CN108022239A - A kind of sparkling wine browning detection device and method based on machine vision - Google Patents

A kind of sparkling wine browning detection device and method based on machine vision Download PDF

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CN108022239A
CN108022239A CN201711266254.8A CN201711266254A CN108022239A CN 108022239 A CN108022239 A CN 108022239A CN 201711266254 A CN201711266254 A CN 201711266254A CN 108022239 A CN108022239 A CN 108022239A
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CN108022239B (en
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张卫正
钱慎
钱慎一
张伟伟
刘岩
李萌
张焕龙
陈启强
邹东尧
甘勇
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Zhengzhou University of Light Industry
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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    • G06T5/30—Erosion or dilatation, e.g. thinning
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
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Abstract

The problem of present invention proposes a kind of sparkling wine browning detection device and method based on machine vision, complicated to solve existing detection process, of high cost, and applicability is not strong；The sample being placed on using the camera collection of smart mobile phone on the diffused light source panel of vasculum bottom, obtain the digital picture of sparkling wine, the processing such as gray processing, binaryzation, corrosion carries out digital picture by machine vision and image processing techniques, by analyzing tri- passages of image RGB, quality status stamp using blue channel attenuation percentage as sparkling wine, its content height correlation with 2 furfural of the absorbance at 420nm and 5 methylol.The present invention only needs to obtain the transmission image of the sparkling wine sample in different brown stain stages, can be carried out at the same time the analysis of multiple brand sparkling wine samples, avoids the sample treatment of complexity, it can carry out Multi-example analysis, speed is fast, and cost is low, without expensive special instrument and other chemical reagent.

Description

A kind of sparkling wine browning detection device and method based on machine vision
Technical field
The present invention relates to sparkling wine brown stain and the technical field of image procossing, more particularly to one kind to be based on machine vision Sparkling wine browning detection device and method.
Background technology
At this stage, nearly all also exist to the good opinion and demand of fizz in production sparkling wine, consumer all over the world Constantly increase.The principle of fizz is to add sugar and yeast in the wine made to carry out second of alcohol hair in the container of closing Ferment, fermentation process produce carbon dioxide be limited in bottle become wine in bubble source (Serra-Cayuela A, Jourdes M,Riu-Aumatell M,et al.Kinetics of browning,phenolics,and 5- hydroxymethylfurfural in commercial sparkling wines.[J].Journal of Agricultural&Food Chemistry,2014,62(5):1159-1166).The sugar of addition is converted into by the yeast of addition Alcohol and carbon dioxide so that bubble is produced and seethed, this is one of most important feature of sparkling wine.
Sparkling wine consumption figure rapid development, according to the statistics of international grape wine tissue, in recent years sparkling wine life Production increases 40%, and fizz grape wine accounts for the 8% of global vintage.China and the U.S. become the master for promoting this growth Power army.
Spain's slips and French champagne are foremost sparkling wine brands.Slips be by produced in conventional processes by Name the high-quality fizz of regulation protection in original producton location.Grape used in slips wine is by strictly selecting, in juice-extracting process It must be careful as, optimal grape juice could be so obtained in preferable maturity.Grape juice is then poured into stainless steel barrel Cold fermentation is carried out, goes and tastes to determine its quality again after standing, and selectively allocated.Last allotment is carried out Afterwards, wine is fitted into bottle, and is at least placed 9 months in wine cabinet, but usually this storage period can be longer.During this period, grape Wine will produce secondary fermentation in bottle, form complicated organoleptic attribute such as fragrance, color and the foam performance of grape wine, become Steep in wine, and produce yeast precipitation.After aging period, saccharomycete can carefully be taken out, is filled to bottleneck with a collection of grape wine, With cork closure, and add strip of paper used for sealing.In addition, according to the duration of ageing in bottle, there are two kinds of different classifications：At least 15 The Reserva, Gran Reserva (Serra-Cayuela A, Aguilera-Curiel the M A, Riu- of at least 30 months of the moon Aumatell M,et al.Browning during biological aging and commercial storage of Cava sparkling wine and the use of 5-HMF as a quality marker[J].Food Research International,2013,53(1):226-231).Except improving grape quality, the ageing time can also influence production cost, The final price of aging wine is higher than new grape wine.
In terms of physical chemistry and organoleptic attribute, grape wine is a kind of dynamic products, therefore in biological ageing and is stored Complicated chemical change occurs for Cheng Zhonghui.These chemical changes may cause the change of organoleptic feature, particularly fragrance, flavor and Color (research [D] the Jinan of Wang Yu peaks Wine Aroma influence factors:Shandong Light Ind College, 2010).
Brown stain is to be related to oxidizing process (Li H, Guo A, the Wang H.Mechanisms of sugar, lipid, amino acid and phenol of oxidative browning of wine[J].Food chemistry,2008,108(1):1-13.), brown stain reduces Aesthetic quality's (color, the change of flavor and fragrance and increase of astringent taste) of grape wine, therefore during processing and storage Brown stain must be controlled.Once bottle is sealed, brown stain cannot regulate and control again.Therefore, winery, which must be found, to have indicated Steep in wine the mark and parameter of brown stain, and pass through the brown stain that technological means detects sparkling wine.It is necessary in actual production to open The fast and reliable method of hair monitoring browning degree, and the parameter of definition instruction sparkling wine quality.
Existing research has been based on different quality status stamps and e measurement technology, it is proposed that several to be used to quantify and characterize The method of pickled grape wine browning.As UV-Visible absorption spectrum (Ultraviolet-visible spectroscopy, Abbreviation UV-VIS) and high performance liquid chromatography (High Performance Liquid Chromatography, abbreviation HPLC) (Serra-Cayuela A,Aguilera-Curiel M A,Riu-Aumatell M,et al.Browning during biological aging and commercial storage of Cava sparkling wine and the use of 5-HMF as a quality marker[J].Food Research International,2013,53(1):226- 231.)。
Absorbance (A at 420nm wavelength420) be used as white wine brown stain monitoring parameter (Kallithraka S, Salacha M I,Tzourou I.Changes in phenolic composition and antioxidant activity of white wine during bottle storage:Accelerated browning test versus bottle storage[J].Food Chemistry,2009,113(2):500-505.), A420The increase of value and browning Directly related (Ibarz A, Pagan J, Garza S.Kinetic models of non-enzymatic browning in apple puree.[J].Journal of the Science of Food&Agriculture,2000,80(8):1162- 1168.).However, in previous studies, the A as the quality status stamp of slips sparkling wine420Parameter is proved to have low Sensitivity and low specificity, use HMF 5 hydroxymethyl 2 furaldehyde (5-HMF) content instead as more effectively mark (Serra- afterwards Cayuela A,Aguilera-Curiel M A,Riu-Aumatell M,et al.Browning during biological aging and commercial storage of Cava sparkling wine and the use of 5-HMF as a quality marker[J].Food Research International,2013,53(1):226-231.), but to the change Compound, which carries out chromatography in laboratory, to be needed to consume substantial amounts of time and reagent, and cost is expensive.
In existing research method, fluorescent excitation-emmision spectra combination parallel transport is successfully used to monitor The research of the browning of four kinds of sparkling wines, by as by monitoring A420Or 5-HMF contents realize brown stain detection Quick alternative.In order to study more preferable alternative solution, researcher proposes a kind of new method based on Colorimetric techniques, uses Browning in the slips sparkling wine that monitoring accelerates in brown stain experiment.Using fixed by International Commission on Illumination (CIE) Lab color modes measurement browning degree (the Raquel P F that the Lab color modes or Richard.S.Hunter of justice are founded G,Joao Barroca M.Evaluation of the Browning Kinetics for Bananas and Pears Submitted to Convective Drying[J].Current Biochemical Engineering,2014,1:165- 172.), but these methods need to use special colorimeter or spectrophotometer.
In recent years, lifted with the sensor performance of smart mobile phone, the application study based on smart mobile phone is also increasing.Intelligence Can mobile phone relative experimental room specialty instrument and equipment, with cost it is low, it is easy to carry, can collection in worksite and analysis, result it is easy In share the advantages that.
The content of the invention
Brown stain detection for existing sparkling wine needs to use special colorimeter or spectrophotometer, and detection process is multiple Miscellaneous, the not strong technical problem of applicability, the present invention proposes a kind of sparkling wine browning detection dress based on machine vision Put and method, utilize machine vision and image processing techniques to carry out the detection of sparkling wine browning, it is only necessary to obtain different The transmission image of the sparkling wine sample in brown stain stage, it is possible to be carried out at the same time point of the sparkling wine sample of multiple brands Analysis, device is cheap, without expensive special instrument and other chemical reagent.
In order to achieve the above object, the technical proposal of the invention is realized in this way：A kind of blistering based on machine vision Grape wine browning detection device and method, its step are as follows：
Step 1：Experimental provision is built, prepares sample；
Step 2：Sample image is obtained using the camera of smart mobile phone, by the RGB image IG of acquisitioniCarry out gray processing and Binary conversion treatment, obtains binary image BINi, i=1,2 ..., 6；
Step 3：By image BINiIn pixel all negate, obtain image IRi；With structural element to IRiCarry out rotten Erosion, obtains image IERi, the constriction zone in hole of the acquisition equipped with sample；
Step 4：By image IERiConnected component labeling is carried out, each type of sample is identified according to connected component labeling；
Step 5：With image IG1R passages, G passages and the B in region of contraction sample well of primary sample of standard lead to Correction factor is calculated on the basis of the average in road, and to image IGiEach passage carries out color correction, obtains correction chart as IGCi, i= 2,…6；
Step 6：Calculate the image IGC after correctioniThe R, G in region of shrinkage hole of each brand sample, channel B Average, chooses parameter of the channel B as brown stain；
Step 7：Channel B Color invariants and blueness decay percentage are calculated, blueness decay percentage has line with the time Sexual intercourse, blueness decay percentage are consistent with the browning of sparkling wine, the quality status stamp as brown stain detection.
The sample is Brut, Brut Reserva, Brut Gran tetra- kinds of best seller brands of Reserva and Semiseco Slips sparkling wine, four kinds of grape wine have different sugar contents and productive year；10mL is taken to be respectively charged into the amber of 4 20mL In amber color bottle, and in N2Deaerate under air-flow；This 4 amber vials equipped with different brands fizz are placed on dark In environment, and Conservation environment temperature is 8 DEG C；Using the fizz of the different brands in this 4 bottles as the experiment of follow-up 10 days Standard sample, and by this 4 amber vials equipped with standard sample be named as Bottle1, Bottle2, Bottle3, Bottle4；10mL is respectively taken to be respectively charged into the amber vial of 4 20mL the sparkling wine of four brands again, and in N2 Deaerate under air-flow；This 4 amber vials equipped with different brands fizz are placed on to the environment of complete darkness, are then placed In an oven, 65 ± 1 DEG C of constant temperature is set, carries out accelerating brown stain experiment；By this 4 equipped with the amber small of acceleration brown stain sample Bottle is named as Bottle5, Bottle6, Bottle7, Bottle8；Gathered by the sampling time point of 0,2,4,6,8 and 10 day Sample message, when the time interval of collection is 48 small；In each sampling time point, (1) extracts Brut, Brut first Each 1 part of Reserva, the standard sample of Brut Gran Reserva and Semiseco, are placed sequentially in 96 orifice plates from top to bottom In the hole at middle part, 1 row are lined up vertically, are then named as the 1st row；(2) then extraction Brut, Brut Reserva, Brut Gran Reserva and Semiseco acceleration brown stain each 4 parts of sample, then each brand put 1 row, be sequentially placed into the 2nd of 96 orifice plates ~5 row.
The experimental provision includes vasculum and light source, and light source is arranged on the lower section of vasculum, and collection lower box part, which is equipped with, to be used Collection hole is equipped with the top of the orifice plate for holding sample, vasculum, collection hole is located at the surface of orifice plate.
The vasculum is made of black foam core plate, covered with dumb light black velvet paper inside vasculum；Vasculum For the rectangular parallelepiped structure of height 80cm, the difference between diagonal and center vertical optical path is less than 5%；The light source can for intensity The diffusing reflection light source of control；Orifice plate is 96 orifice plates, and orifice plate is manufactured by the pure polystyrene of import optical clear, at gamma rays sterilizing Reason.
Obtain sample image method be：Slips sparkling wine sample is arranged in hole using 0.3ml micropipettes The middle part of plate, by the panel of orifice plate placement light source, vasculum is covered on orifice plate, collection hole is located at the upper of the middle of orifice plate Side, smart mobile phone is placed on the collection hole at the top of vasculum, using the built-in camera of smart mobile phone with highest resolution ratio Image is obtained, and saves as JPEG；The primary sample arrangement of standard in the orifice plate, obtains all samples into same image.
The method that the RGB image of acquisition is carried out to gray processing and binary conversion treatment is：
The RGB image IG that smart mobile phone is obtainediIn each pixel tri- components of R, G, B average value as image Gray value, i.e. Grayi(x, y)=(RIG,i(x,y)+GIG,i(x,y)+BIG,i(x, y))/3, obtain gray level image Grayi；
To gray level image GrayiOptimal threshold T is calculated using Otsu algorithms, optimal threshold T is so that the ash of σ maximums Angle value gt：If the gray level of gray level image is L, then tonal range is [0, L-1], and gray level image is calculated most using Ostu methods Good threshold value is：σ=Max [w0(gt)×(u0(gt)-u)^2+w1(gt)×(u1(gt)-u) ^2)], wherein, w0For prospect ratio, u0For prospect gray average, w1For background ratio, u1For background gray average, u is the average of view picture gray level image.
T is the optimal threshold of segmentation figure picture, and gray level image is divided into 2 parts according to optimal threshold T, obtains binaryzation Image BINi(x,y)：
Wherein, Grayi(x, y) is the value of the pixel at (x, y) place in gray level image；
By binary image BINiInto negate (pixel value is 1 to be changed into 0, pixel value be 0 be changed into 1) obtain binary picture As IRi；Using the imerode function pair binary images IR in MatlabiCarry out etching operation：IERi=imerode (IRi, D1), wherein, IERiRepresent binary image IRiBy a diameter of 8 circular configuration element D1Obtained image, i=after corrosion 1,2,3,6；D1=strel (' disk', 4), disk represents circular shape.
By image IERiCarry out connected component labeling method be：Using connected component labeling function in Matlab bwlabel：[LJi,LNUMi]=bwlabel (IERi, 8), wherein, LNUMiRepresent image IERiThe quantity of middle connected region, LJi Represent and image IERiThe identical matrix of size, matrix L JiContain mark image IERiIn each connected region classification mark Label, the value of these labels is 1,2 ..., LNUMi, 8 represent it is by 8 neighbor searching connected regions；
The method of the position of each type of sample of the identification is：The sample area that connected component labeling shrinks each Domain is as a connected region, for each connected region, using the regionprops functions in Matlab：STATSi= regionprops(LJi, ' Centroid'), ' Centroid' attributes are exactly the barycenter of connected region, STATSiContain image IERiIn each connected region (centre of form coordinate (RXi,nu,LYi,nu), wherein, the value of subscript n u is 1~20.
The acquisition methods of correction chart picture are：With image IG1The R in region of contraction sample well of primary sample of standard lead to The average R in road, G passages and channel B1, ave、G1, aveAnd B1, aveOn the basis of, in follow-up different time sampled point acquired image Standard primary sample where the R passages in region of contraction sample well, the average of G passages and channel B be RI, ave、Gi,ave And BI, aveFor reference, three correction factor C are calculatedi,R、Ci,GAnd Ci,B：
Wherein, Ci,R、Ci,G、Ci,BFor image IGiR passages, the correction factor of G passages and channel B, i=2,3 ..., 6；
By image IGiAll pixels point R passages RIG, iIt is multiplied by Ci,R, G passages GIG, iIt is multiplied by Ci,G, channel B BIG, iIt is multiplied by Ci,B, i=2,3 ..., 6, obtain tri- passages of new RGB, the image of composition is correction chart as IGCi。
By image IER1In the row coordinate of centre of form coordinate be LY1,1~LY1,4The pixel of connected region put 1, it is other Pixel is set to 0, and obtains matrix J Z1, by original image IG1In all pixels point R passages value as matrix RIG,1, by original Beginning image IG1In all pixels point G passages value as matrix GIG,1, by original image IG1In all pixels point B The value of passage is as matrix BIG,1；By matrix J Z1Dot product matrix RIG,1Obtain matrix D CR1, by matrix J Z1Dot product matrix GIG,1 To matrix D CG1, by matrix J Z1Dot product matrix BIG,1Obtain matrix D CB1；
R1, ave=sum (sum (DCR1))/sum(MJ1),
G1, ave=sum (sum (DCG1))/sum(MJ1),
B1, ave=sum (sum (DCB1))/sum(MJ1),
Wherein, sum (sum (DCR1)) it is matrix D CR1The summation of middle all elements value, sum (sum (DCG1)) it is matrix DCG1The summation of middle all elements value, sum (sum (DCB1)) it is matrix D CB1The summation of middle all elements value, sum (MJ1) it is figure As IER1In the row coordinate of centre of form coordinate be LY1,1~LY1,4Connected region sum of all pixels, MJ1=regionprops (JZ1, ' Area'), ' Area' attributes are exactly the number of pixels in image regional.
By sample image IGCiChannel B be converted to respective color invariant：bi=BIGC,i,ave/(RIGC,i,ave+GIGC,i,ave +BIGC,i,ave), calculate blueness decay percentage %Bt：
Wherein, bt0Channel B invariant when being t=0, btIt is the Color invariants of the channel B of time t；Blueness decay hundred Divide and compare %BtRepresent that browning is：Y=Y0+ kt,
Wherein, Y is the absorbance or 5-HMF contents or %B at 420nmt, Y0It is that absorbance at 420nm or 5-HMF contain Amount or %BtInitial value, t is the time, and k is velocity constant.
Beneficial effects of the present invention：Sparkling wine sample is placed in the hole in the centre position of 96 orifice plates, orifice plate is put Put on the diffused light source panel of vasculum bottom, using smart mobile phone as image capture device, from adopting at the top of vasculum Collect the digital picture that sparkling wine is obtained in hole, carry out Digital Image Processing and analysis, blistering is detected in RGB color The brown stain of grape wine, determines the browning degree of sparkling wine；Absorbance and acquisition with two kinds of widely used research 420nm The method of 5-HMF contents is compared, and blue channel attenuation percentage and common browning index height correlation, are that fizz is brown The new mark become.The present invention only needs to obtain the transmission image of the sparkling wine sample in different brown stain stages, it is possible to while into The analysis of the sparkling wine brown stain sample of the multiple brands of row, avoids the sample treatment of complexity, and can synchronously carry out more Sample analysis, speed is fast, and device price is low, without expensive special instrument and other chemical reagent.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing There is attached drawing needed in technology description to be briefly described, it should be apparent that, drawings in the following description are only this Some embodiments of invention, for those of ordinary skill in the art, without creative efforts, can be with Other attached drawings are obtained according to these attached drawings.
Fig. 1 is the flow chart of the present invention.
Fig. 2 is the structural scheme of mechanism of image capture apparatus of the present invention.
Fig. 3 is putting in order for inventive samples.
Fig. 4 is the gray level image of sample of the present invention.
Fig. 5 is the binary image of sample of the present invention.
Fig. 6 is the image after present invention corrosion.
Fig. 7 causes R, G of various brands sample areas, the Change in Mean schematic diagram of channel B for acceleration brown stain.
Fig. 8 is various brands sample B channel attenuation and the chart of percentage comparison picture of heating time.
Fig. 9 is the absorbance at various brands sample 420nm and the graph of a relation picture of blue channel attenuation percentage.
Figure 10 is various brands sample 5-HMF contents and the graph of a relation of blue channel decay percentage.
Figure 11 is the qualitative comparison of several algorithms.
Embodiment
Below in conjunction with the attached drawing in the embodiment of the present invention, the technical solution in the embodiment of the present invention is carried out clear, complete Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, instead of all the embodiments.It is based on Embodiment in the present invention, those of ordinary skill in the art are obtained every other under the premise of not making the creative labor Embodiment, belongs to the scope of protection of the invention.
As shown in Figure 1, a kind of sparkling wine browning detection device and method based on machine vision, its step is such as Under：
Step 1：Experimental provision is built, prepares sample.
Using the slips sparkling wine of four kinds of best seller brands as research object, including Brut, Brut Reserva, Brut Gran Reserva and Semiseco, four kinds of grape wine have different sugar contents and productive year.During initial samples, The quality parameter of each type of slips grape wine is as shown in table 1.Surveyed using the standard method of international grape and grape wine tissue Measure content sugar, alcohol content, pH, the free and total sulfur dioxide of every kind of sample.Wherein, alcohol content meets on slips wine bottle The value of sign.
The quality parameter of 14 kinds of brand Cava sparkling wines of table
10mL is respectively taken to be respectively charged into the amber vial of 4 20mL the sparkling wine of four brands, and in N2Gas Flow down degassing.This 4 amber vials equipped with different brands fizz are placed in the environment of dark afterwards, and preserve ring Border temperature is 8 DEG C, places the time of 10 days in the present context, its brown stain can be ignored.By the different brands in this 4 bottles Standard sample of the fizz as subsequent experimental, and this 4 amber vials equipped with standard sample are named as Bottle1、Bottle2、Bottle3、Bottle4。
And then, then by the sparkling wine of four brands respectively take 10mL to be respectively charged into the amber vial of 4 20mL, And in N2Deaerate under air-flow.This 4 amber vials equipped with different brands fizz are placed on to the environment of complete darkness, so After place in an oven, 65 ± 1 DEG C of constant temperature of setting, carries out accelerating brown stain experiment.And by this 4 equipped with acceleration brown stain sample Amber vial is named as Bottle5, Bottle6, Bottle7, Bottle8.
Sample message is gathered at the 0th, 2,4,6,8 and 10 day (6 sampling time points), the time interval of collection is small for 48 When.In each sampling time point, (1) extracts Brut, Brut Reserva, Brut Gran Reserva and Semiseco first Each 1 part of standard sample, in the hole at the middle part for being placed sequentially in 96 orifice plates from top to bottom, line up 1 row vertically, be then named as 1 row；(2) then extraction Brut, Brut Reserva, Brut Gran Reserva and Semiseco acceleration brown stain sample Each 4 parts, then each brand put 1 row, be sequentially placed into 96 orifice plates the 2nd~5 row, as shown in Figure 3.Therefore, 120 are analyzed altogether Sample (6 sampling time point × 4 Cava brand × 5 part (4 parts accelerate brown stain sample and 1 part of standard sample)).
For experimental provision as shown in Fig. 2, including vasculum 2 and light source 4, light source 4 is arranged on the lower section of vasculum 2, vasculum 2 Lower part, which is equipped with, is used to holding 96 orifice plates 3 of sample, and the top of vasculum 2 is equipped with collection hole 1, collection hole 1 be located at orifice plate 3 just on Side.Vasculum 1 is made of black foam core plate, internal covered with dumb light black velvet paper, to eliminate internal light reflection.Adopt Header 1 is the cuboid of high 80cm so that difference between diagonal and center vertical optical path is less than 5%, so as to reduce due to Light passes through difference caused by different paths.Light source 4 is the diffusing reflection light source of intensity controlled.Orifice plate 3 is 96 orifice plates, by import The pure polystyrene manufacture of optical clear, through gamma rays sterilization treatment.
Step 2：Sample image is obtained using the camera of smart mobile phone, the RGB image of acquisition is subjected to gray processing and two-value Change is handled.
In the hole at the middle part that slips sparkling wine sample is arranged in orifice plate 3 using micropipette (0.3ml), per hole Add 0.15ml samples, orifice plate 3 is placed on the panel of light source 4, vasculum 2 is covered on the aperture plate 3, collection hole 1 is located at orifice plate 3 Surface.Smart mobile phone is placed on the collection hole 1 at the top of vasculum, using the built-in camera of smart mobile phone with highest Resolution ratio obtains image (3888 × 5152=20030976 pixels), and saves as jpeg format.Every time during collection, orifice plate is obtained In all samples into same image, caused by being rocked to avoid the fluctuation due to light source or smart mobile phone influence.For Raising efficiency, and can be compared between different sample images, all can be in the 1st row of orifice plate when gathering image every time The primary sample of placement standard, is derived from each Bottle1, Bottle2, Bottle3, Bottle4 using time point.
Sample is placed in the center of 96 orifice plates, is from left to right arranged in order, the 1st is classified as the primary sample of standard (often Kind of 1 part of sparkling wine, respectively from Bottle1, Bottle2, Bottle3 and Bottle4, totally 4 parts), the 2nd is classified as Brut samples 4 parts of product (come from Bottle5), and the 3rd is classified as 4 parts of Brut Reserva samples (coming from Bottle6), and the 4th is classified as Brut Gran 4 parts of Reserva samples (come from Bottle7), and the 5th is classified as 4 parts of Semiseco samples (coming from Bottle8), as shown in Figure 3.
The image comprising the sparkling wine sample for accelerating brown stain and the sample of standard sparkling wine is obtained first, rear Continuous image of the sampling time point collection comprising brown stain sample and standard sample.By obtaining 6 figures in 6 sampling time points Picture, at the 1st time, obtains the standard sample in orifice plate and accelerated the image IG of the sample of brown stain by 0 day1；At the 2nd time, obtain The image IG of standard sample in orifice plate and the sample by acceleration brown stain in 2 days2；At the 3rd time, the standard sample in orifice plate is obtained With the image IG of the sample by acceleration brown stain in 4 days3；In the 4th, obtain the standard sample in orifice plate and accelerated by 6 days brown The image IG of the sample of change4；In the 5th, obtain the standard sample in orifice plate and accelerated the image of the sample of brown stain by 8 days IG5；At the 6th time, obtain the standard sample in orifice plate and accelerated the image IG of the sample of brown stain by 10 days6.Each sampling time Before point obtains image, the relevant position that sample is put into orifice plate will be taken out from Bottle1~Bottle8, as shown in Figure 3.Adopt When collecting the image of sample, ensure that each column equipped with sample is in vertical state as far as possible in the preview form of camera so that obtain Image close in Fig. 3 sample place ideal position, in order to follow-up image procossing.
The uniformity of illumination and measurement is studied using blank, blank is placed on diffusing reflection light source, and using intelligent hand The built-in camera of machine obtains image IB (3888 × 5152=20030976 pixels) with highest resolution ratio, by image IB average marks For 322 × 243=78246 blocks, (each piece is expressed as BKNum1, num2, the value that the value of num1 is 1~322, num2 is 1~243), Every piece of size is 16 × 16=256 pixels.Count BKNum1, num2RGB triple channels average BKNum1, num2, ave。
Wherein, RIB(x,y)、GIB(x, y) and BIB(x, y) is represented in image IB in the R passages at pixel (x, y) place respectively Value, the value of G passages and the value of channel B.The value range that the value range of x is 0~3887, y is 0~5151.
Count BKNum1, num2, aveIn maximum MAXbkAnd minimum value MINbk, by calculating, maximum difference (MAXbk- MINbk)/MAXbkLess than 1%, show that the influence of exterior lighting is smaller, meet requirement of the present invention to the uniformity of illumination.This hair The bright view data using aperture plate center position, reduces error as far as possible with this.
The RGB image IG that smart mobile phone is obtainedi(value of i is 1~6) is converted to gray level image Grayi, using average value Method, that is, extract the average value of RGB triple channels as gray value：
Grayi(x, y)=(RIG,i(x,y)+GIG,i(x,y)+BIG,i(x,y))/3
Wherein, RIG,i(x,y)、GIG,i(x,y)、BIG,i(x, y) is respectively image IGiIn pixel (x, y) place R, G, The value of channel B；Grayi(x, y) represents gray value of the gray level image at pixel (x, y) place, as shown in Figure 4.
It needs to be determined that threshold value could be to gray level image GrayiCarry out binary conversion treatment, maximum variance between clusters (Ostu) meter Calculate it is simple, stablize it is effective, be passed through in practical application frequently with definite threshold value method.Ostu algorithms are considered as image segmentation Preferred algorithm that middle threshold value is chosen, the algorithm from brightness of image and contrast influence, therefore in Digital Image Processing To being widely applied.Ostu algorithms are the gamma characteristics by image, divide the image into background and prospect two parts.Background is with before Inter-class variance between scape is bigger, illustrates that the two-part difference for forming image is bigger, prospect mistake is divided into background or portion when part Point background mistake, which is divided into prospect, all can cause two parts difference to diminish, therefore, make inter-class variance maximum segmentation mean wrong point it is general Rate is minimum.
If the gray level of gray level image is L, then tonal range is [0, L-1], and gray level image is calculated using Ostu algorithms Optimal threshold is：
σ=Max [w0(gt)×(u0(gt)-u)^2+w1(gt)×(u1(gt)-u)^2)]
Wherein, w0For prospect ratio, u0For prospect gray average, w1For background ratio, u1For background gray average, u is whole The average of width gray level image so that the gray value gt of σ maximums is exactly the optimal threshold T of segmentation figure picture.
To gray level image Grayi(x, y) finds optimal threshold T using criterion above, divides the image into as 2 parts, Obtain binary image BINi(x,y)。
Wherein, Grayi(x, y) is gray level image GrayiIn (x, y) place pixel value, the value of i is 1~6 (collection altogether 6 images, obtain 1 image every time), the image BIN after binaryzationi(x, y) is as shown in Figure 5.
Step 3：By image BINiIn pixel all negate, obtain binary image IRi；With structural element to image IRiCorroded to obtain image IERi, the constriction zone in the hole equipped with sample is obtained, prevents the edge in the hole equipped with sample to sample The image procossing of product interferes.
D1Represent a diameter of 4 circular configuration element, its establishment function is：
D1=strel (' disk', 4)
Its implication is that one radius of establishment is 4 (the circular configuration element of i.e. a diameter of 8), disk expression circular shapes. The function of strel functions is structural texture element (Structuring element), and so-called structural element, can regard one as Small image is opened, it is commonly used in the morphology operations (such as expansion, burn into opening operation and closed operation) of image.
By image BINiIn pixel all negate, obtain image IRi.That is image BINiIn the value of original pixel be 0, then it is changed into 1；The value of pixel originally is 1, then is changed into 0.
Using the imerode functions in Matlab by binary image IRiCarry out the etching operation in Morphological scale-space, tool The operating method of body is：
IERi=imerode (IRi,D1)
Wherein, IERiRepresent binary image IRiBy a diameter of 8 circular configuration element D1Obtained image after corrosion, I=1,2,6.As shown in Figure 6.Utilize circular configuration element D1To binary image IRiCarry out at morphologic corrosion Reason, eliminates the border for the emptying aperture for not holding sample in image, while also removes the border in the hole for holding sample, obtains in sample The constriction zone in the hole of the image in heart district domain, i.e. sample, prevents the edge in the hole equipped with sample from being caused to the image procossing of sample Interference.In image equipped with sample hole region in, by using morphologic caustic solution by the sample areas in image into Go corrosion (achieveed the purpose that shrink sample areas), only with the pixel inside hole, eliminate the influence on the border in hole.
Step 4：By image IERiConnected component labeling is carried out, each type of sample is identified according to connected component labeling Position.
Image IERiConnected component labeling is carried out, for handling the image in the hole equipped with different samples.If E with F is connected, and F is connected with G, then E is connected with G.Visually apparently, the point to communicate with each other forms a region, and disconnected Point forms different regions.Such a all collection formed that communicates with each other are collectively referred to as a connected region.Binaryzation The most important method of graphical analysis is exactly connected component labeling, it passes through the mark to white pixel in binary image (target) Allow each individually connected region to form an identified block, can further obtain the area of these blocks, profile, external Rectangle, barycenter and the not geometric parameter such as bending moment.
The present invention uses connected component labeling function bwlabel in Matlab：[LJi,LNUMi]=bwlabel (IERi, 8), wherein LNUMiRepresent image IERiThe quantity of middle connected region, due to there is 20 samples in each image, so LNUMi =20.LJiExpression and IERiFor the identical matrix of size, LJiMatrix contains mark image IERiIn each connected region class Distinguishing label, the value of these labels is 1,2 ..., LNUMi, 8 in formula represent it is by 8 neighbor searching connected regions.
The algorithm of function bwlabel is a traversing graph picture, and writes down what is continuously rolled into a ball and mark in every a line (or row) It is of equal value right, then by equivalence to being re-flagged to original image.This algorithm is higher one of current efficiency, is calculated It is of equal value right for eliminating with Dulmage-Mendelsohn decomposition algorithms that sparse matrix has been used in method.
By image IERiConnected component labeling is carried out, realizes sample areas that each shrinks as a connected region, For each connected region, using regionprops functions in Matlab：STATSi=regionprops (LJi,' Centroid'), ' Centroid' attributes be exactly the region barycenter (i.e. the centre of form of connected region).
The STATS being calculated according to regionprops functionsiContain image IERiIn each connected region it is (i.e. every A sample areas) centre of form coordinate (RXi,nu,LYi,nu), wherein the value of nu is 1~20.The centre of form of these connected regions is according to row Coordinate is arranged as LY from small to largei,1~LYi,20, the wherein LY of row coordinate minimumi,1~LYi,4This 4 sample areas, are sample (row share four samples, are followed successively by Brut standard samples, Brut from top to bottom in standard sample region in this image Reserva standard samples, Brut Gran Reserva standard samples, Semiseco standard samples)；Row coordinate is LYi,5~ LYi,8This 4 sample areas, are the Brut sample areas in sample image；Row coordinate is LYi,9~LYi,12This 4 sample areas Domain, is the Brut Reserva sample areas in sample image；Row coordinate is LYi,13~LYi,16This 4 sample areas, are Brut Gran Reserva sample areas in sample image；Row coordinate is LYi,17~LYi,20This 4 sample areas, are sample Semiseco sample areas in this image.
Step 5：With image IG1R passages, G passages and the B in region of contraction sample well of primary sample of standard lead to Correction factor is calculated on the basis of the average in road, and to image IGiEach passage carries out color correction, obtains correction chart as IGCi, i= 2,…6。
By image IER1In the row coordinate of centre of form coordinate be LY1,1~LY1,4The pixel of connected region put 1, it is other Pixel is set to 0, and obtains matrix J Z1, then by original image IG1In all pixels point R passages value as matrix RIG,1, By original image IG1In all pixels point G passages value as matrix GIG,1, by original image IG1In all pixels point Channel B value as matrix BIG,1.By JZ1Dot product RIG,1Obtain DCR1, by JZ1Dot product GIG,1Obtain DCG1, by JZ1Dot product BIG,1Obtain DCB1。
Using the regionprops functions in matlab：MJ1=regionprops (JZ1, ' Area'), calculate image IG1 Standard sample shrinkage hole region R passages average.The function include ' Area' attributes are exactly image regional Middle pixel total number.
R1, ave=sum (sum (DCR1))/sum(MJ1),
G1, ave=sum (sum (DCG1))/sum(MJ1),
B1, ave=sum (sum (DCB1))/sum(MJ1),
Wherein, sum (sum (DCR1)) it is matrix D CR1The summation of middle all elements value, sum (sum (DCG1)) it is matrix DCG1The summation of middle all elements value, sum (sum (DCB1)) it is matrix D CB1The summation of middle all elements value, sum (MJ1) it is figure As IER1In the row coordinate of centre of form coordinate be LY1,1~LY1,4Connected region sum of all pixels.Similarly, G is calculated1, ave And B1, ave。
Similarly, image IG is calculatediStandard sample contraction sample well region R passages, G passages and channel B Average RI, ave、GI, aveAnd BI, ave, wherein i=2,3 ..., 6.
Then color correction is carried out.With image IG1Standard primary sample contraction sample well region R passages, G The average R of passage and channel B1, ave、G1, aveAnd B1, aveOn the basis of, follow-up different time sampled point acquired image IGi(i= 2,3 ..., 6) in standard primary sample where the R passages in region in hole, the average of G passages and channel B be Ri,ave、 GI, aveAnd BI, aveTo refer to (wherein i=2,3 ..., 6), three correction factor C are calculatedi,R、Ci,GAnd Ci,B(wherein i=2, 3,…,6)。
Wherein, Ci,R、Ci,G、Ci,BFor image IGiR passages, the correction factor of G passages and channel B.
By image IGiAll pixels point R passages RIG, iIt is multiplied by Ci,R, G passages GIG, i is multiplied by Ci,G, channel B BIG, iIt is multiplied by Ci,B, tri- passages of new RGB are obtained, the image of composition is named as correction chart as IGCi, wherein i=2,3 ..., 6.
Step 6：Calculate the image IGC after correctioniThe R, G in region of shrinkage hole of each brand sample, channel B Average, chooses parameter of the channel B as brown stain.
Using method before, correction chart is obtained as IGCiThe various brands of (i=1,2 ..., 6) accelerate the receipts of brown stain sample The average R of R, G, B triple channel in contracting regionIGC,i,ave、GIGC,i,ave、BIGC,i,ave, and draw, as shown in Figure 7.Fig. 7 shows companion With heating time, R, G of sample area and the differentiation of channel B value.Channel B is mainly reflected in as can be seen that accelerating to change, and R It is held essentially constant with G values.With the increase of heating time, this shows as deeper yellow, i.e., changes to more shallow brown. The change of the channel B of Brut and GRes samples has obvious linear dependence, and in Res and SS, 48 it is small when after brown stain Start, hereafter blue channel value and linear dependence again between the time.Original sparkling wine sample shows it The Light Difference of color, as Brut and Brut Reserva (Res) sample is more like each other.And Semiseco (SS) and Brut Gran Reserva (GRes) show larger difference, and the maximum difference between sample is blue channel.
Step 7：Calculate channel B Color invariants and blueness decay percentage；Blueness decay percentage has line with the time Sexual intercourse, is consistent with the browning of sparkling wine.
Except having used RIGC,i,ave、GIGC,i,ave、BIGC,i,aveThe change of value is analyzed, and additionally uses R, G, B color not Variable studies brown stain.By sample image IGCiChannel B be converted to respective color invariant (bi=BIGC,i,ave/ (RIGC,i,ave+GIGC,i,ave+BIGC,i,ave)), to quantify and compare brown stain.Channel B Color invariants are similar to absorbance, can be with Calculate blueness decay percentage (%Bt).B at this timeiCorresponding to bt。
Wherein, bt0Channel B invariant when being t=0, btIt is the Color invariants of the channel B of time t.Fig. 8 shows institute The %B of the sample of researchtChange with time, it can be seen that brown stain is reflected in %BtIncrease linearly over time.
For Brut,R2For 0.99.
For Brut Reserva,R2For 0.93.
For Semiseco,R2For 0.93.
For Brut Gran Reserva,R2For 0.96.
Wherein, xdayFor chronomere (my god).
Shimadzu ultraviolet specrophotometer (UV-3600, Duisburg, Germany) in use 10mm path lengths The quartz cuvette and distilled water of degree as reference, measure absorbance of the Cava sparkling wines of 4 kinds of brands at 420nm. In 420nm (A420) absorbance be multiplied by 1000 times, be expressed as milli absorbance unit (milli-absorbance units, letter Claim mAU).5-HMF is measured in each sample according to the method for international grape and grape wine tissue, using with level Four L-7100 Hitachi's liquid chromatograph (Hitachi) of pump, L-7455 diode array detector and LaChrom chromatographic columns carries out 4 kinds of brands Cava sparkling wines liquid-phase chromatographic analysis.Image capture device in the present invention is 7 smart mobile phone of Huawei's honor, is had 5.2 inches of touch display screens, resolution ratio are the pixel of 1920 pixels × 1080,20,000,000 pixel camera head (5152 × 3888 pixels Image), support PDAF phase-detection rapid focus, F2.0 apertures.
In order to assess detection result of the present invention, by %BtWith other conventional brown stain monitoring parameters (such as A420With 5-HMF contents) It is compared, Fig. 9 is shown in 420nm (A420) place measurement absorbance and %BtBetween correlation.
Linear regression analysis is carried out to determine the relation between these brown stain indexs：
For Brut,R2For 0.98.
For Brut Reserva,R2For 0.98.
For Semiseco,R2For 0.99.
For Brut Gran Reserva,R2For 0.96.
Wherein, x is sample in 420nm (A420) place absorbance, y %Bt。
The result shows that %BtThe trend similar to the browning index presentation represented by A420.It should be noted that two kinds of distinct methods Between have height correlation.Correspond to the color in the border between blueness and purple in view of 420nm, absorption band will be given Sample band Yellow-to-orange/brown, correspondingly, these changes will reflect in the channel B of image.Equally it is significantly, it is different There are uniformity between sample, their slope is closely similar, particularly Brut, Res and SS sample.
On the other hand, Figure 10 shows %BtCorrelation between 5-HMF contents.Linear regression analysis is carried out to determine Relation between these brown stain indexs：
For Brut,R2For 0.93.
For Brut Reserva,R2For 0.97.
For Semiseco,R2For 0.99.
For Brut Gran Reserva,R2For 0.91.
Wherein, x be sample 5-HMF contents, y %Bt。
As can be seen that there is very high correlation between them, and particularly Brut, Res and SS samples.Should be considerable It is that 5-HMF is not the compound of unique instruction grape wine brown stain, therefore the difference of slope value is attributable to depositing for other compounds Also the color of grape wine is being influenced.
On %Bt, 5-HMF contents and A420The characteristics of the results show of acquisition browning, it can be by following Equation describes：
Y=Y0+kt (8)
Wherein, Y is absorbance (mAU) or 5-HMF contents or the %B at 420nmt, t is time (in units of day), and k is Velocity constant (is expressed as A420MAU/day, mg/L/ days of 5-HMF, for BtThe daily percentage specific damping of blue channel).
Zero order kinetics (zero-order elimination kineics) refers to medicine in vivo with constant speed Eliminate, no matter that is, plasma drug level height, the medication amount eliminated in the unit interval are constant.Produce the master of zero kinetics Will be the reason is that the saturation history that drug metabolic enzyme, drug transporters and medicine are combined with plasma protein, zero kinetics There is the characteristics of active transport.Zero order kinetics is set up from natural force system, such as electromagnetic force, biomethanics, gravitation.Zero The dynamic (dynamical) rich connotation of level, natural day is into enlightenment is far-reaching, is widely used.The present invention uses the correlation theory of zero order kinetics, Carry out the research of sparkling wine brown stain.The formula of zero order kinetics is identical with above formula, and (at this time, k is zero-order constant, Y0For Initial blood concentration, blood concentration when Y is t).The velocity constant calculated for the zero order kinetics of each sample and monitoring side Method is as shown in table 2.As can be seen that use %BtZero order kinetics (table 2 and Fig. 8) is also observed as collative variables.In addition, to the greatest extent Pipe assessment brown stain speed constant value it is different according to different application methods, but the relation between result follow really it is identical Trend.
2 zero order kinetics parameter (A of table420, 5-HMF, %Bt)
As the time increases, sparkling wine ferments (from Brut Gran Reserva to Brut Reserva) continuous, Brown stain velocity constant increases (from Brut to Semiseco) with the increase of sugared content.Show that the constant speed of sugared content increases Add, as was expected, and 5-HMF is formed and initial sugared content height correlation, because 5-HMF synthesis speeds are sugared dependences (Camara J S,Alves M A,Marques J C.Changes in volatile composition of Madeira wines during their oxidative ageing[J].Analytica Chimica Acta,2006,563(1): 188-197.).Therefore, grape wine (the Brut Gran of the sparkling wine at least influenced by browning seemingly first water Reserva, Brut Reserva).Figure 11 summarize to graphically acquisition results, it can be seen that the present invention makes brown stain Journey is characterized, and completely the same with known method.
The present invention proposes a kind of colorimetric method for the browning for being used to monitor sparkling wine, and this method speed is fast, consumption Take low.Using the camera of smart mobile phone as harvester, and for analyzing the transmission image of slips grape wine, avoid Complicated sample treatment, and single step Multi-example analysis can be carried out.The present invention proposes a kind of new control parameter, with the time Passage, blue channel percentage specific damping, this causes brown stain dynamics to be able to study and characterize.That is R, G passage is in browning In be kept approximately constant, browning has time dependence, with the time increase and mainly influence channel B.The present invention proposes will Quality status stamps of the blue channel attenuation percentage %Bt as sparkling wine, the value and the absorbance and 5- hydroxyl first at 420nm The content height correlation of base -2- furfurals, the content of absorbance and HMF 5 hydroxymethyl 2 furaldehyde at 420nm is grape wine brown stain Common quality status stamp, the result of acquisition and existing research method are completely the same.Therefore, new parameter and furfural compounds are deposited In height correlation, be reflected in the correlation of A420 and in lesser degree it is related with 5-HMF concentration because this is only brown stain One of coloring compound involved in process.The present invention the result shows that %Bt is a good brown stain descriptor, it is only necessary to obtain Take the transmission image of the sparkling wine sample in different brown stain stages, it is possible to be carried out at the same time the sparkling wine sample of multiple brands This analysis, device is cheap, without expensive special instrument and other chemical reagent.
Method proposed by the invention can be as the alternative solution of conventional method, and may be used as sparkling wine Key Quality Con trolling index.Method proposed by the invention has been proven that its serviceability, therefore based on the further of this method Research will be significantly.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention With within principle, any modification, equivalent replacement, improvement and so on, should all be included in the protection scope of the present invention god.

Claims (10)

1. a kind of sparkling wine browning detection method based on machine vision, it is characterised in that its step is as follows：
Step 1：Experimental provision is built, prepares sample；
Step 2：Sample image is obtained using the camera of smart mobile phone, by the RGB image IG of acquisitioniCarry out gray processing and binaryzation Processing, obtains binary image BINi, i=1,2 ..., 6；
Step 3：With structural element to binary image BINiCorroded, obtain image IEi, obtain the hole equipped with sample Constriction zone；
Step 4：By image IEiIn pixel all negate, obtain image IERi；By image IERiCarry out connected component labeling, Each type of sample is identified according to connected component labeling；
Step 5：With image IG1The R passages in region of shrinkage hole of primary sample of standard, the average of G passages and channel B be Benchmark correction factor, and to image IGiEach passage carries out color correction, obtains correction chart as IGCi, i=2 ... 6；
Step 6：Calculate the image IGC after correctioniThe R, G in region of shrinkage hole of each brand sample, the average of channel B, Choose parameter of the channel B as brown stain；
Step 7：Channel B Color invariants and blueness decay percentage are calculated, blueness decay percentage has linear close with the time System, blueness decay percentage are consistent with the browning of sparkling wine, the quality status stamp as brown stain detection.
2. the sparkling wine browning detection method according to claim 1 based on machine vision, it is characterised in that The sample is Brut, the blistering of the slips of Brut Reserva, Brut Gran tetra- kinds of best seller brands of Reserva and Semiseco Grape wine, four kinds of grape wine have different sugar contents and productive year；10mL is taken to be respectively charged into the amber vial of 4 20mL In, and in N2Deaerate under air-flow；This 4 amber vials equipped with different brands fizz are placed in the environment of dark, And Conservation environment temperature is 8 DEG C；Standard sample using the fizz of the different brands in this 4 bottles as the experiment of follow-up 10 days Product, and this 4 amber vials equipped with standard sample are named as Bottle1, Bottle2, Bottle3, Bottle4；Again 10mL is respectively taken to be respectively charged into the amber vial of 4 20mL the sparkling wine of four brands, and in N2Deaerate under air-flow； This 4 amber vials equipped with different brands fizz are placed on to the environment of complete darkness, are then placed within baking oven, if Constant temperature 65 ± 1 DEG C fixed, carries out accelerating brown stain experiment；This 4 amber vials equipped with acceleration brown stain sample are named as Bottle5、Bottle6、Bottle7、Bottle8；In the sampling time point collection sample letter by 0,2,4,6,8 and 10 day Breath, when the time interval of collection is 48 small；In each sampling time point, (1) extracts Brut, Brut Reserva, Brut first Each 1 part of the standard sample of Gran Reserva and Semiseco, in the hole at the middle part for being placed sequentially in 96 orifice plates from top to bottom, is erected It is in line to be arranged into 1, then it is named as the 1st row；(2) then extraction Brut, Brut Reserva, Brut Gran Reserva and Semiseco acceleration brown stain each 4 parts of sample, then each brand put 1 row, be sequentially placed into 96 orifice plates the 2nd~5 row.
3. the sparkling wine browning detection method according to claim 1 based on machine vision, it is characterised in that The experimental provision includes vasculum (2) and light source (4), and light source (4) is arranged on the lower section of vasculum (2), vasculum (2) lower part Equipped with the orifice plate (3) for holding sample, collection hole (1) is equipped with the top of vasculum (2), collection hole (1) is being located at orifice plate (3) just Top.
4. the sparkling wine browning detection method according to claim 3 based on machine vision, it is characterised in that The vasculum (1) is made of black foam core plate, and vasculum (1) is internal covered with dumb light black velvet paper；Vasculum (1) For the rectangular parallelepiped structure of height 80cm, the difference between diagonal and center vertical optical path is less than 5%；The light source (4) is strong Spend controllable diffusing reflection light source；Orifice plate (3) is 96 orifice plates, and orifice plate (3) is manufactured by the pure polystyrene of import optical clear, through gamma Ray sterilizing processing.
5. the sparkling wine browning detection method based on machine vision according to Claims 2 or 3, its feature exist In the method for obtaining sample image is：Slips sparkling wine sample is arranged in orifice plate (3) using 0.3ml micropipettes Middle part, by orifice plate (3) place light source (4) panel on, vasculum (2) is covered on orifice plate (3), collection hole (1) be located at hole The surface of plate (3), smart mobile phone is placed on the collection hole (1) at the top of vasculum, uses the built-in camera of smart mobile phone Image is obtained with highest resolution ratio, and saves as JPEG；The primary sample arrangement of standard in the orifice plate, obtains all samples and arrives In same image.
6. the sparkling wine browning detection method according to claim 1 based on machine vision, it is characterised in that The method that the RGB image of acquisition is carried out to gray processing and binary conversion treatment is：
The RGB image IG that smart mobile phone is obtainediIn each pixel tri- components of R, G, B gray scale of the average value as image Value, i.e. Grayi(x, y)=(RIG,i(x,y)+G IG,i(x,y)+B IG,i(x, y))/3, obtain gray level image Grayi；
To gray level image GrayiOptimal threshold T is calculated using Otsu algorithms, optimal threshold T is so that the gray value of σ maximums gt：If the gray level of gray level image is L, then tonal range is [0, L-1], and the optimal threshold of gray level image is calculated using Ostu methods It is worth and is：σ=Max [w0(gt)×(u0(gt)-u)^2+w1(gt)×(u1(gt)-u) ^2)], wherein, w0For prospect ratio, u0For Prospect gray average, w1For background ratio, u1For background gray average, u is the average of view picture gray level image.
T is the optimal threshold of segmentation figure picture, and gray level image is divided into 2 parts according to optimal threshold T, obtains binary image BINi(x,y)：
<mrow> <msub> <mi>BIN</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <msub> <mi>Gray</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>&lt;</mo> <mi>T</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mrow> <msub> <mi>Gray</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>&amp;GreaterEqual;</mo> <mi>T</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
Wherein, Grayi(x, y) is the value of the pixel at (x, y) place in gray level image；
By binary image BINiNegate to obtain binary image IRi, using the imerode function pairs IR in MatlabiCarry out rotten Erosion operation：IERi=imerode (IRi,D1), wherein, IERiRepresent binary image IRiBy a diameter of 8 circular configuration element D1Obtained image after corrosion, i=1,2,3 ..., 6；D1=strel (' disk', 4), disk represents circular shape.
7. the sparkling wine browning detection method according to claim 6 based on machine vision, it is characterised in that By image IERiCarry out connected component labeling method be：Using connected component labeling function bwlabel in Matlab：[LJi, LNUMi]=bwlabel (IERi, 8), wherein, LNUMiRepresent image IERiThe quantity of middle connected region, LJiExpression and image IERiThe identical matrix of size, matrix L JiContain mark image IERiIn each connected region class label, these labels Value be 1,2 ..., LNUMi, 8 represent it is by 8 neighbor searching connected regions；
The method of the position of each type of sample of the identification is：The sample areas that connected component labeling shrinks each is made For a connected region, for each connected region, using the regionprops functions in Matlab：STATSi= regionprops(LJi, ' Centroid'), ' Centroid' attributes are exactly the barycenter of connected region, STATSiContain image IERiIn each connected region centre of form coordinate (RXi,nu,LYi,nu), wherein, the value of subscript n u is 1~20.
8. the sparkling wine browning detection method according to claim 1 based on machine vision, it is characterised in that The acquisition methods of correction chart picture are：With image IG1Standard primary sample shrinkage hole region R passages, G passages and B The average R of passage1, ave、G1, aveAnd B1, aveOn the basis of, standard in follow-up different time sampled point acquired image it is original The average of the R passages in the region of the shrinkage hole where sample, G passages and channel B is RI, ave、GI, aveAnd BI, aveFor reference, calculate Three correction factor Ci,R、Ci,GAnd Ci,B：
<mrow> <msub> <mi>C</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>R</mi> </mrow> </msub> <mo>=</mo> <mfrac> <msub> <mi>R</mi> <mrow> <mn>1</mn> <mo>,</mo> <mi>a</mi> <mi>v</mi> <mi>e</mi> </mrow> </msub> <msub> <mi>R</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>a</mi> <mi>v</mi> <mi>e</mi> </mrow> </msub> </mfrac> <mo>,</mo> <msub> <mi>C</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>C</mi> </mrow> </msub> <mo>=</mo> <mfrac> <msub> <mi>G</mi> <mrow> <mn>1</mn> <mo>,</mo> <mi>a</mi> <mi>v</mi> <mi>e</mi> </mrow> </msub> <msub> <mi>G</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>a</mi> <mi>v</mi> <mi>e</mi> </mrow> </msub> </mfrac> <mo>,</mo> <msub> <mi>L</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>B</mi> </mrow> </msub> <mo>=</mo> <mfrac> <msub> <mi>B</mi> <mrow> <mn>1</mn> <mo>,</mo> <mi>a</mi> <mi>v</mi> <mi>e</mi> </mrow> </msub> <msub> <mi>B</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>a</mi> <mi>v</mi> <mi>e</mi> </mrow> </msub> </mfrac> </mrow>
Wherein, Ci,R、Ci,G、Ci,BFor image IGiR passages, the correction factor of G passages and channel B, i=2,3 ..., 6；
By image IGiAll pixels point R passages RIG, iIt is multiplied by Ci,R, G passages GIG, iIt is multiplied by Ci,G, channel B BIG, iIt is multiplied by Ci,B, i =2,3 ..., 6, obtain tri- passages of new RGB, the image of composition is correction chart as IGCi。
9. the sparkling wine browning detection method based on machine vision according to claim 7 or 8, its feature exist In by image IER1In the row coordinate of centre of form coordinate be LY1,1~LY1,4The pixel of connected region put 1, other pixels Set to 0, obtain matrix J Z1, by original image IG1In all pixels point R passages value as matrix RIG,1, by original image IG1In all pixels point G passages value as matrix GIG,1, by original image IG1In all pixels points channel B Value is used as matrix BIG,1；By matrix J Z1Dot product matrix RIG,1Obtain matrix D CR1, by matrix J Z1Dot product matrix GIG,1Obtain matrix DCG1, by matrix J Z1Dot product matrix BIG,1Obtain matrix D CB1；
R1, ave=sum (sum (DCR1))/sum(MJ1),
G1, ave=sum (sum (DCG1))/sum(MJ1),
B1, ave=sum (sum (DCB1))/sum(MJ1),
Wherein, sum (sum (DCR1)) it is matrix D CR1The summation of middle all elements value, sum (sum (DCG1)) it is matrix D CG1In The summation of all elements value, sum (sum (DCB1)) it is matrix D CB1The summation of middle all elements value, sum (MJ1) it is image IER1 In the row coordinate of centre of form coordinate be LY1,1~LY1,4Connected region sum of all pixels, MJ1=regionprops (JZ1,' Area'), ' Area' attributes are exactly number of pixels in image regional.
10. the sparkling wine browning detection method according to claim 1 based on machine vision, its feature exist In by sample image IGCiChannel B be converted to respective color invariant：bi=BIGC,i,ave/(RIGC,i,ave+GIGC,i,ave+ BIGC,i,ave), calculate blueness decay percentage %Bt：
Wherein, bt0Channel B invariant when being t=0, btIt is the Color invariants of the channel B of time t；Blueness decay percentage Compare %BtRepresent that browning is：Y=Y0+ kt,
Wherein, Y is the absorbance or 5-HMF contents or %B at 420nmt, Y0It is the absorbance or 5-HMF contents at 420nm Or %BtInitial value, t is the time, and k is velocity constant.
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