Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM

• Published in: Foods Vol. 11; no. 11; p. 1658
• Main Authors: Ding, Yuhan, Yan, Yuli, Li, Jun, Chen, Xu, Jiang, Hui
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 05.06.2022; MDPI
• Subjects: Accuracy; Algorithms; Classification; CLPSO-SVM; Discriminant analysis; Food science; Huangshan Maofeng tea; I.R. radiation; Infrared spectra; Infrared spectroscopy; Kernel functions; Machine learning; Methods; Near infrared radiation; near-infrared spectroscopy; Neural networks; Optimization; Particle swarm optimization; Principal components analysis; Quality management; Spectrum analysis; Support vector machines; Tea; tea quality level; Transmittance; Wavelengths; China; near-infrared spectroscopy; classification; Huangshan Maofeng tea; CLPSO-SVM; tea quality level
• ISSN: 2304-8158; 2304-8158
• DOI: 10.3390/foods11111658

In this paper, we propose a method for classifying tea quality levels based on near-infrared spectroscopy. Firstly, the absorbance spectra of Huangshan Maofeng tea samples were obtained in a wavenumber range of 10,000~4000 cm−1 using near-infrared spectroscopy. The spectral data were then converted to transmittance and smoothed using the Savitzky–Golay (SG) algorithm. The denoised transmittance spectra were dimensionally reduced using principal component analysis (PCA). The characteristic variables obtained using PCA were used as the input variables and the tea level was used as the output to establish a support vector machine (SVM) classification model. The penalty factor c and the kernel function parameter g in the SVM model were optimized using particle swarm optimization (PSO) and comprehensive-learning particle swarm optimization (CLPSO) algorithms. The final experimental results show that the CLPSO-SVM method had the best classification performance, and the classification accuracy reached 99.17%.