TY - JOUR
T1 - Chemometric challenges in development of paper-based analytical devices
T2 - Optimization and image processing
AU - Hamedpour, Vahid
AU - Oliveri, Paolo
AU - Leardi, Riccardo
AU - Citterio, Daniel
N1 - Funding Information:
We acknowledge Dr. Yuki Hiruta (Keio University, Yokohama, Japan) for help and advice. V. H. kindly acknowledges a scholarship from the Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan.
Funding Information:
We acknowledge Dr. Yuki Hiruta (Keio University, Yokohama, Japan) for help and advice. V. H. kindly acknowledges a scholarship from the Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan.
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2020/3/8
Y1 - 2020/3/8
N2 - Although microfluidic paper-based analytical devices (μPADs) get a lot of attention in the scientific literature, they rarely reach the level of commercialization. One possible reason for this is a lack of application of machine learning techniques supporting the design, optimization and fabrication of such devices. This work demonstrates the potential of two chemometric techniques including design of experiments (DoE) and digital image processing to support the production of μPADs. On the example of a simple colorimetric assay for isoniazid relying on the protonation equilibrium of methyl orange, the experimental conditions were optimized using a D-optimal design (DO) and the impact of multiple factors on the μPAD response was investigated. In addition, this work demonstrates the impact of automatic image processing on accelerating color value analysis and on minimizing errors caused by manual detection area selection. The employed algorithm is based on morphological recognition and allows the analysis of RGB (red, green, and blue) values in a repeatable way. In our belief, DoE and digital image processing methodologies are keys to overcome some of the remaining weaknesses in μPAD development to facilitate their future market entry.
AB - Although microfluidic paper-based analytical devices (μPADs) get a lot of attention in the scientific literature, they rarely reach the level of commercialization. One possible reason for this is a lack of application of machine learning techniques supporting the design, optimization and fabrication of such devices. This work demonstrates the potential of two chemometric techniques including design of experiments (DoE) and digital image processing to support the production of μPADs. On the example of a simple colorimetric assay for isoniazid relying on the protonation equilibrium of methyl orange, the experimental conditions were optimized using a D-optimal design (DO) and the impact of multiple factors on the μPAD response was investigated. In addition, this work demonstrates the impact of automatic image processing on accelerating color value analysis and on minimizing errors caused by manual detection area selection. The employed algorithm is based on morphological recognition and allows the analysis of RGB (red, green, and blue) values in a repeatable way. In our belief, DoE and digital image processing methodologies are keys to overcome some of the remaining weaknesses in μPAD development to facilitate their future market entry.
KW - D-optimal design
KW - Design of experiments
KW - Image processing algorithm
KW - Mathematical morphology recognition (MMR)
KW - Microfluidic paper-based analytical devices (μPADs)
KW - Optimization
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U2 - 10.1016/j.aca.2019.11.064
DO - 10.1016/j.aca.2019.11.064
M3 - Article
C2 - 32029100
AN - SCOPUS:85076237649
VL - 1101
SP - 1
EP - 8
JO - Analytica Chimica Acta
JF - Analytica Chimica Acta
SN - 0003-2670
ER -