Colorimetric detection using microfluidic paper-based analytical devices (µPADs) and smartphones enable lowcost mobile chemical analysis solutions. However, variable illumination conditions and phone characteristics (i.e. camera hardware and software capabilities) limit the accurate interpretation and reproducibility of quantitative results. In this paper, we describe a method to automatically compensate an image captured by a smartphone camera under variable illumination conditions. By incorporating a two-step algorithm, we approximate the mobile camera picture color distribution to resemble a laboratory-grade measurement under reference illumination conditions. For every test image, the algorithm first applies a color mapping step that performs histogram matching of a set of color reference spots printed on the device to a laboratory reference measurement. After this initial correction step, a transformation matrix is computed via a least-square fit to minimize the differences between the device and the laboratory references. This matrix is then applied to the RGB channel values obtained from a µPAD to correct for illumination variations. The methodology was tested by correcting a test dataset captured using a smartphone to approximate to a calibration dataset acquired using a lab-grade camera. After correction, the relative error between the datasets fell to 10-20%, leading to an increase in classification accuracy between 12-33%. This approach enables colorimetric chemical analysis with smartphones outside the lab, removing the need to control external lighting conditions.
KEYWORDS: Data modeling, RGB color model, Microfluidics, Machine learning, Chemical analysis, Cameras, Image analysis, Environmental sensing, Cell phones, Biological and chemical sensing
Colorimetric analysis is being broadly applied in chemical sensing today; however, detection ranges and resolution limits are typically modest. In this paper, we introduce a methodology to quantify the colorimetric chemical response on a paper-based microfluidic device that enables high-resolution colorimetric detection over a broad pH range. We have achieved this by combining data from various indicators displaying sensitivity on partially overlapping small pH ranges and training machine learning classification models to the colorimetric output. The training dataset consists of images taken from the colorimetric response of three different pH indicators previously deposited on circular spots of a multilayer paper-based device, captured with a reference lab-grade camera. Instead of restricting the use of each pH indicator to their linear response regime within the RGB space, the models are trained against data spanning the entire range of pH values, from 3 to 9, in increments of 0.1, exploring the optimum combination of feature engineering and classification model to maximize the overall model accuracy. The combined analysis of image data captured simultaneously with the three indicators resulted in a pH detection accuracy above 85% with over the entire pH range with resolution down to 0.2 pH points. The demonstrated detection range and resolution are well-suited to support various applications in environmental and industrial analysis.
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