Rapid identification of geographical origins and determination of polysaccharides contents in Ganoderma lucidum based on near infrared spectroscopy and chemometrics.
10.19540/j.cnki.cjcmm.20180514.002
- Author:
LAI CJS
1
;
Rong-Rong ZHOU
2
;
Yi YU
3
;
Wen ZENG
1
;
Ming-Hua HU
3
;
Luo-di FAN
3
;
Lin CHEN
2
;
Zi-Dong QIU
1
;
Chuan SONG
1
;
Shui-Han ZHANG
2
;
Lan-Ping GUO
1
;
Lu-Qi HUANG
1
Author Information
1. State Key Laboratory of Dao-di Herbs， National Resource Center for Chinese Materia Medica， China Academy of Chinese Medical Sciences， Beijing 100700， China.
2. Institute of Chinese Materia Medica, Hunan Academy of Chinese Medicine, Changsha 410013, China.
3. Infinitus (China) Company Ltd., Guangzhou 510663， China.
- Publication Type:Journal Article
- Keywords:
Ganoderma lucidum;
near infrared spectroscopy;
partial least square regression;
polysaccharides contents;
random forest
- MeSH:
Fungal Polysaccharides;
analysis;
Geography;
Least-Squares Analysis;
Reishi;
chemistry;
Spectroscopy, Near-Infrared
- From:
China Journal of Chinese Materia Medica
2018;43(16):3243-3248
- CountryChina
- Language:Chinese
-
Abstract:
Near infrared spectroscopy combined with chemometrics methods was used to distinguish Ganoderma lucidum samples collected from different origins, and a prediction model was established for rapid determine polysaccharides contents in these samples. The classification accuracy for training dataset was 96.87%, while for independent dataset was 93.33%; as for the prediction model, 5-fold cross-validation was used to optimize the parameters, and different signal processing methods were also optimized to improve the prediction ability of the model. The best square of correlation coefficients for training dataset was 0.965 4, and 0.851 6 for validation dataset; while the root-mean-square deviation values for training dataset and validation dataset were 0.018 5 and 0.023 6, respectively. These results showed that combining near infrared spectroscopy with suitable chemometrics approaches could accuracy distinguish different origins of G. lucidum samples; the established prediction model could precious predict polysaccharides contents, the proposed method can help determine the activity compounds and quality evaluation of G. lucidum.
