Unlocking the full potential of voltammetric data analysis: A novel peak recognition approach for (bio)analytical applications

• Published in: Talanta (Oxford) Vol. 233; p. 122605
• Main Authors: Van Echelpoel, Robin, de Jong, Mats, Daems, Devin, Van Espen, Piet, De Wael, Karolien
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2021
• Subjects: Chemometrics; Data analysis software; Data preprocessing; Peak recognition; Signal data; Voltammetry; Peak recognition; Voltammetry; Signal data; Data analysis software; Data preprocessing; Chemometrics
• ISSN: 0039-9140; 1873-3573
• DOI: 10.1016/j.talanta.2021.122605

Bridging the gap between complex signal data output and clear interpretation by non-expert end-users is a major challenge many scientists face when converting their scientific technology into a real-life application. Currently, pattern recognition algorithms are the most frequently encountered signal data interpretation algorithms to close this gap, not in the least because of their straight-forward implementation via convenient software packages. Paradoxically, just because their implementation is so straight-forward, it becomes cumbersome to integrate the expert's domain-specific knowledge. In this work, a novel signal data interpretation approach is presented that uses this domain-specific knowledge as its fundament, thereby fully exploiting the unique expertise of the scientist. The new approach applies data preprocessing in an innovative way that transcends its usual purpose and is easy to translate into a software application. Multiple case studies illustrate the straight-forward application of the novel approach. Ultimately, the approach is highly suited for integration in various (bio)analytical applications that require interpretation of signal data. [Display omitted] •Peak recognition approach exploiting domain-specific knowledge of researcher•Approach optimally suited for integration in (bio)analytical applications•Data preprocessing tandem highlights hidden features in signal data•Applicability approach is demonstrated on electrochemical sensors