Nondestructive Determination and Visualization of Quality Attributes in Fresh and Dry Chrysanthemum morifolium Using Near-Infrared Hyperspectral Imaging
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sample Preparation
2.2. Hyperspectral Image Acquisition
2.2.1. Hyperspectral Imaging System
2.2.2. Spectra Extraction and Preprocessing
2.3. Chemical Compositions Measurement
2.3.1. Sample Preparation
2.3.2. Preparation of the Standard Solution
2.3.3. HPLC Operating Conditions
2.3.4. Method Validation and Quantitative Analysis
2.4. Multivariate Analysis
2.4.1. Calibration Models
PLS
ELM
LS-SVM
2.4.2. Optimal Wavelength Selection
- (1)
- Manually define range of the number of variables to be selected.
- (2)
- Randomly select a variable and calculate the projection of this variable on the other variables.
- (3)
- Select the variable with the largest projections into the candidate subset, then the corresponding variable for projection is used for projecting on the residual variables.
- (4)
- Repeat steps (2) and (3) until the number of variables in the candidate subset is equal to the maximum number.
- (5)
- Build multiple linear regression (MLR) models using different numbers of variables in the subset, and the variables corresponding to the model with the minimum RMSE are selected as optimal variables.
2.4.3. Model Evaluation and Software
2.5. Visualization of Chemical Compositions
3. Results and Discussion
3.1. Spectral Profiles
3.2. Outlier Detection and Sample Set Split
3.3. Calibration Models Using Full Spectra
3.4. Optimal Wavelength Selection
3.5. Calibration Models Using Optimal Wavelengths
3.6. Visualization of Chlorogenic Acid, Luteolin-7-O-glucoside, and 3,5-O-Dicaffeoylquinic Acid in Chrysanthemum morifolium
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Sample Status | Compositions | Calibration | Prediction | ||||
---|---|---|---|---|---|---|---|
Range | Mean | SD | Range | Mean | SD a | ||
Fresh | chlorogenic acid | 0.33–0.59 | 0.48 | 0.067 | 0.34–0.58 | 0.48 | 0.067 |
luteolin-7-O-glucoside | 0.21–0.42 | 0.32 | 0.046 | 0.22–0.40 | 0.32 | 0.046 | |
3,5-O-dicaffeoylquinic acid | 0.79–1.29 | 1.07 | 0.13 | 0.81–1.29 | 1.07 | 0.13 | |
Dry | chlorogenic acid | 0.33–0.61 | 0.48 | 0.067 | 0.34–0.60 | 0.48 | 0.067 |
luteolin-7-O-glucoside | 0.22–0.42 | 0.33 | 0.046 | 0.23–0.41 | 0.33 | 0.046 | |
3,5-O-dicaffeoylquinic acid | 0.79–1.29 | 1.07 | 0.13 | 0.79–1.27 | 1.07 | 0.13 |
Compositions | Models | Parameters a | Calibration | Prediction | |||||
---|---|---|---|---|---|---|---|---|---|
R2c | RMSEC | Biasc | R2p | RMSEP | RPD | Biasp | |||
chlorogenic acid | PLS | 8 | 0.90 ** | 0.021 | 1.04 × 10−7 | 0.87 ** | 0.024 | 2.79 | 0.0024 |
ELM | 19 | 0.91 ** | 0.020 | 3.03 × 10−10 | 0.88 ** | 0.023 | 2.91 | 1.54 × 10−4 | |
LS-SVM | 38.7638, 184.1695 | 0.91 ** | 0.020 | −8.10 × 10−16 | 0.87 ** | 0.024 | 2.79 | 2.58 × 10−4 | |
luteolin-7-O-glucoside | PLS | 7 | 0.82 ** | 0.020 | 2.63 × 10−7 | 0.82 ** | 0.019 | 2.42 | 4.69 × 10−4 |
ELM | 22 | 0.86 ** | 0.018 | 6.71 × 10−12 | 0.82 ** | 0.019 | 2.42 | 0.0017 | |
LS-SVM | 13.19329, 165.0049 | 0.84 ** | 0.019 | 2.88 × 10−16 | 0.79 ** | 0.021 | 2.19 | −0.0039 | |
3,5-O-dicaffeoylquinic acid | PLS | 8 | 0.85 ** | 0.049 | −2.41 × 10−7 | 0.81 ** | 0.058 | 2.24 | 0.00057 |
ELM | 22 | 0.87 ** | 0.047 | −1.89 × 10−10 | 0.82 ** | 0.057 | 2.28 | 0.0084 | |
LS-SVM | 1,063,548.01366, 29,309.77831 | 0.86 ** | 0.048 | 1.23 × 10−11 | 0.82 ** | 0.056 | 2.32 | 0.0016 |
Compositions | Models | Parameters | Calibration | Prediction | |||||
---|---|---|---|---|---|---|---|---|---|
R2c | RMSEC | Biasc | R2p | RMSEP | RPD | Biasp | |||
chlorogenic acid | PLS | 9 | 0.89 ** | 0.022 | −2.25 × 10−7 | 0.85 ** | 0.026 | 2.58 | 0.0027 |
ELM | 17 | 0.90 ** | 0.022 | 1.79 × 10−9 | 0.86 ** | 0.025 | 2.68 | 8.47 × 10−4 | |
LS-SVM | 144.6281, 201.9949 | 0.93 ** | 0.018 | 6.80 × 10−15 | 0.83 ** | 0.029 | 2.31 | 0.0049 | |
luteolin-7-O-glucoside | PLS | 7 | 0.82 ** | 0.019 | 1.35 × 10−7 | 0.77 ** | 0.022 | 2.09 | 0.0046 |
ELM | 31 | 0.86 ** | 0.017 | 5.32 × 10−10 | 0.81 ** | 0.020 | 2.30 | 0.0037 | |
LS-SVM | 2783.4589, 2382.1537 | 0.85 ** | 0.018 | −3.21 × 10−13 | 0.77 ** | 0.023 | 2.00 | 0.0056 | |
3,5-O-dicaffeoylquinic acid | PLS | 9 | 0.84 ** | 0.050 | 1.66 × 10−7 | 0.83 ** | 0.054 | 2.41 | −0.0017 |
ELM | 23 | 0.85 ** | 0.050 | −1.62 × 10−9 | 0.83 ** | 0.053 | 2.45 | −2.07 × 10−4 | |
LS-SVM | 49,789.1255, 9996.34004 | 0.86 ** | 0.049 | 2.02 × 10−11 | 0.83 ** | 0.055 | 2.36 | 7.07 × 10−4 |
Sample Status | Compositions | Number | Wavelength (nm) |
---|---|---|---|
Fresh | chlorogenic acid | 8 | 1463, 1082, 1419, 1615, 1399, 1005, 1164, 1325 |
luteolin-7-O-glucoside | 7 | 1025, 1082, 992, 1429, 1646, 1281, 1406 | |
3,5-O-dicaffeoylquinic acid | 8 | 1046, 1126, 1005, 1436, 1615, 975, 1164, 1288 | |
Dry | chlorogenic acid | 8 | 1470, 1076, 1419, 1315, 988, 1396, 1227, 1646 |
luteolin-7-O-glucoside | 5 | 1072, 1612, 1419, 1318, 1646 | |
3,5-O-dicaffeoylquinic acid | 10 | 1126, 1180, 1029, 1210, 1227, 1463, 975, 995, 1646, 1389 |
Compositions | Models | Parameters | Calibration | Prediction | |||||
---|---|---|---|---|---|---|---|---|---|
R2c | RMSEC | Biasc | R2p | RMSEP | RPD | Biasp | |||
chlorogenic acid | PLS | 7 | 0.90 ** | 0.021 | 1.87 × 10−6 | 0.88 ** | 0.023 | 2.91 | 0.0018 |
ELM | 10 | 0.91 ** | 0.020 | −6.50 × 10−10 | 0.87 ** | 0.024 | 2.79 | 0.0023 | |
LS-SVM | 8.6896, 6.2569 | 0.91 ** | 0.020 | 2.80 × 10−16 | 0.87 ** | 0.024 | 2.79 | −8.77 × 10−5 | |
luteolin-7-O-glucoside | PLS | 7 | 0.83 ** | 0.019 | 2.61 × 10−6 | 0.80 ** | 0.020 | 2.3 | 0.0012 |
ELM | 18 | 0.85 ** | 0.018 | −9.42 × 10−7 | 0.82 ** | 0.019 | 2.42 | −0.0014 | |
LS-SVM | 4.9733, 0.50549 | 0.87 ** | 0.017 | −1.18 × 10−16 | 0.81 ** | 0.020 | 2.3 | −0.0019 | |
3,5-O-dicaffeoylquinic acid | PLS | 7 | 0.84 ** | 0.051 | −4.57 × 10−7 | 0.80 ** | 0.062 | 2.10 | 0.0039 |
ELM | 19 | 0.87 ** | 0.047 | −2.11 × 10−6 | 0.83 ** | 0.055 | 2.36 | 0.0016 | |
LS-SVM | 3,119,660.6357, 1108.4923983 | 0.86 ** | 0.048 | 6.35 × 10−10 | 0.81 ** | 0.059 | 2.20 | 0.0035 |
Compositions | Models | Parameters | Calibration | Prediction | |||||
---|---|---|---|---|---|---|---|---|---|
R2c | RMSEC | Biasc | R2p | RMSEP | RPD | Biasp | |||
chlorogenic acid | PLS | 8 | 0.89 ** | 0.022 | −7.10 × 10−7 | 0.84 ** | 0.027 | 2.48 | 0.0032 |
ELM | 39 | 0.93 ** | 0.018 | −4.77 × 10−6 | 0.87 ** | 0.025 | 2.68 | 0.0042 | |
LS-SVM | 146.7564, 9.64007 | 0.92 ** | 0.018 | 1.27 × 10−14 | 0.81 ** | 0.030 | 2.23 | 0.0050 | |
luteolin-7-O-glucoside | PLS | 5 | 0.78 ** | 0.021 | 2.30 × 10−7 | 0.68 ** | 0.026 | 1.77 | 0.0030 |
ELM | 18 | 0.83 ** | 0.019 | 1.65 × 10−6 | 0.78 ** | 0.022 | 2.09 | 0.0041 | |
LS-SVM | 16.9896, 0.39888 | 0.90 ** | 0.014 | −1.28 × 10−16 | 0.68 ** | 0.026 | 1.77 | 0.0020 | |
3,5-O-dicaffeoylquinic acid | PLS | 8 | 0.84 ** | 0.051 | −3.02 × 10−6 | 0.83 ** | 0.054 | 2.41 | −0.0050 |
ELM | 20 | 0.85 ** | 0.049 | −1.45 × 10−6 | 0.83 ** | 0.054 | 2.41 | −0.0013 | |
LS-SVM | 2,163,016.0391, 32,811.479235 | 0.84 ** | 0.050 | −7.30 × 10−10 | 0.84 ** | 0.054 | 2.41 | −0.0025 |
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He, J.; Zhu, S.; Chu, B.; Bai, X.; Xiao, Q.; Zhang, C.; Gong, J. Nondestructive Determination and Visualization of Quality Attributes in Fresh and Dry Chrysanthemum morifolium Using Near-Infrared Hyperspectral Imaging. Appl. Sci. 2019, 9, 1959. https://doi.org/10.3390/app9091959
He J, Zhu S, Chu B, Bai X, Xiao Q, Zhang C, Gong J. Nondestructive Determination and Visualization of Quality Attributes in Fresh and Dry Chrysanthemum morifolium Using Near-Infrared Hyperspectral Imaging. Applied Sciences. 2019; 9(9):1959. https://doi.org/10.3390/app9091959
Chicago/Turabian StyleHe, Juan, Susu Zhu, Bingquan Chu, Xiulin Bai, Qinlin Xiao, Chu Zhang, and Jinyan Gong. 2019. "Nondestructive Determination and Visualization of Quality Attributes in Fresh and Dry Chrysanthemum morifolium Using Near-Infrared Hyperspectral Imaging" Applied Sciences 9, no. 9: 1959. https://doi.org/10.3390/app9091959