Rapid Non-Destructive Detection Method for Black Tea With Exogenous Sucrose Based on Near-Infrared Spectroscopy
LUO Zheng-fei1, GONG Zheng-li1, 2, YANG Jian1, 2*, YANG Chong-shan2, 3, DONG Chun-wang3*
1. School of Biotechnology and Engineering, West Yunnan Normal University of Science and Technology, Lincang 677000, China
2. School of Engineering and Technology, Southwest University, Chongqing 400715, China
3. Tea Research Institute, Shandong Academy of Agricultural Sciences，Jinan 250100，China
Abstract：In order to realize the rapid and effective detection of exogenous sucrose content in finished black tea, Fengqing large-leaved 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 (Rc) was 0.907, the prediction set correlation coefficient (Rp) 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 Rc value is 0.950, Rp 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.
罗正飞，龚正礼，杨 坚，杨崇山，董春旺. 基于近红外的掺糖红茶快速无损检测方法[J]. 光谱学与光谱分析, 2023, 43(08): 2649-2656.
LUO Zheng-fei, GONG Zheng-li, YANG Jian, YANG Chong-shan, DONG Chun-wang. Rapid Non-Destructive Detection Method for Black Tea With Exogenous Sucrose Based on Near-Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2649-2656.
[1] PAN Ke, FANG Shi-mao, LIU Zhong-ying, et al(潘 科，方仕茂，刘忠英，等). Journal of Fujian Agriculture and Forestry University(Natural Science Edition)[福建农林大学学报(自然科学版)], 2021, 50(4): 490.
[2] Liu Zhongyuan, Yang Chongshan, Luo Xin, et al. Journal of Food Process Engineering, 2021, 44(11): e13853.
[3] Dong Chunwang, Li Jia, Wang Jinjin, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2018, 205: 227.
[4] Li Luqing, Jin Shanshan, Wang Yujie, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2021, 247: 119096.
[5] Yang Chongshan, Zhao Yan, An Ting, et al. LWT- Food Science and Technology, 2021, 141: 110975.
[6] ZHOU Tian-shan, MI Xiao-ling, YU You-ben, et al（周天山，米晓玲，余有本，等）. Journal of Northwest Forestry University（西北林学院学报）, 2016, 31(5): 154.
[7] Liu Z, Zhang R, Yang C, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2022, 271.
[8] Grisanti E, Totska M, Huber S, et al. Journal of Spectroscopy, 2018: 1.
[9] WU Jian-ping, MING Yue-hong, ZENG Rong-shen, et al(吴建平，明跃红，曾融生，等). Acta Seismologica Sinica(地震学报), 2001, (1): 49.
[10] BAO Min-ze, HU Xiu-ting, XIE Yu-ying, et al（包敏泽，胡秀婷，谢玉莹，等). Computer Engineering and Science（计算机工程与科学）, 2020, 42(6): 1127.
[11] Sun Hongmin, Huang Yu, Wang Yan, et al. Journal of Northeast Agricultural University(English Edition), 2020, 27(4): 88.
[12] XIAO Shi-jie, WANG Qiao-hua, LI Chun-fang, et al（肖仕杰，王巧华，李春芳，等). Smart Agriculture[智慧农业（中英文）], 2021, 3(4): 77.
[13] WANG Sheng-lin, YANG Chong-shan, LIU Zhong-yuan, et al（王盛琳，杨崇山，刘中原，等）. Journal of Tea Science（茶叶科学）, 2021, 41(2): 251.
[14] LONG Yan, MA Min-juan, WANG Ying-yun, et al（龙 燕，马敏娟，王英允，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2021, 37(11): 172.
[15] Paymode A S, Malode V B. Artificial Intelligence in Agriculture, 2022 ，6: 23.
[16] Raghavendra A, Guru D S, Rao M K. Artificial Intelligence in Agriculture, 2021, 5: 43.
[17] ZHAO Peng, LI Yue（赵 鹏, 李 悦）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2019, 39(11): 3525.
[18] YIN Xiao-jun, LI Man-chun, ZHAO Si-feng, et al（尹小君, 李满春, 赵思峰, 等）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报）, 2012, 43(2): 175.
[19] Jin S, Su Z, Xu Z, et al. Journal of Northeast Agricultural University (English Edition), 2019, 26(1): 53.
[20] WANG Kai, WANG Ju-xiang, XING Zhi-na, et al（王 凯，王菊香，邢志娜，等）. Application Research of Computers（计算机应用研究）, 2018, 35(10): 3000.
[21] TANG Hai-tao, MENG Xiang-tian, SU Xun-xin, et al（唐海涛，孟祥添，苏循新，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2021, 37(2): 105.
[22] WANG Yuan-zhong, ZHAO Yan-li, ZHANG Ji, et al（王元忠，赵艳丽，张 霁，等）. Food Science（食品科学）, 2016, 37(4): 169.
[23] LONG Yan, MA Min-juan, WANG Ying-yun, et al（龙 燕，马敏娟，王英允，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2021, 37(11): 172.
[24] HUANG Jun-shi, WANG Dong-xin, XIONG Ai-hua, et al（黄俊仕，王冬欣，熊爱华，等）. Acta Agriculturae Universitatis Jiangxiensis（江西农业大学学报）, 2020, 42(6): 1270.
