The Red Component of Analytical Chemistry: Assessing the Performance of Analytical Methods

The increasing emphasis on sustainability, regulatory compliance, and method transparency in analytical chemistry has led to the emergence of holistic evaluation frameworks such as white analytical chemistry (WAC). This approach integrates environmental friendliness (green), analytical performance (red), and practical/economic considerations (blue) into a unified assessment of method quality. Among these, the red dimension, focusing on analytical performance, remains foundational, as no method can be deemed reliable or useful without robust validation of its analytical capabilities. Despite the availability of well-established figures of merit (such as sensitivity, precision, and accuracy), their evaluation is often fragmented and subjective, hindering consistent comparisons between methods. To address this gap, the Red Analytical Performance Index (RAPI) was recently developed as a standardized and quantitative tool for evaluating the core performance characteristics of analytical methods. RAPI consolidates key validation parameters into a single, interpretable score, enhancing transparency and comparability in method development and selection. This article highlights the importance of the red dimension within the WAC framework and presents the RAPI tool as a critical advancement for harmonizing analytical performance assessment. A case study comparing two chromatographic methods for non-steroidal anti-inflammatory drug (NSAID) determination in water is used to demonstrate the practical application of RAPI. The results confirm its potential to support informed decision-making in both research and routine laboratories, emphasizing that high-quality analytical performance must remain a central pillar in sustainable and responsible analytical science.

The selection and development of analytical methods have traditionally centered on maximizing analytical performance understood through parameters such as sensitivity, precision, selectivity, and accuracy. These figures of merit of method validation guidelines issued by regulatory bodies like the International Council for Harmonisation (ICH), the U.S. Food and Drug Administration (FDA), and the European Medicines Agency (EMA) (1-3). However, in recent years, the growing global demand for sustainable science has transformed how analytical chemists assess method quality.

This transformation is exemplified by the development of white analytical chemistry (WAC), a conceptual framework proposed to integrate multiple dimensions of method evaluation:

• Red, representing analytical performance,
• Green, representing environmental sustainability,
• and Blue, reflecting practicality and economic feasibility (4).

By merging these three colors into a single evaluative space, WAC aligns analytical chemistry with the broader principles of green chemistry, sustainability, and responsible innovation (4). While several tools have been developed to address the green part, such as the Green Analytical Procedure Index (GAPI) (5), Analytical GREEness (AGREE) (6), and blue aspects (7), the red dimension has often been neglected in structured assessment efforts.

The analytical performance of a method is often assumed to be implicitly understood through the validation process. However, challenges persist in how these data are interpreted and communicated. Figures of merit such as limit of detection (LOD), limit of quantification (LOQ), linearity, precision, trueness, and selectivity are frequently reported in heterogeneous formats, lacking standardized benchmarks or weighting schemes (8,9). This variability complicates objective comparisons between methods, even in peer-reviewed literature.

Moreover, the increasing complexity of modern analytical tasks such as trace-level contaminant monitoring, multi-residue food analysis, or high-throughput omics workflows, demands rigorous, transparent, and quantitative tools to evaluate how well a method performs its intended function. Without such tools, method selection often relies on subjective judgment, legacy practices, or economic constraints rather than on a rigorous performance comparison.

To address this challenge, Nowak and associates introduced the Red Analytical Performance Index (RAPI) in 2025; a novel scoring tool that consolidates core analytical performance criteria into a single, normalized score ranging from 0 (poor) to 10 (ideal) (10). RAPI is designed to be modular, reproducible, and adaptable to various types of analytical procedures. It enables:

• transparent comparison of methods during method development,
• evidence-based decision-making in method validation and regulatory submissions,
• integration into the broader WAC framework as the quantifier of the red dimension.

This article aims to present the conceptual foundation and structure of RAPI, discuss its significance in the context of WAC, and illustrate its practical application using a comparative case study involving chromatographic methods for non-steroidal anti-inflammatory drug (NSAID) determination in environmental water. The RAPI tool represents a critical step toward holistic, yet analytically rigorous, assessment of method quality.

Underlining the Red Dimension in White Analytical Chemistry

The red dimension of the WAC framework reflects the core analytical performance of a method. This aspect is firmly grounded in traditional analytical figures of merit, which serve as the cornerstone of method validation protocols defined in regulatory documents such as ICH Q2(R2), United States Pharmacopeia (USP) <1225>, and International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 17025 (1,11-12), and many more.

The validation parameters that are usually mentioned in case of analytical procedure validation include:

·Selectivity: The method’s ability to differentiate and accurately measure the analyte of interest in the presence of other components (interferents) in a sample;

·Sensitivity: The capacity to detect small changes in analyte concentration;

·Linearity: The proportional relationship between analyte concentration and signal response;

·Limit of detection (LOD)andlimit of quantification (LOQ): The smallest concentrations of analyte that can be reliably detected and quantified, respectively;

·Precision: The closeness of repeated measurements, often expressed as relative standard deviation (RSD);

·Accuracy (Trueness): The closeness of a measured value to a reference or true value;

·Robustness: The method’s capacity to remain unaffected by small, deliberate variations in conditions.

These parameters are often evaluated during method development, optimization, and validation stages and are essential in determining whether a method is fit for purpose (9). However, while the scientific community largely agrees on the importance of these metrics, there is no unified framework for how they should be assessed, weighted, or compared across methods or laboratories.

In the context of WAC, the red dimension is not optional, it is the minimum analytical requirement. A method cannot be deemed green or practical if it fails to produce reliable analytical results. However, WAC does not prescribe exact scoring rules for how the red component should be quantified, leaving that to the discretion of the user. As a result, the assessment of analytical performance remains prone to:

·Subjective bias in how results are interpreted (for example, is R² = 0.995 acceptable or not?),

·Heterogeneous reporting practices, especially in interdisciplinary fields, and

·Difficulty in comparing competing methods that differ in sophistication, instrumentation, or application domain.

This lack of standardization has serious implications. In industrial and regulatory environments, method selection and validation are expected to follow clear, auditable criteria. Similarly, in academic research, the absence of structured analytical performance assessment weakens claims of method superiority or novelty (13).

Moreover, the trend toward multi-dimensional evaluation further underscores the need for a standardized, reproducible, and user-friendly approach to capturing red dimension performance in a quantitative way.

To resolve this issue, efforts have emerged to develop unified scoring systems. The most notable and recent of these is the RAPI proposed by Nowak and associates, which seeks to normalize and quantify red-dimensional metrics into a composite score that can be easily interpreted and compared (10). RAPI is particularly valuable because it enables developers and end-users to preserve analytical rigor while integrating sustainability and practicality within a broader WAC assessment.

Tools for Evaluating Analytical Performance: The Red Analytical Performance Index (RAPI)

RAPI is a novel tool developed to objectively quantify the analytical performance of quantitative methods in line with the principles of WAC. While traditional method validation focuses on multiple performance indicators, their evaluation is typically descriptive, fragmented, and rarely consolidated into a unified score. RAPI addresses this by introducing a structured, semi-quantitative scoring system designed to support transparent comparison and interpretation of method validation data across laboratories and publications.

Inspired by previous tools such as BAGI (used in the blue dimension of WAC), RAPI employs an open-source Python-based software that allows users to select validation results from dropdown menus and instantly obtain a composite score, visually integrated into a characteristic radial pictogram. The tool is offered under the Massachusetts Institute of Technology (MIT) license, ensuring open access, reproducibility, and flexibility (14).

The RAPI model is built upon ten analytical parameters, selected based on ICH Q2(R2) and ISO 17025 guidelines, generally accepted validation practice (15-17), the need for universal applicability to all types of quantitative analytical methods. These parameters are:

Repeatability (RSD%): Variation under same conditions, short timescale, one operator.

Intermediate precision (RSD%): Variation under variable but controlled conditions (such as different days or analysts).

Reproducibility (RSD%): Variation across laboratories, equipment, and operators (where applicable).

Trueness: Expressed as relative bias (%) using CRMs, spiking, or comparison to reference method.

Recovery and Matrix Effect: % recovery and qualitative matrix impact.

Limit of Quantification (LOQ): Expressed as % of average expected analyte concentration.

Working Range: Distance between LOQ and the method’s upper quantifiable limit.

Linearity (R²): Simplified, using the coefficient of determination.

Robustness/Ruggedness: Number of factors (pH, temperature) tested not to affect performance.

Selectivity: Number of interferents that do not influence precision/trueness.

This set of parameters is both broad and universal. While some (like LOQ or R²) are commonly reported in analytical studies, others such as ruggedness and reproducibility, are often overlooked, despite being critical for comprehensive method validation. Their inclusion in RAPI encourages more complete and harmonized validation efforts. Each parameter is scored independently on a five-level scale (see Table I).

The absence of data (due to a method not evaluated for a given parameter) also results in a score of 0, penalizing incomplete validation. This is a crucial feature of RAPI, promoting both thoroughness and transparency. The final RAPI score is calculated as the sum of the ten individual parameter scores, resulting in a value ranging from 0 to 100. This total score is visualized at the center of a radial pictogram (see Figure 1), where each parameter is represented as a spoke with its individual value (0–10).

The shape and area of the pictogram provide immediate visual cues about method strengths and weaknesses. The interpretation of the score can be followed as presented in Table II.

RAPI does not weigh the importance of parameters, each of the ten contributes equally to the final score. While in specific applications some criteria (such as, for example, LOQ in trace analysis) may be more relevant than others, RAPI does not attempt to generalize or assume such priorities. This egalitarian structure reinforces the comprehensive nature of the evaluation and allows users to apply context-specific judgment where needed.

Importantly, the RAPI score should not be the sole determinant of whether a method is fit for purpose. Rather, it provides a structured, comparative baseline to support decision-making, publication review, regulatory communication, and internal quality assurance. Advantages of RAPI are 1) open-source and user-friendly interface available online; 2) standardized criteria based on global validation guidelines; 3) encouragement of completeness in method validation reporting; 4) visual and numerical output enhancing communication and decision-making; and 5) adaptability to various analytical techniques and sample types.

Application of RAPI: Comparative Assessment of Two Green TF-SPME Methods

Two well-documented, innovative, and green analytical procedures published in peer-reviewed journals were selected to present the applicability of RAPI. Method A: Natural deep eutectic solvent (NADES) combined with powdered cork used as a sorbent for thin-film solid-phase microextraction (TF-SPME), applied to the determination of selected ultraviolet (UV) filters (benzophenone‑3 and octocrylene) in lake waters (18), and Method B: Thin-film SPME using recycled diatomaceous earth as sorbent, combined with a 96-well plate TF-SPME system, used for the determination of endocrine-disrupting chemicals (bisphenol A, benzophenone, triclocarban) (19). Although both methods target trace-level pollutants in environmental waters and share green features, they differ significantly in analyte classes, concentration ranges, and throughput. The results for RAPI are presented in Figure 2.

Method A demonstrates excellent analytical performance with a total RAPI score of 82.5, confirming comprehensive validation, outstanding sensitivity, and robust repeatability. This makes it highly suitable for trace analysis of ultraviolet (UV) filters in natural waters. Method B, despite its innovative and high-throughput approach, scores significantly lower (~47.5) due to the lack of data on reproducibility, robustness, and intermediate precision, along with limited sensitivity for trace analysis of endocrine disruptors. The RAPI tool enables transparent and reproducible comparison of validation completeness and performance between analytical methods, supporting more informed selection in method development or regulatory decision-making.

Conclusion

RAPI provides a structured, transparent, and reproducible framework for evaluating the analytical quality of methods within the WAC paradigm. By consolidating key validation parameters into a single score, RAPI supports comparative analysis and strengthens decision-making in method development, regulatory assessment, and routine laboratory practice.

The comparative case study of two TF-SPME methods demonstrates how RAPI can reveal substantial differences in analytical rigor, despite similarities in methodological intent. While one method excelled in validation completeness and sensitivity, the other highlighted the need for further performance data despite offering higher throughput.

RAPI serves not only as a scoring tool but as a critical aid for promoting comprehensive validation and analytical reliability across diverse techniques.

References

1. ICH Q2(R2) Guideline on Validation of Analytical Procedures. International Council for Harmonisation 2023. https://www.ich.org/page/quality-guidelines
2. Analytical Procedures and Methods Validation for Drugs and Biologics: Guidance for Industry.U.S. Food and Drug Administration (FDA), 2015. https://www.fda.gov/files/drugs/published/Analytical-Procedures-and-Methods-Validation-for-Drugs-and-Biologics.pdf
3. European Medicines Agency, ICH Q2(R2) Validation of Analytical Procedures - Scientific Guideline. European Medicines Agency 2024. https://www.ema.europa.eu/en/ich-q2r2-1
4. Nowak, P. M.; Wietecha-Posłuszny, R.; Pawliszyn, J. White Analytical Chemistry: An Approach to Reconcile the Principles of Green Analytical Chemistry and Functionality. Trends Anal. Chem. 2021, 138, 116223. DOI: 10.1016/j.trac.2021.116223
5. J. Płotka-Wasylka, J. A New Tool for the Evaluation of the Analytical Procedure: Green Analytical Procedure Index. Talanta 2018, 181, 204-209. DOI: 10.1016/j.talanta.2018.01.013
6. Pena-Pereira, F.; Wojnowski, W.; Tobiszewski, M. AGREE—Analytical GREEnness Metric Approach and Software. Anal. Chem.2020,92, 10076-10082. DOI: 10.1021/acs.analchem.0c01887
7. Manousi, N.; Wojnowski, W.; Płotka-Wasylka, J. et al. Blue Applicability Grade Index (BAGI) and Software: A New Tool for the Evaluation of Method Practicality. Green Chem. 2023, 25, 7598-7604. DOI: 10.1039/D3GC02347H
8. González, A. G.; Herrador, M. A. A Practical Guide to Analytical Method Validation, Including Measurement Uncertainty and Accuracy Profiles. Trends Anal. Chem. 2007, 26 (3), 227-238. DOI: 10.1016/j.trac.2007.01.009
9. Gustavo González, A.; Ángeles Herrador, M.; Asuero, A. G. Intra-Laboratory Assessment of Method Accuracy (Trueness and Precision) by Using Validation Standards. Talanta 2010, 82, 1995–1998. DOI: 10.1016/j.talanta.2010.07.071
10. Nowak, P. M.; Wojnowski, W.; Manousi, N. et al. Red Analytical Performance Index (RAPI) and Software: The Missing Tool for Assessing Methods in Terms of Analytical Performance, Green Chem.2025,27, 5546-5553. DOI: 10.1039/D4GC05298F
11. United States Pharmacopeia (USP) General Chapter <1225>: Validation of Compendial Procedures. USP 43–NF 38, 2020.
12. ISO/IEC 17025:2017 – General Requirements for the Competence of Testing and Calibration Laboratories. International Organization for Standardization, 2017.
13. V. Ravichandran, V.; S. Shalini, S.; Sundram K. M. et al. Validation of Analytical Methods – Strategies and Importance, Int. J. Pharm. Pharm. Sci. 2010, 2 (3), 18-22.
14. https://mostwiedzy.pl/pl/justyna-plotka-wasylka,647762-1/rapi
15. Konieczka, P.; Namieśnik, J.; Zygmunt, B. Ocena i kontrola jakości wyników pomiarów analitycznych; WNT, 2013.
16. Magnusson, B.; Ornemark, U. The Fitness for Purpose of Analytical Methods – A Laboratory Guide to Method Validation and Related Topics; Eurachem, 2014.
17. Taverniers, I.; De Loose, M.; Bockstaele, E. V. Trends in Quality in the Analytical Laboratory. II. Analytical Method Validation and Quality Assurance.TrAC, Trends Anal. Chem.2004, 23, 535-552. DOI:10.1016/j.trac.2004.04.001
18. Werner, J.; Płatkiewicz, D.; Mysiak, Ł. et al. Natural Deep Eutectic Solvent Mixed with Powdered Cork as a Green Approach for Thin Film SPME and Determination of Selected Ultraviolet Filters in Lake Waters, Green Anal. Chem. 2025, 12,100256. DOI:10.1016/j.greeac.2025.100256
19. Kirschner, N.; A.N. Dias, A. N.; D. Budziak, D. et al. Novel Approach to High-Throughput Determination of Endocrine Disruptors Using Recycled Diatomaceous Earth as a Green Sorbent Phase for Thin-Film Solid-Phase Microextraction Combined with 96-Well Plate System. Anal. Chim. Acta 2017, 996, 29-37. DOI: 10.1016/j.aca.2017.09.047

Justyna Płotka-Wasylka is with the Department of Analytical Chemistry of the Faculty of Chemistry at Gdańsk University of Technology in Gdańsk. Poland. Adrián Fuente-Ballesterosis with the Analytical Chemistry Group (TESEA) of I. U. CINQUIMA, Faculty of Sciences at the University of Valladolid, in Valladolid, Spain. Direct correspondence to: justyna.plotka-wasylka@pg.edu.pland adrian.fuente.ballesteros@uva.es