CN112730269B - Aflatoxin intelligent detection method based on deep learning - Google Patents
Aflatoxin intelligent detection method based on deep learning Download PDFInfo
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- 229930195730 Aflatoxin Natural products 0.000 title claims abstract description 23
- XWIYFDMXXLINPU-UHFFFAOYSA-N Aflatoxin G Chemical compound O=C1OCCC2=C1C(=O)OC1=C2C(OC)=CC2=C1C1C=COC1O2 XWIYFDMXXLINPU-UHFFFAOYSA-N 0.000 title claims abstract description 23
- 239000005409 aflatoxin Substances 0.000 title claims abstract description 23
- 238000013135 deep learning Methods 0.000 title claims abstract description 19
- 238000001514 detection method Methods 0.000 title claims abstract description 16
- 238000001228 spectrum Methods 0.000 claims abstract description 17
- 239000003053 toxin Substances 0.000 claims abstract description 9
- 231100000765 toxin Toxicity 0.000 claims abstract description 9
- 238000004811 liquid chromatography Methods 0.000 claims abstract description 4
- 230000004913 activation Effects 0.000 claims description 23
- 230000006870 function Effects 0.000 claims description 23
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- 238000013136 deep learning model Methods 0.000 claims description 2
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- 244000105624 Arachis hypogaea Species 0.000 description 2
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- 235000018262 Arachis monticola Nutrition 0.000 description 2
- 240000008042 Zea mays Species 0.000 description 2
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- 238000013528 artificial neural network Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 235000005822 corn Nutrition 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 235000020232 peanut Nutrition 0.000 description 2
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- 238000002306 biochemical method Methods 0.000 description 1
- 230000000711 cancerogenic effect Effects 0.000 description 1
- 231100000357 carcinogen Toxicity 0.000 description 1
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- 238000000701 chemical imaging Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
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- 230000036541 health Effects 0.000 description 1
- 238000004128 high performance liquid chromatography Methods 0.000 description 1
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Abstract
The invention relates to an intelligent aflatoxin detection method based on deep learning, which comprises the steps of firstly, obtaining hyperspectral data of single mildewed grains, calibrating the toxin content of each grain by using a liquid chromatography, then, building a one-dimensional deep learning network, defining the number and structure of network layers, importing the average spectrum of each grain, continuously adjusting the network hyper-parameters, and selecting the hyper-parameters with the highest accuracy as a classification model.
Description
Technical Field
The invention discloses an aflatoxin intelligent detection method based on deep learning, and belongs to the field of artificial intelligent detection of food safety.
Background
Aspergillus flavus is classified as a naturally occurring carcinogen by the World Health Organization (WHO) cancer research institution, and the detection method thereof is developed from the beginning mainly by biochemical methods such as thin layer chromatography to the common application of various methods such as high performance liquid chromatography, microcolumn method, enzyme-linked immunosorbent assay and the like, but the methods need long-time experiments by professionals.
The deep learning is a neural network system based on convolution operation, has strong calculation power and self-learning capability, has high accuracy and high calculation speed, is well known in the field of digital image processing at first, and can well process two-dimensional image data classification tasks. A proper network structure can be constructed according to different classification targets and data characteristics thereof, and the hyper-parameters of the neural network need to be adjusted according to a target data set.
The hyperspectral imaging technology can collect images of hundreds of wave bands of an object, different performances of different substances under different wave band spectrum signals can be drawn into a curve related to the spectrum wave bands and the spectrum values by each pixel, each pixel is classified according to the difference of the curve and can be realized through a one-dimensional convolutional neural network (1D-CNN), toxin identification is carried out by analyzing whether the spectrum characteristics of aflatoxin exist in a single pixel in the hyperspectral images, the toxin content can be calculated by accumulation, and the aflatoxin has the distribution characteristics of a surface layer and a shallow surface layer, so that deep learning and hyperspectral are matched, and the defects of complex operation, long time and high detection cost of the traditional method can be overcome.
Disclosure of Invention
The invention mainly aims to overcome the defects of the problems and discloses an aflatoxin intelligent method based on 1D-CNN deep learning.
The technical scheme of the invention is an aflatoxin intelligent detection method based on deep learning, which comprises the following steps:
step 1: acquiring hyperspectral data of the front side and the back side of a single mildewed grain;
step 2: obtaining a binary image of the grains and the background by utilizing the spectrum obtained in the step 1 and Gaussian clustering, and calculating an average spectrum of the single-sided grains;
and step 3: adding the hyperspectral data of the front side and the back side of the single grain obtained in the step 2, and taking an average value as the spectral data of the single grain;
and 4, step 4: calibrating the toxin content of the single grain in the step 1 by using a liquid chromatography to obtain a label for judging whether the aflatoxin of the single grain exceeds the standard or not;
and 5: normalizing the input data;
step 6: building a one-dimensional deep learning network, and defining the number of network layers and structure;
and 7: introducing the average spectrum and toxin content of each grain, continuously adjusting network hyper-parameters, and recording the accuracy of each training network model;
and 8: and selecting the optimal parameters of the hyper-parameters to configure the optimal parameters into a final hyperspectral aspergillus flavus sorting network.
Wherein:
(1) the specific steps of the step 2 are as follows:
step 2-1: setting the clustering number to be 2 by using a Gaussian clustering method for the whole hyperspectral cube, and accurately classifying the background and the grains;
step 2-2: expressing the grain picture by using a binary image, wherein a white area represents the grain, and a black area represents the background;
step 2-3: and taking the average spectrum of the white area as the hyperspectral data of the single-sided grains.
(2) The concrete steps of the step 6 are as follows:
step 6-1: establishing a 7-layer one-dimensional information convolution model: each of the first four layers is a convolution layer with a largest pooling layer, and the last three layers are full-connection layers;
step 6-2: keeping the basic network structure in the step 6-1 unchanged, and setting initial values of three hyper-parameters of iteration times, learning rate and activation function. The number of iterations is set to 30, the learning rate is set to 0.005, and the activation function is set to relu.
(3) The specific steps of step 7 are:
step 7-1: the number of fixed iterations is 30 and the learning rate is 0.005. And replacing the activation function with sigmoid and tanh, importing the activation function into a data training network, and recording the accuracy of the verification set. Recording the accuracy of the activation functions of relu, sigmoid and tanh;
step 7-2: the fixed learning rate is 0.005 and the activation function is relu. And replacing the iteration times with 50 and 100, importing the data training network and recording the accuracy of the verification set. Recording the accuracy rates of 30, 50 and 100 of the iteration times;
and 7-3: the fixed number of iterations is 30 and the activation function is relu. The learning rate was replaced with 0.0005, 0.00005, and 0.000005, the data training network was imported and the validation set accuracy was recorded. Accuracy rates of replacement of learning rates by 0.0005, 0.00005, and 0.000005 are recorded;
and 7-4: and applying the model with the highest target identification accuracy to the aflatoxin of the grains according to the parameters with the highest iteration times, learning rate and the highest accuracy in the activation function.
A1D-CNN deep learning model in the aflatoxin intelligent method based on 1D-CNN deep learning can be one of Alexnet, VGG-16 or models.
Drawings
FIG. 1 is a general schematic diagram of an aflatoxin intelligent detection method based on deep learning.
FIG. 2 shows the result of aflatoxin detection of peanut kernels.
FIG. 3 shows the result of aflatoxin detection of corn kernels.
Detailed Description
The following describes specific embodiments of the present apparatus and method with reference to fig. 1-3, wherein fig. 2 and 3 are the detection results of the specifically implemented peanut and corn kernel using Alexnet and VGG-16 models, respectively:
step 1: acquiring hyperspectral data of the front side and the back side of a single mildewed grain;
step 2: obtaining a binary image of the grains and the background by Gaussian clustering by using the spectrum obtained in the step 1, and calculating an average spectrum of single-sided grains;
and step 3: adding the hyperspectral data of the front side and the back side of the single grain obtained in the step 2, and taking an average value as the spectral data of the single grain;
and 4, step 4: performing toxin content calibration on the single grains in the step 1 by using a liquid chromatography method to obtain a label indicating whether aflatoxin of the single grains exceeds the standard or not;
and 5: normalizing the input data;
step 6: building a one-dimensional deep learning network, and defining the number of network layers and structure;
and 7: introducing the average spectrum and toxin content of each grain, continuously adjusting network hyper-parameters, and recording the accuracy of each training network model;
and 8: and selecting the optimal parameters of the hyper-parameters to configure the optimal parameters into a final hyperspectral aspergillus flavus sorting network.
Wherein:
(1) the specific steps of the step 2 are as follows:
step 2-1: setting the clustering number to be 2 by using a Gaussian clustering method for the whole hyperspectral cube, and accurately classifying the background and the grains;
step 2-2: expressing the grain picture by using a binary image, wherein a white area represents the grain, and a black area represents the background;
step 2-3: and taking the average spectrum of the white area as the hyperspectral data of the single-sided grains.
(2) The concrete steps of the step 6 are as follows:
step 6-1: establishing a 7-layer one-dimensional information convolution model: each of the first four layers is a convolution layer with a largest pooling layer, and the last three layers are full-connection layers;
step 6-2: keeping the basic network structure in the step 6-1 unchanged, and setting initial values of three hyper-parameters of iteration times, learning rate and activation function. The number of iterations is set to 30, the learning rate is set to 0.005, and the activation function is set to relu.
(3) The specific steps of the step 7 are as follows:
step 7-1: the number of fixed iterations is 30 and the learning rate is 0.005. And replacing the activation function with sigmoid and tanh, importing the activation function into a data training network, and recording the accuracy of the verification set. Recording the accuracy of the activation functions of relu, sigmoid and tanh;
step 7-2: the fixed learning rate is 0.005 and the activation function is relu. And replacing the iteration times with 50 and 100, importing the data training network and recording the accuracy of the verification set. Recording the accuracy rates of 30, 50 and 100 of the iteration times;
and 7-3: the fixed number of iterations is 30 and the activation function is relu. The learning rate was replaced with 0.0005, 0.00005, and 0.000005, the data training network was imported and the validation set accuracy was recorded. Recording the accuracy rate of replacing the learning rate by 0.0005, 0.00005 and 0.000005;
and 7-4: and applying the model with the highest target identification accuracy to the aflatoxin of the grains according to the parameters with the highest iteration times, learning rate and the highest accuracy in the activation function.
Claims (5)
1. An aflatoxin intelligent detection method based on deep learning is characterized by comprising the following steps:
step 1: acquiring hyperspectral data of the front side and the back side of a single mildewed grain;
step 2: obtaining a binary image of the grains and the background by Gaussian clustering by using the spectrum obtained in the step 1, and calculating an average spectrum of single-sided grains;
and step 3: adding the hyperspectral data of the front side and the back side of the single grain obtained in the step 2, and taking an average value as the spectral data of the single grain;
and 4, step 4: calibrating the toxin content of the single grain in the step 1 by using a liquid chromatography to obtain a label for judging whether the aflatoxin of the single grain exceeds the standard or not;
and 5: normalizing the input data;
step 6: building a one-dimensional deep learning network, and defining the number of network layers and structure;
and 7: introducing the average spectrum and toxin content of each grain in the step 3, continuously adjusting network hyper-parameters, and recording the accuracy of each training network model;
and step 8: and selecting the optimal parameter of each hyper-parameter to configure the hyper-spectral aspergillus flavus sorting network.
2. The intelligent aflatoxin detection method based on deep learning as claimed in claim 1, which is characterized in that the specific steps in the step 2 are as follows:
step 2-1: setting the clustering number to be 2 by using a Gaussian clustering method for the whole hyperspectral cube, and accurately classifying the background and the grains;
step 2-2: expressing the grain picture by using a binary image, wherein a white area represents the grain, and a black area represents the background;
step 2-3: and taking the average spectrum of the white area as the hyperspectral data of the single-sided grains.
3. The intelligent aflatoxin detection method based on deep learning as claimed in claim 1, which is characterized in that the specific steps of step 6 are as follows:
step 6-1: establishing a one-dimensional information convolution model with a fixed layer number: the front N-1 layers are convolution layers with the largest pooling layer, and the rear three layers are full-connection layers;
step 6-2: keeping the basic network structure in the step 6-1 unchanged, and setting initial values of three hyper-parameters of iteration times, learning rate and activation function.
4. The intelligent aflatoxin detection method based on deep learning as claimed in claim 1, which is characterized in that the specific steps of step 7 are as follows:
step 7-1: replacing the activation function with fixed iteration times and unchanged initial values of the learning rate, importing the activation function into a data training network, recording the accuracy of a verification set, and recording the accuracy of different activation functions;
step 7-2: the initial value of the fixed learning rate is unchanged, the activation function is unchanged, the iteration times are replaced by importing data to a training network, and the accuracy rate of a verification set is recorded;
and 7-3: the initial value of the fixed iteration times is unchanged, the activation function is unchanged, the learning rate is replaced, the learning rate is led into a data training network, the accuracy rate of a verification set is recorded, and the accuracy rates under different learning rates are recorded;
and 7-4: and using the model with the highest target identification accuracy for aflatoxin classification of the grains according to the parameters with the highest iteration times, learning rate and accuracy in the activation function.
5. The method for intelligently detecting aflatoxin based on deep learning of claim 1, wherein the deep learning model can be one of Alexnet and VGG-16 models.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US8563934B2 (en) * | 2010-09-10 | 2013-10-22 | Mississippi State University | Method and detection system for detection of aflatoxin in corn with fluorescence spectra |
| CN102636581A (en) * | 2011-12-26 | 2012-08-15 | 湖北省农业科学院农业质量标准与检测技术研究所 | Liquid chromatogram and fluorescence method for simultaneously detecting aflatoxin B1, ochratoxin A, zearalenone and citrinin in grains |
| CN103575694A (en) * | 2013-11-11 | 2014-02-12 | 东北农业大学 | Quick detection method for aflatoxin B1 of rice based on near infrared spectroscopy |
| CN104598886B (en) * | 2015-01-23 | 2017-07-07 | 中国矿业大学(北京) | A kind of method that utilization near-infrared high spectrum image recognizes the oil crops that go mouldy |
| CN104914052B (en) * | 2015-05-12 | 2018-01-16 | 江苏大学 | A kind of light spectrum image-forming detection method for the peanut that goes mouldy |
| TW201925777A (en) * | 2017-12-08 | 2019-07-01 | 艾思博生物科技股份有限公司 | Non-invasive testing method for aflatoxin, food inspection server, and food inspection system |
| CN108985237A (en) * | 2018-07-20 | 2018-12-11 | 安徽农业大学 | A kind of detection method and its system of the wheat scab based on depth mixing |
| CN109657653B (en) * | 2019-01-21 | 2022-10-04 | 安徽大学 | Wheat grain gibberellic disease identification method based on imaging hyperspectral data |
| CN110298396B (en) * | 2019-06-25 | 2022-02-08 | 北京工业大学 | Hyperspectral image classification method based on deep learning multi-feature fusion |
| CN110470834A (en) * | 2019-09-22 | 2019-11-19 | 河南工业大学 | A kind of aflatoxin B1 detection method of content based on magnetic signal |
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