A highly sensitive optical fiber sensor enables rapid triglycerides-specific detection and measurement at different temperatures using convolutional neural networks

For specific recognition and sensitive detection of triglycerides (TGs), an optical fiber sensor (OFS) based on an enhanced core diameter mismatch was proposed. The sensitivity of the sensor is significantly increased due to the repetitive excitation of the higher-order cladding modes. A technique for immobilizing lipase using covalent binding technology was presented and demonstrated by Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy. The interference dip of the sensor was shifted due to TGs being hydrolyzed in the presence of lipase. The sensor shows an optimal response within 3 min and exhibits a high sensitivity of 0.9933 nm/(mg/ml) and a limit of detection of 0.0822 mg/ml in the concentration range 0-8 mg/ml at a temperature of 37 degrees C and a pH of 7.4. The response of the sensor to TGs concentration at different temperatures and pH was investigated. The reproducibility, reusability, and stability of the proposed sensor were tested and verified experimentally. The biosensor is highly specific for TGs and unaffected by many other interfering substances. Further, the measurement of TGs concentration at different temperatures was realized. This method provides a new way to detect TGs rapidly and reliably and has potential applications in medical research and clinical diagnosis.

Automated summary

This study proposes an optical fiber sensor based on an enhanced core diameter mismatch for specific recognition and sensitive detection of triglycerides. The sensor achieves higher sensitivity through the excitation of higher-order cladding modes. By immobilizing lipase using covalent binding technology, triglycerides can be hydrolyzed, leading to a shift in the interference dip of the sensor. The sensor exhibits high sensitivity and specificity for triglycerides and holds potential applications in medical research and clinical diagnosis.