{"title":"基于双系综模型的近红外光谱快速测定土壤有机质","authors":"Yingxia Li, Jiajing Zhao, Zizhen Zhao, Haiping Huang, Xiaoyao Tan, Xihui Bian","doi":"10.1002/cem.70053","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>An intelligent and accurate modeling method is proposed combining near-infrared (NIR) spectroscopy for measuring organic matter content in soil samples. The proposed method uses Monte Carlo (MC) random sampling in the training set, where subsets were randomly selected from the samples and further selected using the butterfly optimization algorithm (BOA) to construct partial least squares (PLS) submodels, named MC-BOA-PLS. Ultimately, the final prediction was obtained by averaging the predictions of these submodels. The parameters of the MC-BOA-PLS model were optimized, including the iteration number of BOA, the number of butterflies, and the number of PLS submodels. Results show that MC-BOA-PLS exhibited superior predictive performance to predict organic matter content in soil compared with PLS and BOA-PLS.</p>\n </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 8","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Rapid Determination of Soil Organic Matter by Near-Infrared Spectroscopy With a Novel Double Ensemble Modeling Method\",\"authors\":\"Yingxia Li, Jiajing Zhao, Zizhen Zhao, Haiping Huang, Xiaoyao Tan, Xihui Bian\",\"doi\":\"10.1002/cem.70053\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>An intelligent and accurate modeling method is proposed combining near-infrared (NIR) spectroscopy for measuring organic matter content in soil samples. The proposed method uses Monte Carlo (MC) random sampling in the training set, where subsets were randomly selected from the samples and further selected using the butterfly optimization algorithm (BOA) to construct partial least squares (PLS) submodels, named MC-BOA-PLS. Ultimately, the final prediction was obtained by averaging the predictions of these submodels. The parameters of the MC-BOA-PLS model were optimized, including the iteration number of BOA, the number of butterflies, and the number of PLS submodels. Results show that MC-BOA-PLS exhibited superior predictive performance to predict organic matter content in soil compared with PLS and BOA-PLS.</p>\\n </div>\",\"PeriodicalId\":15274,\"journal\":{\"name\":\"Journal of Chemometrics\",\"volume\":\"39 8\",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Chemometrics\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/cem.70053\",\"RegionNum\":4,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"SOCIAL WORK\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemometrics","FirstCategoryId":"92","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cem.70053","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SOCIAL WORK","Score":null,"Total":0}
Rapid Determination of Soil Organic Matter by Near-Infrared Spectroscopy With a Novel Double Ensemble Modeling Method
An intelligent and accurate modeling method is proposed combining near-infrared (NIR) spectroscopy for measuring organic matter content in soil samples. The proposed method uses Monte Carlo (MC) random sampling in the training set, where subsets were randomly selected from the samples and further selected using the butterfly optimization algorithm (BOA) to construct partial least squares (PLS) submodels, named MC-BOA-PLS. Ultimately, the final prediction was obtained by averaging the predictions of these submodels. The parameters of the MC-BOA-PLS model were optimized, including the iteration number of BOA, the number of butterflies, and the number of PLS submodels. Results show that MC-BOA-PLS exhibited superior predictive performance to predict organic matter content in soil compared with PLS and BOA-PLS.
期刊介绍：
The Journal of Chemometrics is devoted to the rapid publication of original scientific papers, reviews and short communications on fundamental and applied aspects of chemometrics. It also provides a forum for the exchange of information on meetings and other news relevant to the growing community of scientists who are interested in chemometrics and its applications. Short, critical review papers are a particularly important feature of the journal, in view of the multidisciplinary readership at which it is aimed.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
