A lab-on-a-tube biosensor for automatic detection of foodborne bacteria using rotated Halbach magnetic separation and Raspberry Pi imaging

A lab-on-a-tube biosensor was established to rapidly, sensitively and automatically detect foodborne bacteria through a rotatable Halbach magnet to form and rotate magnetic nanobead (MNB) chains for specific isolation of target bacteria, gold@platinum nanocatalysts (Au@PtNCs) to label target bacteria for efficient amplification of detection signal and Raspberry Pi App to collect and analyze the image of catalysate. First, the glass tube was successively preloaded with the mixture of MNBs, sample and Au@PtNCs, the washing buffer (skim milk) and the substrate (hydrogen peroxide-3,30,5,50-tetramethylbenzidine), and they were separated by air gaps. After the tube was placed on the biosensor, the MNB chains were stably formed and continuously rotated using the Halbach magnet and the mixture was moved back and forth using a programmable peristaltic pump, thus making the formation of MNB-bacteria-Au@PtNCs complexes. After the washing buffer was moved to wash the complexes, the substrate was then moved to resuspend the complexes, resulting in the catalytic reaction that changed the color of the substrate. Finally, the catalysate was moved to the designated area, the image of which was analyzed by the Raspberry Pi App to quantitatively determine the concentration of bacteria in the samples. This biosensor was able to detect Salmonella in spiked chicken samples in 1 h with lower detection limit of 8 CFU/50 mu L and a recovery from 88.96% to 99.74%. This biosensor based on a single tube is very promising to automatically detect foodborne bacteria due to its low cost, high integration and simple operation.

Automated summary

A lab-on-a-tube biosensor was developed for the rapid and sensitive detection of foodborne bacteria. It utilizes a Halbach magnet to rotate magnetic nanobead chains for specific isolation of target bacteria, gold@platinum nanocatalysts for amplification of detection signal, and a Raspberry Pi App for image analysis. The biosensor showed promise in automatically detecting Salmonella in chicken samples, with a detection limit of 8 CFU/50 μl and high recovery rates.