CN106404692A - Method for detecting freshness grade of instant sea cucumber by using hyperspectral imaging technology - Google Patents
Method for detecting freshness grade of instant sea cucumber by using hyperspectral imaging technology Download PDFInfo
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- CN106404692A CN106404692A CN201610981944.0A CN201610981944A CN106404692A CN 106404692 A CN106404692 A CN 106404692A CN 201610981944 A CN201610981944 A CN 201610981944A CN 106404692 A CN106404692 A CN 106404692A
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- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
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Abstract
The invention discloses a method for detecting the freshness grade of instant sea cucumber by using a hyperspectral imaging technology. The method comprises the following steps: 1, collecting a sample; 2, measuring the sample; 3, carrying out hyperspectral analysis on the sample; 4, carrying out hyperspectral image analysis on the sample; 5, establishing a model; and 6, evaluating the model. The method for detecting the freshness grade of instant sea cucumber by using the hyperspectral imaging technology has the advantages of no pre-treatment of the sample to be measured, good repeatability, short analysis time, and no destroy of the instant sea cucumber, allows the freshness grade of all other instant sea cucumber samples to be detected to be predicated through a network model only by measuring the texture characteristics after the network model used for predication is established, is a non-intruding measurement method, realizes accurate and stable detection values, improves the measurement efficiency, and can meets rapid analysis demands of the production field on the sample.
Description
Technical field
The present invention relates to a kind of method that utilization high light spectrum image-forming technology detects instant sea cucumber grade of freshness.
Background technology
It is rich in aminoacid, acidic mucopolysaccharide, collagen protein, selenka and vitamin, mineral etc. in Stichopus japonicuss, have
Special nutritive value and health care.Due to the instant sea cucumber made by techniques such as high pressure, Short Time Heatings with fresh and alive sea cucumbers
Product moisture and protein content are higher, are processing, the effect corruption that link is easily subject to enzyme and microorganism such as are packing, preserve, transporting, selling
Lose rotten；At present, instant sea cucumber freshness detection mainly has the methods such as subjective appreciation, microorganism and physical and chemical determination, subjective appreciation
Intuitively, easy, easily affected by subjective factorss it is impossible to quantitative conclusion, there is potential danger, microorganism detection mainly passes through cause
The mode such as the quantity of rotten bacterium and pathogenic bacterium presence or absence differentiates；Physico-chemical tests mainly include measuring total volatile basic nitrogen（TVB-N）、
Trimethylamine, pH value and K value etc., microorganism and physico-chemical analysis can objective, accurate response instant sea cucumber freshness, but step is numerous
Trivial, time-consuming, the line being difficult to be applied to said method the course of processing detects, if seeking a kind of quick, lossless instant
Stichopus japonicuss Noninvasive Measuring Method of Freshness, significant at aspects such as the processing of its automatization, storage quality inspection, forecasting shelf lifes.
Content of the invention
It is an object of the invention to provide a kind of utilization EO-1 hyperion of freshness that can quick and precisely detect instant sea cucumber becomes
Method as technology for detection instant sea cucumber grade of freshness.
The present invention be employed technical scheme comprise that for achieving the above object：A kind of instant using high light spectrum image-forming technology detection
The method of Stichopus japonicuss grade of freshness, comprises the following steps：
A, sample collecting：The representative instant sea cucumber sample of collection different sources, different size and Various Seasonal；
B, sample measurement：The instant sea cucumber sample of the different freshnesss of collection is measured, obtains the number of total volatile basic nitrogen
According to；
C, carry out sample EO-1 hyperion spectrum analyses：Instant sea cucumber sample is carried out with EO-1 hyperion instrument collection analysises, chooses Stichopus japonicuss sense
Interest region obtains all band averaged spectrum curve, carries out Data Dimensionality Reduction using the valley point of all band averaged spectrum curve and reduces ripple
Section, chooses best features wave band by main constituent image, selects the optimal main constituent figure under characteristic wave bands to obtain weight coefficient figure,
The flex point of its in figure is optimal wavelength, obtains optimal wavelength corresponding characteristic wavelength image simultaneously, and characteristic wavelength image is done
Wave band ratio；
D, carry out sample high spectrum image analysis：Than image, shade is removed to characteristic wavelength band, removes background and obtain sea
Join entirety as region of interest, by gray level co-occurrence matrixes, Gray level-gradient co-occurrence matrix, improved local pattern texture descriptor
Texture feature extraction；
E, set up model：Using the textural characteristics extracting as |input paramete, set up freshnesss different from instant sea cucumber freshness etc.
Level network model；
F, evaluation model：According to network model, treat the prediction grade of freshness value of determination sample and true grade of freshness value
Accuracy described model is estimated.
Described step（B）Middle TVB-N content measurement adopts Micro-kjoldahl method.
Described step（C）The parameter that middle EO-1 hyperion instrument collection adopts is set to 2.8nm, time of exposure for spectral resolution
It is set to 15ms, object distance is set to 140mm, spectrum sample point is set to 0.65nm, and object stage translational speed is set to 6mm/s.
Described step（C）Middle Data Dimensionality Reduction adopts PCA, and weight coefficient figure adopts linear combination regression algorithm.
Described step（D）In go shade to adopt wave band than algorithm, go background to adopt self adaptation Da-Jin algorithm Threshold segmentation mask
Algorithm.
Described step（E）Middle network model is particle group optimizing neutral net.
The method that a kind of present invention utilization high light spectrum image-forming technology detects instant sea cucumber grade of freshness, testing sample need not
Pre-treatment, reproducible, analysis time is short, and instant sea cucumber is no destroyed, right after establishing the network model for prediction
It can be non-by network model's prediction grade of freshness that every other instant sea cucumber sample to be measured only needs to measure textural characteristics
Intrusive mood measuring method, the numerical value of detection is accurate, stable, improves measurement efficiency, can meet production scene fast to sample
Fast analysis demand.
Brief description
Fig. 1 is that a kind of present invention utilization high light spectrum image-forming technology detects that the method for instant sea cucumber grade of freshness gathers not
With the averaged spectrum curve chart interested of instant sea cucumber under grade of freshness.
Fig. 2 is the instant sea of the method that a kind of present invention utilization high light spectrum image-forming technology detects instant sea cucumber grade of freshness
Join the main constituent figure that all band and 5 sub-band lower eigenvalues are more than 1.
Fig. 3 is the instant sea of the method that a kind of present invention utilization high light spectrum image-forming technology detects instant sea cucumber grade of freshness
The corresponding weight coefficient figure of optimal main constituent figure under ginseng best band.
Fig. 4 is the instant sea of the method that a kind of present invention utilization high light spectrum image-forming technology detects instant sea cucumber grade of freshness
Ginseng 686nm and 985nm wave band is than the figure after computing.
Fig. 5 is the instant sea of the method that a kind of present invention utilization high light spectrum image-forming technology detects instant sea cucumber grade of freshness
Ginseng wave band enters line mask than image and removes the figure after background.
Fig. 6 is the instant sea of the method that a kind of present invention utilization high light spectrum image-forming technology detects instant sea cucumber grade of freshness
Network model's fitness curve chart under ginseng different texture characteristic parameter.
Specific embodiment
As shown in Figures 1 to 6, the method detecting instant sea cucumber grade of freshness using high light spectrum image-forming technology, specifically real
Apply step as follows：A, sample collecting, point different batches collection different sources, different size, the instant sea cucumber of Various Seasonal 20,
So sample has necessarily representative and universality；B, sample measurement, the assay method of TVB-N content, temperature（25
±3℃）Lower using 20 instant sea cucumber samples as experiment sample, put in freshness protection package and be numbered, be placed in 25 DEG C of incubators
Preserve, to be measured, every 6h sample this utilize Micro-kjoldahl method (GB/T5009.44 2003) measure sample TVB-N
Content, the continuous measurement of each sample 4 times, measurement result is as shown in table 1；C, sample EO-1 hyperion spectrum analyses, to described instant sea
Ginseng sample carries out EO-1 hyperion instrument collection analysises, Hyperspectral imager mainly by Image- λ-V10E-LU enhancement mode visible-near
Infrared high spectrum camera, spectrogrph, Halogen light, automatically controlled mobile platform are constituted, and the parameter that collection adopts is arranged for spectral resolution
For 2.8nm, time of exposure is set to 15ms, and object distance is set to 140mm, and spectrum sample point is set to 0.65nm, for avoiding image
Middle instant sea cucumber shape distortion, object stage translational speed is set to 6mm/s, as shown in figure 1, choose Stichopus japonicuss area-of-interest obtaining
To all band averaged spectrum curve（400-1000nm）, carry out Data Dimensionality Reduction using the valley point of all band averaged spectrum curve and obtain
Main constituent image, table 2 shows 6 principal component contributor rates before each wave band, selects the main constituent image that eigenvalue is more than 1, such as Fig. 2
Shown, select best band to replace all band for 686-985nm according to main constituent image effect, reduce operand, improve efficiency,
As shown in figure 3, the weight coefficient figure that the main constituent figure under characteristic wave bands obtains, the flex point of its in figure is optimal wavelength, this enforcement
In example, software used is Spectral Image software（Isuzu Optics Corp, Taiwan）With ENVI 5.3
（Research System Inc, USA）；D, the analysis of sample high spectrum image, table 3 shows the dependency between characteristic wavelength
Size, as shown in figure 4, two minimum characteristic wavelengths of dependency do the image that wave band obtains than computing, eliminates shade simultaneously,
As shown in figure 4, background image is gone than what image carried out that self adaptation Da-Jin algorithm Threshold segmentation does that mask computing obtains to wave band, will go
Except background obtains instant sea cucumber entirety as region of interest, by gray level co-occurrence matrixes, Gray level-gradient co-occurrence matrix, improved office
Portion's pattern texture descriptor texture feature extraction, the dependency selecting best features wavelength is according to as shown in table 3；E, set up mould
Type, the textural characteristics extracting in described three kinds of distinct methods pass through particle group optimizing BP neural network and set up from different freshnesss etc.
The network model of level, in the present embodiment, software used is Matlab 2012b（The MathWorksInc, USA）, need
Bright, described software can set up the software of model for any particle group optimizing BP neural network, is not limited to the present embodiment
Citing；F, evaluation model, table 4 shows predicting the outcome of parameter setting under different models and instant sea cucumber grade of freshness,
Can see from table Gray level-gradient co-occurrence matrix texture network model accuracy be 95%, gray level co-occurrence matrixes texture network
The accuracy of model is 90%, and the accuracy of improved local pattern texture network model is 80%, and high light spectrum image-forming technology is described
The network model that combined with texture feature is set up can predict the grade of freshness of instant sea cucumber exactly.
To sum up, the present invention utilizes the method that high light spectrum image-forming technology detects instant sea cucumber grade of freshness, by recurrence
Model checking it can be seen that using the method for the present invention set up the network mould for predicting instant sea cucumber grade of freshness
Type, either gray level co-occurrence matrixes or Gray level-gradient co-occurrence matrix and improved local pattern texture be predicted, can
It is accurately used for predicting instant sea cucumber grade of freshness, instant sea cucumber sample to be measured is no destroyed, easy and simple to handle, detection can be improved
Speed.
No. 1 instant sea cucumber TVB-N value
Table 1
The first six main constituent adds up variance contribution ratio
Table 2
Wave band dependency
Table 3
Different Model checking results
Table 4
Claims (6)
1. a kind of utilization high light spectrum image-forming technology detects the method for instant sea cucumber grade of freshness it is characterised in that including following
Step：
A, sample collecting：The representative instant sea cucumber sample of collection different sources, different size and Various Seasonal；
B, sample measurement：The instant sea cucumber sample of the different freshnesss of collection is measured, obtains the number of total volatile basic nitrogen
According to；
C, carry out sample EO-1 hyperion spectrum analyses：Instant sea cucumber sample is carried out with EO-1 hyperion instrument collection analysises, chooses Stichopus japonicuss sense
Interest region obtains all band averaged spectrum curve, carries out Data Dimensionality Reduction using the valley point of all band averaged spectrum curve and reduces ripple
Section, chooses best features wave band by main constituent image, selects the optimal main constituent figure under characteristic wave bands to obtain weight coefficient figure,
The flex point of its in figure is optimal wavelength, obtains optimal wavelength corresponding characteristic wavelength image simultaneously, and characteristic wavelength image is done
Wave band ratio；
D, carry out sample high spectrum image analysis：Than image, shade is removed to characteristic wavelength band, removes background and obtain sea
Join entirety as region of interest, by gray level co-occurrence matrixes, Gray level-gradient co-occurrence matrix, improved local pattern texture descriptor
Texture feature extraction；
E, set up model：Using the textural characteristics extracting as |input paramete, set up freshnesss different from instant sea cucumber freshness etc.
Level network model；
F, evaluation model：According to network model, treat the prediction grade of freshness value of determination sample and true grade of freshness value
Accuracy described model is estimated.
2. the method that a kind of utilization high light spectrum image-forming technology according to claim 1 detects instant sea cucumber grade of freshness,
It is characterized in that：Described step（B）Middle TVB-N content measurement adopts Micro-kjoldahl method.
3. the method that a kind of utilization high light spectrum image-forming technology according to claim 1 detects instant sea cucumber grade of freshness,
It is characterized in that：Described step（C）The parameter that middle EO-1 hyperion instrument collection adopts is set to 2.8nm for spectral resolution, exposure
Set of time is 15ms, and object distance is set to 140mm, and spectrum sample point is set to 0.65nm, and object stage translational speed is set to
6mm/s.
4. the method that a kind of utilization high light spectrum image-forming technology according to claim 1 detects instant sea cucumber grade of freshness,
It is characterized in that：Described step（C）Middle Data Dimensionality Reduction adopts PCA, and weight coefficient figure adopts linear combination to return and calculates
Method.
5. the method that a kind of utilization high light spectrum image-forming technology according to claim 1 detects instant sea cucumber grade of freshness,
It is characterized in that：Described step（D）In go shade to adopt wave band than algorithm, go background to cover using self adaptation Da-Jin algorithm Threshold segmentation
Film algorithm.
6. the method that a kind of utilization high light spectrum image-forming technology according to claim 1 detects instant sea cucumber grade of freshness,
It is characterized in that：Described step（E）Middle network model is particle group optimizing neutral net.
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Cited By (16)
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| CN107247026A (en) * | 2017-07-26 | 2017-10-13 | 成都九维云联科技有限公司 | A kind of pre-judging method of perishable items |
| CN107796766A (en) * | 2017-10-18 | 2018-03-13 | 盐城工学院 | A kind of smelly pin salt place of production discrimination method, device and computer-readable recording medium |
| CN108613942A (en) * | 2018-03-19 | 2018-10-02 | 河南科技学院 | The method of on-line quick detection chicken hardness |
| CN108613941A (en) * | 2018-03-19 | 2018-10-02 | 河南科技学院 | The method of on-line quick detection chicken ash content |
| CN108627475A (en) * | 2018-03-19 | 2018-10-09 | 河南科技学院 | Application in high light spectrum image-forming technology on-line checking chicken content of microorganisms |
| CN108627471A (en) * | 2018-03-19 | 2018-10-09 | 河南科技学院 | The method that on-line quick detection chicken heat kills rope silk bacterial content |
| CN108645798A (en) * | 2018-03-19 | 2018-10-12 | 河南科技学院 | The method of on-line quick detection chicken content of lactic acid bacteria |
| CN108872137A (en) * | 2018-03-19 | 2018-11-23 | 河南科技学院 | Method based on multispectral on-line checking chicken thiobarbituricacidα- |
| CN108872138A (en) * | 2018-03-19 | 2018-11-23 | 河南科技学院 | The method of on-line quick detection chicken enterobacteriaceae content |
| CN109668857A (en) * | 2019-01-25 | 2019-04-23 | 江苏大学 | Tealeaves total plate count detection device and method based on near-infrared hyper-spectral image technique |
| CN109883548A (en) * | 2019-03-05 | 2019-06-14 | 北京理工大学 | The Encoding Optimization of the spectrum imaging system of neural network based on optimization inspiration |
| CN110849828A (en) * | 2019-12-13 | 2020-02-28 | 嘉兴职业技术学院 | Saffron crocus classification method based on hyperspectral image technology |
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| CN115713694A (en) * | 2023-01-06 | 2023-02-24 | 东营国图信息科技有限公司 | Land surveying and mapping information management method |
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