Calibration‐free quantitation in microchip zone electrophoresis with conductivity detection

Noblitt, Scott D.; Henry, Charles S.

doi:10.1002/elps.201500098

Cited by 1 publication

(2 citation statements)

References 55 publications

(67 reference statements)

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“…For the MCE analysis, devices were prepared using a Sylgard 184 elastomer/crosslinker kit (Dow Corning, Midland, MI, USA) and standard soft lithography techniques [26]. Contact conductivity detection was performed using a bubble-cell detection zone [27], specific microfluidic geometry was used previously [28], and injection mode was gated injection [29]. is known to lead to a small bias [1], but the bias is inconsequential for many applications.…”

Section: Instrumentationmentioning

confidence: 99%

“…Inclusion of the internal standard corrects for several injection-related variances, greatly improving reproducibility [30,31]. Additionally, the same MCE device is reusable for both calibration and sample analyses, removing the device-to-device contribution to variance, although utilization of the internal standard may eliminate most device-todevice variability [28,32]. Through the use of an internal standard, capillary electrophoresis can achieve RSD values of 1% or better [33].…”

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confidence: 99%

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Characterizing nonconstant instrumental variance in emerging miniaturized analytical techniques

Noblitt

Berg

Cate

et al. 2016

Analytica Chimica Acta

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Measurement variance is a crucial aspect of quantitative chemical analysis. Variance directly affects important analytical figures of merit, including detection limit, quantitation limit, and confidence intervals. Most reported analyses for emerging analytical techniques implicitly assume constant variance (homoskedasticity) by using unweighted regression calibrations. Despite the assumption of constant variance, it is known that most instruments exhibit heteroskedasticity, where variance changes with signal intensity. Ignoring nonconstant variance results in suboptimal calibrations, invalid uncertainty estimates, and incorrect detection limits. Three techniques where homoskedasticity is often assumed were covered in this work to evaluate if heteroskedasticity had a significant quantitative impact-naked-eye, distance-based detection using paper-based analytical devices (PADs), cathodic stripping voltammetry (CSV) with disposable carbon-ink electrode devices, and microchip electrophoresis (MCE) with conductivity detection. Despite these techniques representing a wide range of chemistries and precision, heteroskedastic behavior was confirmed for each. The general variance forms were analyzed, and recommendations for accounting for nonconstant variance discussed. Monte Carlo simulations of instrument responses were performed to quantify the benefits of weighted regression, and the sensitivity to uncertainty in the variance function was tested. Results show that heteroskedasticity should be considered during development of new techniques; even moderate uncertainty (30%) in the variance function still results in weighted regression outperforming unweighted regressions. We recommend utilizing the power model of variance because it is easy to apply, requires little additional experimentation, and produces higher-precision results and more reliable uncertainty estimates than assuming homoskedasticity.

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