A systematic calibration transfer and quantification method based on principal components extreme learning machine for near-infrared spectroscopy
The comprehensive results guarantee that PCELM-ELM is an accurate and practical method to transfer the NIR spectra of the slave instrument toward a well-established and maintained calibration model without costly and time-consuming recalibration. Results across diverse datasets confirm PCELM-ELM is a promising calibration transfer and quantitative method in NIR application.
Authors
- Jingjing Lv (ORCID: https://orcid.org/0000-0003-0337-449X)
- Kun Xu (ORCID: https://orcid.org/0000-0002-1663-9998)
- Tiejun Yang (ORCID: https://orcid.org/0000-0002-8644-4651)
- Hongchao Ji (ORCID: https://orcid.org/0000-0002-7364-0741)
- Ling‐Ling Gao (ORCID: https://orcid.org/0000-0002-4221-2816)
- Xiaqiong Fan
- Xiaolong Chen (ORCID: https://orcid.org/0000-0001-9042-0284)
- Xuefeng Li (ORCID: https://orcid.org/0000-0002-2413-9790)
- Bo Li (ORCID: https://orcid.org/0000-0002-0439-5666)
- Yun Shao
Institutions
- China Tobacco (CN)
- Henan University of Technology (CN)
- Agricultural Genomics Institute at Shenzhen (CN)
- Henan Academy of Agricultural Sciences (CN)
Publication Details
- Journal
- Analytica Chimica Acta
- Published
- 2025-05-06
- DOI
- https://doi.org/10.1016/j.aca.2025.344151
- Citations
- 2
- Primary Topic
- Machine Learning and ELM
- Type
- article
- Field-Weighted Citation Impact
- 4.96