文献类型： 外文期刊

第一作者： Luo Zheng-fei

作者： Luo Zheng-fei;Gong Zheng-li;Yang Jian;Gong Zheng-li;Yang Jian;Yang Chong-shan;Yang Chong-shan;Dong Chun-wang

作者机构：

关键词： Black tea; Adding exogenous sucrose; Near-infrared spectroscopy; Non-destructive testing

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.7； 五年影响因子：0.6 ）

ISSN： 1000-0593

年卷期： 2023 年 43 卷 8 期

页码：

收录情况： SCI

摘要： In order to realize the rapid and effective detection of exogenous sucrose content in finished black tea, Fengqing largeleaved species tea was used as a research sample, and a quantitative prediction model for exogenous sucrose content in finished black tea was constructed by using near-infrared spectroscopy. First, near-infrared spectral data were collected during the production of finished black tea samples with different exogenous sucrose contents (0, 250, 500 and 750 g). When processing the data, in order to improve the prediction accuracy of the model, four different preprocessing methods, standard normal transformation (SNV), multivariate scattering correction (MSC), smoothing (Smooth) and centering (Center), were selected to reduce noise and establish partial least squares regression (PLSR) model, according to the effect of the model, the best SNV preprocessing method was selected, the correction set correlation coefficient (R-c) was 0. 907, the prediction set correlation coefficient (R-d) was 0. 826, and the relative percent deviation (RPD) was 1. 75. In order to reduce the impact of redundant information in the spectrum on the model operation speed, the competitive adaptive reweighted sampling (CARS), shuffled frog leaping algorithm(SFLA), variable combination population analysis iteratively retaining informative variables (VCPA-IRIV) and variable iterative space shrinkage algorithm (VISSA) to extract the characteristic wavelengths sensitive to sucrose from the SNV preprocessed spectrum. After the full spectrum and the selected characteristic wavelengths were dimensionally reduced by principal component analysis (PCA), linear PLSR and nonlinear support vector regression (SVR) and random forest (RF) quantitative prediction models were established respectively. The results show that after SNV preprocessing, the performance of the nonlinear SVR and RF models is better than that of the linear PLSR model, among which VCPA-IRIV-SVR is the optimal model, its R-c value is 0. 950, R-p value is 0. 924, and RPD value is 2. 51. The research shows that near-infrared spectroscopy is feasible for the quantitative prediction of sucrose content in black tea processing, which provides a theoretical support for the non-destructive testing of black tea safety and quality.

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