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 experts). 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.