This leaflet is intended to help participants in quantitative proficiency testing (PT) schemes to better understand the performance assessment made by the PT provider [1-4].<h3>Performance assessment parameters&nbsp;</h3><h4>Assigned value&nbsp;</h4>In order to assess individual performances, the results reported by participants are compared to the value assigned by the PT provider (xpt). ISO 13528 [1] suggests five different ways to obtain xpt (Figure 1). In this context, the design of the PT scheme must reflect the objectives of the scheme, comparing results either to a predetermined xpt (independent from the participant results), or to a value derived from the reported results. The PT provider is responsible for defining xpt , taking into account the advantages and disadvantages of the various possibilities when establishing metrological traceability or estimating the uncertainty of the assigned value, u(xpt).<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Figure_1_e5ef5d1f9c.jpg" alt="Eurachem - Figure 1 Options for obtaining the assigned value, Xpt" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Figure_1_e5ef5d1f9c.jpg 245w," sizes="100vw" width="640" height="382"><h4>Uncertainty of the assigned value&nbsp;</h4>The uncertainty of the assigned value u(xpt) can be estimated in at least five different ways [1], each corresponding to the value assignment strategy illustrated in Figure 1. The different uncertainty evaluation methods are listed in Figure 2. This uncertainty can be reported as a standard uncertainty (u (xpt)), or as an expanded uncertainty U (xpt) with a coverage factor k, where U (xpt) = k . u (xpt). The choice of k will impact the level of confidence (e.g. approximately 95 % for k = 2).<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Figure_2_f41ebc0c61.jpg" alt="Eurachem: Figure 2 Options for obtaining the uncertainity of the assigned value, u(Xpt)" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Figure_2_f41ebc0c61.jpg 245w," sizes="100vw" width="727" height="378"><h4>Standard deviation for proficiency assessment&nbsp;</h4>Five possibilities are suggested by ISO 13528 to set the standard deviation for proficiency assessment spt (Figure 3). The chosen approach must comply with the objectives of the PT scheme.&nbsp;<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Figure_3_f09cb3969e.jpg" alt="Eurachem: Figure 3 Options for obtaining the standard deviation for proficiency assesment" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Figure_3_f09cb3969e.jpg 245w," sizes="100vw" width="638" height="355">Two approaches for performance assessments use spt as shown in Table 1.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Table_1_Scoring_for_performance_assessment_3dd03aa3fb.jpg" alt="Eurachem: Table 1 - Scoring for performance assessment." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Table_1_Scoring_for_performance_assessment_3dd03aa3fb.jpg 245w," sizes="100vw" width="1390" height="571"><h3>Performance assessment&nbsp;</h3>All performance assessments are based on the difference between the participant’s results (xi) and the assigned value (xpt) divided by a normalisation factor (Table 1).&nbsp;<ul><li>The percent difference D% is the normalised difference expressed as a percentage of xpt , for which a prescribed maximum relative permissible error (dE% = dE / xpt) is defined by the PT provider.&nbsp;</li><li>The z score (unitless) compares the participant’s deviation from the assigned value with the standard deviation for proficiency assessment (spt). When the uncertainty of the assigned value is significant (u(xpt) &gt; 0.3 spt), the z’ score should be used instead, to include the contribution of u(xpt) in the denominator.&nbsp;</li><li>The zeta (ζ) score (unitless) states whether the laboratory’s result agrees with the assigned value within the respective uncertainty. The denominator is the combined (standard) uncertainty of the assigned value u(xpt) and the (standard) measurement uncertainty reported by the laboratory u(xi). An unsatisfactory zeta score can either be caused by an inappropriate estimation of the measured quantity value (e.g. concentration, content), or of its measurement uncertainty, or both.&nbsp;</li><li>The En score (unitless), unlike the zeta score, uses a combination of expanded uncertainties as the normalisation factor. This combination corresponds to a confidence interval of approximately 95 %. En scores are often used in metrology comparisons among calibration laboratories.</li></ul><h3>Interpretation of performance scores&nbsp;</h3>According to ISO 13528:2022 clause 9.4.2, the conventional interpretation of the z, z’ and ζ scores is as follows:&nbsp;<ul><li>A result that gives |score| ≤ 2.0 is considered to be acceptable, indicating satisfactory performance.&nbsp;</li><li>A result that gives 2.0 &lt; |score| &lt; 3.0 is considered to give a warning signal, indicating questionable performance.&nbsp;</li><li>A result that gives |score| ≥ 3.0 is considered to be unacceptable (or action signal), indicating unsatisfactory performance.&nbsp;</li></ul>Similarly, |En| &lt; 1 and |D%| &lt; dE% indicate successful (acceptable) performance.

Understanding PT performance assessment

The increasing focus on nitrosamine impurities has prompted analytical development teams across the industry to revisit long-standing assumptions about sensitivity, selectivity, and method versatility. Among the impurity classes now receiving heightened attention are nitrosamine drug substance–related impurities (NDSRIs), a group distinguished by their structural resemblance to parent APIs and their tendency to form under specific manufacturing or storage conditions. For beta blockers, which often contain amine functionalities susceptible to nitrosation, this presents a particularly relevant analytical challenge.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Thermo_Fisher_Scientific_Exploring_a_Versatile_LC_HRMS_Strategy_for_NDSRI_Determination_in_Beta_Blocker_Drug_Substances_d824f864cd.jpg" alt="Thermo Fisher Scientific: Exploring a Versatile LC–HRMS Strategy for NDSRI Determination in Beta Blocker Drug Substances" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Thermo_Fisher_Scientific_Exploring_a_Versatile_LC_HRMS_Strategy_for_NDSRI_Determination_in_Beta_Blocker_Drug_Substances_d824f864cd.jpg 245w," sizes="100vw" width="1024" height="512">A recent <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_003910_pb_lc_hrms_beta_blocker_ndsri_an003910_na_en_61a31cd182.pdf">Thermo Fisher Scientific application note</a> examines this challenge through the lens of LC–HRMS, presenting a unified analytical approach capable of addressing NDSRI quantitation across six different beta blocker drug substances.<h3>Revisiting the complexity of NDSRIs</h3>The scientific and regulatory landscape surrounding NDSRIs continues to evolve. As outlined early in the study (pages 2–3), the scarcity of toxicological data for many of these impurities has led agencies to adopt structure-based frameworks such as the Carcinogenic Potency Categorization Approach (CPCA). These classifications, coupled with low acceptable-intake values, have placed new emphasis on achieving both trace-level sensitivity and high structural specificity.For many labs, that means rethinking the interplay between chromatographic separations, ionization behavior, adduct formation, and mass accuracy, especially when the impurity under investigation differs only subtly from its corresponding drug substance.<h3>A single method across six APIs</h3>One aspect of the work that may be of interest to analytical scientists is the decision to pursue one LC–HRMS method applicable across six beta blocker drug substances: atenolol, bisoprolol fumarate, carvedilol, labetalol HCl, metoprolol fumarate, and propranolol HCl.This required careful consideration of:<ul><li>Column chemistry and dimensions</li><li>Mobile phase composition and gradient design</li><li>Ion source settings suitable for diverse analyte structures</li><li>Product ion selection guided by theoretical fragmentation analysis (pages 6–8)</li><li>Valve switching to manage potential interferences</li></ul>The resulting method offers a perspective on how unified analytical strategies may help streamline workflows in environments where multiple drug substances must be assessed for similar impurity risks.<h3>Method performance: key observations</h3>The study presents a series of experimental evaluations, reproducibility, robustness, linearity, recovery, and ion-ratio confirmation, that provide insight into how the method behaves under different analytical conditions.A few noteworthy observations include:<ul><li>Linearity (R² &gt; 0.99 for all NDSRIs): Calibration plots (page 11) illustrate consistent detector response across a broad concentration range.</li><li>Sensitivity at low levels: LOQs aligned to 10% of each impurity specification limit (Table 14, page 10), with LODs at approximately one-third of the LOQ.</li><li>Long-run robustness: A 21-hour sequence showed stable peak-area performance, with cumulative %RSD values that speak to the system’s suitability for extended runs (page 9).</li><li>Chromatographic clarity: Representative chromatograms (pages 13–14) demonstrate distinct separation between NDSRIs and their related APIs, even within a 20-minute gradient.</li></ul>These results collectively provide a framework for understanding how LC–HRMS can be optimized for structurally similar, low-level impurities in complex matrices.<h3>Key considerations</h3>While every laboratory faces unique constraints, sample types, regulatory expectations, and throughput demands, the work offers several themes that may resonate with teams currently evaluating or refining nitrosamine methodologies:<ul><li>The emphasis on accurate mass and interference avoidance highlights the value of high-resolution MS when dealing with closely related structures.</li><li>The method’s consistency across structurally diverse beta blockers underscores how column and gradient design influence versatility.</li><li>The detailed precursor and product ion selection process illustrates a practical approach to balancing sensitivity and selectivity in impurity analysis.</li></ul>These elements, taken together, form a basis for discussion as laboratories consider how best to adapt their workflows to emerging impurity requirements.<h3>A closer look at the study</h3>For those interested in the nuances of CPCA-driven specification setting, chromatographic optimization, or the application of <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products?category=134&amp;manufacturerItems=3">Orbitrap-based mass spectrometry</a> to low-level impurity work, the full application note provides additional technical depth. It includes complete parameter tables, ion-ratio confirmation data, chromatographic profiles, and experimental rationales that further illuminate the method’s development and evaluation.<blockquote>Visit us on&nbsp;<a target="_blank" rel="noopener noreferrer" href="https://www.linkedin.com/showcase/72003132/admin/dashboard/">LinkedIn:</a>&nbsp;#NitrosamineImpurities #NDSRI #NitrosamineAnalysis #ImpurityAnalysis</blockquote>

Exploring a Versatile LC–HRMS Strategy for NDSRI Determination in Beta Blocker Drug Substances

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