Ultra-low dual detection of tetrahydrocannabinol and cannabidiol in saliva based on electrochemical sensing and machine learning: overcoming cross-interferences and saliva-to-saliva variations

Detection of tetrahydrocannabinol and cannabidiol in saliva using m-Z-THC and m-Z-CBD electrochemical sensors and machine learning as a powerful tool to overcome cross-interferences and saliva-to-saliva variations. A novel alternative to cope with saliva-to-saliva variations and cross-interference while sensing delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) is reported here using two voltammetric sensors coupled with machine learning. The screen-printed electrodes modified with the same analyte molecules (m-Z-THC and m-Z-CBD) were employed for sensing ultra-low concentrations of THC and CBD in the 0 to 5 ng mL −1 range in real human saliva samples. Simultaneous detection of THC and CBD was carried out using m-Z-THC or m-Z-CBD to study the performance of each modified sensor. Also, CBD and THC have the same molecular structure; there is only a slight difference in how the atoms are arranged, and therefore both molecules will have similar electrochemical performance. Consequently, CBD can be a potential interference while detecting THC and THC can be an interference during CBD detection using electrochemical sensors. Therefore, machine learning was introduced to analyze the sensor analytical responses to overcome such issues. The data processing results provide suitable accuracies of 100% for training in the case of both sensors and 92 and 83% for m-Z-THC and m-Z-CBD, respectively, for dataset testing THC and CBD in saliva samples. Additionally, the saliva samples containing CBD and THC as cross-interference were accurately identified and classified.