Decade of advancements in light–matter interaction-based optical fiber biosensing: innovations, challenges, and future directions
Optical fiber biosensors (OFBs) have emerged as a versatile and highly sensitive technology, with various implementations such as fiber Bragg gratings, interferometric techniques, and D-shaped fibers, particularly those that leverage surface plasmon resonance effects. Classifying these sensors based on target analytes underscores their adaptability and broad range of applications. Despite significant progress, challenges such as repeatability, multiplexing, and data interpretation remain key barriers to widespread practical adoption. To address these limitations, recent innovations focus on artificial intelligence algorithms for enhanced data processing, as well as novel materials that improve sensitivity and the repeatability of functionalization. The integration of label-free detection, enabled by surface functionalization, further enhances biosensing capabilities. Moreover, the unique combination of small fiber dimensions, low sample-volume detection, and multiplexing capabilities positions OFBs as a promising alternative to existing commercial solutions. As demand for precise, real-time, and multi-analyte sensing grows, OFBs are poised to become a cornerstone technology, particularly in the context of Healthcare 5.0, where personalized and intelligent diagnostics are essential to advancing medical applications. We summarize advances in OFBs over the past decade, highlighting their functionalities and applications across multiple fields, including medical diagnostics, environmental monitoring, and industrial process control. Finally, we explore future directions for this field. Reported detection limits below 1 femtomolar (10−15 mol/L), selectivity ratios exceeding 100:1, and operational stability over 60 days illustrate ongoing progress in overcoming challenges related to sensitivity, specificity, and long-term performance. The integration of the Internet of Things will enable seamless communication with external devices. Innovations in material science can further improve sensitivity and repeatability, allowing probing in low-sample environments and supporting new designs with label-free and multiplexing capabilities. Continued progress in these areas will pave the way for large-scale production and commercialization of OFBs.
Multiplexing technique using photodetector arrays for quasi-distributed intensity variation optical fiber sensors system
We address the problem of simultaneously estimating the water-oil interface and total levels in three-phase separator tanks by applying three machine learning methods: Multilayer Perceptron (MLP), Kolmogorov-Arnold Networks (KAN), and Random Forest (RF). Data was collected from Fiber Bragg Grating-based optical sensors and processed to suppress outliers using the Local Outlier Factor algorithm. Hyperparameters for each model were optimized using Grid Search, and their performance was compared. The trained models were also exposed to scenarios with different levels of noise, and performance was evaluated. The results suggest that KAN performs effectively in predicting liquid levels, achieving a Root Mean Square Error of less than 3 mm and a Mean Absolute Percentage Error below 0.3% in scenarios without noise. Both MLP and KAN exhibit similar accuracy when the noise level is up to 1%. However, the MLP model outperforms KAN in higher noisy scenarios. On the other hand, the RF model shows the least effectiveness overall in noisy environments, though it does maintain a relatively stable maximum error across different noise levels. Therefore, we demonstrate that KAN has advantages in low-noise scenarios compared to conventional MLP and RF models. Conversely, MLP is more effective under higher noise conditions. These findings can aid in the research and development of monitoring systems.