Abstract 086
Chemometric analysis of proteolysis during ripening of Ragusano cheese
J. Dairy Sci. 87 (10): 3138-3152, 2004
Fallico, P.L.H. McSweeney, K.J. Siebert, J. Horne, S. Carpino, and G. Licitra
Chemometric modelling of peptide and free amino acid data was used to study proteolysis in PDO Ragusano cheese. Twelve cheeses ripened 3-7 mo were selected from local farmers and were analysed in four layers: rind, external, middle and internal. Proteolysis was significantly affected by cheese layer and age. Significant increases in nitrogen soluble in pH 4.6 acetate buffer and 12% trichloroacetic acid were found from rind to core and throughout ripening. Patterns of proteolysis by urea-PAGE showed that rind-to-core and age-related gradients of moisture and salt contents influenced coagulant and plasmin activities, as reflected in varying rates of hydrolysis of the caseins. Analysis of significant inter-correlations among chemical parameters revealed that moisture, more than salt content, had the largest single influence on rates of proteolysis. Lower levels of 70% ethanol-insoluble peptides coupled to higher levels of 70% ethanol-soluble peptides were found by reversed phase-HPLC in the innermost cheese layers and as the cheeses aged. Non-significant increases of individual free amino acids were found with cheese age and layer. Total free amino acids ranged from 14.3 mg/g 6.2% of total protein) at 3 mo to 22.0 mg/g (8.4% of total protein) after 7 mo. Glutamic acid had the largest concentration in all samples at each time and, jointly with lysine and leucine, accounted for 48% of total free amino acids. Principal components analysis and hierarchical cluster analysis of the data from reversed phase-HPLC chromatograms and free amino acids analysis showed that the peptide profiles were more useful in differentiating Ragusano cheese by age and farm origin than the amino acid data. Combining free amino acid and peptide data resulted in the best Partial Least Squares Regression model (R2 = 0.976; Q2 = 0.952) predicting cheese age, even though the peptide data alone led to a similarly precise prediction (R2 = 0.961; Q2 = 0.923). The most important predictors of age were soluble and insoluble peptides with medium hydrophobicity. The combined peptide dataset also resulted in a 100% correct classification by Partial Least Squares Discriminant Analysis of cheeses according to age and farm origin. Hydrophobic peptides were again discriminatory for distinguishing among sample classes in both cases.
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