CN104132896A - Method for rapidly identifying adulterated meat - Google Patents
Method for rapidly identifying adulterated meat Download PDFInfo
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- CN104132896A CN104132896A CN201410335229.0A CN201410335229A CN104132896A CN 104132896 A CN104132896 A CN 104132896A CN 201410335229 A CN201410335229 A CN 201410335229A CN 104132896 A CN104132896 A CN 104132896A
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Abstract
The invention provides a method for rapidly identifying adulterated meat. The method comprises the following steps: 1, respectively preparing a pure meat sample, an adulterated meat sample and an unknown meat sample to be measured; 2, respectively acquiring the near infrared diffuse reflection spectra of the pure meat sample and the adulterated meat sample, continuously each of the above samples multiple times, and respectively taking the average value of obtained data as an original spectrum; 3, carrying out modeling wave band optimization selection on the original spectrum, and carrying out spectrum pretreatment on selected modeling wave bands; 4, extracting spectrum characteristic information by adopting a main component analysis process; 5, establishing an adulterated meat identifying model by adopting a discriminant analysis process; and 6, calculating the mahalanobis distance of each of the pure meat sample and the adulterated meat sample to determine the type of each of the pure meat sample and the adulterated meat sample; and 7, utilizing the adulterated meat identifying model to identify and analyze the sample to be measured, and calculating the mahalanobis distance of the sample to be measured in order to determine the type of the sample to be measured. The method can realize the rapid, accurate and stable qualitative classification discrimination of unknown meat.
Description
Technical field
The present invention relates to the adulterated differentiation of meat field, be specially the method for the adulterated meat of a kind of quick discriminating
,can realize the qualitative discrimination to adulterated meat.
Background technology
According to investigations, one of modal method of the adulterated fraud of meat is to utilize low value meat to serve as high value meat, along with meat price variance progressively widens, this " it is adulterated that economic interests drive " phenomenon is more and more common, as events such as " the false beef ", " the duck variable body mutton cubes roasted on a skewer " that expose repeatedly in recent years, " false mutton rolls ", with pork, duck, forging beef and mutton is current main adulterated means.Separately have report, it is all much assorted poultry meat that frozen mutton volume, the sliced mutton that sell the market of farm produce, Beijing has.A research discovery in somewhere, south last year, 25% meat exists adulterated.Although these data can not represent national integral level, it has opened the adulterated tip of the iceberg of meat.These lawbreaking activitiess not only serious infringement consumer's interests, and the interests of a lot of enterprises are encroached in this inequitable competition.On May 8th, 2013, Premier of the State Council presides over standing meeting in State Council, specializes in to dispose and does a good job of it the work such as agricultural production, guarantee market supply and price are steady, wherein proposes especially " sternly hitting the unlawful practices such as the adulterated sell-fake-products of meat products ".For the interests of Protection of consumer with guarantee a fair play environment, the importance that adulterated meat detects is apparent.
The common method that detects adulterated meat comprises organoleptic analysis, chromatographic technique and DNA technique etc.Wherein organoleptic analysis's result is subject to the interference of human factor and external environment very large, and result subjectivity is strong.And it is former flavoursome that adulterated meat is used essence and flavoring agent to cover mostly, and finished product is impalpable on sense organ; Chromatographic technique is complicated to sample pretreatment, time-consuming, needs to consume a large amount of solvents; And DNA technique is to adopt the rapid amplifying of PCR method to a small amount of thing DNA sequence dna to be detected, by the analysis to nucleotide sequence, mix the detection of pseudo-product, though accuracy is high, but complex operation, expense are high, consuming time, are difficult to adapt to the needs of fast detecting in batches, and are often restricted while using, ordinary people cannot use it to detect, and the scope of application is limited to.This is the deficiencies in the prior art part.
At present, the spectral technique that the near infrared technology of take is representative has that sample pre-treatments is simple, green, running cost is low, detection speed is fast, good stability, can realize the advantages such as on-line analysis, selectivity and antijamming capability are strong, is suitable for qualitative analysis fast.The present invention utilizes near infrared spectrum can effectively to adulterated meat, carry out qualitative discriminating in conjunction with principal component analysis (PCA), techniques of discriminant analysis, significant to solving the food-safety problem and the stable meat market that cause because of the adulterated fraud of meat.
Summary of the invention
Technical matters to be solved by this invention is, for the deficiencies in the prior art, provides the method for the adulterated meat of a kind of quick discriminating, and the method can realize quick, accurate, the stable qualitative classification to unknown meat and differentiate.
For solving the problems of the technologies described above, the present invention adopts following technical scheme:
A method for the adulterated meat of quick discriminating, comprises step:
(1) adulterated meat two samples of class meat and the testing sample of unknown meat preparing respectively pure meat and made by described pure meat;
(2) near-infrared diffuse reflection spectrum of the sample of pure meat and adulterated meat described in difference acquisition step (1), repeatedly, the number of times of each sample collection is identical for each sample continuous acquisition, gets respectively average as original spectrum;
(3) above-mentioned original spectrum is carried out respectively to the optimization selection of modeling wave band, and the modeling wave band of selecting is carried out to spectrum pre-service;
(4) adopt principal component analysis (PCA) to extract spectral signature information;
(5) adopt discriminant analysis method to set up adulterated meat and differentiate model;
(6) calculate the mahalanobis distance of each sample, by mahalanobis distance, differentiate the classification under each sample;
(7) gather the near-infrared diffuse reflection spectrum of testing sample, utilize above-mentioned adulterated meat to differentiate that model carries out discriminatory analysis to testing sample, and calculate the mahalanobis distance of testing sample, by mahalanobis distance, differentiate the classification under testing sample.
Wherein, between above-mentioned steps (6) and step (7), be provided with verification step, the sample of known class is carried out to identification and classification, and evaluate differentiating result, its implementation is: continuous several times gathers the near-infrared diffuse reflection spectrum of the sample of above-mentioned known class, get average as original spectrum, adopt the adulterated meat described in step (5) to differentiate that model carries out discriminatory analysis to the sample of known class, and calculate the mahalanobis distance of the sample of above-mentioned known class, by mahalanobis distance, differentiate the classification under the sample of above-mentioned known class, and the known class of differentiating result and sample is compared, thereby the differentiation result to above-mentioned adulterated meat discriminating model is evaluated
.
Wherein, in the application, each related near-infrared diffuse reflection spectrum obtains by same near infrared spectrometer scanning respectively.This can guarantee that each parameter related in this programme obtains under same acquisition hardware condition, can avoid the impact of systematic error.
Wherein, described pure meat is any two kinds in mutton, beef, pork and duck.
Wherein, the number of principal components of the principal component analysis (PCA) of above-mentioned steps (4) is 10.
Wherein, the scanning wave-number range of described near infrared spectrometer is 10000～4000cm
-1, resolution is 8 cm
-1, scanning times 128 times.
The described concrete grammar that differentiation result is verified is the accuracy rate that calculates differentiation.Might as well make the N that adds up to of sample, differentiating correct sample number is M, has the accuracy rate of differentiation to be
.
Described modeling wave band is 7283.09～4478.60cm
-1.
The pretreated method of described spectrum is that polynary scatter correction is in conjunction with Savitzky-Golay filter method.
The number of times of each the sample continuous acquisition described in above-mentioned steps (2) is 3 times.
Compared with prior art, advantage of the present invention is:
The present invention adopts near-infrared spectrum technique, principal component analysis (PCA) and techniques of discriminant analysis, sets up the discrimination model of adulterated meat, can carry out identification and classification qualitatively to unknown meat.This technology has avoided Organoleptic method subjectivity strong, be subject to the shortcomings such as environmental interference, also overcome chromatographic technique sample pretreatment complicated, time-consuming, need to consume the deficiency of a large amount of solvents, can also overcome DNA technique complex operation, expense is high, deficiency consuming time and be difficult to meet batch and differentiate fast demand, there is sample pre-treatments simple, green, running cost is low, discriminating speed is fast, good stability, can realize on-line analysis, the advantages such as selectivity and antijamming capability are strong, easy and simple to handle, being convenient to ordinary people uses, applied widely, be convenient to promote the use of, practical.
As can be seen here, the present invention compared with prior art, has outstanding substantive distinguishing features and significant progressive, and the beneficial effect of its enforcement is also apparent.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the specific embodiment of the present invention 1.
Fig. 2 is the discriminating figure of the specific embodiment of the present invention 1.
Wherein, zero-mutton, △-pork,-the mix mutton of pork.
Embodiment
Below in conjunction with accompanying drawing and preferred embodiment, the present invention is further described in detail.
Embodiment 1:
As depicted in figs. 1 and 2, pure meat is mutton and pork, and adulterated meat is the mutton of mixing pork.
(1) sample of mutton and the testing sample of unknown meat preparing respectively mutton, pork and mix pork.
Unknown meat and pure mutton and pork are ground into respectively to rotten shape, the meat samples of making respectively testing sample, meat samples, pork sample and mixing pork.Wherein, while preparing adulterated meat, two kinds of meat gruels are mixed.Sample preparation is simple, convenient operation.
(2) near-infrared diffuse reflection spectrum that gathers respectively meat samples, pork sample and mix the meat samples of pork, each sample continuous acquisition 3 times, get respectively average as original spectrum, and described original spectrum is divided into training set and forecast set two parts at random.
The near-infrared diffuse reflection spectrum that is gathered respectively meat samples, pork sample and mixed the meat samples of pork by near infrared spectrometer, each sample continuous acquisition 3 times, averages as original spectrum respectively.Wherein, the scanning wave-number range of described near infrared spectrometer is 10000～4000 cm
-1, resolution 8 cm
-1, scanning times 128 times, take built-in background as reference.
Gathered original spectrum is divided into respectively to training set (69 samples of mutton, 83 samples of pork are mixed 80 samples of mutton of pork) and forecast set (34 samples of mutton, 40 samples of pork are mixed 40 samples of mutton of pork) two parts at random.Wherein, training set is for the foundation of calibration model, and forecast set is for the differentiation performance of verification model.
(3) original spectrum of above-mentioned training set is carried out respectively to the optimization selection of modeling wave band, and the modeling wave band of selecting is carried out to spectrum pre-service.
Wherein, modeling wavelength band is 7283.09～4478.60cm
-1, selected modeling wave band is carried out to spectrum pre-service by polynary scatter correction and Savitzky-Golay filter method.
(4) adopt the spectral signature information of the spectrum obtaining in principal component analysis (PCA) extraction step (3).
Wherein, the number of principal components that above-mentioned principal component analysis (PCA) adopts is 10.
(5) take the spectral signature information of obtaining in step (4) is parameter, adopts discriminant analysis method to set up adulterated meat and differentiates model.
(6) calculation training is concentrated the mahalanobis distance of each sample, and differentiates the classification under each sample by mahalanobis distance.
In the present embodiment, as shown in table 1 to the differentiation result of training set.
(7) the forecast set sample in step (2) is carried out to identification and classification, and evaluate differentiating result.
By the quantization function in TQ Analyst 8 softwares, forecast set sample spectra is analyzed, as shown in Figure 2, adopt above-mentioned adulterated meat to differentiate that model carries out discriminatory analysis to forecast set sample, and calculate the mahalanobis distance of each sample in forecast set, be that correlation spectrum data are differentiated horizontal ordinate and the ordinate in model at adulterated meat, and differentiate the affiliated classification of forecast set sample according to above-mentioned mahalanobis distance, the real known class of differentiating result and forecast set sample is compared, thereby the differentiation result of above-mentioned adulterated meat discriminating model is evaluated
.
In the present embodiment, as shown in table 2 to the differentiation result of forecast set.
As shown in table 2, model is to the correct recognition rata of forecast set sample all higher than 90%, and visible model has good differentiation performance.
(8) the continuous near-infrared diffuse reflection spectrum that gathers testing sample for 3 times, get average as original spectrum, utilize above-mentioned adulterated meat to differentiate that model carries out discriminatory analysis to testing sample, and calculate the mahalanobis distance of testing sample, by mahalanobis distance, differentiate the classification under testing sample.
Wherein, in present embodiment, the method for the near-infrared diffuse reflection spectrum of related each sample of collection is identical.
Certainly, above-mentioned explanation is not limitation of the present invention, and the present invention is also not limited only to above-mentioned giving an example, and the variation that those skilled in the art make in essential scope of the present invention, remodeling, interpolation or replacement, also belong to protection scope of the present invention.
Claims (9)
1. differentiate fast a method for adulterated meat, it is characterized in that, comprise step:
(1) adulterated meat two samples of class meat and the testing sample of unknown meat preparing respectively pure meat and made by described pure meat;
(2) near-infrared diffuse reflection spectrum of the sample of pure meat and adulterated meat described in difference acquisition step (1), repeatedly, the number of times of each sample collection is identical for each sample continuous acquisition, gets respectively average as original spectrum;
(3) above-mentioned original spectrum is carried out respectively to the optimization selection of modeling wave band, and the modeling wave band of selecting is carried out to spectrum pre-service;
(4) adopt principal component analysis (PCA) to extract spectral signature information;
(5) adopt discriminant analysis method to set up adulterated meat and differentiate model;
(6) calculate the mahalanobis distance of each sample, by mahalanobis distance, differentiate the classification under each sample;
(7) gather the near-infrared diffuse reflection spectrum of testing sample, utilize above-mentioned adulterated meat to differentiate that model carries out discriminatory analysis to testing sample, and calculate the mahalanobis distance of testing sample, by mahalanobis distance, differentiate the classification under testing sample.
2. the method for the adulterated meat of quick discriminating according to claim 1, it is characterized in that, between above-mentioned steps (6) and step (7), be provided with verification step, the sample of known class is carried out to identification and classification, and evaluate differentiating result, its implementation is: continuous several times gathers the near-infrared diffuse reflection spectrum of the sample of above-mentioned known class, get average as original spectrum, adopt the adulterated meat described in step (5) to differentiate that model carries out discriminatory analysis to the sample of known class, and calculate the mahalanobis distance of the sample of above-mentioned known class, by mahalanobis distance, differentiate the classification under the sample of above-mentioned known class, and the known class of differentiating result and sample is compared, thereby the differentiation result to above-mentioned adulterated meat discriminating model is evaluated.
3. the method for the adulterated meat of quick discriminating according to claim 1, is characterized in that, described each near-infrared diffuse reflection spectrum obtains by same near infrared spectrometer scanning respectively.
4. the method for the adulterated meat of quick discriminating according to claim 3, is characterized in that, the scanning wave-number range of described near infrared spectrometer is 10000～4000cm
-1, resolution is 8 cm
-1, scanning times 128 times.
5. the method for the adulterated meat of quick discriminating according to claim 1, is characterized in that, the number of principal components of the principal component analysis (PCA) of described step (4) is 10.
6. the method for the adulterated meat of quick discriminating according to claim 1, is characterized in that, described pure meat is any two kinds in mutton, beef, pork and duck.
7. according to the method for the adulterated meat of arbitrary quick discriminating described in claim 1-6, it is characterized in that, described modeling wave band is 7283.09～4478.60cm
-1.
8. according to the method for the adulterated meat of arbitrary quick discriminating described in claim 1-6, it is characterized in that, the pretreated method of described spectrum is that polynary scatter correction is in conjunction with Savitzky-Golay filter method.
9. according to the method for the adulterated meat of arbitrary quick discriminating described in claim 1-6, it is characterized in that, the number of times of each the sample continuous acquisition described in step (2) is 3 times.
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105092525A (en) * | 2015-08-31 | 2015-11-25 | 河南省产品质量监督检验院 | Near-infrared spectral discrimination method for mutton adulterated with duck meat |
| CN105223140A (en) * | 2015-10-10 | 2016-01-06 | 中国科学院苏州生物医学工程技术研究所 | The method for quickly identifying of homology material |
| CN107219184A (en) * | 2017-04-24 | 2017-09-29 | 仲恺农业工程学院 | A kind of meat discrimination method and device traced to the source applied to the place of production |
| CN107677637A (en) * | 2017-08-31 | 2018-02-09 | 维沃移动通信有限公司 | The authenticity verification method and mobile terminal of a kind of meat |
| CN108444798A (en) * | 2018-01-29 | 2018-08-24 | 华中农业大学 | A kind of beef adulteration detection method based on Biospeckles and the moment of inertia spectrum analysis |
| CN109001146A (en) * | 2018-07-26 | 2018-12-14 | 江苏大学 | A kind of method for quick identification of chilled beef and the fresh beef of jellyization |
| CN109916990A (en) * | 2019-04-04 | 2019-06-21 | 宁夏大学 | A method of based on the mineral element fingerprint verification sheep known for its fine thick wool meat place of production |
| CN110865044A (en) * | 2018-08-28 | 2020-03-06 | 中蓝晨光成都检测技术有限公司 | Spectral analysis method for identifying white oil-doped organic silicon product |
| CN112415050A (en) * | 2020-11-13 | 2021-02-26 | 石河子大学 | Mutton adulteration qualitative discrimination method based on temperature distribution difference |
| CN113567359A (en) * | 2021-08-10 | 2021-10-29 | 江苏大学 | Identification method of raw cut meat and high meat imitation thereof based on component linear array gradient characteristics |
| CN115436510A (en) * | 2022-08-29 | 2022-12-06 | 中国农业科学院北京畜牧兽医研究所 | Method for identifying difference of flavor compounds in livestock and poultry meat based on gas chromatography-electrostatic field orbit trap high-resolution mass spectrometry |
| US11940435B2 (en) | 2021-08-10 | 2024-03-26 | Jiangsu University | Method for identifying raw meat and high-quality fake meat based on gradual linear array change of component |
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Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105092525A (en) * | 2015-08-31 | 2015-11-25 | 河南省产品质量监督检验院 | Near-infrared spectral discrimination method for mutton adulterated with duck meat |
| CN105223140A (en) * | 2015-10-10 | 2016-01-06 | 中国科学院苏州生物医学工程技术研究所 | The method for quickly identifying of homology material |
| CN107219184A (en) * | 2017-04-24 | 2017-09-29 | 仲恺农业工程学院 | A kind of meat discrimination method and device traced to the source applied to the place of production |
| CN107677637A (en) * | 2017-08-31 | 2018-02-09 | 维沃移动通信有限公司 | The authenticity verification method and mobile terminal of a kind of meat |
| CN108444798A (en) * | 2018-01-29 | 2018-08-24 | 华中农业大学 | A kind of beef adulteration detection method based on Biospeckles and the moment of inertia spectrum analysis |
| CN109001146A (en) * | 2018-07-26 | 2018-12-14 | 江苏大学 | A kind of method for quick identification of chilled beef and the fresh beef of jellyization |
| CN110865044A (en) * | 2018-08-28 | 2020-03-06 | 中蓝晨光成都检测技术有限公司 | Spectral analysis method for identifying white oil-doped organic silicon product |
| CN109916990A (en) * | 2019-04-04 | 2019-06-21 | 宁夏大学 | A method of based on the mineral element fingerprint verification sheep known for its fine thick wool meat place of production |
| CN112415050A (en) * | 2020-11-13 | 2021-02-26 | 石河子大学 | Mutton adulteration qualitative discrimination method based on temperature distribution difference |
| CN112415050B (en) * | 2020-11-13 | 2024-01-23 | 石河子大学 | Mutton mixing supposition discriminating method based on temperature distribution difference |
| CN113567359A (en) * | 2021-08-10 | 2021-10-29 | 江苏大学 | Identification method of raw cut meat and high meat imitation thereof based on component linear array gradient characteristics |
| CN113567359B (en) * | 2021-08-10 | 2022-05-20 | 江苏大学 | Raw cut meat and high meat-imitation identification method thereof based on component linear array gradient characteristics |
| WO2023015609A1 (en) * | 2021-08-10 | 2023-02-16 | 江苏大学 | Method for identifying raw cut meat and high imitation meat thereof on basis of component linear array gradient feature |
| US11940435B2 (en) | 2021-08-10 | 2024-03-26 | Jiangsu University | Method for identifying raw meat and high-quality fake meat based on gradual linear array change of component |
| CN115436510A (en) * | 2022-08-29 | 2022-12-06 | 中国农业科学院北京畜牧兽医研究所 | Method for identifying difference of flavor compounds in livestock and poultry meat based on gas chromatography-electrostatic field orbit trap high-resolution mass spectrometry |
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Application publication date: 20141105 |