Local partial least squares based on global PLS scores

A local‐based method for near‐infrared spectroscopy predictions, the local partial least squares regression on global PLS scores (LPLS‐S), is proposed in this work and compared with the usual local PLS (LPLS) regression approach. LPLS‐S is based on the idea of replacing the original spectra with a global PLS score matrix before using the usual LPLS. This is done with the aim of increasing the speed of the calculations, which can be an important parameter for online applications in particular, especially when implemented on large databases. In this study, the performance of the two local approaches was compared in terms of efficiency and speed. It could be concluded that the root‐mean‐square error of prediction of LPLS and LPLS‐S were 1.1962 and 1.1602, respectively, but the calculation speed for LPLS‐S was more than 20 times faster than for the LPLS algorithm. The performance of LPLS‐S was compared with usual local PLS regression approach (LPLS) in terms of efficiency and speed. Compared with the LPLS algorithm, the calculation speed of LPLS‐S was greatly improved. The optimum parameters for the LPLS‐S algorithm were easier and more repeatable to be obtained whatever the test set than the LPLS algorithm.