A rapid non-destructive detection system and method for matsutake based on convolutional neural network

Abstract

The invention discloses a fast non-destructive detection system and method for matsutake based on convolutional neural network, including a deep learning convolutional neural network model, a control terminal and a consumer terminal; the deep learning convolutional neural network model includes sample collection, data collection, Deep learning convolutional neural network modeling and optimization; sample collection completes sample screening of detection objects to establish a sample set, and divides the sample set into training set, verification set and test set; data collection includes sample chemical content measurement and spectral data collection ; Deep learning convolutional neural network modeling and optimization Utilize deep learning convolutional neural network model and pooling processing to model preprocessed spectral data and corresponding chemical content; deep learning convolutional neural network model for matsutake The detection results of the matsutake are stored in the control terminal; the consumer terminal can obtain the detection data of the matsutake by accessing the control terminal. The invention can effectively reduce the detection cost, and is beneficial to the supervision department to supervise the market.

Description

technical field [0001] The invention relates to the technical field of non-destructive testing, in particular to a rapid non-destructive testing system and method for fungus food based on deep learning convolutional neural network and near-infrared analysis technology. Background technique [0002] Modern near-infrared spectroscopy analysis technology is a new physical measurement technology that uses the optical characteristics of chemical substances in the NIR spectral region to quickly determine the content and characteristics of one or more chemical components in a sample. Fast speed, simultaneous determination of multiple indicators, no waste pollution, low cost, high utilization rate and other advantages that cannot be compared with conventional methods, have been widely used in food quality inspection, petrochemical, agricultural production, clinical pharmacy and other fields. [0003] Deep learning is an information extraction method that uses multi-layer nonlinear t...

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Problems solved by technology

[0007] 1. For the detection of wild matsutake in remote areas, the grade evaluation is mainly done by relying on experience and the size of matsutake, and the elements or components contained in it cannot be quantitatively determined, and the internal decay that cannot be detected will seriously affect the safety of eating or transportation

The market value of matsutake cannot be reflected only by the previous experience based on the size of the classification. Moreover, there are many heterogeneous fungi similar to matsutake in the edible fungus. The accuracy of classification based on appearance and experience alone is low, and consumers cannot distinguish the specific ones. Substance content, reduce the market competitiveness of matsutake

[0008] 2. The traditional detection method is time-consuming, laborious and costly, and some methods complete the detection by destroying the matsutake entity

Because wild matsutake generally grows in the forests of Sichuan, Yunnan and Northeast China, if the traditional detection method is used, the matsutake needs to be transported from the picking place to a specific place for testing. During this transportation and testing process, the water loss of matsutake is very obvious. The content of internal elements will change, which will affect the taste of fresh matsutake and even lose its market value

[0009] 3. The traditional detection method has low accuracy and slow speed, and cannot complete the rapid and simultaneous measurement of multiple elements

Because matsutake is a rare and precious edible fungus, the market has strict requirements on the content of specific components in it. The detection method with low accuracy leads to people's disapproval of matsutake on the market, which indirectly damages the local and national economic development.

[0010] 4. The existing testing agency system is mainly established between the testing agency and the manufacturer, which fails to effectively protect the rights and interests of consumers

Consumers fail to timely or effectively understand the specific information of matsutake, such as grades, trace elements, and content of matsutake polysaccharides, etc., and there are phenomena such as blind consumption and misleading consumption, which is not conducive to fair market competition and long-term development

[0011] 5. The traditional matsutake detection model is established based on the relationship between absorbance and chemical content in different wavelength ranges. This method needs to use matrix multiplication to establish the link relationship between input and output, that is, the link between each output and input There is interaction between them, the calculation is large and the accuracy cannot be guaranteed

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