Spectroscopy and Spectral Analysis, Volume. 40, Issue 10, 3254(2020)
Application of Different Smoothing Ensemble CARS Algorithm in Spectral Discrimination of Black Tea Grade
Moving window smoothing ensemble CARS (MWS-ECARS) is a stable algorithm for extracting characteristic variables. Based on the previous studies, two improved MWS-ECARS are proposed to reduce the dimension of black tea spectrum based on different window smoothing algorithms in this paper, and compared with the original MWS-ECARS, the commonly used successive projections algorithm (SPA), the competitive adaptive reweighting algorithm (CARS) and the moving window partial least squares method (MWPLS). A partial least square regression model (PLSR) was established to select the best black tea grade discrimination model. Two improved MWS-ECARS methods are Gaussian filter ECARS (GF-ECARS) and Median filter smoothing ECARS (MF-ECARS), respectively. The CARS algorithm runs n times (n=1 000 in this paper). The wavelength and its corresponding selected frequency are sorted out and different window smoothing algorithms are used to smooth the selection frequency. The window widths are all 3~31, and the window step sizes are all 2. The threshold is set through the selection frequency smoothed by different window widths and smoothing algorithm, and the starting threshold and step size are both 20. Finally, the wavelength whose selection frequency is higher than the threshold is selected and the PLSR model is established. The correlation coefficient of prediction set (
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Li YUAN, Bin SHI, Jian-cheng YU, Tian-yu TANG, Yuan YUAN, Yan-lin TANG. Application of Different Smoothing Ensemble CARS Algorithm in Spectral Discrimination of Black Tea Grade[J]. Spectroscopy and Spectral Analysis, 2020, 40(10): 3254
Category: Research Articles
Received: Aug. 22, 2019
Accepted: --
Published Online: Jun. 18, 2021
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