Application of outlier sample analysis

In order to optimize calibration set and increase prediction accuracy of the calibration model when near infrared spectroscopy was used to develop the model for rice amylose content, 18 abnormal spectrums produced by subjective and objective factors were eliminated based on Mahalanobis distance criterion combined with prediction concentration residual standard. The calibration results showed that the correlation coefficient of calibration model increased from 0.86287 to 0.9350, and root mean square error of calibration reduced from 2.53 to 1.54. The correlation coefficient of cross validation using Leave-One-Out method increased from 0.62785 to 0.86850, and root mean square error of cross validation reduced from 4.05 to 2.18.