An Improved Ensemble Learning Method for Protein Content Analysis of Corn with Small Sample by Near-Infrared Spectroscopy
文献类型：期刊论文
| 作者 | Liu, Jing2; Yu, Shaohui1 |
| 刊名 | FOOD ANALYTICAL METHODS
 |
| 出版日期 | 2024-09-01 |
| 卷号 | 17 |
| 关键词 | Near-infrared spectroscopy Ensemble learning Partial least squares Weighted clustering |
| ISSN号 | 1936-9751 |
| DOI | 10.1007/s12161-024-02669-8 |
| 通讯作者 | Yu, Shaohui(yushaohui2005@163.com) |
| 英文摘要 | Near-infrared spectroscopy has become an important methodology for rapid and non-destructive detection in food and agricultural fields. However, the accuracy of quantitative analysis was seriously restricted by the severe overlap of spectra and the high cost of standard samples. In order to reduce the impact of these problems especially that of small sample size problem, a novel method named weighted clustering ensemble partial least squares (WCE-PLS) is proposed for the protein content analysis of corn. Firstly, the clustering and sampling strategy is introduced in the calibration sets of corn to create different subsets for generating sub-models. Then, root mean square errors of cross-validation (RMSECV) in those sub-models as the crucial criterion are computed for model optimization. Finally, in integrating step, two Gaussian weighted functions used to determine the weights of sub-models are defined. The validation performance of the proposed method is tested with the near infrared spectral data sets of corn and compared with single PLS, bagging PLS, boosting PLS, and data augmentation (DA) PLS. To further demonstrate the effectiveness of the method, another data set of soil was used for supplementary verification. Results of the prediction sets indicated that the RMSEP values of the WCE-PLS are obviously smaller than that of boosting PLS. And the RMSEP of WCE-PLS and bagging PLS is relatively small in most cases. Furthermore, the correlation coefficients between predicted value and chemical value are respectively 0.96587 and 0.90849 for two data sets, which computed by the WCE-PLS is obviously higher than that computed by the other four methods. And the t test also showed the WCE-PLS has smaller t values and larger p values. |
| WOS关键词 | PARTIAL LEAST-SQUARES ; MULTIVARIATE CALIBRATION ; REGRESSION ; ALGORITHM ; MODEL |
| 资助项目 | National Natural Science Foundation of China[61405257] ; National Natural Science Foundation of China[61378041] ; Higher School outstanding young talent support project of Anhui province[gxyqZD2018071] ; Anhui Provincial Natural Science Foundation[1508085MF138] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA08040107-2] ; CASHIPS Director's Fund[YZJJ201515] |
| WOS研究方向 | Food Science & Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001297781500001 |
| 出版者 | SPRINGER |
| 资助机构 | National Natural Science Foundation of China ; Higher School outstanding young talent support project of Anhui province ; Anhui Provincial Natural Science Foundation ; Strategic Priority Research Program of the Chinese Academy of Sciences ; CASHIPS Director's Fund |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/134954]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Yu, Shaohui |
| 作者单位 | 1.Nanjing Forestry Univ, Coll Sci, Nanjing 210037, Peoples R China 2.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Jing,Yu, Shaohui. An Improved Ensemble Learning Method for Protein Content Analysis of Corn with Small Sample by Near-Infrared Spectroscopy[J]. FOOD ANALYTICAL METHODS,2024,17. |
| APA | Liu, Jing,&Yu, Shaohui.(2024).An Improved Ensemble Learning Method for Protein Content Analysis of Corn with Small Sample by Near-Infrared Spectroscopy.FOOD ANALYTICAL METHODS,17. |
| MLA | Liu, Jing,et al."An Improved Ensemble Learning Method for Protein Content Analysis of Corn with Small Sample by Near-Infrared Spectroscopy".FOOD ANALYTICAL METHODS 17(2024). |
入库方式： OAI收割
来源：合肥物质科学研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。