{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1363670318727049600.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.1002/cem.1339"}},{"identifier":{"@type":"URI","@value":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fcem.1339"}},{"identifier":{"@type":"URI","@value":"https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/pdf/10.1002/cem.1339"}}],"dc:title":[{"@value":"Genetic algorithm‐based wavelength selection method for spectral calibration"}],"description":[{"type":"abstract","notation":[{"@value":"<jats:title>Abstract</jats:title><jats:p>In this paper, we propose a genetic algorithm‐based wavelength selection (GAWLS) method for visible and near‐infrared (Vis/NIR) spectral calibration. The objective of GAWLS is to construct robust and predictive regression models by selecting informative wavelength regions. To demonstrate the ability of the proposed method, regression models for soil properties and sugar content of apples are constructed by using GAWLS and other variable selection methods. Copyright © 2010 John Wiley & Sons, Ltd.</jats:p>"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1383670318727049600","@type":"Researcher","foaf:name":[{"@value":"Masamoto Arakawa"}]},{"@id":"https://cir.nii.ac.jp/crid/1383670318727049601","@type":"Researcher","foaf:name":[{"@value":"Yosuke Yamashita"}]},{"@id":"https://cir.nii.ac.jp/crid/1383670318727049602","@type":"Researcher","foaf:name":[{"@value":"Kimito Funatsu"}]}],"publication":{"publicationIdentifier":[{"@type":"PISSN","@value":"08869383"},{"@type":"EISSN","@value":"1099128X"}],"prism:publicationName":[{"@value":"Journal of Chemometrics"}],"dc:publisher":[{"@value":"Wiley"}],"prism:publicationDate":"2010-09","prism:volume":"25","prism:number":"1","prism:startingPage":"10","prism:endingPage":"19"},"reviewed":"false","dc:rights":["http://onlinelibrary.wiley.com/termsAndConditions#vor"],"url":[{"@id":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fcem.1339"},{"@id":"https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/pdf/10.1002/cem.1339"}],"createdAt":"2010-09-01","modifiedAt":"2025-10-12","relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1050001335723713664","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@language":"en","@value":"Estimation of active pharmaceutical ingredients content using locally weighted partial least squares and statistical wavelength selection."}]},{"@id":"https://cir.nii.ac.jp/crid/1050585407780051968","@type":"Article","resourceType":"学術雑誌論文(journal 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Considering Process Dynamics"},{"@value":"プロセスの動特性を考慮した非線型ソフトセンサー手法の開発"},{"@language":"ja-Kana","@value":"プロセス ノ ドウトクセイ オ コウリョ シタ ヒセンケイ ソフトセンサー シュホウ ノ カイハツ"}]},{"@id":"https://cir.nii.ac.jp/crid/1390001205181313792","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@language":"en","@value":"Development of a Wavelength Region Selection Method Basedon Genetic Algorithm-based WaveLength Selectionand Support Vector Regression"},{"@language":"ja","@value":"Ｇｅｎｅｔｉｃ　Ａｌｇｏｒｉｔｈｍ－ｂａｓｅｄ　ＷａｖｅＬｅｎｇｔｈ　ＳｅｌｅｃｔｉｏｎとＳｕｐｐｏｒｔ　Ｖｅｃｔｏｒ　Ｒｅｇｒｅｓｓｉｏｎを組み合わせた変数領域選択手法の開発"},{"@language":"ja-Kana","@value":"Genetic Algorithm-based WaveLength Selection ト Support Vector Regression オ クミアワセタ ヘンスウ リョウイキ センタク シュホウ ノ カイハツ"}]},{"@id":"https://cir.nii.ac.jp/crid/1390282680157059840","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@language":"en","@value":"Development of Soft Sensor Methods 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