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Although fabrication of colorimetric paper-based analytical devices (PADs) has drawn increasing attention recently, the signal readout method is still a crucial challenge on the way to practical exploitations. We herein introduce an integration of digital image processing with a model PAD for easing and improving the signal readout procedure. The colorimetric detection mechanism of PAD relies on in-situ induced yellowish silver nanoparticles via the interaction of silver ions, poly(vinyl alcohol), and ammonia with isoniazid. The observed color value is related with the concentration of isoniazid. The proposed algorithm is based on mathematical morphology recognition, and minimizes the errors arising from manual area selection. Besides, it allows the recognition of both circle and square shapes in 96-well plate and A4 size array designs. Since this algorithm automatically provides the blank-corrected numerical matrixes and image profiles of red, green and blue channels, further investigations such as outlier classification, construction of regression/prediction models and calculation of detection limit can be easily performed. Comparison of signal readout results of the developed algorithm with ImageJ software demonstrates significant improvements in analysis speed, reproducibility, accuracy and color values. Therefore, application of the proposed algorithm is promising as a robust technique for practical applications.

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.