Deep Learning Combined with Hyperspectral Imaging Technology for Variety Discrimination of Fritillaria thunbergii.
Molecules
; 27(18)2022 Sep 16.
Article
in En
| MEDLINE
| ID: mdl-36144775
ABSTRACT
Traditional Chinese herbal medicine (TCHM) plays an essential role in the international pharmaceutical industry due to its rich resources and unique curative properties. The flowers, stems, and leaves of Fritillaria contain a wide range of phytochemical compounds, including flavonoids, essential oils, saponins, and alkaloids, which may be useful for medicinal purposes. Fritillaria thunbergii Miq. Bulbs are commonly used in traditional Chinese medicine as expectorants and antitussives. In this paper, a feasibility study is presented that examines the use of hyperspectral imaging integrated with convolutional neural networks (CNN) to distinguish twelve (12) Fritillaria varieties (n = 360). The performance of support vector machines (SVM) and partial least squares-discriminant analysis (PLS-DA) was compared with that of convolutional neural network (CNN). Principal component analysis (PCA) was used to assess the presence of cluster trends in the spectral data. To optimize the performance of the models, cross-validation was used. Among all the discriminant models, CNN was the most accurate with 98.88%, 88.89% in training and test sets, followed by PLS-DA and SVM with 92.59%, 81.94% and 99.65%, 79.17%, respectively. The results obtained in the present study revealed that application of HSI in conjunction with the deep learning technique can be used for classification of Fritillaria thunbergii varieties rapidly and non-destructively.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Antitussive Agents
/
Saponins
/
Oils, Volatile
/
Drugs, Chinese Herbal
/
Fritillaria
/
Alkaloids
/
Deep Learning
Type of study:
Prognostic_studies
Language:
En
Journal:
Molecules
Journal subject:
BIOLOGIA
Year:
2022
Document type:
Article
Affiliation country:
China