A Cascaded Vernier Sensing Structure Combined With Ensemble Learning for High-Sensitivity and Large-Range Bovine Serum Albumin Detection

In this work, we establish a Bovine Serum Albumin(BSA) concentration measurement model based on a cascaded Vernier sensing structure combined with ensemble learning algorithm, which combines adaptive feature selection based on random forest (RF), anomaly diagnosis by kernel-ridge regression (KRR), and the lasso regression algorithm (LASSO) to achieve BSA concentration detection with high-sensitivity, large-range and high-accuracy. The refractive index measurement sensitivity can reach 11951.24 nm/RIU, with good repeatability and stability (0.22 nm). The sensitivity of BSA measurement is 2.03 nm (mg/ml). In the detection range of 0~122 mg/ml, its average absolute percentage error is 2.05%, Compared with traditional wavelength demodulation algorithm, its measurement accuracy has been improved 9.29%, and more importantly, its detection range has been expanded by 8 times (16 mg/ml). The method achieves the intelligent detection of BSA concentration. It provides a new intelligent demodulation approach for fiber optic sensing demodulation methods.