Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Bacteria are everywhere in the natural environment. Although most of them are harmless, there are still some hazardous bacteria that will harm human health, so it is particularly important to identify bacteria quickly. Compared with traditional time-consuming and complicated identification methods, laser-induced breakdown spectroscopy (LIBS) is one of the potential technologies for rapid identification of bacteria. In this paper, six weakly active bacteria, including , , , , and , are taken as analysis samples. The thawed bacteria are placed in deionized water, and then uniformly smeared on five kinds of substrates to verify the feasibility of using LIBS to identify these bacteria. Spectrum filtering, normalization and principal component analysis (PCA) are used to preprocess the spectra, and a multi-class identification method based on the one-against-all linear kernel function of support vector machine (SVM) is proposed to establish the prediction model. The identification performance is evaluated by using precision and recall. The experimental results show that high-purity graphite is the best substrate with the least interference to the LIBS spectrum of bacteria. The prediction precision of these six bacteria is 77.27%, 92.86%, 84.21%, 94.12%, 81.82% and 76.92%, respectively, recall is 85%, 100%, 94.12%, 80%, 81.82% and 75% respectively, and the identification rate is 84.17%. It can be seen that the direct identification of bacteria can be preliminarily realized by smearing bacteria on the graphite substrate and analyzing its LIBS spectra, which provides a feasible way for simple, rapid and on-site bacterial identification.
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Source |
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http://dx.doi.org/10.1039/d2ay01840c | DOI Listing |
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