Difference between revisions of "Template:Article of the week"

From LIMSWiki
Jump to navigationJump to search
(Updated article of the week text)
(Updated article of the week text)
(9 intermediate revisions by the same user not shown)
Line 1: Line 1:
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Villegas-Pérez Foods23 12-22.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
'''"[[Journal:A quality assurance discrimination tool for the evaluation of satellite laboratory practice excellence in the context of European regulatory meat inspection for Trichinella spp.|A quality assurance discrimination tool for the evaluation of satellite laboratory practice excellence in the context of European regulatory meat inspection for ''Trichinella spp.'']]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


[[Trichinosis|Trichinellosis]] is a parasitic foodborne zoonotic disease transmitted by ingestion of raw or undercooked meat containing the first larval stage (L1) of the nematode. To ensure the [[Quality (business)|quality]] and safety of food intended for human consumption, meat inspection for detection of ''Trichinella'' spp. larvae is a mandatory procedure per European Union (E.U.) regulations. The implementation of [[quality assurance]] (QA) practices in [[Laboratory|laboratories]] that are responsible for ''Trichinella'' spp. detection is essential given that the detection of this parasite is still a pivotal threat to public health, and it is included in List A of Annex I, Directive 2003/99/EC, which determines the agents to be monitored on a mandatory basis ... ('''[[Journal:A quality assurance discrimination tool for the evaluation of satellite laboratory practice excellence in the context of European regulatory meat inspection for Trichinella spp.|Full article...]]''')<br />
[[Chromatography|Chromatographic]] oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of [[convolutional neural network]]s (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The [[open-source software]] Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... ('''[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Full article...]]''')<br />
''Recently featured'':
''Recently featured'':
{{flowlist |
{{flowlist |
* [[Journal:Developing a framework for open and FAIR data management practices for next generation risk- and benefit assessment of fish and seafood|Developing a framework for open and FAIR data management practices for next generation risk- and benefit assessment of fish and seafood]]
* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]
* [[Journal:An extract-transform-load process design for the incremental loading of German real-world data based on FHIR and OMOP CDM: Algorithm development and validation|An extract-transform-load process design for the incremental loading of German real-world data based on FHIR and OMOP CDM: Algorithm development and validation]]
* [[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]
* [[Journal:Potency and safety analysis of hemp-derived delta-9 products: The hemp vs. cannabis demarcation problem|Potency and safety analysis of hemp-derived delta-9 products: The hemp vs. cannabis demarcation problem]]
* [[Journal:Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems|Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems]]
}}
}}

Revision as of 13:37, 13 May 2024

Fig1 Bispo-Silva Geosciences23 13-11.png

"Geochemical biodegraded oil classification using a machine learning approach"

Chromatographic oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of convolutional neural networks (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The open-source software Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... (Full article...)
Recently featured:

Retrieved from "https://www.limswiki.org/index.php?title=Template:Article_of_the_week&oldid=63551"