writer：Kaiyi Wang, Xihui Bian*, Meng Zheng, Peng Liu, Ligang Lin, Xiaoyao Tan
keywords：Extreme learning machine, Monte Carlo sampling, Least absolute shrinkage and selection operator, Multivariate calibration, Hemoglobin concentration
source：期刊
specific source：Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2021, 263, 120138
Issue time：2021年

A novel ensemble extreme learning machine (ELM) approach that combines Monte Carlo (MC) sampling and least absolute shrinkage and selection operator (LASSO), named as MC-LASSO-ELM, is proposed to determine hemoglobin concentration of blood. It employs MC sampling to randomly select samples from the training set and LASSO further to choose variables from selected samples to establish plenty of ELM sub-models. The final prediction is obtained by combining the predictions of these sub-models. Combined with near-infrared spectroscopy, MC-LASSO-ELM is used to determine the hemoglobin concentration of blood. Compared with ELM, MC-ELM and LASSO-ELM, MC-LASSO-ELM can obtain the best stability and highest accuracy