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Partial least squares models sensitivity

A surface method of measurement of a sizing agent (partly hydrolysed polyvinyl alcohol) on the warp yam substrate (polyester/cotton) using NIR diffuse reflectance spectroscopy has been described (337). A partial least-squares (PLS) modelling procedure used a frequency segment of the NIR spectrum that is most sensitive to changes in size concentration relative to the warp yarn. [Pg.17]

Multivariable calibration permits the simultaneous determination of multicomponent mixtures and it is mainly based on spectroscopy data. Full-spectrum multivariate calibration methods offer the advantage of speed in the determination of the analytes, avoiding separation steps in the analytical procedures. Partial least squares (PLS) has become the usual first-order multivariate tool because of the quality of the calibration models obtained, the ease of its implementation, and the availability of software [27]. However, all first-order methods, of which PLS is no exception, are sensitive to the presence of unmodeled interferents, that is, compounds occurring in new samples that have not been included during the training step of the multivariate model. This situation is encountered... [Pg.172]

See other pages where Partial least squares models sensitivity is mentioned: [Pg.726]    [Pg.12]    [Pg.189]    [Pg.103]    [Pg.253]    [Pg.367]    [Pg.58]    [Pg.710]    [Pg.317]    [Pg.202]    [Pg.199]    [Pg.121]    [Pg.8]    [Pg.236]    [Pg.685]    [Pg.205]   
See also in sourсe #XX -- [ Pg.228 ]

See also in sourсe #XX -- [ Pg.325 ]



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