Journal article
Temperature correction strategy for improving concentration predictions with near-infrared spectra of aqueous-based samples
Analytica chimica acta, Vol.1095, pp.20-29
01/25/2020
DOI: 10.1016/j.aca.2019.09.034
PMID: 31864623
Abstract
Concentration predictions from near-infrared spectra are used across a range of application areas. When aqueous samples are employed, the extreme temperature sensitivity of underlying water absorption bands can lead to significant errors in predicted analyte concentrations, even when efforts are made to control sample temperatures. To address this issue, a temperature-correction procedure was developed on the basis of modeling the systematic error that occurs in predicted concentrations as a function of variation in sample temperature. With this approach, a quantitative calibration model was developed for samples at a fixed temperature. This model was subsequently applied to the spectra of a second set of samples with known analyte concentrations collected under conditions of varying temperature. Using either measured temperatures or those estimated from a spectral temperature prediction model, a least-squares polynomial fit was performed between concentration residuals and temperature. Going forward, for a given sample temperature, the polynomial model was used to estimate the concentration residual at that temperature. The estimated residual was then used to correct the predicted concentration. For spectra collected in the 5000-4000 cm
near-infrared region, this methodology was tested for samples of glucose in buffer and mixture samples of glucose and lactate in buffer over the temperature range of 20.0-40.5 °C.
Details
- Title: Subtitle
- Temperature correction strategy for improving concentration predictions with near-infrared spectra of aqueous-based samples
- Creators
- Chamathca P S Kuda-Malwathumullage - Department of Chemistry, University of Iowa, Iowa City, IA, 52242, USAGary W Small - Department of Chemistry, University of Iowa, Iowa City, IA, 52242, USA. Electronic address: gary-small@uiowa.edu
- Resource Type
- Journal article
- Publication Details
- Analytica chimica acta, Vol.1095, pp.20-29
- DOI
- 10.1016/j.aca.2019.09.034
- PMID
- 31864623
- ISSN
- 0003-2670
- eISSN
- 1873-4324
- Language
- English
- Date published
- 01/25/2020
- Academic Unit
- Chemistry
- Record Identifier
- 9984217459402771
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