Hybrid machine learning driven optimization of multilayer SPR sensor for high sensitivity milk fat detection.

Accurate determination of fat content in milk is crucial for ensuring the quality, nutritional value, and economic grading of dairy products. In this study, a highly sensitive multilayer surface plasmon resonance biosensor is proposed for fat content detection in milk samples using the angular interrogation method at a fixed wavelength of 633 nm. The proposed sensor is designed based on a Kretschmann configuration and includes a SiO2 prism and optimized multilayer films of MgO, Ag, BaTiO3, and BP. The combination of high refractive index dielectric materials like MgO and BaTiO3 with ultrathin BP plays a vital role in enhancing the performance of SPR biosensors. To ensure accuracy in the SPR biosensor design, the performance of the SPR biosensor has been rigorously analyzed using the transfer matrix method, finite element method, and finite difference time-domain method. Moreover, the parameter optimization process is performed by a hybrid technique that uses the brute-force method, ML, and refinement processes, making it possible to determine the optimal thickness of the layers for the highest sensitivity. The proposed sensor has a refractive index range of 1.345 to 1.3621, which relates to the fat content in the milk product. When the refractive index of the sensor was set at 1.3621, the sensitivity was found to be high at 401.40 deg/RIU, the minimum reflectance was 0.086, the quality factor was 148.33 RIU-1, and the accuracy was 0.369 deg-1. This study has made a promising numerical SPR sensing technique in the development of conventional SPR biosensors and has the potential to be used in the food industry for the monitoring of milk quality and adulteration.