An optimization strategy for waveband selection in FT-NIR quantitative analysis of corn protein

Abstract

An optimization strategy for waveband selection of corn protein was developed based on Fourier transform near infrared (FT-NIR) spectrometry. The optimized moving window partial least squares (OMWPLS) framework was proposed based on different sample partitions for modeling stability. A global-optimal model and some local-optimal models were selected by OMWPLS screening through the full scanning range. The modified optical path length estimation and correction (OPLECm) technique was utilized for further data preprocessing of the OMWPLS-selected wavebands. We finally acquired an optimal and stable model, of which the root mean square error and the correlation coefficients of prediction were 0.413 (%) and 0.939, respectively, and the modeling waveband was 5158–4857 cm−1 with 40 wavenumbers. This selected waveband achieved high accuracy in validation. Moreover, many alternative wavebands with acceptable predictive results were also found. This finding seems valuable and quite practical for the design of a corn-specific NIR instrument. The waveband selection framework confirms the feasibility that FT-NIR quantitative analysis of corn protein can be determined. FT-NIR spectrometry combined with waveband optimization is expected to be an alternative technology for detection of corn chemical components, which is essential for the implementation of chemometric methods in the analysis of corn quality.