Impact of sample data repeatability on NIR calibration model
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. Method: Yinhuang decoction was used as the subject, and x\IR 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. Result: 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. Conclusion: Averaging of multiple measurements can dramatically improve the predictive ability of the model and make the model more stable.
near-infrared spectroscopy (NIRs)partial least square (PLS)repetitive samples
NETL
NSTL
万方数据
维普
