Six defined strains of bacteriophage-insensitive Streptococcus cremoris, grown in whey-based starter media, were used over a period of 10 months to produce more than 2 million kg of Cheddar cheese on continuous cheesemaking equipment. Flavor development in this cheese was less than that in cheese made with conventional bulk starter. Proteolysis in the trichloracetic acid (TCA) soluble cheese fraction was less in cheese manufactured using S. cremoris compared to conventional bulk starter. Rheological properties were studied and the force-compression curves related to the age, composition and pH of the cheese. Pattern recognition techniques were used to analyze the multivariate data. The texture was affected by the mechanical process, moisture content and yield point of the cheese. Casein proteolysis, age, culture type and fiiness were the most discriminating variables affecting maturity.

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Cheese ripening accelerated by adding enzyme extracts derived from lactic bacteria promoted proteolysis and produced an acceptable novel cheese based on sensory tests. However, HPLC profiles for watersoluble compounds were considerably different from those for control Cheddar cheese without the added enzyme extracts, probably indicating a different mechanism of proteolysis. The samples did not follow the usual aging pathway on a similarlity scattergram, when HPLC data were processed by principal component similarity (PCS) analysis. Such analysis was useful for evaluating effects of accelerated cheese ripening.

As unsupervised classifications, principal component similarity (PCS) and cluster analysis (CA) were compared for outlier detectability in panel evaluation. By rotating the reference, PCS can define outlying panelists based on the similarity of their evaluation patterns with that of the reference panelist. As a result, the outliers detected on PCS scattergrams are dependent on the reference selected, whereas, outliers detected by CA are based on dissimilarity, thus being rather unilateral. The definition of outliers in PCS is new as it is different from the currently most popular definitions based on dissimilarity. For verifying the outliers thus obtained, random-centroid optimization (RCO) was applied for selecting the best samples by each cluster of panelists. This combination of PCS=RCO may be useful in finding the likeness distribution among consumers and then in creating food products to correctly respond to the demands of different consumer groups. *Corresponding

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