Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/39522
Title: Semi-supervised chemometric methodology for improved multivariate calibration of spectrometers.
Authors: Chen, Zheng Yu.
Keywords: DRNTU::Science::Chemistry::Biochemistry::Spectroscopy
Issue Date: 2010
Abstract: In this work, a semi-supervised chemometric methodology is designed to improve the predicted accuracy for the multivariate calibration in spectrometers. The simulation is carried out on MATLAB platform. The available toolbox of partial least-squares regression (PLS), which is a very powerful algorithm designed to build quantitative models, is used extensively in this work. Three algorithms are developed and evaluated based on the given data set. The finalized algorithm, which utilizes the co-training method, is further evaluated with three different data sets. The results of the prediction accuracy improvement are obtained and analyzed. Although the improvement is not significant, the feasibility of the algorithm is still valuable to be discussed. Recommendations on future directions are given to the development of a better algorithm.
URI: http://hdl.handle.net/10356/39522
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCBE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
SCBE74.pdf
  Restricted Access
641.38 kBAdobe PDFView/Open

Page view(s) 5

246
checked on Oct 22, 2020

Download(s) 5

4
checked on Oct 22, 2020

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.