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

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 DiNardo Toxins2020 12-4.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:Enzyme immunoassay for measuring aflatoxin B1 in legal cannabis|Enzyme immunoassay for measuring aflatoxin B1 in legal cannabis]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


The diffusion of the legalization of [[wikipedia:Cannabis|cannabis]] for recreational, medicinal, and nutraceutical uses requires the development of adequate analytical methods to assure the safety and security of such products. In particular, [[wikipedia:Aflatoxin|aflatoxins]] are considered to pose a major risk for the health of cannabis consumers. Among analytical methods that allow for adequate monitoring of food safety, [[immunoassay]]s play a major role thanks to their cost-effectiveness, high-throughput capacity, simplicity, and limited requirement for equipment and skilled operators. Therefore, a rapid and sensitive [[enzyme immunoassay]] has been adapted to measure the most hazardous [[wikipedia:Aflatoxin B1|aflatoxin B<sub>1</sub>]] in cannabis products. The assay was acceptably accurate (recovery rate: 78–136%), reproducible (intra- and inter-assay means coefficients of variation 11.8% and 13.8%, respectively), and sensitive (limit of detection and range of quantification: 0.35 ng mL<sup>−1</sup> and 0.4–2 ng mL<sup>−1</sup> ... ('''[[Journal:Enzyme immunoassay for measuring aflatoxin B1 in legal cannabis|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 />
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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...)
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