Rapid determination of hemoglobin concentration by a novel ensemble extreme learning machine method combined with near-infrared spectroscopy

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Molecular and biomolecular spectroscopy, 2021-12, Vol.263, p.120138, Article 120138
• Main Authors: Wang, Kaiyi, Bian, Xihui, Zheng, Meng, Liu, Peng, Lin, Ligang, Tan, Xiaoyao
• Format: Article
• Language: English
• Subjects: Extreme learning machine; Hemoglobin concentration; Least absolute shrinkage and selection operator; Monte Carlo sampling; Multivariate calibration
• ISSN: 1386-1425
• DOI: 10.1016/j.saa.2021.120138

[Display omitted] •A novel ensemble multivariate calibration method named MC-LASSO-ELM is proposed.•Monte Carlo (MC) sampling is used to select a certain number of samples from the original training set.•LASSO is further used to select variables for the selected samples by MC. 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.