  We report the application of machine learning to smartphone based
colorimetric detection of pH values. The strip images were used as the training
set for Least Squares-Support Vector Machine (LS-SVM) classifier algorithms
that were able to successfully classify the distinct pH values. The difference
in the obtained image formats was found not to significantly affect the
performance of the proposed machine learning approach. Moreover, the influence
of the illumination conditions on the perceived color of pH strips was
investigated and further experiments were carried out to study effect of color
change on the learning model. Test results on JPEG, RAW and RAW-corrected image
formats captured in different lighting conditions lead to perfect
classification accuracy, sensitivity and specificity, which proves that the
colorimetric detection using machine learning based systems is able to adapt to
various experimental conditions and is a great candidate for smartphone based
sensing in paper-based colorimetric assays.
