Identification of informative spectral ranges for predicting major chemical constituents in corn using NIR spectroscopy

Many studies have been conducted using NIR spectroscopy to predict corn constituents; however, a systematic investigation of the spectral sub-regions under the scope of overtones and combinations has not been performed. In this study, the corn spectra were divided into second overtones (1100–1388 nm), first overtones (1390–1852 nm), and combinations (1852–2498 nm). Then, using variable importance in projection and genetic algorithm, each region was inspected sequentially to identify the most informative sub-region for each attribute to improve interpretability. The identified spectral subsets were further tuned to select the most influential bands for each attribute. The sub-regions in combinations bands was most informative for predicting water (1908–2108 nm, 2 bands), oil (2176–2304 nm, 6 bands), and protein (2130–2190 nm, 3 bands), whereas the first overtones region was the best for predicting starch (1452–1770 nm, 5 bands). Results provided valuable information for potential hardware and software improvements.