A new method for the selection of wavelengths from near infrared spectra using partial least squares(PLS) analysis is presented. The method aims to find wavelengths that produce significant improvements in PLS prediction accuracy over using all wavelengths. The method is based on data splitting and evaluation of the appropriate prediction errors. Analysis of interactance spectra of kiwifruit using three evaluation criteria are compared with the results obtained from full spectrum analysis and with the recently proposed feature selection method. Using the recommended criterion, the method was found to produce models with lower standard errors than the optimum model obtained using the feature selection method 87% of the time. Properties of initiating the search method from starting points selected by three procedures are compared and recommendations are given for selecting the initial wavelengths. The new search method also has a low probability of obtaining significant correlations through chance.
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