In determination of free amino acid content in tea by FT-NIR spectroscopy

in order to improve its precision and robustness

it attempted to build predictive model by the efficient spectral region selection and PLS algorithm. In this research

synergy interval partial least-squares (siPLS) and genetic algorithm partial least-squares (GA-PLS) were applied to select the efficient spectral regions. The number of PLS components and the number of intervals were optimized according to cross-validation in calibration set. The performance of the final model was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction and calibration sets. Experimental results showed that the two methods were able to produce better prediction models in relation to the full-spectrum model

and the performance of siPLS model was better than GA-PLS. The optimal model was achieved with R= 0.9542 and RMSEP =0.2560 in prediction set. This study demonstrated that NIR spectroscopy with efficient selection wavelength regions algorithm could be used successfully to analyze free amino acid content in tea

and revealed superiority of siPLS algorithm in contrast with other multivariate calibration methods.