Abstract
For regression analysis, the calculation of the PRESS (PRediction Error Sum of Squares) statistic has become a popular method for the determination of the number of regressors to use. This paper presents a new PRESS algorithm for use with partial least squares (PLS), which calculates the PRESS statistic more efficiently than repeated use of a fast PLS regression model. As a case study, this fast PLS/PRESS approach is applied to the calibration of an NIR soft sensor to monitor the density of the product of a pilot scale food extruder.
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