title: [Prediction of maize stover components with near infrared reflectance spectroscopy].
abstract: The components concentrations in maize stover were analyzed with 67 samples selected from 380 samples of different provinces and varieties in order to serve the biomass utilization of our country. The technique of near infrared reflectance spectroscopy (NIRS) and partial least square (PLS) regression were used to establish the models. The results showed that the calibration models developed by the spectral data pretreatment of the first derivative+Karl Norris derivative filter were the best for ash, hemicellulose, cellulose, Klason lignin, acid unsolvable ash, and water in the spectral region of 4100-7500 cm(-1). The root mean square error of cross validation (RMSECV) for the above six components was 0.991, 1.27, 1.44, 0.599, 0.0903 and 0.547, respectively; the root mean square error of prediction (RMSEP) was 0.7746%, 1.8072%, 0.2569%, 2.5819%, 0.5158% and 1.0325%, respectively. The models can be used to measure various samples in biomass transformation industry.
authors.affiliation: State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100080, China.
authors.forename: Li-ying
authors.initials: LY
authors.lastname: Liu
authors.forename: Hong-zhang
authors.initials: HZ
authors.lastname: Chen
mesh_terms: D002138:Calibration; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D003313:Zea mays / Q000033:anatomy & histology / Q000737:chemistry*