Food-borne pathogenic bacteria Raman spectrum classification model training method based on adaboost

A food-borne pathogenic bacteria and Raman spectroscopy technology is applied in the training field of food-borne pathogenic bacteria Raman spectroscopy classification model, which can solve the problems of poor timeliness, long operation cycle, complicated process, etc. Improve accuracy and timeliness

Pending Publication Date: 2020-06-12
SHANGHAI INST OF TECH
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However, these methods often have a long operatio

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  • Food-borne pathogenic bacteria Raman spectrum classification model training method based on adaboost
  • Food-borne pathogenic bacteria Raman spectrum classification model training method based on adaboost

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[0021] In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0022] Such as figure 1 and 2 As shown, the present invention provides a kind of adaboost-based foodborne pathogen Raman spectrum classification model training method, comprising:

[0023] Step S1, smoothing and denoising the Raman spectrum data stream of food-borne pathogens, while keeping the shape and width of the signal in the Raman spectrum data stream of food-borne pathogens after smoothing and denoising unchanged;

[0024] Step S2: Sampling the Raman spectral data stream of food-borne pathogenic bacteria after smoothing and denoising, normalizing, and then performing PCA dimensionality reduction, and calculating each feature in the Raman spectral data stream of food-borne pathogenic bacteria The correlation coefficient re...

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Abstract

The invention provides a food-borne pathogenic bacteria Raman spectrum classification model training method based on adaboost, and provides a Raman spectrum classification method based on an adaboostintegration algorithm for escherichia coli and brucella. The method comprises the following steps: performing data preprocessing on Raman spectra of two different germs; including deburring, the invention relates to a noise reduction (Savitzky-Golay filter). Sampling into numeric data; according to the method, firstly, a mesh search model is established, then PCA dimension reduction is carried outon data, then an adaboost algorithm based on a meta-classifier as a decision tree is used for calling the mesh search model to find out the most appropriate parameters, and it is verified that the adaboost algorithm of the integrated algorithm has higher classification accuracy compared with a single algorithm such as KNN and SVM.

Description

technical field [0001] The invention relates to an adaboost-based Raman spectrum classification model training method for food-borne pathogenic bacteria. Background technique [0002] At present, the methods used for the detection of foodborne pathogens mainly include: traditional biological methods, chromogenic medium methods, and polymerase chain reaction. However, these methods often have a long operation period, complicated process and poor timeliness. Contents of the invention [0003] The object of the present invention is to provide a Raman spectrum classification model training method based on adaboost for food-borne pathogenic bacteria. [0004] In order to solve the above problems, the present invention provides a method for training a Raman spectrum classification model of foodborne pathogenic bacteria based on adaboost, including: [0005] Smoothing and denoising the Raman spectral data stream of food-borne pathogenic bacteria, while keeping the shape and wid...

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Application Information

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IPC IPC(8): G06K9/00G06K9/44G06K9/62G06N20/20
CPCG06N20/20G06V20/695G06V20/698G06V10/34G06F18/2135G06F18/214
Inventor 曾万聃黄杰伦夏志平王其
Owner SHANGHAI INST OF TECH
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