Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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This original work investigated the optical properties and Monte-Carlo (MC) based simulation of light propagation in the flavedo of Nanfeng tangerine (NF) and Gannan navel orange (GN) infected by Penicillium italicum. The increase of absorption coefficient (μ) at around 482 nm and the decrease at around 675 nm were both observed in infected NF and GN during storage, indicating the accumulation of carotenoids and loss of chlorophyll. Particularly, the μ in NF varied more intensively than GN, but the limited differences of reduced scattering coefficient (μ') were detected while postharvest infection. Besides, MC simulation of light propagation indicated that the photon packets weight and penetration depth at 482 nm in NF were reduced more than in GN flavedo, while there were almost no changes at the relatively low absorption wavelength of 926 nm. The simulated absorption energy at 482 nm in NF and GN presented more changes than those at 675 nm during infection, thus could provide better detection of citrus diseases. Furthermore, PLS-DA models can discriminate healthy and infected citrus, with the accuracy of 95.24 % for NF and 98.67 % for GN, respectively. Consequently, these results can provide theoretical fundamentals to improve modelling prediction robustness and accuracy.
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http://dx.doi.org/10.1016/j.foodres.2024.114787 | DOI Listing |