Pentagonal hollow core photonic crystal fiber: Highly sensitive detection of sorbitol and butyl acetate using machine learning

Food safety monitoring requires highly sensitive and low-loss detection techniques for identifying hazardous food additives. In this study, a pentagonal hollow-core photonic crystal fiber (HC-PCF) sensor is introduced to detect two widely used but potentially harmful food additives, Sorbitol, and Butyl Acetate, in the terahertz (THz) regime. The objective of this work is to develop a PCF structure that ensures strong light analyte interaction, ultra-low loss, and high detection accuracy. The sensor was numerically modeled using COMSOL Multiphysics 6.1 based on the Finite Element Method (FEM) to evaluate key optical parameters such as relative sensitivity (RS), confinement loss (CL), effective material loss (EML), numerical aperture (NA), effective area (EA), and spot size. In addition, a Random Forest Regressor (RFR) machine learning model was employed to predict sensor performance and validate the simulation results. The optimized design, operating at 2.4 THz, achieved maximum RS values of 95.98% for Butyl Acetate and 95.07% for Sorbitol, along with ultra-low CL of 1.18 × 10⁻¹³ dB/m and 3.84 × 10⁻¹⁴ dB/m, respectively. The corresponding EML values were 0.0073 cm⁻¹ and 0.0083 cm⁻¹. The RFR model yielded a high R² score of 0.9935, confirming its predictive reliability and consistency with FEM results. These results demonstrate the effectiveness of the proposed sensor for accurate and reliable food additive detection.