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'''[[Clinical chemistry]]''' (sometimes referred to as '''chemical pathology''') is the area of [[clinical pathology]] that is generally concerned with analysis of bodily fluids. The discipline originated in the late nineteenth century with the use of simple chemical tests for various components of blood and urine. Subsequent to this, other techniques were applied including the use and measurement of enzyme activities, spectrophotometry, [[electrophoresis]], and [[immunoassay]].
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Today [[Clinical laboratory|clinical laboratories]] are now highly automated to accommodate the high workload typical of a hospital laboratory or [[reference laboratory]]. A large clinical laboratory will accept samples for up to about 700 different kinds of tests. Even the largest of laboratories rarely do all these tests themselves, and some must be referred to other labs. This large array of tests can be further sub-categorized into sub-specialties like general or routine chemistry, special chemistry, clinical endocrinology, [[toxicology]], therapeutic drug monitoring, urinalysis, and fecal analysis. ('''[[Clinical chemistry|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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''Recently featured'':
''Recently featured'': [[Desktop virtualization]], [[Comprehensive outpatient rehabilitation facility]], [[National Institutes of Health]]
{{flowlist |
* [[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: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: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]]
}}

Latest 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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