A new NIR method based onmultivariate calibration for determination of ethanol in industrially packed wholemeal bread was developed and validated. GC-FID was used as reference method for the determination of actual ethanol concentration of different samples of wholemeal bread with proper content of added ethanol, ranging from 0 to 3.5% (w/w). Stepwise discriminant analysis was carried out on the NIR dataset, in order to reduce the number of original variables by selecting those that were able to discriminate between the samples of different ethanol concentrations. With the so selected variables a multivariate calibration model was then obtained by multiple linear regression. The prediction power of the linear model was optimized by a new “leave one out” method, so that the number of original variables resulted further reduced.
Multivariate Calibration on NIR Data: Development of a Model for the Rapid Evaluation of Ethanol Content in Bakery Products / Bello, A; Bianchi, Federica; Careri, Maria; Giannetto, Marco; Mori, Giovanni; Musci, Marilena. - In: ANALYTICA CHIMICA ACTA. - ISSN 0003-2670. - 603:(2007), pp. 8-12. [10.1016/j.aca.2007.09.037]
Multivariate Calibration on NIR Data: Development of a Model for the Rapid Evaluation of Ethanol Content in Bakery Products
BIANCHI, Federica;CARERI, Maria;GIANNETTO, Marco;MORI, Giovanni;MUSCI, Marilena
2007-01-01
Abstract
A new NIR method based onmultivariate calibration for determination of ethanol in industrially packed wholemeal bread was developed and validated. GC-FID was used as reference method for the determination of actual ethanol concentration of different samples of wholemeal bread with proper content of added ethanol, ranging from 0 to 3.5% (w/w). Stepwise discriminant analysis was carried out on the NIR dataset, in order to reduce the number of original variables by selecting those that were able to discriminate between the samples of different ethanol concentrations. With the so selected variables a multivariate calibration model was then obtained by multiple linear regression. The prediction power of the linear model was optimized by a new “leave one out” method, so that the number of original variables resulted further reduced.| File | Dimensione | Formato | |
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