铁皮石斛品质影响因素快速无损检测及光谱特性分析

doi:10.3964/j.issn.1000-0593(2021)10-3276-05

Abstract
References
Related Citation (15)

Download: PDF (2068 KB)
Export: BibTeX | EndNote (RIS)

Abstract With the improvement of people’s living standards, people pay more and more attention to the health care function of Chinese herbal medicine. Dendrobium officinale Kimura et Migo (Dendrobium officinale) is a rare Chinese herbal medicine, known as “life-saving fairy grass”. In this study, we tried to evaluate the quality of Dendrobium officinale by sugar content, pH value and other related physical and chemical characteristics. We selected three different habitats of Dendrobium officinale from Huoshan in Anhui, Yandang Mountain in Zhejiang and Yunnan Province as research objects, extracted the spectral data and physical and chemical parameters of different Dendrobium officinale, and then carried out the inversion of quality indicators. Finally, the correlation model between quality and spectrum was established. In the experiment, the leaves, roots and flowers of Dendrobium were removed first, and then the stems to be studied. The spectral data of three Dendrobium officinale with different quality grades were obtained by the ASD specter. The same samples was ground, put into a centrifugal tube, sealed with methanol solution, and packaged with tin foil paper to make corresponding solutions. The chlorophyll content, sugar content and pH value were measured by spectrophotometer, sugar meter and pH meter. The upper layer, middle layer and lower part of the centrifuge tube were selected for each sample. Each sample was measured 3 times, and the average value was taken as the control sample. The original spectral data were denoised and dimensionally reduced by wavelet transform. The correlation between the energy coefficients (including band and scale) and the physicochemical parameters of the Dendrobium officinale control group was analyzed. The higher energy coefficient in the determination coefficient was selected as the wavelet feature, and the wavelet feature was fitted by the least square method. Using all experimental samples as test set and 70% as verification set, the determination coefficient (R²) of chlorophyll content inversion model was 0.819, 0.820 and 0.865, the root mean square error (RMSE) were 0.035, 0.013 and 0.017, respectively; the determination coefficient (R²) of sugar content inversion model was 0.756, 0.764 and 0.823, respectively. The results showed that the root means square error (RMSE) was 0.025, 0.030 and 0.036 8; the determination coefficient (R²) of the inversion model for pH value was 0.819, 0.820 and 0.865, and the root mean square error (RMSE) was 0.034 5, 0.013 and 0.017, respectively. It can be found that the quality inversion model and determination coefficient (R²) of three kinds of Dendrobium officinale are all greater than 0.80, and the root means square error (RMSE) is less than 0.10. This study proved that the spectral characteristics of chlorophyll, sugar content and pH value in Dendrobium officinale were feasible for quality evaluation.

Key words：Dendrobium officinale Kimura et Migo; Quality; Spectral characteristics; Wavelet transform; Fast and nondestructive detection

Received: 2020-10-12 Accepted: 2021-02-19

ZTFLH:

S237

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)10-3276-05 OR https://www.gpxygpfx.com/EN/Y2021/V41/I10/3276

[1] BAI Gang, HOU Yuan-yuan, DING Guo-yu, et al(白钢, 侯媛媛, 丁国钰, 等). Acta Pharmaceutica Sinica(药学学报), 2019, 54(2): 197.
[2] JIANG Meng-yuan, HU Lin, YANG Qing-song, et al(蒋孟圆, 胡琳, 杨青松, 等). Environmental Chemistry(环境化学), 2018, 37(10): 236.
[3] MA Hong(马红). China Journal of Traditional Chinese Medicine and Pharmacy(中华中医药杂志), 2017, 32(9): 4056.
[4] YANG Bin-bin, LI Ming-cheng, WANG Bin(严斌斌, 李明成, 王彬). General Journal of Stomatology(全科口腔医学电子杂志), 2018, 5(28): 96.
[5] Chinese Pharmacopoeia Commission(中华人民共和国药典委员会). Pharmacopoeia of the People’s Republic of China(中华人民共和国药典).Beijing: China Medical Science Press(北京：中国医药科技出版社), 2010. 245.
[6] TANG Li, LI Jing, LONG Hua, et al(唐丽, 李菁, 龙华, 等). Guangdong Agricultural Sciences(广东农业科学), 2015, 42(8): 17.
[7] WANG Li-xia, LIU Meng-zong, WANG Fang, et al(王丽霞, 刘孟宗, 王芳, 等). China Food Additives(中国食品添加剂), 2019，(2): 85.
[8] GE Ying-hua, WANG Jie, ZHOU Cui, et al(葛颖华, 王杰, 周萃, 等). Chinese Journal of Traditional Medical Science and Technology(中国中医药科技), 2015, 22(5): 527, 602.
[9] YU Meng-meng, WU Ling-shang，SI Jin-ping, et al(于萌萌, 吴令上, 斯金平, 等）. China Journal of Chinese Materia Medica(中国中药杂志), 2016, 41(12): 2208.
[10] Tang C L. Agricultural Science & Technology, 2014, (11): 1928.
[11] YAN Zhen, NIE Ji-yun, CHENG Yang, et al(闫震, 聂继云, 程杨, 等). China Fruits(中国果树), 2018, (2): 59, 72.
[12] ZHANG Yin-tao, GE Hong-li, SUN Zhen, et al(张银涛, 葛宏立, 孙贞, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(6): 1645.
[13] CHEN Zhen-xing, WANG Wan-jun(陈振兴，王万军). Biology Teaching(生物学教育)，2020，45(9)：2.
[14] MA Xiao-shuang, LI Cheng-cheng(马小双, 李程程). Heilongjiang Agricultural Sciences(黑龙江农业科学), 2015, (9): 116.
[15] HU Bing-feng, HUANG Hua-li, JI Yuan-zu, et al(胡秉芬, 黄华梨, 季元祖, 等). Pratacultural Science（草业科学）, 2018, 35(8): 1965.
[16] HE Li-si, SU Jia-le, LIU Xiao-qing, et al(何丽斯，苏家乐，刘晓青，等). Jiangsu Agricultural Sciences(江苏农业科学)，2012，40(11)：190.
[17] WANG Xi-yao(汪希尧). China Agricultural Information（中国农业信息）, 2014, (3): 211.