title: [Impact of sample data repeatability on NIR calibration model].
abstract: OBJECTIVE
  To investigate the impact of repeated data acquisition on the stability of NIR quantitative calibration model, and make a preliminary analysis on reasons for the impact.
  
  
  METHODS
  Yinhuang decoction was used as the subject, and NIR spectrum samples were collected. By reference to HPLC's determination value, the baicalin quantitative calibration model was established by using recursive least square algorithm to detect cumulative-LVs curve of latent variables. The impact of calibration model caused by repetitive samples was explained in latent variance space.
  
  
  RESULTS
  After averaging the repetitive spectrum samples, quantitative prediction model, which was built by optimal method of spectrum pretreatment, showed the ideal prediction result (RMSECV = 1.824). The area under the cumulative-LVs curve of latent variables was obviously larger than other modeling methods, i. e., this model is more stable.
  
  
  CONCLUSIONS
  Averaging of multiple measurements can dramatically improve the predictive ability of the model and make the model more stable.
authors.affiliation: Research Center of Traditional Chinese Medicine Information Engineering, Beijing University of Chinese Medicine, Beijing 100102, China.
authors.forename: Chenglin
authors.initials: C
authors.lastname: Sui
authors.forename: Zhisheng
authors.initials: Z
authors.lastname: Wu
authors.forename: Zhaozhou
authors.initials: Z
authors.lastname: Lin
authors.forename: Bing
authors.initials: B
authors.lastname: Xu
authors.forename: Min
authors.initials: M
authors.lastname: Du
authors.forename: Xinyuan
authors.initials: X
authors.lastname: Shi
authors.forename: Yanjiang
authors.initials: Y
authors.lastname: Qiao
mesh_terms: D002138:Calibration; D004365:Drugs, Chinese Herbal / Q000737:chemistry; D015233:Models, Statistical; D015203:Reproducibility of Results; D013055:Spectrophotometry, Infrared / Q000379:methods*; D013997:Time Factors
chemical_list: D004365:Drugs, Chinese Herbal; C508895:yin-huang