The LCGC Blog: An RGB Additive Color Model for Analytical Method Evaluation

Traveling to interesting places for conferences and hearing new ideas is certainly one of the most enjoyable aspects of a faculty position. Recently, I attended the 16^th International Interdisciplinary Meeting on Bioanalysis (CECE 2019) in Gdansk, Poland. I was absolutely delighted both by the city and the scientific quality of the meeting. 

One talk in particular caught my eye as something unique and different. A group in the Department of Analytical Chemistry at Jagiellonian University in Krakow, Poland recently developed a new means for a more holistic evaluation of analytical methods (1). The so-called RGB (red-green-blue) additive color model provides a way to more completely evaluate the effectiveness, safety/sustainability, and practicality of an analytical method.

We are all familiar with typical measures of the analytical performance of a method, sometimes referred to as analytical figures of merit: Accuracy, precision, linear range, limit of detection, specificity, and so on. Guidances for these assessments are commonly provided by a range of governing bodies, like the Environmental Protection Agency or the Food and Drug Administration, depending on the application associated with the method. These figures of merit are very often explicitly tabulated and published for a new method. In fact, it would be near impossible to get a new quantitative method published without a full assessment of these method validation parameters.

Similarly, we are quite familiar with other aspects of a method, which can often be less transparent or less explicitly reported.  We know, to some degree, what is meant for a method to be “green,” as it pertains to a method’s safety for the user and the environment. Low energy consumption, low occupational risk, and minimal to no hazards related to reagents and waste are desirable characteristics of any process. These are all concepts consistent with “green analytical chemistry.”

As a community, we are also keen to develop methods, which are productive and practical. We value time- and cost-effectiveness, limited complexity, low susceptibility to failure, and robustness, broadly defined. 

The RGB Model provides a new means for assessing these less reported, but still inherently very important, aspects of an analytical method.

Analytical performance is red, “greenness” of the method is green, and productivity/practical effectiveness of the method is blue. The study authors conceived an Excel sheet which allows a quantitative assessment of multiple attributes in each color, in order to calculate a final composite color of the method. If a method ends up being red, green, or blue, then it lacks any benefit in the other two attributes. Of course, this would be rare, and as such there are nine possible color outcomes, once all attributes are assessed. Besides a final color of red, blue, and green, the method can be characterized as white (ideal combination of all), magenta, cyan, yellow, colorless/gray, and black. Magenta, cyan, and yellow indicate some reasonably good combination of multiple primary attributes. The authors also included a method “brilliance” measure, where certain attributes can be weighted according to user preference.

As you can imagine, there are many ways to interpret various measures of various performance metrics. The presenting author emphasized that this current model may not be perfect, but at least it is a start to build from – it is a flexible tool, which could be adapted, altered, or augmented. Currently, there is very little in the way of such transparent holistic assessments available to the analytical community. The authors point out that this concept need not apply only to analytical methods, but could also be applied to various other scientific processes. Kudos to the study authors for thinking outside of the box and bringing some concrete attention and tools to things we all consider and value, but perhaps don’t explicitly express. In fact, we likely only express assessments of greenness and enhanced productivity if there are clear advantages to be conveyed; we might not be always fully honest about the drawbacks of our methods in these contexts-perhaps it is because we have lacked the appropriate tools to perform adequate assessments.

Reference

(1) P.M. Nowak and P. Koscielniak. Anal. Chem.91, 10343–10352 (2019).

Kevin A. Schug is a Full Professor and Shimadzu Distinguished Professor of Analytical Chemistry in the Department of Chemistry & Biochemistry at The University of Texas (UT) at Arlington. He joined the faculty at UT Arlington in 2005 after completing a Ph.D. in Chemistry at Virginia Tech under the direction of Prof. Harold M. McNair and a post-doctoral fellowship at the University of Vienna under Prof. Wolfgang Lindner. Research in the Schug group spans fundamental and applied areas of separation science and mass spectrometry. Schug was named the LCGC Emerging Leader in Chromatography in 2009 and the 2012 American Chemical Society Division of Analytical Chemistry Young Investigator in Separation Science. He is a fellow of both the U.T. Arlington and U.T. System-Wide Academies of Distinguished Teachers.