Calibration transfer between NIR spectrometers: New proposals and a comparative study. (27th February 2017)

• Record Type: Journal Article
• Authors: Folch‐Fortuny, Abel; Vitale, Raffaele; de Noord, Onno E.; Ferrer, Alberto
• Is Part Of: Journal of chemometrics. Volume 31:Number 3(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-02-27
• Subjects: calibration transfer -- joint‐Y partial least squares regression (JYPLS) -- multivariate calibration -- missing data imputation -- trimmed scores regression (TSR); Chemistry -- Mathematics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; 542.85
• DOI: 10.1002/cem.2874
• Languages: English
• ISSNs: 0886-9383

Abstract

Abstract : Calibration transfer between near‐infrared (NIR) spectrometers is a subtle issue in chemometrics and process industry. In fact, as even very similar instruments may generate strongly different spectral responses, regression models developed on a first NIR system can rarely be used with spectra collected by a second apparatus. In this work, two novel methods to perform calibration transfer between NIR spectrometers are proposed. Both of them permit to exploit the specific relationships between instruments for imputing new unmeasured spectra, which will be then resorted to for building an improved predictive model, suitable for the analysis of future incoming data. Specifically, the two approaches are based on trimmed scores regression and joint‐Y partial least squares regression, respectively. The performance of these novel strategies will be assessed and compared to that of well‐established techniques such as maximum likelihood principal component analysis and piecewise direct standardisation in two real case studies. Abstract : Two novel methods to perform calibration transfer between near‐infrared spectrometers are proposed. Both of them permit to exploit the specific relationships between instruments for imputing new unmeasured spectra, which will be then utilised for building an improved predictive model, suitable for the analysis of future incoming data. Specifically, these approaches are based on trimmed scores regression and joint‐Y partial least squares regression, respectively. The performance of these novel strategies will be assessed and compared to that of well‐established techniques.