| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 4561999 | Food Research International | 2011 | 8 Pages |
Two unsupervised pattern recognition techniques such as stepwise linear discriminant analysis (SLDA) and Soft Independent Modeling of Class Analogy (SIMCA) were used to classify tomato samples in categories corresponding to the cultivation areas. The same approach was used for triple concentrated pastes for discrimination between two different Italian production areas. Accordingly, HS-SPME-GC-MS with 85 μm carboxen/polydimethylsiloxane fiber was used for the determination of the volatile fraction in tomatoes and triple concentrate tomato pastes samples. Ethyl isobututanoate was used as internal standard for semiquantitative analysis and the concentration data (μg/kg) of 38 compounds for tomatoes and of 32 compounds for triple concentrates were used in following chemometric analysis. Sixteen and three variables were selected by forward stepwise LDA for tomatoes and pastes, respectively. SLDA and SIMCA models showed respectively 96% and 94% in term of prediction ability for tomatoes. The two supervised techniques provided 100% and 97% in prediction of the production areas of tomato pastes, respectively.
