Issue 24, 2019

Molecular spectroscopic wavelength selection using combined interval partial least squares and correlation coefficient optimization

Abstract

Wavelength selection plays a vital role in employing near-infrared spectroscopy for analyzing samples. Existing wavelength selection algorithms present certain drawbacks that can be mitigated by combining algorithms. In this study, we employed a combination of algorithms to quantitatively analyze corn components using near-infrared spectroscopy data. We combined Savitzky–Golay (SG) preprocessing, the correlation coefficient (CC) method, and synergy interval partial least squares (siPLS) algorithms to propose CC-SiPLS and CC-SG-SiPLS methods. The results of applying full-spectrum partial least squares (PLS), correlation coefficient partial least squares (CC-PLS), synergy interval partial least squares (SiPLS), CC-SiPLS, and CC-SG-SiPLS methods to the near-infrared spectral wavelength selection were compared. The results showed that the mathematical models established from the spectral data after wavelength selection using CC, SiPLS, CC-SiPLS, and CC-SG-SiPLS were simplified, and the numbers of wavelengths were 33.6% (CC) and 14.3% (SiPLS), 11.1% (CC-SiPLS), and 6.3% (CC-SG-SiPLS) of that using the full spectrum. The accuracy of predicting the oil content of corn was improved compared to PLS. The CC-SG-SIPLS wavelength selection algorithm combined with the preprocessing method reduced the number of wavelengths from 700 to 44 and the model complexity was the most simplified. The root mean square error in prediction (RMSEP) and relative percent deviation (RPD) were 0.0552 and 2.5706, respectively, demonstrating adequate prediction accuracy. This result indicates that a combination strategy provides an effective way for multiple waveband selection, and that CC-SG-SiPLS can provide high analysis accuracy using molecular absorption bands composed of several wavelength intervals. Thus, this algorithm is an effective and robust wavelength selection strategy.

Graphical abstract: Molecular spectroscopic wavelength selection using combined interval partial least squares and correlation coefficient optimization

Article information

Article type
Paper
Submitted
29 Apr 2019
Accepted
15 May 2019
First published
16 May 2019

Anal. Methods, 2019,11, 3108-3116

Molecular spectroscopic wavelength selection using combined interval partial least squares and correlation coefficient optimization

W. Jiang, C. Lu, Y. Zhang, W. Ju, J. Wang and M. Xiao, Anal. Methods, 2019, 11, 3108 DOI: 10.1039/C9AY00898E

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements