Calibration transfer via an extreme learning machine auto-encoder. Issue 6 (5th February 2016)

• Record Type: Journal Article
• Authors: Chen, Wo-Ruo; Bin, Jun; Lu, Hong-Mei; Zhang, Zhi-Min; Liang, Yi-Zeng
• Is Part Of: Analyst. Volume 141:Issue 6(2016)
• Page Start: 1973
• Page End: 1980
• Publication Date: 2016-02-05
• Subjects: Chemistry, Analytic -- Periodicals; 543
• DOI: 10.1039/c5an02243f
• Languages: English
• ISSNs: 0003-2654

Abstract

Abstract : A novel spectra standardization algorithm titled Transfer via Extreme learning machine Auto-encoder Method (TEAM) has been proposed. Compared with commonly used methods like PDS, GLS and CCA, TEAM is more stable and can significantly reduce prediction errors. 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 ; Web:https://github.com/zmzhang/TEAM .