Combining local wavelength information and ensemble learning to enhance the specificity of class modeling techniques: Identification of food geographical origins and adulteration

Analytica Chimica Acta

Volumn 754, Issue , 2012, Pages 31-38

  Xu, Lu ^a   Ye, Zi Hong ^a   Yan, Si Min ^a   Shi, Peng Tao ^a   Cui, Hai Feng ^a   Fu, Xian Shu ^a   Yu, Xiao Ping ^a  

^a CHINA JILIANG UNIVERSITY   (China)

Author keywords

Ensemble class models; Infrared spectroscopy; One class partial least squares; Soft independent modeling of class analogy; Spectral interval selection

Indexed keywords

CLASS MODELING; CLASS MODELS; CROSS VALIDATION; DATA-DRIVEN METHODS; ENSEMBLE LEARNING; ENSEMBLE MODELS; FEATURE REDUCTION; GEOGRAPHICAL ORIGINS; INFRARED SPECTRAL; INTERVAL SELECTION; ONE-CLASS PROBLEMS; PARTIAL LEAST SQUARE (PLS); SESAME OIL; SOFT INDEPENDENT MODELING OF CLASS ANALOGIES; SPECTRAL INFORMATION; SUBMODELS; TARGET CLASS; TEST OBJECT;

INFRARED SPECTROSCOPY; VEGETABLE OILS;

WAVELENGTH;

ARTICLE; CLASS MODELING TECHNIQUE; DATA ANALYSIS; FOOD QUALITY; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PARTIAL LEAST SQUARES REGRESSION; PRIORITY JOURNAL; QUALITY CONTROL; SENSITIVITY AND SPECIFICITY; STATISTICAL CONCEPTS; WAVELET ANALYSIS;

FOOD; FOOD CONTAMINATION; LEAST-SQUARES ANALYSIS; MODELS, STATISTICAL;

SESAMUM INDICUM;

EID: 84868688510     PISSN: 00032670     EISSN: 18734324     Source Type: Journal    
DOI: 10.1016/j.aca.2012.10.011     Document Type: Article

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