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- Title
Soybean Saponin Content Detection Based on Spectral and Image Information Combination.
- Authors
Sun, Hongmin; Meng, Xifan; Han, Yingpeng; Li, Xiao; Li, Xiaoming; Li, Yongguang
- Abstract
Soybean saponin is a natural antioxidant and is anti-inflammatory. Hyperspectral analysis technology was applied to detect soybean saponin content rapidly and nondestructively in this paper. Firstly, spectral preprocessing methods were studied, and standard normal variable (SNV) was used to remove noise information. Secondly, a two-step hybrid variable selection approach based on synergy interval partial least squares (SiPLS) and iteratively retains informative variables (IRIV) was proposed to extract characteristic variables. Then, the ensemble learning model was constructed by back propagation neural network (BPNN), deep forest (DF), partial least squares regression (PLSR), and extreme gradient boosting (EXG). Finally, image information was combined into spectral data to improve model accuracy. The prediction coefficient of determination ( R 2 ) of the final model reached 0.9216. It can provide rapid, nondestructive, and accurate detection technology of soybean saponin content. A combination of spectral and image information will provide a new idea for application of hyperspectral.
- Subjects
SPECTRAL imaging; PARTIAL least squares regression; BACK propagation; SOYBEAN
- Publication
Journal of Spectroscopy, 2024, Vol 2024, p1
- ISSN
2314-4920
- Publication type
Article
- DOI
10.1155/2024/7599132