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| Effects of Scalable One-pass Self-representation Learning on Near Infrared Spectroscopy Regression Modeling |
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| DOI： |
| KeyWord:near infrared spectroscopy wavelength selection scalable one-pass self-representation learning（SOP－SRL） partial least-squares（PLS） target ingredients determination |
| Author | Institution |
| GUO Tuo，LIANG Xiao-juan，MA Jin-fang，YUAN Kai，GE Fa-huan，XIAO Huan-xian |
1. School of Electrical Information and Artificial Intelligence，Shaanxi University of Science and Technology，Xi'an ，China; 2. Department of Electro－Optical Engineering，Jinan University，Guangzhou ，China; 3. Nansha Research Institute，Sun Yat-sen University，Guangzhou ，China; 4. Jiangxi Poly Pharmaceutical Co. Ltd.，Ganzhou ，China |
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| Abstract: |
| Near-infrared spectroscopy is widely applied in the quality monitoring process of traditional Chinese medicine since it features with rapid detection，and making no damage to the samples and no pollution to the environment in the meantime. In order to realize the rapid prediction of the target ingredients of Antai Pills，a new near-infrared spectroscopy modeling method was proposed，which combines scalable one-pass self-representation learning（SOP－SRL） with partial least-squares（PLS）. Taking ferulicacid，baicalin and wogonoside in Antai pills as the research objects，the representative bands selected by SOP－SRL were compared with three band selection algorithms，such as correlation coefficient method（CC），regularized self-representation algorithm（RSR） and sparse subspace clustering （SSC）. Then，the quantitative model was established by PLS. The evaluation criteria of the model are root mean squares error of cross validation（RMSECV），corrected determination coefficient（R2c），predicted root mean square error（RMSEP） and predicted determination coefficient（R2p）. Results indicated that the SOP－SRL had good results on all three datasets. Compared with all bands，the selected bands of SOP－SRL were reduced from 800（FULL） to 70，67 and 87， respectively. The RMSEP decreased from 0.080 1，6.349 5，0.742 5 to 0.065 3，3.620 8，0.407 3，decreased by 18%，43% and 45%，respectively. The R2p increased from 0.911 9，0.879 4，0.915 8 to 0.938 8，0.952 6，0.970 1，respectively，increased by 3%，8% and 6%. Therefore，the results of the SOP－SRL algorithm were significantly better than other comparison algorithms. The SOP－SRL algorithm could improve the accuracy of quantative model. The model combining SOP－SRL with PLS could rapidly detect the target ingredients of Antai pills. |
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