Real-time monitoring of total polyphenols content in tea using a developed optical sensors system.

A portable and low-cost optical sensors system consisting of hardware and software was developed and used for real-time monitoring total polyphenols content in tea in this work. This developed system was used for data acquisition. Partial least square (PLS) with several variable selection algorithms was used for modeling. Synergy interval partial least square (Si-PLS) was first used to select spectral subintervals of interest, and then competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA) were comparatively employed to select the variables of interest from the subintervals of interest. The optimum model was achieved and stored in the developed software. Next, 20 independent samples were used to test the performance of this system. And the coefficient of variation (CV) of the final results was used to state the stability and reliability of this system. The results also showed that GA-Si-PLS performed better than CARS-Si-PLS model and the CVs for most of the samples were <5%. This study demonstrated this developed optical sensors system as a promising tool that could be used for real-time monitoring tea quality.