Optimal partner wavelength combination method with application to near-infrared spectroscopic analysis

Abstract

Using binary linear regression, the optimal partner wavelength of each wavelength is selected in an initial wavelength screening region. On the basis of strategy above, a novel approach for selecting appropriate wavelengths combination, named optimal partner wavelength combination (OPWC) coupled with partial least squares (PLS), is proposed, and was successfully applied for reagent-free near-infrared spectroscopic analysis of organic matter in soil. Moving window PLS (MW-PLS), successive projections algorithm (SPA) and Monte Carlo uninformative variable elimination (MC-UVE), which are well-performed wavelength selection methods, were also conducted for comparison.The OPWC-PLS, MW-PLS, SPA-PLS and MC-UVE-PLS methods selected 14, 210, 63, 199 wavelengths, respectively. The root-mean-square error and correlation coefficients for leave-one-out cross validation were 0.165gkg−1 and 0.967 for OPWC-PLS, 0.163gkg−1 and 0.968 for MW-PLS, 0.198gkg−1 and 0.953 for SPA-PLS, and 0.190gkg−1 and 0.956 for MC-UVE-PLS, respectively. The results indicate that OPWC-PLS and MW-PLS methods were almost the same, and were obvious better than SPA-PLS and MC-UVE-PLS methods. But the OPWC only contained 14 wavelengths, which is a high efficient approach for extracting information wavelengths and mitigating redundant wavelengths. OPWC can be also provided valuable reference for designing small dedicated spectrometers with a high signal-to-noise ratio.OPWC can be programmed determined, which has small amount of calculation and high operating speed, and it is a deterministic search technique whose results are reproducible. We believe that OPWC has such applicability and can be applied to other fields of spectroscopic analysis.