CN104089883B - Laser array based detection device and pseudosciaena polyactis storage time detection method - Google Patents
Laser array based detection device and pseudosciaena polyactis storage time detection method Download PDFInfo
- Publication number
- CN104089883B CN104089883B CN201310695970.3A CN201310695970A CN104089883B CN 104089883 B CN104089883 B CN 104089883B CN 201310695970 A CN201310695970 A CN 201310695970A CN 104089883 B CN104089883 B CN 104089883B
- Authority
- CN
- China
- Prior art keywords
- signal
- storage time
- detection
- sample
- computer
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Landscapes
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses a laser array based detection device and a pseudosciaena polyactis storage time detection method. The method includes: firstly making the laser emitted by a laser array irradiate a sample, collecting the light reflected by the sample by an optical fiber probe and inputting the collected light into a spectrometer so as to obtain n detection signals corresponding to the wavelengths of n lasers; then adjusting the weight of each detection signal to obtain a satisfactory detection signal x(t), inputting the x(t) into a stochastic resonance system model prestored in a computer to make the stochastic resonance system model undergo stochastic resonance, then drawing an output signal noise ratio curve of the stochastic resonance system model by the computer, and conducting calculation by the computer to obtain a predicted pseudosciaena polyactis storage Time. With the characteristics of fast detection speed, high accuracy and good economical efficiency, the device and the method provided by the invention eliminates the interference information of useless band stray light, and the sample storage time is better differentiated.
Description
Technical field
The present invention relates to food quality detection technical field, especially relate to the base that a kind of detection speed is fast, accuracy is high
Detection means and Carnis Pseudosciaenae storage time detection method in laser array.
Background technology
Carnis Pseudosciaenae is Sciaenidae (sciaenidae), also known as Channa argus, Carnis Pseudosciaenae.Carnis Pseudosciaenae is that warm warm nature coastal waters cluster dries up
Swimming fish class, main activity region is on the south Central South China Sea.Because Carnis Pseudosciaenae contains substantial amounts of selenium element, therefore can preferably remove
Free radical, looks improving and the skin nourishing.In addition, Carnis Pseudosciaenae contains very rich in protein, meat is more fine and smooth, can be used as ideals of human being
One of animal protein source.Carnis Pseudosciaenae is described as China four master greatly and catches economic fish, especially in the sea of China and west of pacific ocean
There is in foreign Fisheries Development considerable status, wherein Fujian east area Carnis Pseudosciaenae export volume just can reach the 80% of yield,
Carnis Pseudosciaenae at home and abroad all occupies larger share in fish market, and it is only in marine product industry that these have all established Carnis Pseudosciaenae
Characteristic and importance.
Storage time can characterize the freshness of the flesh of fish, and freshness is an important finger of Fish or fish product quality
Mark, particularly significant to product final mass.Now have been developed in a series of index and method to evaluate fish freshness, such as sense organ
Evaluation methodology, micro-biological process, physics and chemical method etc., but the more difficult satisfaction of above detection method is accurate, quick detection
Require.Generally existing that detection process is loaded down with trivial details, high cost, the shortcomings of time-consuming.
Chinese patent Authorization Notice No.: cn101769889a, in authorized announcement date on July 7th, 2010, discloses a kind of agricultural production
The electric nasus system of product Quality Detection, mainly completes the gas enrichment module to low concentration odor trap including one, a main handle
Olfactory signal is converted into air chamber gas path module and the sensor array of the signal of telecommunication, and one is mainly carried out to sensor array output signal
Filtering, analog digital conversion, the Conditioning Circuits of Sensor of feature extraction and data preprocessing module, a pair of signal is identified and sentences
Embedded system that is disconnected and carrying data storage, a display and result output module；Described gas enrichment module is by being filled with
The adsorption tube of adsorbent, heating wire and attemperating unit are constituted.This invention has single function, and detection time length is it is impossible to for right
The deficiency that Carnis Pseudosciaenae storage time is detected.
Content of the invention
The goal of the invention of invention be in order to overcome that detection method process of the prior art is loaded down with trivial details, high cost, time-consuming
Not enough, there is provided a kind of detection speed is fast, accuracy the is high detection means based on laser array and Carnis Pseudosciaenae storage time
Detection method.
To achieve these goals, invention employs the following technical solutions:
A kind of detection means based on laser array, including spectrogrph, for place detected sample base plate, located at
Supporting plate above base plate；Described supporting plate is provided with the laser array for irradiating detected sample and is used for detection laser battle array
Arrange the fibre-optical probe of reflection light after sample reflection for the laser sending；Described laser array includes n and bundles
And the single wavelength laser that wavelength is arranged in order, described fibre-optical probe is electrically connected with spectrogrph, spectrogrph be provided with for
Calculate the signal output port of mechatronics.
In traditional spectroscopic analysis methods, detected as light source frequently with Halogen light, halogen light source has ripple
The continuous light of long wider range, but in food samples detection, the band of light of real load detecting information is limited, most of
Optical band characterizes to food samples and does not act on.Therefore, the present invention adopts the mono-colour laser composition of several specific wavelengths to swash
Light device array detects to sample, is equivalent to the light filtering out useless wave band, decreases interference signal.Increase several simultaneously
The light intensity of selected wavelength, improves the accuracy of detection.
Preferably, described n is 6, the wavelength of each single wavelength laser be respectively 635nm, 980nm, 532nm,
808nm, 660nm and 780nm.For the physicochemical property of Carnis Pseudosciaenae meat, carry out as the laser instrument preferably selecting above 6 wavelength
Detection, can more project the detection feature of Carnis Pseudosciaenae meat, improve the accuracy of detection.
Preferably, described base plate is connected with supporting plate by 4 columns.
Preferably, described spectrogrph is usb2000+ Vis/NIR instrument.
The detection method that a kind of storage time of Carnis Pseudosciaenae is detected, comprises the steps:
(5-1) sample preparation:
Carnis Pseudosciaenae is cleaned, gills, scale, head and tail, lamellar is taken out from fish body and oppresses as detected sample；
(5-2) detected: computer is electrically connected with spectrogrph, sample is kept flat on base plate, makes laser array
The laser sending is radiated on sample, and the light that fibre-optical probe gathers sample reflection enters in spectrogrph, and spectrogrph obtains swashing with n
N corresponding detection signal x of the wavelength of light device1(t), x2(t) ..., xn(t), and by x1(t), x2(t) ..., xnT () inputs
In computer；
(5-3) data processing:
(5-3-1) set and one-to-one one group of weight w of each detection signal in a computer1, w2..., wn, and
Make
(5-3-2) utilize formulaIt is calculated detection signal x (t), and calculate total mean square deviation
WhenProceed to step (5-3-3)；
WhenOrThen proceed to step (5-3-1):
Illustrate that the weight of each signal now chosen is unreasonable, proceed to step (5-3-1) and reselect one group of weight, directly
To obtaining the rational detection signal of weight, so that it is guaranteed that detection signal can be accurately by the storage time information of Carnis Pseudosciaenae
Characterize.
(5-3-3) the stochastic resonance system model that x (t) input prestores in a computer
In, make stochastic resonance system model that accidental resonance to occur,
Wherein, ξ (t) is the gaussian noise of auto-correlation function e [ξ (t) ξ (0)]=2 α δ (t), and its intensity is α；A is signal sin (2
πf0T+ ψ) intensity；f0Frequency for signal；A, b are the real parameter of setting, and s is the movement locus of Brownian Particles, and t is fortune
The dynamic time, ψ is phase place；
(5-3-4) computer utilizes signal-to-noise ratio computation formula to calculate the output signal-to-noise ratio snr of stochastic resonance system model；
(5-3-5) computer draws the output signal-to-noise ratio curve of stochastic resonance system model, obtains the letter of signal to noise ratio curve
Make an uproar ratio maximum, and using the absolute value of signal to noise ratio maximum as signal to noise ratio eigenvalue k；
(5-4) storage time prediction:
K is substituted into the storage time forecast model being stored in computer
In, computer calculates and obtains Carnis Pseudosciaenae storage time time predicted.
The present invention makes the laser that laser array sends be radiated on sample first, and fibre-optical probe gathers the light of sample reflection
Enter in spectrogrph, spectrogrph obtains the n detection signal corresponding with the wavelength of n laser instrument, then, present invention adjustment is each
The weight of individual detection signal, obtains satisfactory detection signalx(t), the accidental resonance that x (t) input is prestored in a computer
System model, makes stochastic resonance system model that accidental resonance to occur, computer draws the output signal-to-noise ratio of stochastic resonance system model
Curve, obtains the signal to noise ratio maximum of signal to noise ratio curve, and using the absolute value of signal to noise ratio maximum as signal to noise ratio eigenvalue k；
And k is substituted into the storage time forecast model being stored in computer
In, computer calculates and obtains Carnis Pseudosciaenae storage time time predicted.
Therefore, the present invention has the characteristics that detection speed is fast, accuracy is high and good economy performance.
Preferably, described signal-to-noise ratio computation formula is
Wherein, ω is signal frequency, and ω is angular frequency, and s (ω) is signal spectral density, sn(ω) it is signal frequency range
Interior noise intensity.
Preferably, a length of 50 to 70 seconds during detection in step (5-2).
Therefore, invention has the advantages that (1) detection speed is fast, accuracy is high, good economy performance；(2) eliminate no
With the interference information of wave band veiling glare, the storage time of sample is preferably distinguished.
Brief description
Fig. 1 is a kind of structural representation of invention；
Fig. 2 is a kind of flow chart of inventive embodiment；
Fig. 3 is the visible/near infrared diffusing reflection spectrum of embodiments of the invention；
Fig. 4 is the principal component analysiss result of embodiments of the invention；
Fig. 5 is the storage time forecast model matched curve of the present invention.
In figure: base plate 1, supporting plate 2, laser array 3, fibre-optical probe 4, column 7, sample 8.
Specific embodiment
With reference to the accompanying drawings and detailed description invention is further described.
Embodiment as shown in Figure 1 is a kind of detection means based on laser array, treats including spectrogrph, for placement
The base plate 1 of detection sample, the supporting plate 2 above base plate；Supporting plate is provided with 2 jacks, and jack is provided with to be checked for irradiating
The optical fiber of the laser array 3 of test sample product and the laser that sends for the detection laser array reflection light after sample reflection
Probe 4；Laser array includes 6 and bundles and single wavelength laser, fibre-optical probe and light that wavelength is arranged in order
Spectrometer electrically connects, and spectrogrph is provided with for the signal output port with calculating mechatronics；Fibre-optical probe and the folder of horizontal plane
Angle is 70 degree.
The wavelength of 6 single wavelength lasers is respectively 635nm, 980nm, 532nm, 808nm, 660nm and 780nm.Base plate
It is connected with supporting plate by 4 columns 7.Spectrogrph is usb2000+ Vis/NIR instrument.
As shown in Fig. 2 a kind of detection method of Carnis Pseudosciaenae storage time, comprise the steps:
Step 100, sample preparation:
The Carnis Pseudosciaenae taking out from refrigerator is cleaned, gills, scale, head and tail, 25 grams of thickness are taken out from fish body
Lamellar for 5 millimeters is oppressed as detected sample 8；
Step 200, is detected: computer is electrically connected with spectrogrph, sample is kept flat on base plate, make laser instrument battle array
Arrange the laser sending to be radiated on sample, the light that fibre-optical probe gathers sample reflection enters in spectrogrph, obtains as shown in Figure 3
Spectrogram, spectrogrph extracts 6 corresponding with the wavelength of 6 laser instrument detection signal x from spectrogram1(t), x2(t) ...,
x6(t), and by x1(t), x2(t) ..., x6In (t) input computer；A length of 60 seconds during detection in the present embodiment；
Step 300, data processing:
Step 310, sets and one-to-one one group of weight w of each detection signal in a computer1, w2..., w6, and
Make
Step 320, using formulaIt is calculated detection signal x (t), and calculate total mean square deviation
WhenProceed to step 330；
WhenOrThen proceed to step 310:
The stochastic resonance system model that x (t) input is prestored in a computer by step 330
In, make stochastic resonance system model that accidental resonance to occur,
Wherein, ξ (t) is the gaussian noise of auto-correlation function e [ξ (t) ξ (0)]=2 α δ (t), and its intensity is α；A is signal sin (2
πf0T+ ψ) intensity；f0Frequency for signal；A, b are the real parameter of setting, and s is the movement locus of Brownian Particles, and t is fortune
The dynamic time, ψ is phase place；
Step 340, computer utilizes signal-to-noise ratio computation formulaCalculate random
The output signal-to-noise ratio snr of resonator system model；
Step 350, computer draws the output signal-to-noise ratio curve of stochastic resonance system model, obtains the letter of signal to noise ratio curve
Make an uproar ratio maximum, and using the absolute value of signal to noise ratio maximum as signal to noise ratio eigenvalue k.
Step 400, storage time is predicted:
K is substituted into the storage time forecast model being stored in computer
In, computer calculates and obtains Carnis Pseudosciaenae storage time time predicted.
In the present embodiment, k=8.1db, time=3.32 days；Proved by the detection test in laboratory, the present invention's
Precision of prediction r is 0.98498.
Storage time forecast model is by repeat step 100 to 300, to fresh Carnis Pseudosciaenae sample and the ice in 277k
The Carnis Pseudosciaenae sample storing 1 day to 5 days in case is detected, obtains k value corresponding with each detection time, and by each k
Value and corresponding storage time separately constitute 6 points, 6 points are coupled together, constitutes storage time as shown in Figure 5
Forecast model matched curve, storage time forecast modelBent for matching
The formula of line.
Principal component analysiss result after detection signal x (t) normalization of 0 to 5 day as shown in Figure 4, can from Fig. 4
Go out, the accumulative variance contribution ratio sum of two main constituents is 84.40%, the Carnis Pseudosciaenae sample of each storage time in two-dimensional space
Product can preferably be distinguished, and each component distribution comparatively dense relatively.Fig. 4 detection signal from another one side illustration
X (t) can reflect the change of storage time length, and the Forecasting Methodology of the storage time of the present invention is feasible.
It should be understood that the scope that the present embodiment is merely to illustrate invention rather than limits invention.In addition, it is to be understood that reading
After the content that invention is lectured, those skilled in the art can make various changes or modifications to invention, these equivalent form of values with
Sample falls within the application appended claims limited range.
Claims (6)
1. a kind of detection method storage time of Carnis Pseudosciaenae being detected using the detection means based on laser array, institute
State detection means and include spectrogrph, base plate (1), the supporting plate (2) above base plate for placing detected sample；On supporting plate
It is provided with least 2 jacks, jack is provided with the laser array (3) for irradiating detected sample and is used for detection laser battle array
Arrange the fibre-optical probe (4) of reflection light after sample reflection for the laser sending；Described laser array includes n and is bundled in one
Rise and the single wavelength laser that is arranged in order of wavelength, described fibre-optical probe is electrically connected with spectrogrph, spectrogrph be provided with for
With the signal output port calculating mechatronics；Fibre-optical probe is 45 to 75 degree with the angle of horizontal plane；It is characterized in that, described inspection
Survey method comprises the steps:
(1-1) sample preparation:
Carnis Pseudosciaenae is cleaned, gills, scale, head and tail, lamellar is taken out from fish body and oppresses as detected sample (8)；
(1-2) detected: computer is electrically connected with spectrogrph, sample is kept flat on base plate, so that laser array is sent
Laser be radiated on sample, fibre-optical probe gather sample reflection light enter spectrogrph in, spectrogrph obtains and n laser instrument
The corresponding n detection signal x of wavelength1(t), x2(t) ..., xn(t), and by x1(t), x2(t) ..., xnT () input calculates
In machine；
(1-3) data processing:
(1-3-1) set and one-to-one one group of weight w of each detection signal in a computer1, w2..., wn, and make
(1-3-2) utilize formulaIt is calculated detection signal x (t), and calculate total mean square deviation
WhenProceed to step (1-3-3)；
WhenOrThen proceed to step (1-3-1):
(1-3-3) the stochastic resonance system model that x (t) input prestores in a computer
In, make stochastic resonance system model that accidental resonance to occur, its
In, ξ (t) is the gaussian noise of auto-correlation function e [ξ (t) ξ (0)]=2 α δ (t), and its intensity is α；A is signal sin (2 π
f0T+ ψ) intensity；f0Frequency for signal；A, b are the real parameter of setting, and s is the movement locus of Brownian Particles, and t is motion
Time, ψ is phase place；
(1-3-4) computer utilizes signal-to-noise ratio computation formula to calculate the output signal-to-noise ratio snr of stochastic resonance system model；
(1-3-5) computer draws the output signal-to-noise ratio curve of stochastic resonance system model, obtains the signal to noise ratio of signal to noise ratio curve
Maximum, and using the absolute value of signal to noise ratio maximum as signal to noise ratio eigenvalue k；
(1-4) storage time prediction:
K is substituted into the storage time forecast model being stored in computer
In, computer calculates and obtains Carnis Pseudosciaenae storage time time predicted.
2. according to claim 1 using the detection means based on laser array, the storage time of Carnis Pseudosciaenae is examined
The detection method surveyed, is characterized in that, described n is 6, the wavelength of each single wavelength laser be respectively 635nm, 980nm, 532nm,
808nm, 660nm and 780nm.
3. according to claim 1 using the detection means based on laser array, the storage time of Carnis Pseudosciaenae is examined
The detection method surveyed, is characterized in that, described base plate is connected with supporting plate by 4 columns (7).
4. according to claim 1 using the detection means based on laser array, the storage time of Carnis Pseudosciaenae is examined
The detection method surveyed, is characterized in that, described spectrogrph is usb2000+ Vis/NIR instrument.
5. according to claim 1 using the detection means based on laser array, the storage time of Carnis Pseudosciaenae is examined
The detection method surveyed, is characterized in that, described signal-to-noise ratio computation formula isIts
In, ω is signal frequency, and ω is angular frequency, and s (ω) is signal spectral density, sn(ω) it is that noise in signal frequency range is strong
Degree.
6. Carnis Pseudosciaenae is deposited using the detection means based on laser array according to claim 1 or 2 or 3 or 4 or 5
The detection method that the storage time is detected, is characterized in that, a length of 50 to 70 seconds during detection in step (1-2).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310695970.3A CN104089883B (en) | 2013-12-17 | 2013-12-17 | Laser array based detection device and pseudosciaena polyactis storage time detection method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310695970.3A CN104089883B (en) | 2013-12-17 | 2013-12-17 | Laser array based detection device and pseudosciaena polyactis storage time detection method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104089883A CN104089883A (en) | 2014-10-08 |
| CN104089883B true CN104089883B (en) | 2017-02-01 |
Family
ID=51637619
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310695970.3A Expired - Fee Related CN104089883B (en) | 2013-12-17 | 2013-12-17 | Laser array based detection device and pseudosciaena polyactis storage time detection method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104089883B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104568793B (en) * | 2015-01-01 | 2017-06-23 | 浙江工商大学 | Rheum officinale oppresses storage time detection method |
| CN109738899B (en) * | 2018-12-29 | 2024-02-13 | 湖南智晶电子科技有限公司 | Low-altitude aircraft detection method and system based on stochastic resonance detection array |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7494771B2 (en) * | 2001-07-19 | 2009-02-24 | Geneohm Sciences Canada, Inc. | Universal method and composition for the rapid lysis of cells for the release of nucleic acids and their detection |
| CN102495027A (en) * | 2011-12-05 | 2012-06-13 | 天津大学 | Method and device for measuring optical parameter of chaotic medium by multi-wavelength space resolution phase lock |
| CN103234917A (en) * | 2013-04-08 | 2013-08-07 | 中国工程物理研究院流体物理研究所 | Real-time measuring system for impact temperature and spectral emissivity |
| CN103278455A (en) * | 2013-05-16 | 2013-09-04 | 宁波大学 | Measurement device and measurement method of optical parameters of dielectric film |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4414771B2 (en) * | 2004-01-08 | 2010-02-10 | オリンパス株式会社 | Confocal microspectroscope |
-
2013
- 2013-12-17 CN CN201310695970.3A patent/CN104089883B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7494771B2 (en) * | 2001-07-19 | 2009-02-24 | Geneohm Sciences Canada, Inc. | Universal method and composition for the rapid lysis of cells for the release of nucleic acids and their detection |
| CN102495027A (en) * | 2011-12-05 | 2012-06-13 | 天津大学 | Method and device for measuring optical parameter of chaotic medium by multi-wavelength space resolution phase lock |
| CN103234917A (en) * | 2013-04-08 | 2013-08-07 | 中国工程物理研究院流体物理研究所 | Real-time measuring system for impact temperature and spectral emissivity |
| CN103278455A (en) * | 2013-05-16 | 2013-09-04 | 宁波大学 | Measurement device and measurement method of optical parameters of dielectric film |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104089883A (en) | 2014-10-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Xu et al. | An overview on nondestructive spectroscopic techniques for lipid and lipid oxidation analysis in fish and fish products | |
| CN111157511B (en) | Egg freshness nondestructive testing method based on Raman spectrum technology | |
| CN103257118A (en) | Fish tenderness hyperspectral detection method based on characteristic wave band | |
| CN103954567B (en) | Soil salt assay method based on continuum removal method | |
| CN108875913A (en) | A kind of matsutake Fast nondestructive evaluation system and method based on convolutional neural networks | |
| CN102323267A (en) | System and method used for rapidly evaluating freshness of raw meat products | |
| CN104965973B (en) | A kind of Apple Mould Core multiple-factor Non-Destructive Testing discrimination model and method for building up thereof | |
| CN108956545B (en) | Fruit internal quality nondestructive testing model establishing method and system | |
| CN102937575B (en) | Watermelon sugar degree rapid modeling method based on secondary spectrum recombination | |
| CN107024445A (en) | The modeling method and detection method of the quick detection of Nitrate in Vegetable | |
| CN104655585B (en) | A kind of PSE meat screening technique based near infrared spectrum | |
| CN104964963B (en) | Quick determination method based on delicate flavour material inosinicacid in Raman spectrum live fresh pork | |
| Wang et al. | General model of multi-quality detection for apple from different origins by Vis/NIR transmittance spectroscopy | |
| CN113504201A (en) | Crisp meat and Anhui brittleness prediction method based on visible-near infrared hyperspectral technology | |
| CN104089883B (en) | Laser array based detection device and pseudosciaena polyactis storage time detection method | |
| CN109540832A (en) | A kind of detection method based on total nitrogen in the near-to-mid infrared spectrum large-scale milch cow farms liquid manure of fusion | |
| Si et al. | Quality assessment of fruits and vegetables based on spatially resolved spectroscopy: A review | |
| CN106376511B (en) | A kind of fresh-water fishes survival rate prediction device and method based on passive acoustic information | |
| CN104255118A (en) | Rapid lossless testing method based on near infrared spectroscopy technology for paddy rice seed germination percentage | |
| CN110231305A (en) | A method of DPPH free radical scavenging ability in the odd sub- seed of measurement | |
| CN110231302A (en) | A kind of method of the odd sub- seed crude fat content of quick measurement | |
| CN104089881A (en) | Pseudosciaena crocea storage time detection method | |
| CN108535250A (en) | ' Fuji ' ripe apples degree lossless detection method based on Streif indexes | |
| Peng et al. | Application of near-infrared spectroscopy for assessing meat quality and safety | |
| CN110658101A (en) | Method for detecting moisture change of sea cucumber in microwave vacuum drying process |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20170201 Termination date: 20171217 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |