CN113791049B - Method for rapidly detecting freshness of chilled duck meat by combining NIRS and CV - Google Patents
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Abstract
The invention discloses a method for rapidly detecting freshness of cold fresh duck meat by fusing NIRS and CV, and belongs to the technical field of food detection. By extracting characteristic variables of different detection methods, reducing the dimensions of the spectral data and constructing the spectral data and the image transformation data together into a multi-dimensional characteristic variable of the chilled duck meat, enriching the information of the variable, and establishing a freshness prediction model of the chilled duck meat by applying a KNN (K-Nearest Neighbor) method, namely a K Nearest Neighbor method, the multi-dimensional characteristic variable greatly improves the accuracy of modeling, and when the freshness of a sample is verified, the accuracy is as high as 95.24%, and the effect is good.
Description
Technical Field
The invention relates to the technical field of food detection, relates to a method for detecting freshness of chilled duck meat, and particularly relates to a method for rapidly detecting the freshness of the chilled duck meat by fusing a spectrum technology and an image technology.
Background
Freshness is an important index of meat quality safety, nitrogen-containing substances such as ammonia, amines and the like can be generated in the meat deterioration process, the deterioration degree of the meat is usually expressed by measuring the content of volatile basic nitrogen (TVB-N) traditionally, and the method needs a certain detection period and special instruments and equipment and is not suitable for field detection.
At present, the nondestructive detection technology is gradually replacing the conventional detection method, wherein the near infrared spectroscopy (NIRS) technology is widely applied, because meat contains a large amount of organic compounds such as protein, carbohydrate and the like, the substances containing hydrogen groups can cause frequency combination and frequency doubling absorption of near infrared to generate an absorption spectrum, and then a relation model between the spectrum and a detection target is established through chemometrics to realize rapid detection, and the NIRS is applied to the aspects of meat adulteration and component prediction at present. In addition, computer vision technology (CV) has been widely used for acquiring image information of a sample by an image sensor, converting the image information into a digital signal, and analyzing the digital signal by a computer, and also for characteristic information of meat such as color, texture, and darkness. Although these non-destructive testing methods have the advantages of rapidness, safety, low cost, etc., the nature of these non-destructive testing methods is an indirect testing method, and the accuracy is always an important factor that restricts the further popularization thereof.
The near infrared spectrum technology and the computer vision technology can evaluate the quality of meat from different angles, and although the detection of the freshness of the chilled meat by applying the two technologies is reported individually, the accuracy is still not high, mainly because a single detection technology can only be used for measuring from one aspect, and the characterization of sample characteristic information is not comprehensive.
The method for constructing the characteristic variables of the samples by fusing a plurality of detection technologies is an effective means for enriching sample information and improving the accuracy of a nondestructive detection method, but the method for detecting the freshness of the chilled duck meat by fusing a spectrum technology and an image technology is not reported at present.
Disclosure of Invention
Aiming at the problem that the detection accuracy of the existing single detection technology on the chilled fresh duck meat is not high, the invention integrates multidimensional data by using two detection technologies of NIRS and CV and fusing the two detection technologies through special treatment to construct a more accurate prediction model so as to realize the rapid detection on the freshness of the chilled fresh duck meat.
In order to achieve the purpose, the invention adopts the following technical means:
a method for rapidly detecting freshness of chilled duck meat by fusing NIRS and CV comprises the steps of establishing a KNN model and detecting the freshness of the chilled duck meat by using the KNN model, wherein the establishment of the KNN model comprises the following steps:
1) Cutting chilled fresh duck meat for modeling, refrigerating at 4 deg.C, and periodically collecting near infrared spectrum data and image data of sample, wherein the near infrared spectrum data is collected in a diffuse reflection mode of fiber probe with wave band of 4,000-10,000cm -1 The response value of the spectrum is expressed in log (1/R), wherein R is the reflectivity; the acquisition of image data uses a high-resolution digital camera with the resolution not lower than 500 ten thousand pixels; the frequency of data acquisition is once every 1-3 h, and the data acquisition lasts for 14 days;
2) The method comprises the steps of measuring physical and chemical indexes of a sample while acquiring near infrared spectrum data and image data, and grading and marking the freshness grade of the sample according to the measurement result of the physical and chemical indexes;
3) Carrying out principal component analysis on the near infrared spectrum data acquired in the step 1) by using computer software, and reducing the original multiple principal components into data with only one principal component, wherein the data is represented by P;
4) Obtaining color values in the image data acquired in the step 1) by using computer software, and calculating a color distance between the color values and an initial color value, wherein the data is represented by S;
5) And (3) carrying out normalization processing on the P value and the S value by using computer software, and constructing a characteristic variable matrix X of n samples as follows:
6) And judging the category of the freshness of the sample by using a k-nearest neighbor algorithm, and establishing a KNN model of the characteristic variable and the freshness grade of the sample.
Wherein, the physicochemical index measurement in the step 2) is pH measurement and TVB-N value measurement.
When the freshness grade of the sample is graded according to the measurement result of the physicochemical indexes, the fresh meat is graded with the pH = 5.8-6.2 and the TVB-N <15mg/100 g; rating pH = 6.3-6.6, TVB-N = 15-25 mg/100g as secondary fresh meat: the samples with pH >6.7 and TVB-N >25mg/100g are rated as spoiled meat and classified as low freshness grade according to the principle of low if the TVB-N value and the pH value are not in the same freshness grade.
Wherein the near infrared spectrum data in the step 3) is 5500-6500 and 7500-10000cm -1 Data in two band ranges.
Wherein, the color values in the step 4) comprise a red color value, a green color value and a blue color value which are respectively represented by R, G, B.
Wherein the color space S n The calculation formula of (2) is as follows:
R 0 、G 0 、B 0 representing the color value at the beginning, R n 、G n 、B n Representing the color values actually detected by the sample.
When the class to which the freshness of the sample belongs is judged by using a K-nearest neighbor algorithm, the accuracy is highest when the K value is 1, 2 or 3, and any one of the K values is selected.
The invention has the beneficial effects that:
the invention integrates the spectrum and the image to construct a detection model of the characteristic variable of the sample, can meet the requirement of on-line continuous detection, has higher detection accuracy than a single near infrared method or computer vision, and has great application potential in the aspects of production and processing intellectualization and digitization.
Drawings
FIG. 1 near infrared spectra of duck meat at various refrigeration times.
FIG. 2 images of duck meat at different refrigeration times.
FIG. 3 shows the physical and chemical indexes of duck meat under different refrigeration time.
FIG. 4 is a graph of the accuracy distribution of models for different K values.
FIG. 5 is a prediction of test set freshness by an optimal KNN model.
Detailed Description
The present invention will be further illustrated with reference to the following specific examples, but the present invention is not limited to the following examples.
Example 1
(1) Duck sample preparation
A supermarket purchases fresh and fresh duck meat after being slaughtered, duck breast meat is selected, sliced and trimmed into square blocks with the shapes of 10cm multiplied by 10cm and the thickness of 2cm, the square blocks are 14 blocks in total, and the square blocks are stored under the refrigeration condition of 4 ℃.
(2) Sample spectral and image data acquisition
The sample is refrigerated and placed for 0 to 14 days, and the near infrared spectrum collection and the image data collection of the sample are carried out every 1 to 3 hours (random time points and random sample blocks).
The instrument used for near infrared spectrum acquisition is an Antaris II Fourier transform near infrared analyzer, and log (1/R) represents the response value of the spectrum, wherein R is the reflectivity. The acquisition parameters are shown in table 1.
TABLE 1 acquisition parameters of near infrared spectra
The near infrared spectrum data is response values of a sample under different wave numbers, the wave number interval is related to the resolution of a near infrared instrument, and the shorter the interval is, the more the wave numbers are, the higher the resolution is, the more the variable numbers are, and the more comprehensive spectrum information can be reflected. Of course, the huge number of variables brings inconvenience to modeling, and there are some interferences not related to interval information while consuming time, which affects the accuracy of the model.
The image data adopts a Sony IMX219 camera, can continuously shoot high-definition pictures with 800 ten thousand pixels, and is connected with a computer through a USB.
Fig. 1 and 2 are raw spectra and images collected at 1 day, 6 days, and 12 days, respectively, for a random sample.
The spectral data of FIG. 1 show that in the wavenumber range 5500-6500, 7500-10000cm -1 The duck meat shows obvious difference for different refrigeration time, which shows that the near infrared spectrum can obtain distinctive spectrum data for the duck meat with different refrigeration time, and provides a basis for establishing a correlation model between the near infrared spectrum and the freshness of the duck meat; meanwhile, when the spectral data is processed at the later stage, 5500-6500 and 7500-10000cm can be considered in an important way for reducing the data volume -1 Data in two band ranges.
The image data of fig. 2 shows that the duck meat was bright-colored, glossy, dark-colored and matt on day 1 of refrigeration, and dark-colored and matt on day 6, and that the duck meat was dark-colored and dry in meat quality and was contaminated with germs visible to the naked eye on day 12. The result shows that the image data at different time points also have difference, and the difference has certain correlation with the quality of the chilled duck meat.
(3) Physical and chemical index measurement and analysis
The pH value of the sample is determined by referring to a method for determining the pH value of a meat product in GB 5009.237-2016, and the TVB-N value is determined by referring to a semi-microscale nitrogen determination method in GB 5009.228-2016. After each time of spectrum and image data acquisition, a part of samples are cut for measuring physicochemical indexes, and the rest of samples are continuously preserved in cold and fresh conditions.
Fig. 3 shows the change of physicochemical indexes of one sample at random, and the result shows that the TVB-N value and the pH value of the chilled fresh duck meat both have an increasing trend with the extension of the storage time. According to the national evaluation of the freshness grade of meat products, fresh meat: pH = 5.8-6.2, tvb-N <15mg/100g, secondary fresh meat: pH = 6.3-6.6, tvb-N = 15-25 mg/100g, spoiled meat: pH >6.7 and TVB-N >25mg/100g. On this basis, the present study stipulates that if the sample TVB-N value and the pH range are not at the same freshness level, then it should be classified as a low freshness level (from the low principle). The physical and chemical values are analyzed to find that: fresh meat is taken for 0-4 days, sub-fresh meat is taken for 4-8 days, rotten meat is taken for 8-14 days, and the freshness of 168 samples is marked according to the cluster analysis result.
(4) Spectral and image data processing and characteristic variable construction
In order to construct the feature variables, the spectral and image data need to be processed separately. The spectral data is subjected to principal component analysis (realized by a princomp function in Matlab software), so that when the principal component (variable) is 1, the contribution rate reaches 97.2%, and therefore, the spectral data is subjected to principal component analysis to reduce the original 1557 principal components (determined by the resolution of the instrument) into data with only one principal component, wherein the variable is represented by P. And (3) performing dimensionality reduction on the multivariate data through principal component analysis, and greatly reducing data variables under the condition of retaining most of original spectral data characteristics.
Processing of image data by color distance S n It is shown that the image color obtained first is composed of three values of R (red), G (green), and B (blue), and the color distance can be obtained by the following formula:
wherein the color at initial (0 h) is (R) 0 、G 0 、B 0 ) The color value of the target sample is (R) n 、G n 、B n )。
The data are normalized by normalizing the image data (realized by a mapminmax function in Matlab software) to the spectral data, and then matrix merging is carried out on the spectral data after dimensionality reduction and color distance data to form a matrix with multiple rows and 2 columns, wherein the first column represents the spectral data, the second list represents the image data, and each row represents a sample. The finally constructed 168 sample characteristic variable matrixes X are expressed as follows:
(5) Establishment of duck freshness KNN model
The KNN (K-Nearest Neighbor) method, namely a K Nearest Neighbor method, finds out K samples ranked closest by calculating the distance between an unknown sample and a training sample and sorting according to the distance, and finally judges the category to which the freshness of the duck meat sample to be classified belongs according to the categories of the K samples, wherein the method is from Neighbor classification methods in python third party package scibit-lean. The accuracy of the model is affected by different K values, the fused feature matrix and the measured value are trained to obtain the KNN optimal model, and the accuracy of the K values and the model is shown in figure 4.
As can be seen from fig. 4, the accuracy is highest when the K value is 1, 2, or 3, and any one of them may be selected.
(6) Evaluation of optimal KNN model for duck freshness
When the model is built, the proportion of the training set and the prediction set of 168 samples is randomly distributed according to 3:1, 126 is used for training the model in the step (5), and 42 are used as external verification sets for evaluating the model.
Fig. 5 shows the prediction result of the KNN optimal model on the freshness of duck meat, and shows that only 2 duck meat which is originally fresh is identified as fresh and one rotten in 42 samples of the prediction set, the identification accuracy rate reaches 95.24%, and the prediction effect is good.
Example 2
The results of predicting freshness of the prediction set based on the freshness of duck meat detected in example 1, compared with the non-fusion detection method, are shown in table 2.
TABLE 2 comparison of detection methods for freshness of different duck meat
As can be seen from table 2, the accuracy of the traditional freshness detection method, such as a semi-trace azotometry and a microbial counting method, reaches 100%, but the whole detection process consumes a long time and is complex to operate, and can only be used as a standard method during modeling, and cannot meet the requirement for quickly detecting freshness in the actual production process, the near-infrared and computer vision technology can meet the requirement for quickly detecting freshness, and the electronic tongue and the electronic nose can carry out quick detection, but need to carry out sample change operation after detecting a batch of samples, and have a certain time consumption and cannot meet the requirement for continuous online detection, and the freshness indicator card can carry out real-time freshness detection, but the indicator card is often disposable and must be in contact detection, and the accuracy determination needs to be further improved.
Claims (1)
1. The method for rapidly detecting the freshness of the chilled duck meat by fusing the NIRS and the CV is characterized by comprising the steps of establishing a KNN model and detecting the freshness of the chilled duck meat by using the KNN model, wherein the step of establishing the KNN model comprises the following steps:
1) Cutting chilled fresh duck meat for modeling into blocks, refrigerating at 4 deg.C, and periodically collecting near infrared spectrum data and image data of sample, wherein the near infrared spectrum data is collected in a diffuse reflection mode with a fiber probe and a wave band of 4,000-10,000cm -1 The response value of the spectrum is expressed in log (1/R), wherein R is the reflectivity; the acquisition of image data uses a high-resolution digital camera with the resolution not lower than 500 ten thousand pixels; the data acquisition frequency is once every 1 to 3h, and the data acquisition is continuously carried out for 14 days;
2) The method comprises the steps of collecting near infrared spectrum data and image data, simultaneously carrying out physical and chemical index measurement on a sample, and grading and marking the freshness grade of the sample according to the physical and chemical index measurement result; the physicochemical index measurement is pH measurement and TVB-N value measurement, and when the freshness grade of a sample is graded according to the physicochemical index measurement result, fresh meat is graded with the pH = 5.8-6.2 and the TVB-N value less than 15mg/100 g; evaluation of pH =6.3 to 6.6, TVB-N =15 to 25mg/100g as secondary fresh meat: the samples with the pH value of more than 6.7 and the TVB-N value of more than 25mg/100g are evaluated as the spoiled meat, and if the TVB-N value and the pH value of the samples are not in the same freshness grade, the samples are classified into a low freshness grade according to a low principle;
3) Using computer software to process 5500-6500, 7500-10000cm collected in the step 1) -1 Performing principal component analysis on near infrared spectrum data in two waveband ranges to reduce original multiple principal components into data with only one principal component, and using the dataPRepresents;
4) Obtaining the red value, the green value and the blue value in the image data collected in the step 1) by using computer software, respectively representing the red value, the green value and the blue value by R, G, B, calculating the color distance between the data and the initial color value, and using the dataSRepresents; the color distanceS n The calculation formula of (2) is as follows:
wherein n represents the nth sample,
、
、
represents the color value at the time of the initialization,
、
、
representing the color values actually detected by the sample,S n representing a color distance characteristic value of the nth sample;
5) Using computer software pairsPValue sumSNormalizing the values to construct a characteristic variable matrix of n samplesXAs follows:
6) And judging the category of the freshness of the sample by using a K nearest neighbor algorithm, and establishing a KNN model of the characteristic variable and the freshness grade of the sample by taking any one of 1, 2 and 3 as a K value.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2189789A1 (en) * | 2008-11-20 | 2010-05-26 | Sedna | Method and device for checking the freshness of a fish |
| CN102323267A (en) * | 2011-08-10 | 2012-01-18 | 中国农业大学 | System and method used for rapidly evaluating freshness of raw meat products |
| CN103278609A (en) * | 2013-06-27 | 2013-09-04 | 山东商业职业技术学院 | Meat product freshness detection method based on multisource perceptual information fusion |
| CN105548028A (en) * | 2015-12-11 | 2016-05-04 | 华中农业大学 | Fowl egg freshness optical fiber spectroscopic grading detection device and method |
| WO2021053737A1 (en) * | 2019-09-18 | 2021-03-25 | ルミアナ ツェンコヴァ | Visible/near infrared spectroscopy analysis device and visible/near infrared spectroscopy analysis method |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103424374B (en) * | 2013-06-19 | 2015-10-21 | 浙江省海洋开发研究院 | A kind of near-infrared spectrum technique detects the method for hairtail freshness fast |
| JP6499430B2 (en) * | 2014-02-28 | 2019-04-10 | パナソニック株式会社 | Freshness information output method, freshness information output device, control program |
| CN106153576B (en) * | 2016-07-28 | 2019-08-20 | 华南理工大学 | The method of quick predict Frozen Pork storage time based on Near-infrared Double wave band ratio |
| CN108872132A (en) * | 2018-08-24 | 2018-11-23 | 湖北省农业科学院果树茶叶研究所 | A method of fresh tea leaves kind is differentiated using near infrared spectrum |
-
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2189789A1 (en) * | 2008-11-20 | 2010-05-26 | Sedna | Method and device for checking the freshness of a fish |
| CN102323267A (en) * | 2011-08-10 | 2012-01-18 | 中国农业大学 | System and method used for rapidly evaluating freshness of raw meat products |
| CN103278609A (en) * | 2013-06-27 | 2013-09-04 | 山东商业职业技术学院 | Meat product freshness detection method based on multisource perceptual information fusion |
| CN105548028A (en) * | 2015-12-11 | 2016-05-04 | 华中农业大学 | Fowl egg freshness optical fiber spectroscopic grading detection device and method |
| WO2021053737A1 (en) * | 2019-09-18 | 2021-03-25 | ルミアナ ツェンコヴァ | Visible/near infrared spectroscopy analysis device and visible/near infrared spectroscopy analysis method |
Non-Patent Citations (5)
| Title |
|---|
| Beef freshness classification by using color analysis, multi-wavelet transformation, and artificial neural network;Danika Trientin 等;《2015 International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT)》;20151030;第181、185页，及图1 * |
| 光谱和成像融合技术检测猪肉中挥发性盐基氮;赵杰文 等;《激光与光电子学进展》;20120610;第49卷(第06期);第063003-1-063003-6页 * |
| 基于多源感知信息融合的牛肉新鲜度分级检测;姜沛宏 等;《食品科学》;20160315;第37卷(第06期);第161-164页 * |
| 基于计算机视觉的鲶鱼肉色泽测定系统;曹雷鹏 等;《食品科学》;20170815;第38卷(第15期);第135-138页 * |
| 多源信息融合技术的猪肉新鲜度检测方法研究;黄懿 等;《湖北农业科学》;20110620;第50卷(第12期);第2536-2540页 * |
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