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Bacterial biochemical identification system based on artificial neural network and identification method
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An artificial neural network and biochemical identification technology, applied in biochemical equipment and methods, biochemical instruments, biochemical cleaning devices, etc., can solve the problems of cumbersome operation, high cost of identification system, inflexible use, etc.
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Problems solved by technology
[0004] However, these identification methods still have shortcomings such as cumbersome operation, unstable results, high cost of identification system, inflexible use, and defects in classification and calculation methods, which cannot well meet the needs of grassroots microbial testing laboratories.
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Embodiment 1
[0183] Such as figure 1 Shown, a kind of bacterial biochemical identification system based on artificial neural network comprises the following modules: a probabilistic artificial neural network processor module, a database module, a manual input module, an automatic biochemical result reading input module, and a result output display module; the above-mentioned In the bacterial biochemical identification system, the probabilistic artificial neural network processor module is the center, and the database module, manual input module, biochemical result automatic reading input module, and result output display module are all connected to it, and the database module is bidirectionally connected to it .
[0184] In the above-mentioned bacterial biochemical identification system, the probabilistic artificial neural network module is a processor combining computer hardware with an artificial neural network toolbox and a graphical interactive interface; the information in the databas...
Embodiment 2
[0197] The detection process of the system of the present invention will be described below with a specific detection example of Enterobacter cloacae.
[0198] Based on the principle that a positive biochemical result is recorded as "1" and a negative biochemical result is recorded as "0", the results of 20 biochemical items of Enterobacter cloacae obtained through actual experimental detection are as follows: 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1.
[0199] The specific procedure is as follows:
[0200] >>p=[]; (Explanation: [] after P is the positive probability matrix of 20 biochemical items in Attachment 2)
[0201] >>t=ind2vec([]'); (Explanation: [] after t is the bacterial number of 285 species in Attachment 1)
[0202] >>p=p';
[0203] >>[R,Q]=size(p);
[0204] >>[S,Q]=size(t);
[0205] >>p_test=[11011000101011111111]'; (Note: the [] after p_test is the biochemical identification result of Enterobacter cloacae)
[0206] >>dist(p',p_test);
[0...
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Abstract
The invention discloses a bacterial biochemical identification system based on an artificial neural network and a bacterial identification method. The identification system comprises the following modules: a probabilistic artificial neural network processor module, a database module, a manual input module, a biochemical result automatic reading and input module and a result output and display module. The bacterial biochemical identification system takes the probabilistic artificial neural network processor module as a center. The identification method comprises the following steps of: identifying any number of biochemical items of a sample, inputting an identification result into the bacterial biochemical identification system, enabling the probabilistic artificial neural network processor module in the system to automatically perform matching identification on the result and data information in a database, and displaying a result through the result output and display module. The bacterial biochemical identification system disclosed by the invention has the advantages of more advanced identification principle, great convenience in use, low cost, combination with visual analysis and more intuitive result output.
Description
technical field [0001] The invention relates to a bacterial biochemical identification system and identification method based on an artificial neural network, belonging to the technical field of microbial identification. Background technique [0002] In recent years, the number and scale of outbreaks of major foodborne diseases caused by microorganisms have been increasing and expanding worldwide. Illness caused by food contamination has become the most widespread health problem in the world today, and it is also one of the main reasons for the reduction of economic production. In order to prevent the contamination of pathogenic bacteria in food, it is first necessary to find out the types of pathogenic bacteria in food. [0003] Traditional identification relies on phenotypic determination steps such as bacterial culture, isolation, and physiology and biochemistry, and then searches for authoritative bacterial classification manuals or inspection standards such as Bergey's...
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