Optimization and Verification of a Near Infrared Quantitative Model for the Theabrownin in Yinghong No.9 Fermented Leaves
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Abstract:
In order to improve the near-infrared spectroscopy analysis method for realizing quickly the use of the quantitative model for non-destructive and rapid detection of the theabrownin in black tea products, this research used near-infrared spectroscopy to collect, extract and analyze the theabrownin in the fermented leaves of Yinghong No.9 as the example. The construction and optimization of the near-infrared quantitative detection model are performed. Firstly, the original spectra were preprocessed and analyzed by five preprocessing methods: Normalization, baseline correction, S-G first derivative (Savitzky-Golay, 1st S-G), S-G second derivative (2nd S-G) and standard normal variable transform (SNV). Then, the best 1st S-G preprocessing method was used to extract the wavelength features, using the interval partial least squares algorithm (iPLS), competitive adaptive weighting algorithm (CARS) and the variable iterative space shrinkage approach (VISSA), respectively. Finally, the partial least squares regression (PLS) prediction model was used for regression modeling. The results show that the 1st-CARS-PLS model established by using the first-order derivative for preprocessing and the CARS method has more significant effect characteristics, with the number of eigenvalues being 53. The research shows that the model method used in this experiment can rapidly and non-destructively detect the theabrownin content in the fermented leaves of Yinghong No.9.