Difference between revisions of "Template:Article of the week"
Shawndouglas (talk | contribs) (Updated article of the week text) |
Shawndouglas (talk | contribs) (Updated article of the week text) |
||
| Line 1: | Line 1: | ||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 | <div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Mudge AnalBioChem2017 409-12.gif|240px]]</div> | ||
'''"[[Journal: | '''"[[Journal:Leaner and greener analysis of cannabinoids|Leaner and greener analysis of cannabinoids]]"''' | ||
There is an explosion in the number of [[Laboratory|labs]] analyzing [[wikipedia:Cannabinoid|cannabinoids]] in marijuana ([[wikipedia:Cannabis|''Cannabis sativa'' L.]], Cannabaceae); however, existing methods are inefficient, require expert analysts, and use large volumes of potentially environmentally damaging [[wikipedia:Solvent|solvents]]. The objective of this work was to develop and validate an accurate method for analyzing cannabinoids in cannabis raw materials and finished products that is more efficient and uses fewer toxic solvents. A method using [[high-performance liquid chromatography]] (HPLC) with [[Chromatography detector|diode-array detection]] (DAD) was developed for eight cannabinoids in ''Cannabis'' flowers and oils using a statistically guided optimization plan based on the principles of green chemistry. A single-laboratory validation determined the linearity, selectivity, accuracy, repeatability, intermediate precision, limit of detection, and limit of quantitation of the method. Amounts of individual cannabinoids above the limit of quantitation in the flowers ranged from 0.02 to 14.9% concentration (w/w), with repeatability ranging from 0.78 to 10.08% relative standard deviation. ('''[[Journal:Leaner and greener analysis of cannabinoids|Full article...]]''')<br /> | |||
<br /> | <br /> | ||
''Recently featured'': | ''Recently featured'': | ||
: ▪ [[Journal:Laboratory information management software for engineered mini-protein therapeutic workflow|Laboratory information management software for engineered mini-protein therapeutic workflow]] | |||
: ▪ [[Journal:Defending our public biological databases as a global critical infrastructure|Defending our public biological databases as a global critical infrastructure]] | : ▪ [[Journal:Defending our public biological databases as a global critical infrastructure|Defending our public biological databases as a global critical infrastructure]] | ||
: ▪ [[Journal:Determining the hospital information system (HIS) success rate: Development of a new instrument and case study|Determining the hospital information system (HIS) success rate: Development of a new instrument and case study]] | : ▪ [[Journal:Determining the hospital information system (HIS) success rate: Development of a new instrument and case study|Determining the hospital information system (HIS) success rate: Development of a new instrument and case study]] | ||
Revision as of 15:25, 5 August 2019
"Leaner and greener analysis of cannabinoids"
There is an explosion in the number of labs analyzing cannabinoids in marijuana (Cannabis sativa L., Cannabaceae); however, existing methods are inefficient, require expert analysts, and use large volumes of potentially environmentally damaging solvents. The objective of this work was to develop and validate an accurate method for analyzing cannabinoids in cannabis raw materials and finished products that is more efficient and uses fewer toxic solvents. A method using high-performance liquid chromatography (HPLC) with diode-array detection (DAD) was developed for eight cannabinoids in Cannabis flowers and oils using a statistically guided optimization plan based on the principles of green chemistry. A single-laboratory validation determined the linearity, selectivity, accuracy, repeatability, intermediate precision, limit of detection, and limit of quantitation of the method. Amounts of individual cannabinoids above the limit of quantitation in the flowers ranged from 0.02 to 14.9% concentration (w/w), with repeatability ranging from 0.78 to 10.08% relative standard deviation. (Full article...)
Recently featured:
