Characterization of Congou Black Tea by an Electronic Nose with Grey Wolf Optimization (GWO) and Chemometrics

Abstract

The aroma indicator is an important factor in evaluating the quality and grade of black tea and is vital in preventing fraud and reducing financial losses. A rapid method for the characterization of Congou black tea is described using an electronic nose (EN) with grey wolf optimization (GWO) and chemometrics. First, the aroma qualities of the tea were determined by the EN for 700 samples of seven grades and converted into 10 characteristic values. The GWO algorithm was used to optimize the aroma indicators of the EN sensors. Discrimination models based upon the optimized odor sensor features and partial least squares discriminant analysis, k-nearest neighbor (KNN), extreme learning machine, and support vector machine were created to assess the quality of the samples. The results showed that the KNN model constructed with the five odor sensor response values optimized by the GWO algorithm performed best with an accuracy of 97.85%. The results demonstrate that the EN is suitable for the rapid assessment of the quality and grades of Congou black tea products.