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Characterization of Moisture Absorption Process of Stevia and Rapid Determination of Rebaudioside a Content by Using Near-Infrared Spectroscopy |
GAO Le-le1, ZHONG Liang1, DONG Hai-ling1, LAI Yu-qiang5, LI Lian1,3*, ZANG Heng-chang1, 2, 3, 4* |
1. School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Ji’nan 250012, China
2. National Glycoengineering Research Center, Shandong University, Ji’nan 250012, China
3. Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Ji’nan 250012, China
4. Key Laboratory of Characteristic Biological Resources on the Qinghai-Tibet Plateau, Xining 810008, China
5. Shandong Shengwang Pharmaceutical Co., Ltd., Jining 273100, China
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Abstract As a green and safe food and drug ingredient, stevia has broad application prospects. However, moisture absorption is a major problem it faces, which is also a common problem in most preparations raw materials. Research and analyze the process state of moisture absorption, then proposed targeted solutions have important theoretical significance and application value. Near-infrared spectroscopy analysis technology combined with chemometric methods were used to analyze the moisture absorption process of stevia in this study. On this basis, the External Parameter Orthogonalisation (EPO) algorithm was used to eliminate the influence of sample moisture, and to establish a rapid analysis method for Rebaudioside A (RA) content in stevia. The results showed that at the beginning of the moisture absorption process of stevia, water molecules were rapidly adsorbed on the surface of the stevia powder to form a monomolecular layer; after that, the surface adsorption sites became fewer, the moisture absorption rate became significantly slower, and water molecules would be adsorbed on top of the monomolecular layer at the same time; finally, the overall moisture absorption of stevia reached its saturated state, and the water content remained stable. After revealing the law of moisture absorption, the RA quantitative model was established using the spectrum preprocessed by the EPO algorithm, the root mean square error, coefficient of determination, and predicted relative standard deviation of the external test set of the model were 0.669 5%, 0.957 0 and 4.336 8, respectively. Compared with the model built before EPO treatment, there was a big improvement, indicating that the EPO algorithm could effectively remove the influence of moisture absorption. In this study, near infrared spectroscopy was used for the first time to characterize the water changes during the moisture absorption of stevia, at the same time, the EPO algorithm was used to effectively eliminate the influence of moisture absorption and realize the rapid determination of RA in stevia products, which provides a reference for its further research and use.
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Received: 2020-12-24
Accepted: 2021-03-12
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Corresponding Authors:
LI Lian, ZANG Heng-chang
E-mail: lilian@sdu.edu.cn; zanghcw@126.com
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[1] HE Yu-long, LI Hong-xia(何玉龙,李红侠). Sugar Crops of China(中国糖料), 2017, 39(6): 67.
[2] Sun X, Subedi P, Walsh K B. Postharvest Biology and Technology, 2020, 162: 111117.
[3] Liu Y, Deng C, Lu Y, et al. Geoderma, 2020, 376: 114568.
[4] SUN Dao-kai, FAN Yi-qin(孙道开,范益芹). Journal of Chinese Medicinal Materials(中药材), 2018, 41(3): 677.
[5] WANG Ya-jie, JIA Ai-ling, TANG Cheng-cheng, et al(王雅洁,贾艾玲,汤成成, 等). Lishizhen Medicine and Materia Medica Research(时珍国医国药), 2018, 29(8): 1874.
[6] Minasny B, McBratney A B, Bellon-Maurel V, et al. Geoderma, 2011, 167-68: 118.
[7] PENG Xiang, HU Dan, ZENG Wen-zhi, et al(彭 翔,胡 丹,曾文治, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(11): 167.
[8] ZHANG Shu-dan, GAO Jian-de, SONG Ping, et al(张淑丹,高建德,宋 萍, 等). Chinese Traditional Patent Medicine(中成药), 2020, 42(6): 1406.
[9] DU Jun-chao, CHENG Jian-ming, LIU Rui, et al(杜俊潮,程建明,刘 睿, 等). China Journal of Chinese Materia Medica(中国中药杂志), 2017, 42(2): 285.
[10] Zhou G X, Ge Z H, Dorwart J, et al. Journal of Pharmaceutical Sciences, 2003, 92(5): 1058.
[11] Watanabe A, Morita S, Ozaki Y. Applied Spectroscopy, 2006, 60(9): 1054.
[12] Dong Q, Yu C, Li L, et al. Spectrochim Acta A: Mol. Biomol. Spectrosc., 2019, 222: 117183.
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