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What Color Is Your Method? Adaptation of the RGB Additive Color Model to Analytical Method Evaluation Pawe# Mateusz Nowak, and Pawel Koscielniak Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.9b01872 • Publication Date (Web): 15 Jul 2019 Downloaded from pubs.acs.org on July 18, 2019
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Analytical Chemistry
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What Color Is Your Method? Adaptation of the RGB Additive Color Model to Analytical Method Evaluation Paweł M. Nowak* and Paweł Kościelniak Department of Analytical Chemistry, Jagiellonian University in Kraków, Gronostajowa 2, 30-387 Kraków, Poland Keywords: method evaluation, RGB model, green chemistry principles, green analytical chemistry (GAC), metrology
Abstract. Evaluation of an analytical method is a fundamental problem in analytical chemistry, and it is never straightforward. In this article we show a perspective for facing this issue using an original tool. We propose a model that allows one to evaluate any analytical method/procedure in a global manner. It refers to the RGB additive color model, and uses three primary colors to represent three main attributes of the evaluated method: analytical performance - Red, compliance with the “green” chemistry principles – Green, and productivity/practical effectiveness – Blue. A final color of the method results from the additive synthesis of the primary colors. To simplify classifications, we propose the set of nine final colors of the method (white, magenta, cyan, yellow, red, green, blue, colorless/gray, and black). The model provides also a quantitative parameter, named as the “method brilliance”, which integrates all primary colors and treats them with the varying importance, adjusted to the evaluation context and subjective user preferences. The evaluation is performed using standard Excel worksheets interpretable “at-a-glance”, and adjustable to the particular method specifications. We discuss the opportunities offered by this model, potential obstacles and related countermeasures, as well as future perspective for its utilization. The paper shows also examples of using the model for the evaluation of real methods. We believe that the model can be applied not only in analytical science, but also in other chemical sub-disciplines.
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Introduction Central problems in the analytical science include the critical evaluation of newly developed methods, the comparison of various analytical procedures, and the choice of the best method in a particular situation. A newly developed method is commonly validated on the basis of its analytical performance, including such criteria as accuracy, precision, linearity, LOD, LOQ, robustness, selectivity, and a freedom from interferences, etc. Specific regulations for the validation of analytical methods are systematically established and updated by various formal bodies, like FDA, ISO and EPA. Even though method validation helps to compare and appraise individual features of various analytical methods, it does not allow one to analyze and express the analytical power of a method in a holistic manner (using one parameter). Another issue, more and more popular in analytical science, is the concept of green analytical chemistry (GAC), which puts emphasis on assessing how “green” – that is, how safe for user and the environment – a method is.1-9 In recent years many qualitative, semi-quantitative and quantitative approaches for measuring the “greenness” have been developed,10-13 e.g. an Eco-Scale according to which an ideally green method receives 100 points, and any deviation from the GAC rules decreases the score (penalty points).13 While the growing interest in GAC is a positive phenomenon, finding an appropriate balance between the analytical performance of a method and its desired greenness is challenging, often raises controversies, and complicates the evaluation process. No tool for performing transparent method evaluation in terms of both attributes and their variable counterbalance has been proposed. Importantly, in evaluating and comparing the adequacy and efficiency of newly developed methods, some other criteria are broadly overlooked or treated without appropriate attention. Examples include cost- and timeeffectiveness, destruction of sample material per single analysis, theoretical/practical complexity, time and cost related to appropriate staff training, a required service/instrument-calibration frequency, and a risk of random instrumental failures. Even though everyone seems to be aware of these aspects and some of them are oftentimes mentioned in discussions on new methods, they are very rarely considered and evaluated in the same transparent way as the result-oriented features, e.g. accuracy or precision. They are, notwithstanding, often decisive for potential users interested equally in the analytical and practical effectiveness of a method. The above circumstances prompt us that the current validation standards and approaches to assessing method greenness do not comprise all these aspects, so they do not enable performing of comprehensive and global method evaluations. From our point of view, this rises a need for proposing the first integrated scale/metrics for comparing and assessing efficiency of methods taking into account these so diverse criteria. To be useful such a scale should: (i) incorporate various method attributes and enable their individual evaluation/comparison; (ii) provide a measure of the overall method’s suitability and potential; (iii) be transparent, simple and fast in use; (iv) be flexible enough to be adjusted to a given method specifications; and finally, (v) enable fast method re-evaluation by other analysts according to the changed standards, e.g. to reduce a bias caused by an insufficiently critical look, or to consider a different application of the method. In addition, the possibility to assess a method’s potential both in a qualitative (simplified) and quantitative (more precise) way seems to be also desired. Herein, we propose the first tool allowing to face this key and general question. It is an extension of the GAC concept, according to which the method gains the “attribute of greenness” if it meets specific demands. We named our approach as the “RGB model”, because it refers to the RGB additive color model commonly used in representing and displaying colors in electronic systems.
Theory Method colors. Analogously, the name of our tool comes from the initials of the three primary colors (Red, Green, Blue) which in our model, correspond to the three primary attributes of any analytical method: analytical performance assessed typically by a classical validation process – R; safety/eco-friendliness which includes the widely discussed aspects of GAC, e.g. hazards related to reagents and waste, occupational risks or energy consumption – G; and productivity/practical effectiveness which includes such criteria like cost- and time-
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Analytical Chemistry
effectiveness, the extent of sample destruction, methodological complexity, time and cost related to appropriate staff training, and some ordinary instrument operation-related aspects like service frequency or risk of random malfunction – B. Like in electronic devices, mixing these three primary colors of light produces an impression of whiteness, so an analytical method becomes white if it has all the primary attributes to a satisfactory degree. Such a white method is thus complete and coherent. According to another analogy, mixing only two primary components gives one of the secondary colors. A yellow, magenta or cyan method is satisfactory in terms of two attributes but lacks the remaining primary color, thus being neither complete nor coherent. Logically, a method is red, green, or blue if it has only one attribute and lacks the other two. Whether a method conforms with the idea of redness, greenness and blueness is quantitatively measured by a Color Score (CS) ranging from 0 to 100%. We assumed that the method gains one of these elementary colors if the corresponding CS is ≥66.6%, a boundary value which we named the “satisfaction range”. Otherwise, we assumed that the method loses this primary color. If its particular CS is ≥33.3%, named the “tolerance range”, the method is colorless with regard to this primary attribute and transparent/neutral for two other attributes. Hence, if one CS is tolerable and two other CS values are satisfactory, the method is magenta, yellow or cyan. If two CS values are in the tolerance range and one in the satisfaction range, the method is red, green or blue. If all three CS values are tolerable but not satisfactory, the method is colorless (this is represented by the gray color). However, if at least one CS is
