Variable selection, outlier detection, and figures of merit estimation in a partial least-squares regression multivariate calibration model. A case study for the determination of quality parameters in the alcohol industry by near-infrared spectroscopy

dc.creatorValderrama, P
dc.creatorBraga, JWB
dc.creatorPoppi, RJ
dc.date2007
dc.dateOCT 17
dc.date2014-11-20T05:28:03Z
dc.date2015-11-26T17:15:09Z
dc.date2014-11-20T05:28:03Z
dc.date2015-11-26T17:15:09Z
dc.date.accessioned2018-03-29T00:03:25Z
dc.date.available2018-03-29T00:03:25Z
dc.identifierJournal Of Agricultural And Food Chemistry. Amer Chemical Soc, v. 55, n. 21, n. 8331, n. 8338, 2007.
dc.identifier0021-8561
dc.identifierWOS:000250110600007
dc.identifier10.1021/jf071538s
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/73190
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/73190
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/73190
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1281976
dc.descriptionPractical implementation of multivariate calibration models has been limited in several areas due to the requirement of appropriate development and validation to prove their performance to standardization agencies. Herein, a detailed description of the application of multivariate calibration based on partial least-squares regression models (PLSR) for the determination of soluble solids (BRIX), polarizable sugars (POL), and reducing sugars (RS) in sugar cane juice, based on near infrared spectroscopy (NIR), for the alcohol industries is presented. The development of the models, including variable selection and outlier elimination, and their validation by determination of figures of merit, such as accuracy, precision, sensitivity, analytical sensitivity, prediction intervals, and limits of detection and quantification, are described for a representative data set of 1381 sugar cane samples. Values estimated by PLSR are compared with appropriate reference methods, where the results indicated that the PLSR models can be used in the alcohol industry as an alternative to refractometry and lead clarification before polarization measurements (standard methods for BRIX and POL, respectively). For RS, the results of a titration reference method were compared with the PLSR estimates and also with an estimate based on BRIX and POL values, as actually used in the alcohol industry. The PLSR method presented a better agreement with the titration method. However, the results indicated that the RS estimates from both PLSR and those based on the BRIX and POL values, actually used, should be improved to a safe determination of RS.
dc.description55
dc.description21
dc.description8331
dc.description8338
dc.languageen
dc.publisherAmer Chemical Soc
dc.publisherWashington
dc.publisherEUA
dc.relationJournal Of Agricultural And Food Chemistry
dc.relationJ. Agric. Food Chem.
dc.rightsfechado
dc.sourceWeb of Science
dc.subjectvalidation
dc.subjectnear-infrared spectroscopy
dc.subjectPLSR
dc.subjectoutliers
dc.subjectalcohol industry
dc.subjectDetection Limits
dc.subjectError
dc.subjectQuantification
dc.subjectPrediction
dc.subjectSpectrophotometry
dc.subjectValidation
dc.subjectExample
dc.titleVariable selection, outlier detection, and figures of merit estimation in a partial least-squares regression multivariate calibration model. A case study for the determination of quality parameters in the alcohol industry by near-infrared spectroscopy
dc.typeArtículos de revistas


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