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: 3122
Function: getPubMedXML
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
Raman spectroscopy, a nondestructive optical technique that provides detailed chemical information, has attracted growing interest in the food industry. Complementary spectroscopic methods, such as near-infrared (NIR) spectroscopy, nuclear magnetic resonance (NMR), terahertz (THz) spectroscopy, laser-induced breakdown spectroscopy (LIBS), and fluorescence spectroscopy (Flu), enhance Raman spectroscopy's capabilities in various applications. The integration of Raman with these techniques, termed "Raman plus X," has shown significant potential in agri-food analysis. This review highlights the latest advances and applications of dual-modal spectroscopy methods combining Raman spectroscopy with NIR, NMR, THz, LIBS, and Flu in food analysis. Key applications include detecting harmful contaminants, evaluating food quality, identifying adulteration, and characterizing structure. The synergistic use of Raman-based dual-modal spectroscopy provides more comprehensive information and improves modeling accuracy compared to single techniques. The review also explores the role of data fusion in multisource spectral analysis and discusses challenges and prospects of "Raman plus X," including the development of integrated hardware and advanced data fusion algorithms. These advancements aim to streamline multisource data analysis, offering valuable insights to select appropriate analytical methods for practical applications in the food industry.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1111/1541-4337.70102 | DOI Listing |
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