L. Peternelli, M. H. Barbosa, J. Roque, R. Teófilo
{"title":"利用有序预测因子选择和偏最小二乘判别分析从甘蔗茎秆直接获得的近红外光谱进行表型分类","authors":"L. Peternelli, M. H. Barbosa, J. Roque, R. Teófilo","doi":"10.1255/NIR2017.157","DOIUrl":null,"url":null,"abstract":"Author Summary: A new method was developed for the early selection of sugarcane genotypes using near infrared spectroscopy combined with partial least squares discriminant analysis (PLS-DA) and a variable selection method named ordered predictors selection (OPS). The use of the OPS method improved the predictive capacity of PLS-DA models to classify the sugarcane samples correctly according to fiber content (FC) and pol percent (PP).","PeriodicalId":20429,"journal":{"name":"Proceedings of the 18th International Conference on Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Phenotypic classification of sugarcane from near infrared spectra obtained directly from stalk using ordered predictors selection and partial least squares-discriminant analysis\",\"authors\":\"L. Peternelli, M. H. Barbosa, J. Roque, R. Teófilo\",\"doi\":\"10.1255/NIR2017.157\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Author Summary: A new method was developed for the early selection of sugarcane genotypes using near infrared spectroscopy combined with partial least squares discriminant analysis (PLS-DA) and a variable selection method named ordered predictors selection (OPS). The use of the OPS method improved the predictive capacity of PLS-DA models to classify the sugarcane samples correctly according to fiber content (FC) and pol percent (PP).\",\"PeriodicalId\":20429,\"journal\":{\"name\":\"Proceedings of the 18th International Conference on Near Infrared Spectroscopy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 18th International Conference on Near Infrared Spectroscopy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1255/NIR2017.157\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 18th International Conference on Near Infrared Spectroscopy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1255/NIR2017.157","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Phenotypic classification of sugarcane from near infrared spectra obtained directly from stalk using ordered predictors selection and partial least squares-discriminant analysis
Author Summary: A new method was developed for the early selection of sugarcane genotypes using near infrared spectroscopy combined with partial least squares discriminant analysis (PLS-DA) and a variable selection method named ordered predictors selection (OPS). The use of the OPS method improved the predictive capacity of PLS-DA models to classify the sugarcane samples correctly according to fiber content (FC) and pol percent (PP).