Application of characteristic wavelengths selection in determination of pear firmness by near infrared (NIR) spectroscopy

In order to improve the detecting precision and robustness in determination of pear firmness by the FT-NIR spectroscopy, in this research, Synergy interval partial least square coupled with genetic algorithm (siPLS-GA) was used to select the efficient spectral regions and wavelengths in calibrating model. The number of components and the number of variables were implemented by the cross-validation. The performance of the final model was evaluated according to the root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction and calibration sets. The optimal model based on siPLS-GA was obtained with 10 PLS factors, while 4 spectral regions and 96 variables were selected, respectively. The results of final model show that the optimal model can obtain correlation coefficient of 0.9083, and RMSEP of 0.1573 respectively by a prediction set. The research demonstrated that pear firmness could be determined by NIR spectroscopy technique is feasible, and siPLS-GA the superiority in calibrating model.