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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Baratta FrontPharmaco2019 10.jpg|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:Development of standard operating protocols for the optimization of Cannabis-based formulations for medical purposes|Development of standard operating protocols for the optimization of Cannabis-based formulations for medical purposes]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Under current legislation in Italy, using the ''[[wikipedia:Cannabis|Cannabis]]'' plant for medical purposes requires administering it orally in the form of a decoction or as ''Cannabis'' oil extract. The scientific literature reports a number of preparation methods, mainly for oils, but no study is available that compares thoroughly, from a technological viewpoint, the ''Cannabis''-based formulations currently administered to patients. With this in mind, this research work aimed to carry out specific formulation studies to design standard operating procedures for the preparation and optimization of ''Cannabis''-based galenic formulations. Both decoctions and oils were prepared under different operating conditions to identify the most efficient process for the production of formulations with a high concentration of [[wikipedia:Decarboxylation|decarboxylated]] [[wikipedia:Tetrahydrocannabinol|delta-9-tetrahydrocannabinol]] (THC) and [[wikipedia:Cannabidiol|cannabidiol]] (CBD). Regarding ''Cannabis'' oil, a new procedure has been developed that allows significantly higher recovery rates for THC and CBD compared with those for water-based extraction methods (decoction) and those for oil-based methods currently in use. ('''[[Journal:Development of standard operating protocols for the optimization of Cannabis-based formulations for medical purposes|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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