Journal article
Longitudinal Study Comparing Orthogonal Signal Correction Algorithms Coupled with Partial Least-Squares for Quantitative Near-Infrared Spectroscopy
Analytical letters, Vol.55(3), pp.449-466
02/11/2022
DOI: 10.1080/00032719.2021.1939362
Abstract
Several orthogonal signal correction (OSC) methods coupled with partial least-squares (PLS) were applied to near-infrared spectra in a longitudinal study for the prediction of glucose, urea, and triacetin in liquid samples. The OSC methods included Wold's OSC, Fearn's OSC, orthogonal projection to latent structures (OPLS), and direct orthogonal signal correction (DOSC). These preprocessing methods are designed to simplify the spectra and remove information that is orthogonal to the analyte. Samples consisted of various concentrations of glucose, urea, and triacetin in the range from 1 to 19 mM. Calibration and prediction data were collected over 1.7 years to test how well each of the OSC methods helped to maintain analyte prediction performance over time. When comparing the OSC methods, it was found that all of the OSC methods improved the predictions in most data sets throughout the study but that DOSC clearly outperformed the others. On the basis of 18 prediction sets collected across 617 days after the collection of the calibration data, the average percent improvement in the standard error of prediction (SEP) when applying DOSC was 4.6%, 83.1%, and 43.0%, respectively, for glucose, urea, and triacetin. These comparisons were made relative to the SEP values obtained with unprocessed spectra.
Details
- Title: Subtitle
- Longitudinal Study Comparing Orthogonal Signal Correction Algorithms Coupled with Partial Least-Squares for Quantitative Near-Infrared Spectroscopy
- Creators
- Austin Gessell - Department of Chemistry, University of IowaGary W Small - Department of Chemistry, University of Iowa
- Resource Type
- Journal article
- Publication Details
- Analytical letters, Vol.55(3), pp.449-466
- Publisher
- Taylor & Francis
- DOI
- 10.1080/00032719.2021.1939362
- ISSN
- 0003-2719
- eISSN
- 1532-236X
- Grant note
- DOI: 10.13039/100014553, name: Samsung Advanced Institute of Technology
- Language
- English
- Date published
- 02/11/2022
- Academic Unit
- Chemistry
- Record Identifier
- 9984216675502771
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