Use of mean square prediction error analysis and reproducibility measures to study near infrared calibration equation performance

Monitoring of calibration equation performance is essential if high quality of predicted analytical data is to be sustained. In this paper we outline and illustrate the use of some statistical methods which are well suited fo r postprediction data scrutiny. Mean square prediction error is partitioned into three components, viz, mean bias, systematic bias and random error. R eproducibility measures such as concordance correlation (r(c)), intraclass correlation (r(2)) and correlation between difference and sum (r((X- Y)(X Y))) are also discussed. Other topics discussed include the maximisation o f R-2, type II regression (both variables with error model) and new graphic al displays.