Failure of standard training sets in the analysis of fast-scan cyclic voltammetry data
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The use of principal component regression, a multivariate calibration method, in the analysis of in vivo fast-scan cyclic voltammetry data allows for separation of overlapping signal contributions, permitting evaluation of the temporal dynamics of multiple neurotransmitters simultaneously. To accomplish this, the technique relies on information about current-concentration relationships across the scan-potential window gained from analysis of training sets. The ability of the constructed models to resolve analytes depends critically on the quality of these data. Recently, the use of standard training sets obtained under conditions other than those of the experimental data collection (e.g., with different electrodes, animals, or equipment) has been reported. This study evaluates the analyte resolution capabilities of models constructed using this approach from both a theoretical and experimental viewpoint. A detailed discussion of the theory of principal component regression is provided to inform this discussion. The findings demonstrate that the use of standard training sets leads to misassignment of the current-concentration relationships across the scan-potential window. This directly results in poor analyte resolution and, consequently, inaccurate quantitation, which may lead to erroneous conclusions being drawn from experimental data. Thus, it is strongly advocated that training sets be obtained under the experimental conditions to allow for accurate data analysis.
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Shallow neural networks can achieve better error rates than deep neural networks for text classification in some situations, with deep neural networks outperforming shallower models when the dataset was 2.6 million documents but performing poorly when training data was 120 K documents.
“A study shows that shallow neural networks can achieve better error rates than deep neural networks for text classification in some situations, with deep neural networks outperforming shallower models when the dataset was 2.6 million documents but performing poorly when training data was 120 K documents (Johnson et al., 2016).”
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- Authors
- Johnson, J. A., Rodeberg, N. T., & Wightman, R. M.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Wightman, R. Mark
- Venue
- ACS Chemical Neuroscience
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-01-27
- Volume
- 7
- Issue
- 3
- Pages
- 349-359
- ISSN
- 1948-7193 (ISSN-L)
- PMC
- PMC5136453
- OpenAlex
- W2239735445
- Cited by
- 48
- References
- 42
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 1 · 2022: 4 · 2021: 7 · 2020: 8 · 2019: 7 · 2018: 1 · 2017: 7 · 2016: 7
- FWCI
- 3.92
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- ACS Chemical Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- UNC Libraries
- Funders
- National Institute on Drug Abuse (DA010900)
- Topics
- Physical Sciences › Chemistry › Electrochemistry › Electrochemical Analysis and Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrochemical sensors and biosensors
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Keywords
- fast-scan cyclic voltammetry, principal component regression, multivariate calibration, training data, model transferability
- MeSH terms
- Animals, Brain, Brain Chemistry, Calibration, Dopamine, Hydrogen-Ion Concentration, Principal Component Analysis, Electrochemical Techniques
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