the TVBN value of expression prawn has exceeded national standard and can't eat again.
Because the Electronic Nose data have reflected the situation of change of sample headspace gas in the testing process, along with characteristic gas flow in the growth head space of detection time is sucked away and reduces, therefore the detection by electronic nose signal first continues to rise, and starts that certain decline is arranged after reaching maximal value.But, because the disturbing factor in testing process exists, therefore need to reject the detection exceptional data point that detects in data, the tentation data value is the amount X of random variation, has
X～N(μ,σ
2)
μ and σ are respectively average and the standard deviation of Electronic Nose single channel data, through deriving, have
P(|x-μ|＞3σ)≤2-2Φ(3)
2-2Φ(3)=0.003
Wherein, P represents probability function, and the probability of Φ (3) expression normal distribution when x=3, drop on apart from 3 times of unexpected probability of standard deviation of average less than 0.3% because any one detects data, this probability is very little, therefore thinks that this data point is that abnormity point can be removed.The check of process abnormity point and the Electronic Nose data of removing are input in stochastic resonance system to be analyzed, and can effectively improve accuracy in detection.
In the present embodiment, after the prawn that will live is transported the use for laboratory clear water back and cleans up,, immediately with the frozen water sudden death, take every part of approximately pack into sterilized sealing freshness protection package and in 4 ℃ of lower preservations of 25g after draining; In preserving process, get sample every day one time, sample carried out sense organ, TVBN and Electronic Nose measure, and measures altogether 7 days, is designated as respectively 0,1,2,3,4,5,6 day.
The mensuration of total volatile basic nitrogen (TVBN): get shrimp and carry out the mensuration of total volatile basic nitrogen by GB/T5009.44-2003, result represents with milligram number nitrogenous in every 100g sample.Concrete operations: get 1 bag of prawn every day, shell and get edible part and rub, take 2 duplicate samples, every part of about 2g, carry out total volatile basic nitrogen and measure.
concrete operation step: take out one bag of prawn from refrigerator-freezer, shell and decaptitate, getting edible part rubs with pulverizer, taking approximately 2g prawn sample and pan paper together drops in distillation cascade, taking 10gMgO adds in distillation cascade, after installing distillation cascade, add approximately 100mL ultrapure water to distill, receiving tube is inserted absorbing volatile alkali nitrogen under the 50mL boric acid liquid level of (containing indicator), after liquid level increases to 100mL, receiving tube is shifted out under liquid level, and with ultrapure washing, drench receiving tube, approximately stop distillation after 30s, carry out titration with the hydrochloric acid of 0.1mol/L again, the hydrochloric acid volume V that record consumes.For getting rid of such environmental effects experiment accuracy, before carrying out the sample distillation at every turn, need to clean azotometer, then carry out blank test, namely add other equivalent reagent except sample to distill in distillation cascade, measure its salt acid consumption v.Finally, be the hydrochloric acid volume that the prawn sample consumes with (V-v), then be scaled TVB-N content, take mg/100g as unit.Parallel experiment every day 2 times, average as the measured value as this time total volatile basic nitrogen.
Volatile species and the content of the main test sample of Electronic Nose sensor array, each sensor has corresponding with it sensitive gas, and this experiment shares to 8 detecting devices, and its corresponding sensitive gas is in Table 1, and the system architecture schematic diagram is seen Fig. 1.
| Sensor number | Sensor model number | The |
| 1 | TGS-825 | Sulfide |
| 2 | TGS-821 | |
| 3 | TGS-826 | Ammonia |
| 4 | TGS-822 | Alcohol, toluene, dimethylbenzene etc. |
| 5 | TGS-842 | Hydrocarbon component gas (C1～C8) |
| 6 | TGS-813 | Methane, propane, |
| 7 | TGS-2610 | Propane, butane |
| 8 | TGS-2210 | Oxides of nitrogen |
Table 1 gas sensor array forms
Get 3 bags of prawns every day and be sub-packed in sample bottle, be positioned under room temperature, sealing 30min, accumulate certain volatile matter, then sample carried out the Electronic Nose test successively, carries out collection and the processing of data with computing machine.Each sample of every day all detects three times, scavenging period 15min, and sampling time 50s, the mean value of 3 duplicate samples is as last measurement result.
Concrete operation step: open electric nasus system power supply and computing machine, treat that sensor is with more than other equipment preheatings 30min, insert in the sensor passage that in sample bottle, escaping gas is drawn onto Electronic Nose by syringe needle, computing machine carries out the acquisition and processing of data, after sampling 50s finishes, save data, then syringe needle is extracted from sample bottle, by the inlet flow blowing air, sample residual gas in pipeline is cleaned up to baseline steady " making zero " finally, scavenging period is 15min.Afterwards, then carry out second and take turns Head-space sampling.Every survey is once complete, all will carry out the zero clearing work to sample channel.
Prawn can be accompanied by physical and chemical quality and change accordingly in preserving process, soften, organize the escaping gases such as putrid and deteriorated, growth of microorganism, the black change of outward appearance, generation ammonia flavor, urea flavor as musculature.Therefore, physical and chemical index that can be by measuring prawn and the data of detection by electronic nose gained its variation tendency of comparing, confirm that Electronic Nose is applied to the reliability of aquatic products Quality Detection.
The variation of total volatile basic nitrogen: in 4 ℃ of sealing preservation processes of prawn, total volatile basic nitrogen changes as Fig. 2.As shown in Figure 2, in 4 ℃ of sealing preservation processes of prawn, total volatile basic nitrogen changes obviously, and the variation of 4,5,6 days is slightly not obvious, and is consistent with results of sensory evaluation.Prawn total volatile basic nitrogen value at the 4th day in the preserving process of 7 days surpasses 30mg/100g, so first three sky meets the GB/2733 aquatic foods, freezes the requirement of animality aquatic products hygienic standard.
The response of Electronic Nose to the prawn smell: the prawn to the different preservation time carries out detection by electronic nose, the response diagram of 8 sensors of electron gain nose to the prawn smell.Fig. 3 is the response diagram of the 5th day sample of 4 ℃ of sealing preservations of detection by electronic nose prawn, and in Fig. 3, each curve represents the response of a sensor, and when the odoring substance of expression prawn passed through sensor passage, this respective signal value was with the situation of change of sample injection time.As seen from Figure 3, first to the stationary process of final sample gas from sample introduction, the response intensity of sensor strengthens gradually, then tends towards stability.Electronic Nose has obvious response to the smell of prawn, and each response is different, and 1, No. 4 sensor has higher response than other sensors.
The PCA of the Electronic Nose response of difference preservation time prawn analyzes: electric nasus system is analyzed metal sensor to the response signal of the volatile ingredient of different samples after, the raw data that obtains is the matrix ordered series of numbers of a multidimensional.Use the PCA method to carry out dimension-reduction treatment to the original multi-dimensional matrix data that obtain, representative characteristic variable is carried out linear analysis, thereby reach with less sensor response signal information data, and analyze under 4 ℃ of temperature conditions the correlativity that in the different preservation times, the prawn volatile flavor changes.This kind method has become most sensing type Electronic Nose Chemical Measurement data processing method commonly used.
Fig. 4 is the Electronic Nose response that adopts prawn of different preservation time of PCA methods analyst, Fig. 4 is X-Y scheme, it is that the information of the many indexs of sensor that will extract is carried out data-switching and dimensionality reduction that PCA analyzes, and the proper vector after dimensionality reduction is carried out linear classification, show finally main bidimensional or three-dimensional scatter diagram on the scatter diagram that PCA analyzes.As can be seen from Figure 4, the first principal component contribution rate is 82.04%, and the Second principal component, contribution rate is 12.46%, and total contribution rate is 94.50%.As shown in Figure 4, the escaping gas of prawn changes along with the variation of preservation time, and the prawn smell response of different time is broadly divided into 3 zones.The PCA X-Y scheme roughly can be distinguished the prawn of different preservation times.See that along the PC1 axle obvious differentiation was arranged in front 4 days, the 4th day and last two days; See with other times, obvious differentiation was arranged on the 4th day along the PC2 axle, this variation with above-mentioned traditional prawn index of fish freshness is consistent.
Be provided with in advance stochastic resonance system model dx/dt=5ax-2bx in computing machine
3+ 4MI (t)+D ξ (t), will
In input stochastic resonance system model, make the stochastic resonance system model produce accidental resonance;
Computing machine utilizes formula
Calculate output signal-to-noise ratio SNR; Wherein, A, M are constant, and t is the Brownian movement Particles Moving time, and x is the coordinate of Particles Moving, f is signal frequency, and D is external noise intensity, and N (t) grasps noise in being, Δ U is barrier height, and a and b are bistable state potential well parameter, and ξ (t) is the input external noise;
Extract the accidental resonance curve maximum value (SNRMax) of prawn sample as eigenwert, the eigenwert of different resting period prawn samples carried out linear fit, obtain prawn freshness detection model:
Time=(SNRMax+68.25713）/3.52485 （2）
As can be seen from Figure 2, as storage time Time〉2 the time, the TVBN value of prawn sample has exceeded national standard and can't eat again, therefore can derive according to formula (2), as Time〉2 the time, SNRMax 〉-61.20743, namely when detection by electronic nose numerical value greater than-61.20743 the time, the prawn sample is stale and edible not.
Claims (2)
1. Noninvasive Measuring Method of Freshness of the prawn based on fire detection, it is characterized in that: described detection method comprises the following steps:
1) sample learning process, select the prawn sample of different grade of freshness, described prawn sample is placed in cavity, sample gas in cavity is passed into the air chamber of electric nasus system, sensor in described air chamber obtains detecting data, to detect data and be input to the signal to noise ratio (S/N ratio) spectral analysis system, regulate the resonance signal to noise ratio (S/N ratio) curve that noise intensity obtains detecting data, obtain characteristic peak maximal value SNRMax according to described resonance signal to noise ratio (S/N ratio) curve, build the prawn freshness detection model of described characteristic peak maximal value SNRMax and storage time Time:
Time=(SNRMax+68.25713）/3.52485 （1）
2), prawn to be detected is placed on cavity, gas in cavity is passed into the air chamber of electric nasus system, to detect data and be input to the signal to noise ratio (S/N ratio) spectral analysis system, regulate the resonance signal to noise ratio (S/N ratio) curve that noise intensity obtains detecting data, obtain characteristic peak maximal value SNRMax according to described resonance signal to noise ratio (S/N ratio) curve, according to current characteristic peak maximal value SNRMax, obtain storage time according to prawn freshness detection model, as storage time Time〉2, the TVBN value of expression prawn has exceeded national standard and can't eat again.
2. the Noninvasive Measuring Method of Freshness of the prawn based on fire detection as claimed in claim 1, it is characterized in that: in described step 1), sensor obtains detecting data and whether belongs to the abnormity point differentiation, and setting the data value that detects data is the amount X of random variation, has
X～N(μ,σ
2)
μ and σ are respectively average and the standard deviation of Electronic Nose single channel data, determine whether satisfied:
P(|x-μ|＞3σ)≤2-2Φ(3)
Wherein, P represents probability function, the probability of Φ (3) expression normal distribution when x=3, if meet, the current detection data are abnormity point, with its deletion, otherwise judge that current data is normal data.
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| CN106596860A (en) * | 2016-12-19 | 2017-04-26 | 深圳市北测检测技术有限公司 | Automobile odor detection method and detection system |
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| CN104076069A (en) * | 2013-12-04 | 2014-10-01 | 浙江工商大学 | Pork storage time detection method |
| CN104897729A (en) * | 2015-06-04 | 2015-09-09 | 安徽农业大学 | Sorting method of storage time of melon slice tea by using electronic nose |
| CN106596860A (en) * | 2016-12-19 | 2017-04-26 | 深圳市北测检测技术有限公司 | Automobile odor detection method and detection system |
| CN108872493A (en) * | 2018-05-23 | 2018-11-23 | 浙江农林大学 | The anti-erroneous judgement detection system of foodstuff quality and method in a kind of refrigerator |
| CN108872493B (en) * | 2018-05-23 | 2021-02-19 | 浙江农林大学 | Misjudgment-preventing detection system and method for quality of food materials in refrigerator |
| CN112285296A (en) * | 2020-11-11 | 2021-01-29 | 重庆长安汽车股份有限公司 | Automobile interior part smell evaluation method based on electronic nose |
| CN113984946A (en) * | 2021-10-21 | 2022-01-28 | 江南大学 | Crayfish freshness detection method based on gas phase electronic nose and machine learning |
| CN113984946B (en) * | 2021-10-21 | 2022-12-27 | 江南大学 | Crayfish freshness detection method based on gas phase electronic nose and machine learning |
| CN114324781A (en) * | 2022-03-11 | 2022-04-12 | 中汽研汽车零部件检验中心(宁波)有限公司 | Intelligent sniffing method and system |
| CN114324781B (en) * | 2022-03-11 | 2022-12-02 | 中汽研汽车零部件检验中心(宁波)有限公司 | Intelligent sniffing method and system |
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Application publication date: 20131113 |