Variable Selection in Near-Infrared Spectra for Modeling of Hemoglobin Content in Bio-Water Solutions
文献类型：期刊论文
| 作者 | Fang, Renjie1,2; Han, Xin2 ; Li, Xiangxian2; Tong, Jingjing2; Gao, Minguang2; Wang, Yang2
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| 刊名 | JOURNAL OF APPLIED SPECTROSCOPY
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| 出版日期 | 2024-09-01 |
| 卷号 | 91 |
| 关键词 | near-infrared spectroscopy hemoglobin variable selection algorithm water robust model |
| ISSN号 | 0021-9037 |
| DOI | 10.1007/s10812-024-01801-0 |
| 通讯作者 | Han, Xin(xhan@aiofm.ac.cn) |
| 英文摘要 | The background differences in water content of different samples have a very strong influence on the robustness of near-infrared spectroscopy (NIRS). For this reason, this study simulated typical biological water matrix samples with formulated hemoglobin (Hb), glucose (Glc), and distilled water, and attempted to use four different intelligent spectral variable selection algorithms [Competitive Adaptive Reweighted Sampling (CARS), Randomized Frog Hopping Algorithm (RF), Genetic Algorithm (GA), and Variable Projection Importance Algorithm (VIP)] to perform the Hb water interference-resistant feature band preferences, while combining partial least squares (PLS) in parallel to build a robust quantitative model of Hb. In addition, the applicability and validity of the model were validated using three prediction sets P1, P2, P3 with different water backgrounds (the formulation method and composition were kept the same, and only the water content increased sequentially). The results showed that RF, GA, and VIP could effectively screen out the characteristic wavelengths of Hb with low sensitivity to water changes and successfully correct the water effect, but due to the large number of characteristic variables they screened out and the existence of a large number of redundant and water interference variables, this ultimately made the model's robustness less than ideal. The CARS algorithm performed the best, and the RMSEP of the three prediction sets were 0.016, 0.017, and 0.038, which is closer to the RMSECV of the calibration set. Therefore, NIRS combined with the variable selection can reduce the effect of water on model robustness and improve the prediction accuracy of the model by the method of selecting effective wave number intervals, and CARS may be one of the ideal algorithms to solve such problems. |
| WOS关键词 | AQUEOUS-SOLUTIONS ; PREDICTION ; PLS |
| 资助项目 | National Natural Science Foundation of China[42075135] ; President's Fund Project of Hefei Institute of Materials Science, CAS[YYJJ2022QN09] |
| WOS研究方向 | Spectroscopy |
| 语种 | 英语 |
| WOS记录号 | WOS:001313283700001 |
| 出版者 | SPRINGER |
| 资助机构 | National Natural Science Foundation of China ; President's Fund Project of Hefei Institute of Materials Science, CAS |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/135257]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Han, Xin |
| 作者单位 | 1.Univ Sci & Technol China, Hefei, Peoples R China 2.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fang, Renjie,Han, Xin,Li, Xiangxian,et al. Variable Selection in Near-Infrared Spectra for Modeling of Hemoglobin Content in Bio-Water Solutions[J]. JOURNAL OF APPLIED SPECTROSCOPY,2024,91. |
| APA | Fang, Renjie,Han, Xin,Li, Xiangxian,Tong, Jingjing,Gao, Minguang,&Wang, Yang.(2024).Variable Selection in Near-Infrared Spectra for Modeling of Hemoglobin Content in Bio-Water Solutions.JOURNAL OF APPLIED SPECTROSCOPY,91. |
| MLA | Fang, Renjie,et al."Variable Selection in Near-Infrared Spectra for Modeling of Hemoglobin Content in Bio-Water Solutions".JOURNAL OF APPLIED SPECTROSCOPY 91(2024). |
入库方式： OAI收割
来源：合肥物质科学研究院
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