Simultaneous determination of five micro-components in Chrysanthemum morifolium (Hangbaiju) using near-infrared hyperspectral imaging coupled with deep learning with wavelength selection

2021 ◽  
Vol 116 ◽  
pp. 103802
Author(s):  
Juan He ◽  
Chu Zhang ◽  
Lei Zhou ◽  
Yong He
Keyword(s):  
Deep Learning ◽  
Simultaneous Determination ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Chrysanthemum Morifolium ◽  
Wavelength Selection

scholarly journals Determination of Total Polysaccharides and Total Flavonoids in Chrysanthemum morifolium Using Near-Infrared Hyperspectral Imaging and Multivariate Analysis

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2395 ◽  
Author(s):  
Juan He ◽  
Lidan Chen ◽  
Bingquan Chu ◽  
Chu Zhang
Keyword(s):  
Least Squares ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Prediction Models ◽  
Accurate Determination ◽  
Chrysanthemum Morifolium ◽  
Support Vector ◽  
Total Flavonoids ◽  
Total Flavonoids Content

The rapid and nondestructive determination of active compositions in Chrysanthemum morifolium (Hangbaiju) is of great value for producers and consumers. Hyperspectral imaging as a rapid and nondestructive technique was used to determine total polysaccharides and total flavonoids content in Chrysanthemum morifolium. Hyperspectral images of different sizes of Chrysanthemum morifolium flowers were acquired. Pixel-wise spectra within all samples were preprocessed by wavelet transform (WT) followed by standard normal variate (SNV). Partial least squares (PLS) and least squares-support vector machine (LS-SVM) were used to build prediction models using sample average spectra calculated by preprocessed pixel-wise spectra. The LS-SVM model performed better than the PLS models, with the determination of the coefficient of calibration (R2c) and prediction (R2p) being over 0.90 and the residual predictive deviation (RPD) being over 3 for total polysaccharides and total flavonoids content prediction. Prediction maps of total polysaccharides and total flavonoids content in Chrysanthemum morifolium flowers were successfully obtained by LS-SVM models, which exhibited the best performances. The overall results showed that hyperspectral imaging was a promising technique for the rapid and accurate determination of active ingredients in Chrysanthemum morifolium, indicating the great potential to develop an online system for the quality determination of Chrysanthemum morifolium.

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