title: Calibration transfer via an extreme learning machine auto-encoder.
abstract: In order to solve the spectra standardization problem in near-infrared (NIR) spectroscopy, a Transfer via Extreme learning machine Auto-encoder Method (TEAM) has been proposed in this study. A comparative study among TEAM, piecewise direct standardization (PDS), generalized least squares (GLS) and calibration transfer methods based on canonical correlation analysis (CCA) was conducted, and the performances of these algorithms were benchmarked with three spectral datasets: corn, tobacco and pharmaceutical tablet spectra. The results show that TEAM is a stable method and can significantly reduce prediction errors compared with PDS, GLS and CCA. TEAM can also achieve the best RMSEPs in most cases with a small number of calibration sets. TEAM is implemented in Python language and available as an open source package at https://github.com/zmzhang/TEAM.
authors: College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China.
authors: Wo-Ruo
authors: WR
authors: Chen
authors: College of Bioscience and Biotechnology, Hunan Agriculture University, Changsha 410128, China. zmzhang@csu.edu.cn yizeng_liang@263.net.
authors: Jun
authors: J
authors: Bin
authors: College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China.
authors: Hong-Mei
authors: HM
authors: Lu
authors: College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China.
authors: Zhi-Min
authors: ZM
authors: Zhang
authors: College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China.
authors: Yi-Zeng
authors: YZ
authors: Liang