Journal
ANALYTICA CHIMICA ACTA
Volume 558, Issue 1-2, Pages 125-131Publisher
ELSEVIER
DOI: 10.1016/j.aca.2005.11.038
Keywords
NIR; dry-cured ham; sensorial analysis; partial least squares class model; risk curve
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The two objectives of this work were to evaluate near infrared reflectance spectroscopy (NIR) as a tool for on-line classification of dry-cured ham samples according to their sensory characteristics and propose a method for obtaining a set of qualified class models that enables accurate decisions to be taken. With these aims, 117 dry-cured ham samples were classified by expert judges as compliant or non-compliant concerning sensory variables as pastiness, colour, crusting, marbling and ring colour. These samples were also scanned using a remote reflectance fiber optic probe. Each class model built for each sensory variable is evaluated for its sensitivity and specificity, parameters related with the probability of false non-compliance (alpha) and false compliance (beta) of H-0: the sample is compliant hypothesis test. With the five sets of PLS-class modelling the five risk curves, graphs beta versus alpha, are estimated. It is therefore possible to choose the risks of false compliance and false non-compliance for each sensorial variable according to the needs of the decision-maker. (c) 2005 Elsevier B.V. All rights reserved.
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