A Machine Learning Approach for Simultaneous Electrochemical Detection of Dopamine and Serotonin in an Optimized Carbon Thread-Based Miniaturized Device

To further optimize output from electrochemical sensing technology and minimize human intervention, machine learning (ML) models are capable of imparting data-driven predictions. The present work focuses on developing a miniaturized EC sensing platform for the simultaneous detection of neurotransmitters such as dopamine (DA) and serotonin or 5-hydroxytryptamine (5-HT). A modified carbon thread-based miniaturized device (CTMD) was developed using a CO 2 laser scriber to detect dopamine and serotonin. The devices showed a linear range for DA and 5-HT as 0.5 μM – 150 μM and 0.5 μM – 200 μM, respectively. The Limit of detection (LOD) and Limit of quantification (LOQ) for DA and 5-HT were 0.25 μM, 0.76 μM (R 2 = 0.99, N = 3), and 0.22 μM, 0.78 μM (R 2 = 0.98, N = 3), respectively. Further, real sample analysis in blood serum was performed, demonstrating good recovery and selectivity. Finally, ML prediction was performed over 100 % of the generated data through analytical methods, whereas 80% of the data was used for training purposes, and 20% of the data was used for testing purposes. Various ML regression models such as linear regression, decision tree, k-NN, Support vector regression, gradient, adaptive boosting, and random forest were used to obtain the best accurate prediction, low error values, and increased R2-scores. Apart from support vector and linear regression, all other techniques provided the best R2-scores of over 0.98 with low error values. Based on the obtained results, the fabricated device, including the ML approach, can effectively be leveraged in diagnostics devices.