Comparisons of different regressions tools in measurement of antioxidant activity in green tea using near infrared spectroscopy

To rapidly and efficiently measure antioxidant activity (AA) in green tea, near infrared (NIR) spectroscopy was employed with the help of a regression tool in this work. Three different linear and nonlinear regressions tools (i.e. partial least squares (PLS), back propagation artificial neural network (BP-ANN), and support vector machine regression (SVMR)), were systemically studied and compared in developing the model. The model was optimized by a leave-one-out cross-validation, and its performance was tested according to root mean square error of prediction (RMSEP) and correlation coefficient (Rp) in the prediction set. Experimental results showed that the performance of SVMR model was superior to the others, and the optimum results of the SVMR model were achieved as follow: RMSEP = 0.02161 and Rp = 0.9691 in the prediction set. The overall results sufficiently demonstrate that the spectroscopy coupled with the SVMR regression tool has the potential to measure AA in green tea.

► Rapid measurement of antioxidant activity in tea by NIR spectroscopy technique. ► Optimization of spectral preprocessing and PLS factors for PLS model. ► Construction of nonlinear regression models based on the efficient spectral preprocessing. ► Comparing and discussing linear and nonlinear regression tools used in this work.