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
Mineral water is a natural water that originated from an underground water table, a well, or a natural spring which is considered microbiologically intact. The revenue from the bottled mineral water industry will be USD 342.40 billion in 2023, and it is expected to grow at a compound annual growth rate (CAGR) of 5.24 %. Consequently, the discrimination of original bottled mineral water from tap water is an important issue that requires designing sensors for simple and portable identification of these two types of water. In this work, we have developed a Dip-Type colorimetric paper-based sensor array with three organic dyes (Bromothymol Blue, Bromophenol Blue, and Methyl Red) followed by chemometrics' pattern recognition methods (PCA and LDA) for discrimination of original bottled mineral waters from tap waters based on differences in ion variety and ion quantity. Forty brands of mineral water and twenty-six Tap water samples from different regions of Shiraz and other Iranian cities were analyzed by this sensor array. Moreover, these experiments were performed in two consecutive years to check the versatility of the sensor with seasonal changes in waters. This sensor array was able to discriminate these two water types from each other with an accuracy of > 95 % based on the analysis of 85 water samples.
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Source |
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http://dx.doi.org/10.1016/j.saa.2024.124719 | DOI Listing |
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