Prediction of texture and colour of dry-cured ham by visible and near infrared spectroscopy using a fiber optic probe

The potential of visible and near infrared spectroscopy to predict texture and colour of dry-cured ham samples was investigated. Sensory evaluation was performed on 117 boned and cross-sectioned dry-cured ham samples. Slices of approximate thickness 4 cm were cut, vacuum-packaged and kept under frozen storage until spectral analysis. Then, Biceps femoris muscle from the thawed slices was taken and scanned (400-2200 nm) using a fiber optic probe. The exploratory analysis using principal component analysis shows that there are two ham groups according to the appearance or not of defects. Then, a K nearest neighbours was used to classify dry-cured hams into defective or no defective classes. The overall accuracy of the classification as a function of pastiness was 88.5%; meanwhile, according to colour was 79.7%. Partial least squares regression was used to formulate prediction equations for pastiness and colour. The correlation coefficients of calibration and cross-validation were 0.97 and 0.86 for optimal equation predicting pastiness, and 0.82 and 0.69 for optimal equation predicting colour. The standard error of cross-validation for predicting pastiness and colour is between 1 and 2 times the standard deviation of the reference method (the error involved in the sensory evaluation by the e;cperts). The magnitude of this error demonstrates the good precision of the methods for predicting pastiness and colour. Furthermore, the samples were classified into defective or no defective classes, with a correct classification of 94.2%, according to pasty texture evaluation and 75.7% as regard to colour evaluation. (c) 2005 Elsevier Ltd. All rights reserved.