Issue 6, 2016
From the journal:

Analyst

Calibration transfer via an extreme learning machine auto-encoder†

Wo-Ruo Chen,a   Jun Bin,b   Hong-Mei Lu,a   Zhi-Min Zhang*a  and  Yi-Zeng Liang*a  

* Corresponding authors

a College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China

b College of Bioscience and Biotechnology, Hunan Agriculture University, Changsha 410128, China
E-mail: zmzhang@csu.edu.cn, yizeng_liang@263.net

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.

Graphical abstract: Calibration transfer via an extreme learning machine auto-encoder

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Article information

DOI
https://doi.org/10.1039/C5AN02243F
Article type
Paper
Submitted
29 Oct 2015
Accepted
26 Jan 2016
First published
27 Jan 2016

Analyst, 2016,141, 1973-1980

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Calibration transfer via an extreme learning machine auto-encoder

W. Chen, J. Bin, H. Lu, Z. Zhang and Y. Liang, Analyst, 2016, 141, 1973 DOI: 10.1039/C5AN02243F

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