Multimodal Imaging and Lighting Bias Correction for Improved μPAD-based Water Quality Monitoring via Smartphones

Smartphone image-based sensing of microfluidic paper analytical devices (mu PADs) offers low-cost and mobile evaluation of water quality. However, consistent quantification is a challenge due to variable environmental, paper, and lighting conditions, especially across large multi-target mu PADs. Compensations must be made for variations between images to achieve reproducible results without a separate lighting enclosure. We thus developed a simple method using triple-reference point normalization and a fast-Fourier transform (FFT)-based pre-processing scheme to quantify consistent reflected light intensity signals under variable lighting and channel conditions. This technique was evaluated using various light sources, lighting angles, imaging backgrounds, and imaging heights. Further testing evaluated its handle of absorbance, quenching, and relative scattering intensity measurements from assays detecting four water contaminants-Cr(VI), total chlorine, caffeine, and E. coli K12-at similar wavelengths using the green channel of RGB images. Between assays, this algorithm reduced error from mu PAD surface inconsistencies and cross-image lighting gradients. Although the algorithm could not completely remove the anomalies arising from point shadows within channels or some non-uniform background reflections, it still afforded order-of-magnitude quantification and stable assay specificity under these conditions, offering one route toward improving smartphone quantification of mu PAD assays for in-field water quality monitoring.National Science Foundation Graduate Research Fellowship [DGE-1143953]; Paul D. Coverdell Fellows Program; Water and Environmental Technology (WET) Center at the University of Arizona; Tucson WaterThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Cellphone-based devices for bioanalytical sciences

During the last decade, there has been a rapidly growing trend toward the use of cellphone-based devices (CBDs) in bioanalytical sciences. For example, they have been used for digital microscopy, cytometry, read-out of immunoassays and lateral flow tests, electrochemical and surface plasmon resonance based bio-sensing, colorimetric detection and healthcare monitoring, among others. Cellphone can be considered as one of the most prospective devices for the development of next-generation point-of-care (POC) diagnostics platforms, enabling mobile healthcare delivery and personalized medicine. With more than 6.5 billion cellphone subscribers worldwide and approximately 1.6 billion new devices being sold each year, cellphone technology is also creating new business and research opportunities. Many cellphone-based devices, such as those targeted for diabetic management, weight management, monitoring of blood pressure and pulse rate, have already become commercially-available in recent years. In addition to such monitoring platforms, several other CBDs are also being introduced, targeting e.g., microscopic imaging and sensing applications for medical diagnostics using novel computational algorithms and components already embedded on cellphones. This manuscript aims to review these recent developments in CBDs for bioanalytical sciences along with some of the challenges involved and the future opportunities