Supervised pattern recognition applied to the discrimination of the floral origin of six types of Italian honey samples

In this work a supervised chemometric approach to the discrimination of Italian honey samples from different floral origin is presented. The analytical data of 73 Italian honey samples from six varieties (chestnut, eucalyptus, heather, sulla, honeydew, and wildflower) have been processed by Linear Discriminant Analysis (LDA), using two different variable selection procedures (Fisher F-based and stepwise LDA). Three and two variables, respectively have been necessary to obtain a 100% predictive ability as evaluated by cross-validation. Successively, a class modeling approach has been followed, using UNEQ. The resulting models showed 100% sensitivity and specificity.

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