Smart electrochemical sensing of xylitol using a combined machine learning and simulation approach

A novel sensor was proposed for the detection of xylitol in sugar free chewing gum using Au nanoparticles (NPs) derived from Callistemon viminalis leaf extract coupled with multiwalled carbon nanotubes (MWCNTs) doped onto glassy carbon electrode (GCE). In comparison to the bare GCE, the modified GCE/MWCNT/AuNPs sensor showed about 45-fold better electrochemical response to xylitol. Under the optimal conditions, the designed sensor achieved a detection limit of 9.8 x 10 (-6) pM for concentrations ranging from 9.9 x 10 (-6) to 2.9 x 10 (-5) pM. The practicability was tested on sugar-free sample yielding recoveries of 97-100% with RSDs of 2.83-3.33%. Machine learning (ML) was used to predict changes in voltammetric signal with changing potential over time demonstrating the fundamental knowledge of the electrochemical reaction. The performance of the Artificial Neural Network (ANN) provides good accuracy and precision in predicting the intensity (I) along with repeated ANN runs, with a mean square error (MSE) of 0.007 (+/- 0.002) and a determination coefficient (R-2) of 0.9992 +/- 0.0006. Additionally, the interaction of xylitol on the electrode surfaces were investigated using Monte Carlo adsorption studies and 1000 ps Molecular Dynamics simulations under NVT conditions. According to the frontier molecular orbitals obtained through Density Functional Theory calculations, the reactive sites of xylitol occur at the hydroxyl group on the second carbon. Using complementary measurement techniques, this new strategy exhibits a great potential for rapid detection of xylitol in food and dental products.

Automated summary

A novel sensor using Au nanoparticles derived from Callistemon viminalis leaf extract and multiwalled carbon nanotubes doped onto glassy carbon electrode was developed for detecting xylitol in sugar free chewing gum. The sensor showed significantly enhanced electrochemical response to xylitol and achieved a low detection limit. Machine learning and molecular dynamics simulations were used to study the electrochemical reaction and interaction of xylitol on the electrode surfaces. This study demonstrates a new strategy for rapid detection of xylitol.