Smartphone embedded deep learning approach for highly accurate and automated colorimetric lactate analysis in sweat

Abstract

Here, a microfluidic paper-based analytical device (μPAD) was first combined with a deep learning-based smartphone app called “DeepLactate” and then applied for quantitative and selective determination of lactate concentration in sweat. The μPAD was made using wax printing protocol and the detection area was modified with horse radish peroxidase, lactate oxidase and the chromogenic agent 3,3′,5,5′-tetramethylbenzidine for enzymatic detection. The images of μPADs taken by smartphones of several brands in different lighting conditions were used to train various deep learning models to make the system more robust and adaptable to lighting changes. The top-performing model, Inception-v3, was then embedded into a smartphone app, offering easy-operation for non-expert users. Deep learning models, unlike machine learning classifiers, can automatically extract features and be embedded in a smartphone app, enabling analysis without internet access. According to the results, the current system showed a classification accuracy of 99.9 % with phone-independent repeatability and a processing time of less than 1 sec. It also showed excellent selectivity towards lactate over different interfering species. Finally, μPAD was turned into a patch to determine the level of sweat lactate in two volunteers after resting and 15 min of jogging. The system successfully detected lactate in human sweat and confirmed that the level of lactate in sweat increased after jogging. Since the μPAD was designed to first absorb a sample and then transfer it to the detection area, avoiding direct contact with the skin, the system reduces the possibility of skin irritation and has great potential for practical use in a variety of fields including self-health monitoring and sports medicine.