Selective and sensitive multiplexed detection of pesticides in food samples using wearable, flexible glove-embedded non-enzymatic sensors

On-site monitoring of pesticides in food, water and in the environment is crucial for human health, and this requires low cost, portable devices for widespread deployment of the technology. In this paper, we report on selective, sensitive detection and discrimination of four classes of pesticides, namely carbendazim (carbamate), diuron (phenylamide), paraquat (bipyridinium) and fenitrothion (organophosphate), using a set of three glove-embedded sensors printed on three fingers of a rubber glove. The sensors consisted of a pre-treated screen-printed carbon electrode and two other such electrodes coated with either carbon spherical shells (CSS) or Printex carbon nanoballs (PCNB). Detection of carbendazim and diuron was performed using differential pulse voltammetry (DPV) and the electrodes coated with CSS and PCNB, respectively, with limits of detection of 4.7 x 10(-8) and 9.2 x 10(-7) mol L-1. Square wave voltammetry (SWV) was applied to detect paraquat and fenitrothion with limits of detection 2.4 x 10(-8) and 6.4 x 10(-7) mol L-1 using the pretreated electrode in sulfuric acid solution. The high performance and discrimination of the pesticides at distinct concentrations in real samples of apple and cabbage by simply touching with the glove, and in orange juice by immersing the fingers was demonstrated with multidimensional projections. The sensors were robust against flexion in multiple times, stable and had reproducible response with no interference from other pesticides. With their high selectivity, sensitivity, easy operation and rapid pesticide detection, these glove-embedded sensors may also be employed in on-site analysis of other chemical threats and be extended to environmental and water samples.

Automated summary

This study presents a low-cost, portable method for selective and sensitive detection of four classes of pesticides using glove-embedded sensors. The sensors showed high performance in discriminating pesticides in real samples, demonstrating their potential for on-site analysis of chemical threats.