Food-borne pathogenic bacterium classification method

A food-borne pathogenic bacteria and classification method technology, applied in the direction of instruments, biological neural network models, calculations, etc., can solve the problems of ineffective operation, cumbersome operation, high cost, etc., and achieve high accuracy and few network parameters , the effect of alleviating the problem of misjudgment

CN110245713APending Publication Date: 2019-09-17SHANGHAI INST OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2019-09-17

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Abstract

The invention discloses a food-borne pathogenic bacterium classification method. The method comprises the steps of constructing a convolutional neural network and carrying out k-fold cross validation for model evaluation. The method based on the convolutional neural network is used, and the accuracy of food-borne pathogenic bacterium spectral classification is improved through structural configuration and optimization of the convolutional neural network. The technical problem mainly solved by the invention is to realize automation of spectral data classification of the food-borne pathogenic bacteria by modeling the convolutional neural network, provide reference for food safety practitioners and improve the food safety detection efficiency.
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Description

technical field

[0001] The invention relates to the fields of Raman spectroscopy, food-borne pathogenic bacteria, convolutional neural network and machine learning. Background technique

[0002] So far, the most commonly used method for the detection of pathogenic bacteria in food in my country is the traditional detection method of microorganisms established in accordance with the theory of biochemistry and microbiology. The most important identification methods are biochemical identification, visual morphology observation, microbial isolation and culture, and serological typing.

[0003] These methods are cumbersome to operate and have a long cycle, and cannot effectively monitor and prevent them. Such as multiplex PCR detection system, FTA filter-based gene chip detection and other technologies, the classification accuracy is not high, and the number of identified species is also small. Moreover, compared with traditional machine learning algorithms, the cost of manuall...

Examples

Embodiment Construction

[0024] The present invention will be described in detail below in conjunction with specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0025] figure 1 For the convolutional neural network classification method adopted in the present invention, such as figure 1 As shown, the method in this embodiment includes:

[0026] S1. Spectral preprocessing: use the Savitzky-Golay filter and asymmetric least squares in the Origin software to analyze the original E. coli O 157 :H 7 , Brucella S2 strain pathogen spectrum pretreatment to remove the fluorescence background, to obtain the spectrum of pathogenic bacteria;

[0027] S2. Generating a ...