A low-loss detection method for the content of EPA+DHA in live carp muscle based on skin characteristic wavelength

Abstract

The application relates to the technical field of low-loss detection of carps, in particular to a low-loss detection method for the EPA+DHA content of carp muscle based on skin characteristic wavelengths. The method comprises the following steps: constructing a radial basis function neural network, obtaining a training set composed of multiple samples containing wavelength information and unsaturated fatty acid information, training the network to obtain a detection model, and inputting the wavelength information of fresh carp skin into the detection model to obtain the EPA+DHA content of carp muscle. The method can realize rapid low-loss detection and has high detection accuracy.

Description

Technical Field

[0001] The present application relates to the technical field of low-loss detection of carp, and specifically to a low-loss detection method for the EPA+DHA content in living carp muscle based on skin characteristic wavelengths. Background Art

[0002] Common carp (Cyprinus carpio) mainly inhabits waters with stagnant water flow, such as the middle and lower reaches of rivers, lakes, and reservoirs. It particularly prefers nutrient-rich, bottom-dwelling or aquatic-weed-rich waters. As an economic aquatic product, common carp can meet human food needs. The unsaturated fatty acids contained in it, such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), can effectively prevent human cardiovascular diseases, lower blood cholesterol levels, and reduce the probability of thrombosis. As an edible product, especially fresh common carp, quickly knowing the EPA and DHA content in common carp meat can quickly identify the product quality of fresh common carp. The existing technology uses gas chromatography to sample and test common carp meat. The testing process is cumbersome and costly, and the common carp needs to be broken for sampling, which damages the quality of the product. Summary of the Invention

[0003] Based on this, this application uses machine learning to establish a relationship between the characteristic wavelength of fresh carp skin and the EPA and DHA content in its muscle and constructs a detection model. By inputting the characteristic wavelength information of fresh carp skin into the detection model, the EPA and DHA content in its muscle can be quickly determined, achieving not only rapid and low-loss detection but also high detection accuracy.

[0004] To this end, the embodiments of the present application disclose at least the following technical solutions:

[0005] The embodiment discloses a method for detecting unsaturated fatty acids in fresh carp muscle. The method comprises:

[0006] Obtaining a training set, the training set comprising a plurality of training samples, each of the training samples comprising at least one wavelength information and unsaturated fatty acid information corresponding to the wavelength information;

[0007] Obtain a radial basis function neural network, the radial basis function neural network comprising an input layer, a hidden layer, and an output layer, the input layer being used to receive the wavelength information, the hidden layer being used to perform non-negative nonlinear processing on the wavelength information to obtain an implicit function space, and the output layer being used to linearize the implicit function space to obtain a linearized output result;

[0008] Obtaining each of the training samples to train the radial basis function neural network to determine the number, center, and width of neurons in the hidden layer, and the connection weights between the hidden layer and the output layer;

[0009] Obtaining a detection model according to the determined number, center, and width of neurons in the hidden layer, and the determined connection weights between the hidden layer and the output layer;

[0010] Obtaining wavelength information of the fresh carp sample to be tested;

[0011] The wavelength information of the fresh carp sample to be tested is input into the detection model to obtain the unsaturated fatty acid result. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 The present invention provides a flow chart of the method for detecting unsaturated fatty acids in fresh carp muscle provided in the embodiment.

[0013] Figure 2 The original spectral image of a fresh carp provided in the embodiment.

[0014] Figure 3 For Figure 2 The hyperspectral image is obtained by preprocessing.

[0015] Figure 4 The curve of the Monte Carlo sampling number and the sampling run number is provided for the embodiment with the sampling run number of 50 times.

[0016] Figure 5 The curve of the interactive validation root mean square error and the sampling run number provided in the embodiment with the sampling run number of 50 is shown.

[0017] Figure 6 The curve of the spectral variable regression coefficient and the sampling run number is provided for 50 sampling runs in the embodiment.

[0018] Figure 7 A schematic diagram of the structure of a radial basis function neural network provided in an embodiment.

[0019] Figure 8 This is a statistical chart of unsaturated fatty acid content provided in the examples. In the chart, the horizontal axis represents the test results according to GB / T 28769-2012, and the vertical axis represents the unsaturated fatty acid results of the method for testing unsaturated fatty acids in fresh carp muscle.

[0020] Figure 9 This is a visualization diagram of the unsaturated fatty acid content provided in the examples.

[0021] Figure 10 A schematic flow chart of a method for obtaining wavelength information provided in an embodiment.

[0022] Figure 11 This is a schematic diagram of the method flow of step S705 provided in the embodiment. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is further described in detail below with reference to the following examples. It should be understood that the specific examples described herein are merely for the purpose of explaining this application and are not intended to limit this application. Reagents not described in detail in this application are all conventional reagents and can be obtained from commercial channels; methods not specifically described in detail are all conventional experimental methods and can be obtained from the prior art.

[0024] To address the shortcomings of the aforementioned existing technologies, such as long processing times, high costs, and lethal sampling, this application uses machine learning to establish a relationship between the characteristic wavelength of fresh carp skin and the EPA and DHA content in its muscle and construct a detection model. By inputting the characteristic wavelength information of fresh carp skin into the detection model, the EPA and DHA content in its muscle can be quickly determined, achieving not only rapid and low-loss detection but also high detection accuracy.

[0025] For this reason, Figure 1 and 7 As shown, the embodiment discloses a method for detecting unsaturated fatty acids in fresh carp muscle. The method comprises:

[0026] S10, obtaining a training set, wherein the training set includes a plurality of training samples, each of the training samples including at least one wavelength information and unsaturated fatty acid information corresponding to the wavelength information;

[0027] S20, obtaining a radial basis function neural network, the radial basis function neural network comprising an input layer, a hidden layer, and an output layer, the input layer being used to receive the wavelength information, the hidden layer being used to perform non-negative nonlinear processing on the wavelength information to obtain an implicit function space, and the output layer being used to linearize the implicit function space to obtain a linearized output result;

[0028] S30, acquiring each of the training samples to train the radial basis function neural network to determine the number, center, and width of neurons in the hidden layer, and the connection weights between the hidden layer and the output layer;

[0029] S40, obtaining a detection model according to the determined number, center, and width of neurons in the hidden layer, and the determined connection weights between the hidden layer and the output layer;

[0030] S50, obtaining wavelength information of the fresh carp sample to be tested;

[0031] S60: Inputting the wavelength information of the fresh carp sample to be tested into a detection model to obtain an unsaturated fatty acid result.

[0032] In some embodiments, the wavelength information consists of hyperspectral data of 395 nm, 411 nm, 427 nm, 655 nm, 711 nm, 968 nm, 987 nm, 989 nm, 1000 nm, 1004 nm, 1028 nm, 1030 nm, 1033 nm, and 1035 nm.

[0033] In some embodiments, as Figure 10 As shown, in the method, the step of acquiring the wavelength information includes:

[0034] S701, obtaining a hyperspectral image of a fresh carp, wherein the hyperspectral image is a pixel map of the skin spectrum of the fresh carp, and the wavelength range of the skin spectrum is 400-1000 nm;

[0035] S702, obtaining a target area in the hyperspectral image;

[0036] S703, calculating the average reflectivity of all wavelengths within the wavelength range of 400-1000 nm in the current area;

[0037] S704, averaging the spectrum at the wavelength corresponding to the average reflectivity value;

[0038] S705: Extract the wavelength information from the average spectrum.

[0039] In some embodiments, the hyperspectral image is a 2558×960 pixel image.

[0040] In some embodiments, the target area is a 200×200 pixel area.

[0041] In some embodiments, the step of acquiring wavelength information further includes S700 before step S701: pre-processing the acquired original spectral image to obtain the hyperspectral image.

[0042] To eliminate the effects of equipment, samples, and environmental factors on the raw spectral data collected by the hyperspectral imaging system, convolution smoothing is performed on the raw spectral images obtained by the hyperspectral imaging camera. Convolution smoothing is a preprocessing method used to smooth spectral data and reduce noise. It smoothes the data by fitting a polynomial curve to a local region, thereby eliminating or reducing random noise in the data and making the data curve smoother.

[0043] like Figure 2 The original spectrum of a fresh carp is shown. Due to factors such as uneven surface flatness of the carp's skin after descaling and noise, the hyperspectral lines are irregular. Figure 3As shown in the figure, the hyperspectral image obtained by convolution smoothing the original spectrum reduces interference such as noise and is more conducive to machine learning.

[0044] In addition, since the hyperspectral image contains a lot of variable information and there is a lot of redundant information between the variables, it will seriously affect the modeling efficiency and accuracy of the model. In order to improve the prediction accuracy of the model and improve the efficiency of the model prediction, in some embodiments, such as Figure 11 As shown, step S705 specifically includes:

[0045] S711, obtaining an average spectrum of each sample;

[0046] S712, obtaining Monte Carlo sampling from the average spectrum of each sample;

[0047] S713. Obtain at least two variable subsets from the Monte Carlo sampling;

[0048] S714. Perform cross-validation on each variable subset to obtain a root mean square error for each variable subset;

[0049] S715: If it is detected that a certain variable subset has the minimum root mean square error, extract the variables in the variable subset as the extracted wavelength information.

[0050] In step S712 , the Monte Carlo sampling runs 50 times.

[0051] Figure 4 FIG. 4 shows a curve of the Monte Carlo sampling times and the sampling times when the Monte Carlo sampling times in step S712 is 50. Figure 4 It can be seen that as the number of sampling runs increases, the number of Monte Carlo sampling decreases rapidly. The sampling frequency band decreases rapidly in the initial stage (the first 10 sampling processes) and then decreases slowly (the number of sampling runs is 10-50 times), revealing the two stages of coarse selection and fine selection in the CARS algorithm.

[0052] Figure 5 The graph shows the cross-validation root mean square error (RMSEC) versus the number of sampling runs for the Monte Carlo model in step S714, with the number of sampling runs being 50. When the number of sampling runs gradually increases to 28, the RMSEC value gradually decreases with the increase in the number of samples, and then gradually increases.

[0053] Figure 6 A curve showing the regression coefficient of the spectral variable and the number of sampling runs for 50 Monte Carlo sampling runs is shown.

[0054] This shows that after the Monte Carlo sampling is run 50 times, step S705 eliminates useless information and can more accurately extract wavelength information, which helps model training and improves the accuracy of the detection model.

[0055] In some embodiments, the wavelength information obtained is spectral information of 395 nm, 411 nm, 427 nm, 655 nm, 711 nm, 968 nm, 987 nm, 989 nm, 1000 nm, 1004 nm, 1028 nm, 1030 nm, 1033 nm and 1035 nm.

[0056] In some embodiments, the unsaturated fatty acid information is detected by the method recommended by GB / T 28769-2012 standard.

[0057] In some embodiments, 400 fresh carp were randomly sampled using the Kennard-Stone (KS) method. The wavelength information and corresponding unsaturated fatty acid information of 320 of the fish were extracted as a training set for training a constructed radial basis function neural network. Additionally, the wavelength information and corresponding unsaturated fatty acid information of 80 of the fish were extracted as a validation set for validating the trained radial basis function neural network to obtain a detection model. Table 1 shows the statistical results of the sum of the absolute contents of EPA and DHA (mg / g) in fresh carp muscle for the training and validation sets.

[0058] Table 1

[0059]

[0060] In some embodiments, the unsaturated fatty acid information is a normalized vector of the content of eicosapentaenoic acid and docosahexaenoic acid in the fresh carp muscle.

[0061] In some embodiments, the output expression of the radial basis function neural network is:

[0062]

[0063] Where J(ω) represents the objective function, N is the number of samples, M is the number of hidden layer neurons (2), ωj is the weight of the jth hidden layer neuron, xi is the wavelength information of the ith sample, and yi is the eicosapentaenoic acid and docosahexaenoic acid content of the ith sample.

[0064] Among them, radial basis function

[0065] Among them, μ jis the center vector of the jth hidden layer neuron, and σ is the width parameter of the Gaussian function, which controls the width or "radius" of the function.

[0066] In some embodiments, the radial basis function neural network is initialized before training, including initializing the training speed, initializing the training error, and initializing the number of training iterations. For example, the training speed is initialized to 0.01, and the training error is initialized to 1×10 -6 , the number of training iterations is initialized to 1000.

[0067] In some embodiments, the training of the radial basis function neural network further includes detecting that the training is completed when the coefficient of determination of the training set is the highest, the root mean square error is the lowest, and the mean absolute error is the lowest. Root mean square error Mean absolute error Among them, n represents the number of samples, xi and yi represent the estimated value and the measured value respectively, and denote the mean of the estimated and measured values, respectively.

[0068] In some embodiments, the method further comprises validating the trained radial basis function neural network.

[0069] like Figure 8 As shown, the unsaturated fatty acid results are statistical graphs.

[0070] Table 3 shows the prediction results of the detection model for samples in the training and validation sets. The R² values ​​for the training and validation sets were 0.9934 and 0.9913, respectively; the root mean square errors were 0.3326 and 0.3352, respectively; and the mean absolute errors were 0.2704 and 0.2659, respectively. This demonstrates that the method provided in this example can rapidly and low-lossly detect the sum of EPA and DHA content in fresh carp muscle.

[0071] Table 3

[0072]

[0073] In addition, different machine learning models were trained using the samples of the above embodiment, and the error results of the training set and the validation set are shown in Table 3. As can be seen from Table 3, the detection model obtained by training with the radial basis function neural network provided by the present application has the lowest error.

[0074] In some embodiments, the unsaturated fatty acid result is a pixel image composed of R, G, and B components, wherein the pixel image displays the whole fresh carp via a first pixel or a dot matrix composed of the first pixel, and the pixel image displays the unsaturated fatty acids via a second pixel or a dot matrix composed of the second pixel. The levels of the components in the first pixel or its dot matrix are different from those in the second pixel or its dot matrix.

[0075] Furthermore, the pixel image displays the content of the unsaturated fatty acid through the levels of the R, G, and B components in the second pixel or its dot matrix.

[0076] like Figure 9 As shown, for example, the first pixel is a green to light yellow pixel composed of RGB components, so in this pixel image, the fresh carp as a whole is displayed as a region displayed in any color between green and light yellow. For example, the second pixel is a region displayed in any color between red and dark red composed of RGB components. For example, the darker the red displayed by the second pixel, the higher the content of unsaturated fatty acids in the fresh carp muscle in this region.

[0077] In this way, through the visualized pixel image, the user can directly observe the distribution and content of unsaturated fatty acids in the muscles of fresh carp.

[0078] The method for detecting unsaturated fatty acids in fresh carp muscle provided in the embodiments of the present application can be performed by a device for detecting unsaturated fatty acids in fresh carp muscle, or a control unit in the device for detecting unsaturated fatty acids in fresh carp muscle for performing the method for detecting unsaturated fatty acids in fresh carp muscle. In the embodiments of the present application, the method for detecting unsaturated fatty acids in fresh carp muscle performed by the device for detecting unsaturated fatty acids in fresh carp muscle is used as an example to illustrate the device for detecting unsaturated fatty acids in fresh carp muscle provided in the embodiments of the present application.

[0079] Optionally, an embodiment of the present application also provides an electronic device, including a processor, a memory, and a program or instruction stored in the memory and executable on the processor. When the program or instruction is executed by the processor, each process of the above-mentioned method embodiment for detecting unsaturated fatty acids in fresh carp muscle is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0080] The above is only a preferred specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any changes or replacements that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed in this application should be covered by the scope of protection of the present application.

Claims

1. The method for detecting unsaturated fatty acids in fresh carp muscle includes: Obtaining a training set, the training set comprising a plurality of training samples, each of the training samples comprising at least one wavelength information and unsaturated fatty acid information corresponding to the wavelength information; Obtain a radial basis function neural network, the radial basis function neural network comprising an input layer, a hidden layer, and an output layer, the input layer being used to receive the wavelength information, the hidden layer being used to perform non-negative nonlinear processing on the wavelength information to obtain an implicit function space, and the output layer being used to linearize the implicit function space to obtain a linearized output result; Obtaining each of the training samples to train the radial basis function neural network to determine the number, center, and width of neurons in the hidden layer, and the connection weights between the hidden layer and the output layer; Obtaining a detection model based on the determined number, center, and width of neurons in the hidden layer, and the determined connection weights between the hidden layer and the output layer; Obtaining wavelength information of the fresh carp sample to be tested; as well as Inputting the wavelength information of the fresh carp sample to be tested into a detection model to obtain an unsaturated fatty acid result; The wavelength information is characteristic wavelength information of the skin of a fresh carp.

2. The method according to claim 1, wherein the unsaturated fatty acids include eicosapentaenoic acid and docosahexaenoic acid, and the unsaturated fatty acid results include the sum of the contents of eicosapentaenoic acid and docosahexaenoic acid, and the distribution of the eicosapentaenoic acid and docosahexaenoic acid in the fresh carp individuals.

3. The method according to claim 1, wherein the step of acquiring the wavelength information comprises: Obtaining a hyperspectral image of a fresh carp, wherein the hyperspectral image is a pixel map of the skin spectrum of the fresh carp, and the wavelength range of the skin spectrum is 400-1000 nm; Obtaining a target area in the hyperspectral image; Calculating the average reflectivity of all wavelengths within the wavelength range of 400-1000 nm in the target area; An average spectrum at a wavelength corresponding to the average reflectivity value; The wavelength information is extracted from the average spectrum.

4. The method according to claim 3, wherein the hyperspectral image is a 2558×960 pixel image, and the target area is a 200×200 pixel area.

5. The method according to claim 3, wherein the step of extracting the wavelength information from the average spectrum comprises: Obtain the average spectrum of each sample; obtaining Monte Carlo sampling from the average spectrum of each sample; obtaining at least two subsets of variables from the Monte Carlo sampling; Perform cross-validation on each variable subset to obtain the root mean square error of each variable subset; If it is detected that a certain variable subset has the minimum root mean square error, the variables in the variable subset are extracted as the extracted wavelength information.

6. The method according to any one of claims 3 to 5, wherein the wavelength information obtained is spectral information of 395 nm, 411 nm, 427 nm, 655 nm, 711 nm, 968 nm, 987 nm, 989 nm, 1000 nm, 1004 nm, 1028 nm, 1030 nm, 1033 nm and 1035 nm.

7. The method according to claim 1, further comprising initializing the training speed, training error and number of training iterations of the radial basis function neural network before training the radial basis function neural network.

8. The method according to claim 1, further comprising: training the radial basis function neural network; When it is detected that the determination coefficient, the root mean square error and the mean absolute error of the training set are the highest, the training is confirmed to be completed.

9. The method according to claim 1, wherein the unsaturated fatty acid result is a pixel image composed of R, G, and B components, the pixel image displays the whole fresh carp through a first pixel point or a dot matrix composed of the first pixel points, and the pixel image displays the unsaturated fatty acids through a second pixel point or a dot matrix composed of the second pixel points, wherein: The levels of components in the first pixel or its dot matrix are different from those in the second pixel or its dot matrix.

10. The method according to claim 9, wherein the pixel image displays the unsaturated fatty acid content through the levels of the R, G, and B components in the second pixel or its pixel matrix.

Images