title: [Effects of the accuracy of reference data on NIR prediction results].
abstract: Reference data are indispensable to build near-infrared spectroscopy (NIR)calibration models. In the present paper, the effects of the accuracy of reference data on NIR calibration models and its prediction results were studied through two routine applications based on partial least square regression methods. The results indicate that the best NIR calibration statistics and the most accurate prediction results were aligned with the most accurate reference data. However, based on statistical analysis of numerous calibration samples, it is possible for NIR calibration models to obtain more accurate prediction results than the laboratory reference data used in the calibration sets. It is better to make less search for high accurate reference data and instead to introduce more calibration samples to improve the ruggedness of the calibration models.
authors.affiliation: Research Institute of Petroleum Processing, Beijing 100083, China.
authors.forename: Xiao-li
authors.initials: XL
authors.lastname: Chu
authors.forename: Hong-fu
authors.initials: HF
authors.lastname: Yuan
authors.forename: Wan-zhen
authors.initials: WZ
authors.lastname: Lu
mesh_terms: D001554:Benzene / Q000032:analysis / Q000592:standards; D002138:Calibration; D003198:Computer Simulation; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D012015:Reference Standards; D012044:Regression Analysis; D015203:Reproducibility of Results; D012996:Solutions / Q000737:chemistry; D019265:Spectroscopy, Near-Infrared / Q000379:methods / Q000592:standards*
chemical_list: D012996:Solutions; D001554:Benzene