What do you do if your biosensor is not detecting toxins in food as well as you'd like?

Achieving perfect calibration is a constant pursuit. We should rely on a meticulous dance of buffers, standard solutions, and reference electrodes. Standard solutions with known target molecule concentrations are serially diluted to create a calibration curve, translating the sensor's signal into actual concentrations. Multiple points, specificity checks, and repeatability ensure the curve's accuracy. Regular recalibration and advanced techniques like EIS and surface characterization further refine performance, ensuring the sensor remains a reliable tool for gathering trustworthy scientific data.

A biosensor's capacity to identify toxins in food can be enhanced by assessing its design, optimising detection parameters, calibrating and standardising the device, validating results using contaminated or spiked samples, and enhancing sensitivity. Through methodical assessment and optimisation, the biosensor's performance in food safety applications can be greatly enhanced, guaranteeing accurate and reliable toxin detection.

If biosensor is not detecting toxins in food as expected, I’ll begin by recalibrating the equipment to ensure accurate readings. Verify that all components of the biosensor are properly maintained and functioning optimally. Double-check the sample preparation process to rule out any potential sources of interference or contamination. If the issues persist, I’ll consider consulting with experts in biosensor technology for further troubleshooting and potentially explore alternative detection methods or equipment.

Specificity is key - it should only bind the target molecule. Techniques like phage display for antibodies or aptamer design can achieve this. Sensitivity is also crucial - tiny amounts need detection. Amplification strategies or surface engineering can boost this. Stability and reusability are important too. Immobilization techniques or choosing stable enzymes ensure the element functions for longer. By optimizing these aspects, scientists create highly specific, sensitive, and reusable bio-recognition elements, leading to powerful biosensors.

When addressing issues with the bio-recognition element in a biosensor, ensure proper preparation and attachment, verify compatibility with target toxins, and assess activity retention. Optimize immobilization conditions if necessary and consider alternative bio-recognition elements for enhanced sensitivity. Conduct thorough quality control checks and make adjustments as needed to improve detection capabilities.

This is likely the last resort in an industrial setting, given the large investment capital required to execute reoptimization. Good for academic research.

I disagree with the positioning of this step if this is a priority list. This should be step 2, after result validation. Important considerations are 1) high molarity-low affinity interfering agents such as commonly found lipids and carbohydrates and 2) low molarity-high affinity reagents. The first category may be dealt with by for example, passing of diluted sample repeatedly over the sensor matrix. The second category of agents could be overcome by adding a blocking agent or increasing the amount of sensor element. The solution is likely much more complex and this is merely a recommended set of starting points for finding that.

If this is a priority list, then I disagree with it. This is step one and should include a reasonably diverse range of testing conditions. The testing conditions in general, should focus on key physical and chemical characteristics that vary among foods.

As biosensor technology evolves, so too does the software that supports its operation. Keeping this software up to date is crucial for maintaining functionality. We experienced a situation where outdated firmware was limiting the sensitivity of a biosensor designed to detect lead in drinking water. After updating the firmware, immediate improvements were seen. At a food safety laboratory, new biosensor was introduced for detecting pesticides in produce. Validation tests were undertaken comparing its readings with those obtained from high-performance liquid chromatography (HPLC). The validation helped understand the biosensor’s limits and capabilities, leading to adjustments in its use for specific types of produce.