Detection of Lead Chrome Green in Tea Based on Near-Infrared Reflectance Spectroscopy

Abstract

Tea color is a part of tea quality, and illegal addition of lead chrome green (LCG) to improve tea quality cannot be identified by human eyes. This paper is based on near-infrared (NIR) reflectance spectroscopy to detect LCG stained tea and to investigate the feasibility of qualitative and quantitative methods. Firstly, the LCG in tea was qualitatively analyzed by partial least squares discriminant analysis (PLS-DA), random forest (RF), and least squares support vector machine (LSSVM) classification models, and the results showed that the classification accuracy of LSSVM reached 100%. For quantitative analysis, Savitzky–Golay convolutional smoothing (S-G) preprocessing combined with three feature extraction algorithms, namely, joint competitive adaptive weighted sampling (CARS), uninformative variable elimination (UVE), and successive projection algorithm (SPA), were used to build partial least squares (PLS), RF, and LSSVM regression models sequentially on the preprocessed data. The S-G-UVE-LSSVM showed the best regression prediction ability in detecting LCG in tea, with a tested R² of 0.96. These results show the feasibility of NIR spectroscopy for the detection of added LCG in tea.