Spectroscopy and Spectral Analysis, Volume. 42, Issue 7, 2025(2022)
Quantitative Analysis Method of Key Nutrients in Lanzhou Lily Based on NIR and SOM-RBF
Figures & Tables(15)
Fig. 4. SG+Detrend_SPA_PLSR method for the prediction results of protein
Fig. 5. Detrend_SPA_PLSR method for the prediction results of polysaccharide
Fig. 6. SOM neural network structure of protein and polysaccharide
Fig. 8. SOM-RBF prediction algorithm flow chart for protein and polysaccharide[10]
Table 1. Basic statistics of Lanzhou lily samples
View tableView in ArticleTable 1. Basic statistics of Lanzhou lily samples
营养
物质样本集 样本
个数最小值
g/100 g最大值
g/100 g平均值
g/100 g标准
偏差蛋白质 建模集 45 4.93 14.20 9.42 1.86 预测集 14 4.93 12.10 9.61 1.82 多糖 建模集 45 16.78 32.57 21.86 3.16 预测集 14 17.35 29.65 21.64 3.57
Table 2. PLSR modeling results of Lanzhou lily protein using the spectra pretreated by different methods
View tableView in ArticleTable 2. PLSR modeling results of Lanzhou lily protein using the spectra pretreated by different methods
处理参数 预处理方法 Rc RMSEC Rv RMSECV Rp RMSEP 蛋白质 None 0.824 8 0.910 7 0.706 0 1.157 9 0.771 8 1.424 4 SG 0.824 1 0.912 3 0.705 9 1.158 0 0.771 7 1.424 3 Normalize 0.895 3 0.719 6 0.835 5 0.891 8 0.846 8 1.130 5 SNV 0.864 2 0.850 7 0.748 4 1.134 6 0.848 6 0.992 5 MSC 0.801 8 0.920 8 0.666 5 1.163 6 0.759 0 1.212 4 Detrend 0.849 9 0.912 7 0.778 2 1.094 2 0.822 4 1.256 1 OSC 0.996 0 0.166 9 0.870 5 0.917 0 0.346 2 1.890 6 SG+1D 0.965 0 0.461 7 0.672 2 1.307 8 0.778 1 1.181 2 SG+Normalize 0.808 6 1.046 3 0.693 2 1.294 7 0.796 9 1.221 9 SG+SNV 0.749 4 1.067 1 0.599 7 1.306 8 0.814 2 1.138 1 SG+Detrend 0.915 3 0.699 4 0.827 5 0.989 0 0.870 1 1.081 1
Table 3. PLSR modeling results of Lanzhou lily polysaccharide using the spectra pretreated by different methods
View tableView in ArticleTable 3. PLSR modeling results of Lanzhou lily polysaccharide using the spectra pretreated by different methods
处理参数 预处理方法 Rc RMSEC Rv RMSECV Rp RMSEP 多糖 None 0.888 9 1.446 8 0.770 4 2.038 6 0.777 0 2.317 5 SG 0.887 4 1.455 8 0.771 1 2.036 9 0.777 0 2.316 8 Normalize 0.877 8 1.512 5 0.736 2 2.181 8 0.740 7 2.438 6 SNV 0.926 7 1.186 7 0.826 1 1.787 5 0.949 6 1.623 2 MSC 0.941 0 0.919 8 0.833 7 1.511 3 0.901 8 1.648 8 Detrend 0.966 7 0.697 3 0.903 1 1.171 7 0.921 6 1.692 1 OSC 0.999 3 0.117 0 0.966 6 0.809 8 0.835 9 2.131 1 SG+1D 0.953 6 0.941 7 0.518 3 2.696 6 0.452 2 1.241 4 SG+Normalize 0.917 9 1.225 2 0.838 8 1.690 2 0.632 1 2.819 4 SG+SNV 0.946 5 0.877 3 0.850 2 1.438 7 0.914 3 1.611 4 SG+Detrend 0.933 2 1.135 0 0.846 6 1.688 1 0.909 8 1.914 7
Table 4. Comparison of PLSR modeling results based on CARS, SPA and PCA methods
View tableView in ArticleTable 4. Comparison of PLSR modeling results based on CARS, SPA and PCA methods
处理参数 预处理 特征提取法 特征波长数 Rc RMSEC Rv RMSECV Rp RMSEP 蛋白质 SG+Detrend None 831 0.915 3 0.699 4 0.825 7 0.989 0 0.870 1 1.081 1 SG+Detrend CARS 4 0.882 6 0.774 8 0.846 7 0.880 8 0.867 2 1.144 9 SG+Detrend SPA 2 0.884 5 0.722 8 0.853 8 0.807 6 0.810 6 1.195 3 SG+Detrend PCA 3 0.787 5 1.084 1 0.741 5 1.180 4 0.816 4 1.218 3 多糖 Detrend None 831 0.966 7 0.697 3 0.903 1 1.171 7 0.921 6 1.692 1 Detrend CARS 13 0.929 1 1.000 1 0.874 4 1.329 3 0.885 1 1.668 1 Detrend SPA 14 0.967 8 0.632 7 0.920 3 0.988 6 0.810 9 2.094 6 Detrend PCA 3 0.737 2 1.896 2 0.501 5 2.487 7 0.225 3 4.402 6
Table 5. Comparison of modeling results between PLSR and SOM-RBF prediction models
View tableView in ArticleTable 5. Comparison of modeling results between PLSR and SOM-RBF prediction models
营养物质 预测方法 Rp RMSEP 蛋白质 PLSR 0.810 6 1.195 3 SOM-RBF 0.866 6 1.038 5 多糖 PLSR 0.810 9 2.094 6 SOM-RBF 0.868 1 1.799 4
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Xiao-qin LIAN, Qun CHEN, Shen-miao TANG, Jing-zhu WU, Ye-lan WU, Chao GAO. Quantitative Analysis Method of Key Nutrients in Lanzhou Lily Based on NIR and SOM-RBF[J]. Spectroscopy and Spectral Analysis, 2022, 42(7): 2025
Paper Information
Category: Orginal Article
Received: Feb. 8, 2021
Accepted: --
Published Online: Nov. 16, 2022
The Author Email: LIAN Xiao-qin (lianxq@263.net)
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