CN101943661B - Near infrared spectrum and microscopic bacterial plaque area data fusion-based pork freshness non-destructive testing technology - Google Patents
Near infrared spectrum and microscopic bacterial plaque area data fusion-based pork freshness non-destructive testing technology Download PDFInfo
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Abstract
The invention relates to a meat quality detection system. The system comprises an image acquisition module, an image processing module, a characteristic value extraction module, a spectral analysis module and a data fusion processing module, wherein the image acquisition module is used for acquiring an adipose tissue image of a meat sample; the image processing module is used for extracting a culture medium from the adipose tissue image; the characteristic value extraction module is used for acquiring a ratio of the total area of bacterial plaques in the culture medium to the area of the culture medium; the spectral analysis module is used for analyzing a near infrared spectrum of the sample so as to acquire structure composition data of the meat sample; and the data fusion processing module is used for carrying out fusion processing on the ratio acquired by the characteristic value extraction module and the structure composition data of the meat sample so as to obtain an actual output value. The culture medium is a fixed region which is detected in the adipose tissue image and is only provided with adipose tissues of the meat sample. The actual output value represents the quality of the meat sample. The invention also provides a meat quality detection method. The meat quality detection system and the meat quality detection method can rapidly and accurately detect the meat quality and have the advantages of simple structure and detection process.
Description
Technical field
The present invention relates to a kind of detection system, relate in particular to a kind of meat quality detection system and method.
Background technology
In recent years, Public Health Emergencies serious threat national health, therefore, sanitary inspection, has especially caused great attention to the quality testing of the meats such as pork, mutton, beef.At present, the detection technique of meat quality has organoleptic detection, microorganism detection and physics and chemistry detection etc.Organoleptic detection dependence testing staff's sense organ judges the freshness of meat, therefore, higher to testing staff's Capability Requirement, must be that people through system training and long-term practice could be competent at, and it is more affected by subjective factor, testing staff's individual sense organ difference directly affects the accuracy of testing result.Microorganism detection and physics and chemistry detection need to rely on a series of chemical devices and complete, and process is complicated, detection time is long, apparatus expensive, can not carry out fast scene and detect in real time.
Summary of the invention
In view of the above-mentioned problems in the prior art, fundamental purpose of the present invention is to address the deficiencies of the prior art, and provides a kind of and can fast, accurately detect meat quality and structure, the simple meat quality detection system of process and method.
A kind of meat quality detection system, the image capture module that comprises a collection meat sample adipose tissue image, one extracts the image processing module of the nutrient culture media in described adipose tissue image, one obtains the eigenwert extraction module of the total area of the bacterium bacterial plaque in described nutrient culture media and the ratio of nutrient culture media area, one near infrared spectrum of resolving described sample forms the spectral analysis module of data to obtain the structure of described meat sample, and the structure of the ratio that obtains of a pair of described eigenwert extraction module and described meat sample forms data and carries out fusion treatment to obtain the Data Fusion module of a real output value, described nutrient culture media is the fixed area detecting in described adipose tissue image, this fixed area only comprises the adipose tissue image of described meat sample, described real output value characterizes the quality of described meat sample.
A meat quality detection method, comprises the following steps:
Acquisition step: the adipose tissue image that gathers a meat sample;
Nutrient culture media extraction step: extract a fixed area of the adipose tissue only with described meat sample in described adipose tissue image as nutrient culture media;
Eigenwert extraction step: calculate the area of the bacterial plaque in described nutrient culture media and the ratio of described nutrient culture media area;
Spectrometry procedure: the structure of obtaining described meat sample by resolving the near infrared spectrum of described meat sample forms data; And
Data fusion step: the structure of the ratio of described bacterial plaque area and nutrient culture media area and described meat sample is formed in the default algorithm of data substitution one and merges computing, to export a real output value, described real output value is in order to characterize the quality of described meat sample.
Described meat quality detection system and method are by gathering the adipose tissue image of described sample and image being processed and extract nutrient culture media from described image, by obtaining the total area of the bacterium bacterial plaque in described nutrient culture media and the ratio of nutrient culture media area as the First Eigenvalue of analyzing the quality of meat to be detected, avoided only calculating the data deviation that amount of bacterial plaque is brought, and using the structure of obtaining described meat sample and form data as the Second Eigenvalue of analyzing meat quality to be detected by resolving the near infrared spectrum of described sample, finally to described first, Second Eigenvalue carries out Data Fusion and exports the data that characterize meat quality to be detected, can be quick, accurately detect meat quality, and detection architecture and process are simple.
Accompanying drawing explanation
Fig. 1 is the block scheme of meat quality detection system better embodiment of the present invention.
Fig. 2 A-2D is respectively sample at the nutrient culture media image of corrupt 0 hour, 6 hours, 12 hours and 24 hours.
Fig. 3 is the structural representation of the neural network unit in Fig. 1.
Fig. 4 is the process flow diagram of meat quality detection method better embodiment of the present invention.
Fig. 5 is that the neural network unit in Fig. 1 obtains the process flow diagram of the data that characterize meat quality to be detected according to first, second eigenwert of described sample.
Embodiment
The invention will be further described below in conjunction with the accompanying drawings and the specific embodiments.
Please refer to Fig. 1 to Fig. 3, the better embodiment of meat quality detection system 1 of the present invention comprises an image capture module 10, an image processing module 20, an eigenwert extraction module 30, a spectral analysis module 40 and a Data Fusion module 50.Described image processing module 20 comprises an image conversion unit 22, a nutrient culture media extraction unit 24 and an image enhancing unit 26.
Described image capture module 10 comprises a CCD (Charge-coupled Device, charge coupled cell) microscope, for gathering the image of meat sample adipose tissue to be detected, in described adipose tissue, contain the quality index that characterizes described meat to be detected, be grown in the bacterial plaque of the bacterium of described meat to be detected.During concrete operations, the scalpel of available sterilization extracts in the adipose tissue of meat to be detected samples long 2 centimetres, wide 1 centimetre, thick 2 millimeters, the sample of extraction is placed on microslide, again microslide is placed on the microscopical objective table of described CCD, adjust the position of objective table, and described CCD microscope is arranged to a suitable amplification coefficient, as 100 times, by described CCD microscope, sample is taken, to obtain RGB (red channel, green channel, the blue channel) image of described adipose tissue.
Because the green channel of image can well react the bacterium bacterial plaque in adipose tissue, the green channel that described image conversion unit 22 is extracted in described RGB image, and the image that only has green channel extracting is converted to the gray level image of 8, object in described gray level image also comprises double dish, i.e. described microslide.
Described nutrient culture media extraction unit 24 extracts nutrient culture media by Hough transform method from described gray level image, and described nutrient culture media is the border circular areas in described gray level image, and described nutrient culture media is to only include adipose tissue, and does not comprise the image-region of described double dish.According to double dish, nutrient culture media shared position in whole image, if the width that to set the radius of a circle detect by Hough transformation be whole gray level image 1/3rd to 1/2nd between, and the center of circle is when take in the circle of sixth of the width that described gray level image center is whole gray level image as center of circle radius, detected circle is effectively round, extracts the region of the circle of radius minimum in effective circle as described nutrient culture media.In other embodiments, the elliptical region that also can detect in described gray level image by Hough transformation is used as described nutrient culture media, and by Hough transformation, in image, detecting circle and ellipse is the known technology that image is processed, and does not repeat them here.
Described image enhancing unit 26 utilizes histogram equalization converter technique the image at described nutrient culture media place to be strengthened to the processing of picture contrast, and be sent to described eigenwert extraction module 30 by having strengthened the contrast image with nutrient culture media afterwards, the described contrast image afterwards that strengthened is as shown in Fig. 2 A-2D, border circular areas in Fig. 2 A-2D is nutrient culture media, and the spot being dispersed in nutrient culture media is bacterium bacterial plaque.
Described eigenwert extraction module 30 has strengthened contrast image afterwards by detection and has obtained a First Eigenvalue that characterizes sample freshness, because bacteria flora is that form with bacterial plaque is dispersed on nutrient culture media, and bacterial plaque is not of uniform size, therefore, described eigenwert extraction module 30 obtains described the First Eigenvalue by calculating the area of the bacterial plaque in described nutrient culture media, and described the First Eigenvalue is the ratio of the total area of all bacterial plaques and the area of whole nutrient culture media in described nutrient culture media.In present embodiment, described eigenwert extraction module 30 is the quantity of the pixel of statistics nutrient culture media bacterial plaque total areas of obtaining all bacterial plaques, described eigenwert extraction module 30 comprises a pixel detection unit 32, one first totalizer 34, one second totalizer 36 and an arithmetic element 38, described pixel detection unit 32 detect strengthened each pixel of image after contrast gray-scale value and the position of the pixel that detects in this image, as shown in Fig. 2 A-2D, black splotch in image represents bacterium bacterial plaque, because the gray-scale value of black picture element in image is 0, when the gray-scale value that a pixel detected when described pixel detection unit 32 is 0, described the first totalizer 34 adds 1, when a pixel being detected and be arranged in described nutrient culture media, described the second totalizer 36 adds 1, described arithmetic element 38 calculates described first, the ratio of the accumulated value of the second totalizer, be the ratio of the bacterial plaque total area and described nutrient culture media area, this ratio is as described the First Eigenvalue, ratio is larger, the degree of spoilage of interpret sample is darker, as shown in Fig. 2 A-2D, be respectively sample at corrupt 0 hour, 6 hours, the nutrient culture media of 12 hours and 24 hours, wherein the ratio of bacterial plaque area and nutrient culture media area increases along with the increase of degree of spoilage.
Described spectral analysis module 40 is for detection of a Second Eigenvalue of sample, and described Second Eigenvalue comprises that the structure of described sample forms data.Described spectral analysis module 40 comprises a near-infrared light source 42 and a detecting unit 44, described near-infrared light source 42 irradiates described sample, described detecting unit 44 gathers the near infrared spectrum of described sample, the structure of analyzing meat by resolving the near infrared spectrum of described sample forms, as the basis of judgement Meat.As shown in following table (meat hydric group sum of fundamental frequencies and multiple frequency absorption band composition at different levels form table), because near infrared spectrum district is consistent with the sum of fundamental frequencies of hydric group vibration in organic molecule and the uptake zone of frequencys multiplication at different levels, between the structure composition of sample and near infrared spectrum, there is certain funtcional relationship, by the near infrared spectrum of scanning samples, and according to the funtcional relationship between the structure composition of described sample and near infrared spectrum, utilize stoechiometric process to obtain the characteristic information of organic molecule hydric group in sample, because the composition of meat is (as protein, water, multi-layer biological tissue, protein decomposition etc.) mostly by these hydric groups, formed, the structure that therefore can obtain meat to be detected by resolving the near infrared spectrum of described sample forms data.
Further, in order to reduce the impact of the luminous intensity of near-infrared light source 42 on testing result, described near-infrared light source 42 is by sample described in the near infrared light of two bundle different wave lengths the spectrum of collected specimens under this two different wave length respectively, by calculation sample, to the absorbance of the near infrared light of this two different wave length is poor, is analyzed meat structure to be detected and forms data
Further, described Second Eigenvalue also comprises the content data of each constituent of described sample, described spectral analysis module 40 also can comprise a mid-infrared light source 46, auxiliary described near-infrared light source 42 and described detecting unit 44 carry out the quality testing of meat to be detected, described mid-infrared light source 46 irradiates described sample, the middle infrared spectrum of described detecting unit 44 collected specimens, utilize the relation of sample to the absorption intensity of the absorption band of mid-infrared light and molecular composition or hydric group content, treat sample and carry out quantitative test, further detect the purity of meat.
Described Data Fusion module 50 comprises a BP (Back Propagation, backpropagation) neural network unit 52, in described neural network unit 52, prestore a plurality of training samples and corresponding desired output, each training sample comprises that a ratio data and corresponding sample structure form and component content data, each desired output is the value calculating in the default algorithm of the data substitution in corresponding training sample one, represent the quality of different samples, as shown in Figure 3, described neural network unit 52 has the input layer A of a reception input parameter, one carries out the hidden layer B of data processing and the output layer C of an Output rusults, when detecting the quality of described meat to be detected, the input layer A of institute's neural network unit 52 receives first of described sample, Second Eigenvalue, and at described hidden layer B by described first, described in Second Eigenvalue substitution, in default algorithm, merge computing, to obtain a real output value, find with receive first, the immediate training sample of Second Eigenvalue, union is to desired output that should training sample and the error amount between described real output value, whether the error amount described in identification between real output value and described desired output is in the error range of a permission, if the error amount between described real output value and described desired output is in the error range of described permission, , by described output layer C, export described real output value, in order to characterize the quality of described meat to be detected, if the error between described real output value and described desired output is outside the error range of described permission, described neural network unit 52 changes the weights of described default algorithm at its hidden layer B, again according to described first, second eigenwert, calculate real output value, until the error amount between the real output value calculating and described desired output is in the error range of described permission, then export described real output value by described output layer C.
Please continue to refer to Fig. 4, when the better embodiment of meat quality detection method of the present invention utilizes described meat quality detection system 1 to detect the quality of described meat to be detected, comprise the following steps:
Step S1: described image capture module 10 is by sample described in described CCD microscope photographing, to obtain the RGB image of adipose tissue in described sample;
Step S2: the green channel that described image conversion unit 22 is extracted in described RGB image, and the image that only has green channel extracting is converted to the gray level image of 8;
Step S3: described nutrient culture media extraction unit 24 extracts nutrient culture media by Hough transform method from described gray level image, described nutrient culture media is a border circular areas or the elliptical region in described gray level image, described nutrient culture media is to only include adipose tissue image, and does not comprise the circular or oval-shaped fixed area of described double dish image.
Step S4: described image enhancing unit 26 utilizes histogram equalization converter technique to strengthen the contrast of described nutrient culture media place image;
Step S5: the area of bacterial plaque and the ratio of described nutrient culture media area that described eigenwert extraction module 30 calculates in described nutrient culture media, as described the First Eigenvalue, described eigenwert extraction module 30 is the quantity of the pixel of statistics nutrient culture media bacterial plaque total areas of obtaining all bacterial plaques;
Step S6: described near-infrared spectrum analysis module 40 is obtained sample structure by resolving the near infrared spectrum of described sample forms data, as described Second Eigenvalue.This step is to the absorbance of the near infrared light of two different wave lengths is poor, to come analytic sample structure to form by calculation sample.Described Infrared spectroscopy module 40 is also by the middle infrared spectrum of collected specimens, utilize sample the relation of the absorption intensity of the absorption band of mid-infrared light and molecular composition or hydric group content to be detected to the content data of each constituent of sample, now, described Second Eigenvalue also comprises the content data of each constituent of described sample; And
Step S7: described neural network unit 52 is by merging computing in algorithm default described in described first, second eigenwert substitution, to export a real output value, in order to characterize the quality of described meat to be detected.
Please continue to refer to Fig. 5, described step S7 comprises the following steps:
Step S71: the input layer A of described neural network unit 52 receives described first, second eigenwert;
Step S72: at described hidden layer B by merging computing in algorithm default described in described first, second eigenwert substitution, to obtain a real output value;
Step S73: find and the immediate training sample of first, second eigenwert receiving, union is to desired output that should training sample and the error amount between described real output value;
Step S74: judge that error amount between described real output value and described desired output is whether in the error range of a permission, if the error amount between described real output value and described desired output is in the error range of described permission, perform step S75, otherwise, execution step S76;
Step S75: export described real output value by described output layer C; And
Step S76: the weights that change described default algorithm, and return to step S71 and again according to described first, second eigenwert, calculate real output value, until the error amount between the real output value calculating and described desired output is in the error range of described permission, then carry out described step S75.
Described meat quality detection system and method are by gathering the adipose tissue image of described sample and image being processed and extract nutrient culture media from described image, by obtaining the total area of the bacterium bacterial plaque in described nutrient culture media and the ratio of nutrient culture media area as the First Eigenvalue of analyzing the quality of meat to be detected, avoided only calculating the data deviation that amount of bacterial plaque is brought, and using the structure of obtaining described meat sample and form data as the Second Eigenvalue of analyzing meat quality to be detected by resolving the near infrared spectrum of described sample, finally to described first, Second Eigenvalue carries out Data Fusion and exports the data that characterize meat quality to be detected, can be quick, accurately detect meat quality, and detection architecture and process are simple.
Claims (8)
1. a meat quality detection system, the image capture module that comprises a collection meat sample adipose tissue image, one extracts the image processing module of the nutrient culture media in described adipose tissue image, one obtains the eigenwert extraction module of the total area of the bacterium bacterial plaque in described nutrient culture media and the ratio of nutrient culture media area, one near infrared spectrum of resolving described sample forms the spectral analysis module of data to obtain the structure of described meat sample, and the structure of the ratio that obtains of a pair of described eigenwert extraction module and described meat sample forms data and carries out fusion treatment to obtain the Data Fusion module of a real output value, described spectral analysis module comprises near-infrared light source and detecting unit, described near-infrared light source is by meat sample described in the near infrared light of two bundle different wave lengths, described detecting unit gathers the spectrum of described meat sample under described two different wave lengths, and by calculating described meat sample, the poor structure of analyzing described meat sample of the absorbance log of the near infrared light of described two different wave lengths is formed to data, described nutrient culture media is the fixed area detecting in described adipose tissue image, and this fixed area only comprises the adipose tissue image of described meat sample, and described real output value characterizes the quality of described meat sample.
2. meat quality detection system as claimed in claim 1, it is characterized in that, described image processing module comprises green channel in the described meat sample of extraction adipose tissue image and the image conversion unit, that the image of extraction is converted to 8 gray level images is extracted nutrient culture media from described gray level image nutrient culture media extraction unit and by Hough transform method is strengthened to the image enhancing unit of described nutrient culture media place picture contrast.
3. meat quality detection system as claimed in claim 2, it is characterized in that, described nutrient culture media is the border circular areas extracting in described gray level image, the radius of described border circular areas be whole gray level image width 1/3rd to 1/2nd between, in the circle of the center of circle of described border circular areas in described gray level image, the center of circle of this circle is described gray level image center, the sixth of the width that radius is whole gray level image.
4. meat quality detection system as claimed in claim 1, it is characterized in that, described eigenwert extraction module comprises a pixel detection unit, one first totalizer, one second totalizer and an arithmetic element, the gray-scale value of nutrient culture media place each pixel of image and the position of the pixel that detects described in described pixel detection unit inspection, when the gray-scale value of the pixel in described nutrient culture media place image being detected is 0, described the first totalizer adds 1, when this pixel is arranged in described nutrient culture media, described the second totalizer adds 1, described arithmetic element calculates described first, the ratio of the accumulated value of the second totalizer, be the ratio of the described bacterial plaque total area and described nutrient culture media area.
5. meat quality detection system as claimed in claim 1, it is characterized in that, described spectral analysis module also comprises the mid-infrared light source of the described meat sample of an irradiation, described detecting unit obtains the content data of each constituent of described meat sample by resolving the middle infrared spectrum of described meat sample, the structure of the meat sample that the ratio that described Data Fusion module is obtained described eigenwert extraction module, described spectral analysis module obtain forms the content data of data and each constituent and carries out fusion treatment to obtain described real output value.
6. a meat quality detection method, comprises the following steps:
Acquisition step: the adipose tissue image that gathers a meat sample;
Nutrient culture media extraction step: extract a fixed area of the adipose tissue only with described meat sample in described adipose tissue image as nutrient culture media;
Eigenwert extraction step: calculate the area of the bacterial plaque in described nutrient culture media and the ratio of described nutrient culture media area;
Spectrometry procedure: the structure of obtaining described meat sample by resolving the near infrared spectrum of described meat sample forms data, described spectrometry procedure forms data by calculating described meat sample to the poor structure of analyzing described meat sample of the absorbance of the near infrared light of two kinds of different wave lengths; And
Data fusion step: the structure of the ratio of described bacterial plaque area and nutrient culture media area and described meat sample is formed in the default algorithm of data substitution one and merges computing, to export a real output value, described real output value is in order to characterize the quality of described meat sample.
7. meat quality detection method as claimed in claim 6, is characterized in that, further comprising the steps of between described acquisition step and nutrient culture media extraction step:
Extract the green channel in described adipose tissue image, and the image that only has green channel extracting is converted to the gray level image of 8; And
By Hough transform method, from described gray level image, extract a border circular areas that comprises adipose tissue image or an elliptical region as described nutrient culture media.
8. meat quality detection method as claimed in claim 6, is characterized in that, described data fusion step comprises:
Input layer by a neural network unit receives bacterial plaque area and the ratio of nutrient culture media area and the structure of the described meat sample composition data in described nutrient culture media;
The structure of the ratio of the bacterial plaque area in described nutrient culture media and nutrient culture media area and described meat sample is formed in the default algorithm of data substitution one and merges computing, to obtain a real output value;
Find and the immediate training sample of the data that receive, union is to desired output that should training sample and the error amount between described real output value, described training sample comprises that a bacterial plaque area forms data with the ratio data of nutrient culture media area and the corresponding composition of sample, each desired output is the value calculating in default algorithm described in the data substitution in corresponding training sample, represents the quality of different samples;
Judge that error amount between described real output value and described desired output is whether in the error range of a permission;
If the error amount between described real output value and described desired output, in the error range of described permission, is exported described real output value by an output layer of described neural network unit; And
If the error amount between described real output value and described desired output is not in the error range of described permission, change the weights of described default algorithm, and again will in algorithm default described in the structure composition data substitution of the ratio of the bacterial plaque area in described nutrient culture media and nutrient culture media area and described meat sample, merge computing, until the error amount between the real output value calculating and described desired output is in the error range of described permission, then export qualified real output value by described output layer.
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