Analytical Chemistry A Practical Approach

[Ane Neemann]

Amethodology to calculate analytical figures of merit is not well established for detection systems that are based on sensor arrays with low sensor selectivity. In this work, we present a practical approach to estimate the Resolving Power of a sensory system, considering non-linear sensors and heteroscedastic sensor noise. We use the definition introduced by Shannon in the field of communication theory to quantify the number of symbols in a noisy environment, and its version adapted by Gardner and Barlett for chemical sensor systems. Our method combines dimensionality reduction and the use of algorithms to compute the convex hull of the empirical data to estimate the data volume in the sensor response space. We validate our methodology with synthetic data and with actual data captured with temperature-modulated MOX gas sensors. Unlike other methodologies, our method does not require the intrinsic dimensionality of the sensor response to be smaller than the dimensionality of the input space. Moreover, our method circumvents the problem to obtain the sensitivity matrix, which usually is not known. Hence, our method is able to successfully compute the Resolving Power of actual chemical sensor arrays. We provide a relevant figure of merit, and a methodology to calculate it, that was missing in the literature to benchmark broad-response gas sensor arrays.

Figure 4. (A) Collection of measurements obtained from the SB-500-12 sensor after exposing it to the experimental dataset. Data is represented as a plot of the resistance of the sensor along the acquisition time. The color of the curves represents the concentration of CO. The plot also includes the heater profile applied to the sensor. (B) Curves corresponding to concentrations of CO of 0, 11, and 20 ppm, colored by their level of relative humidity (RH). Observe the non-linearities introduced in the pattern due to humidity, and the heteroscedasticity of noise for different concentration levels of CO.

Figure 5. Resolving Power for the set of synthetic sensor-pairs against their selectivity value. The plot includes the sensor space constituted by the sensor pairs for (A) totally non-selective sensors, (B) partially selective sensors, and (C) totally selective sensors. Observe that the area of the sensor space strongly depends on the selectivity of the sensors.

Figure 6. Reduced sensor space obtained from the projection of the original high-dimension sensor space to a 2-dimension space through a PCA projection. Samples were colored according to their concentration of CO, while the marker type represents the level of RH.

Copyright 2018 Fernandez, Yan, Fonollosa, Burgus, Gutierrez and Marco. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Guidelines issued by Regulatory Authorities make it clear that validation of analytical methodology is now widely required in support of registration dossiers. Although some attempts are made at defining terms and some vague indications are sometimes provided within these guidelines, no clear advice is provided on how validations should be conducted and what results should be expected. In this paper it is attempted to suggest some practical approaches to conducting validation and in particular to the determination of accuracy, linearity and limit of detection/quantitation.

While the project will focus on utilizing all available tools in the analytical and mechanical characterization processes, the emphasis of this project will be to develop a simple and easy to implement tool kit. The simplicity of the test protocol will be a primary factor in the development process. The University will also develop educational materials as part of this project (such as, implementation manuals and recorded webinars) to aid in implementation of the research outcomes. This project will bring an academic and industry member of NRRA together with complementary strengths in areas of analytical chemistry characterizations and performance testing and modelling.

During the proposal phase and the development of the work plan, key benefits were selected to clearly define the benefits the state will receive from the results and conclusions of this research. Provide an initial assessment of research benefits, a proposed methodology, and potential implementation steps.

Execute the material sampling plan that is developed in Task 2. Utilize several materials already accessible in this project, resulting in only a limited number of new materials to be sampled. Revaluate a total of eight material sources (one source represents at a minimum one virgin binder type and one recycled binder type). Evaluate binder samples extracted and recovered from plant produced asphalt mixtures. Simultaneous to sampling efforts, obtain materials that are currently stored at subcontractor location. Undertake various material processing activities, including binder extraction and recovery from mixtures, mixture long-term lab aging, and preparation of mixture test specimen for use in Task 6. For mixture long term lab aging, perform conditioning at two aging levels. Adapt the long term aging procedures developed through the National Cooperative Highway Research Program (NCHRP) 09-54 study (multi-day aging at 95 degrees C in loose mix). In consultation with TAP, determine the exact number of days for conditioning based on location of material sampling.

Undertake direct analytical assessment of bitumen compatibilities (between various chemical fractions within the binder, between virgin and recycled binders and, between virgin and recycled binders and rejuvenating agents). A list of analytical assessment methods is presented in the Task 4 tab of this spreadsheet (xls).

Undertake various laboratory physical and mechanical tests and data analyses to determine the mechanical performance properties of asphalt binders. A list of binder lab evaluation methods is listed in Task 5 tab of this spreadsheet (xls).

The current asphalt binder specification limits as well as recently developed and validated binder rheological performance properties will provide an initial baseline comparison with analytical measurements as well as provide initial thresholds to assess binder and aging index parameters binder/rejuvenator compatibilities.

Determine fatigue performance of asphalt binder blends at various aging levels. LAS testing protocol determines continuum damage-based fatigue performance indicators by conducting cyclic tests are increasing strain magnitudes on binder samples. It is significant to expand the limited performance dataset obtained from pavement sections.

To ensure that the compatibility methodology does not result in binder blends that are susceptible to rutting, it is important that rutting performance is assessed. The MSCR properties go above and beyond those derived from linear viscoelastic characterization. Refine above list of testing methods in consultation with the TAP. Perform binder performance assessment on the same materials as those assessed in the analytical stage. Compare properties obtained from binder performance assessment against established threshold values from previous and on-going studies.

Produce a final memorandum that clarifies and documents the methodology used to calculate benefits, including any assumptions and steps required. In addition to quantitative calculations (when feasible), include a qualitative discussion of the estimated benefits. Include key steps that agencies can take to implement the research.

Prepare a draft final report, following State publication guidelines, to document project activities, findings, and recommendations. This report will be reviewed by the TAP, updated by the University to incorporate technical comments, and then approved by the Technical Liaison (TL) before this task is considered complete. Schedule a TAP meeting to facilitate the discussion of the draft report.

Work directly with State contract editors to address editorial comments and finalize the document in a timely manner. The contract editors will publish the report and ensure it meets publication standards.

Quality assurance and measurement uncertainty in analytical laboratories has become increasingly important. To meet increased scrutiny and keep up with new methods, practitioners very often have to rely on self-study. A practical textbook for students and a self-study tool for analytical laboratory employees, Quality Assurance and Quality Control in the Analytical Chemical Laboratory: A Practical Approach defines the tools used in QA/QC, especially the application of statistical tools during analytical data treatment.

Clearly written and logically organized, this book delineates the concepts of practical QA/QC, taking a generic approach that can be applied to any field of analysis. Using an approach grounded in hands-on experience, the book begins with the theory behind quality control systems and then moves on to discuss examples of tools such as validation parameter measurements, the use of statistical tests, counting the margin of error, and estimating uncertainty. The authors draw on their experience in uncertainty estimation, traceability, reference materials, statistics, proficiency tests, and method validation to provide practical guidance on each step of the process.

Presenting guidance on all aspects of QA and measurement results, the book covers QC/QA in a more complex and extended manner than other books on this topic. This range of coverage supplies an integrated view on measures like the use of reference materials and method validation. With worked-out examples and Excel spreadsheets that users can use to try the concepts themselves, the book provides not only know-what but know-how.

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