Simultaneous quantification of active constituents and antioxidant capability of green tea using NIR spectroscopy coupled with swarm intelligence algorithm.

A simple, rapid and low-cost analytical method was employed for simultaneous determination of bioactive constituents and antioxidant capability of green tea. The strategy was based on swarm intelligence algorithms with partial least squares (PLS) such as simulated annealing PLS (SA-PLS), ant colony optimization PLS (ACO-PLS), genetic algorithm PLS (GA-PLS), and synergy interval PLS (Si-PLS) coupled with Near-infrared (NIR) spectroscopy. These algorithms were independently applied to select informative spectral variables and improve the prediction of green tea components. Results showed that NIR combined with SA-PLS and Si-PLS had a strong correlation coefficient with the wet-chemical methods for predicting epigallocatechin gallate (R p 2 = 0.97); epigallocatechin (R p 2 = 0.97); epicatechin gallate (R p 2 = 0.96); epicatechin (R p 2 = 0.91); catechin (R p 2 = 0.98); caffeine (R p 2 = 0.96); theanine (R p 2 = 0.93); and antioxidant capability (R p 2 = 0.80) in green tea. Our results revealed the potential utilization of NIR spectroscopy coupled with SA-PLS and Si-PLS algorithms as an effective and robust technique to simultaneously predict active constituents and antioxidant capability of green tea. • Swarm intelligence algorithms were applied to predict green tea constituents. • Tea bioactive compounds and antioxidant capability models were improved. • PLS, enhanced by Si, GA, SA and ACO proved feasible to quantify tea constituents. • NIR coupled with SA-PLS and Si-PLS exhibited best predictive ability. [ABSTRACT FROM AUTHOR]