Abstract
In this paper, the potential of coupling mid- and near-infrared spectroscopic fingerprinting techniques and chemometric classification methods for the traceability of extra virgin olive oil samples from the PDO Sabina was investigated. To this purpose, two different pattern recognition algorithm representative of the discriminant (PLS-DA) and modeling (SIMCA) approach to classification were employed. Results obtained after processing the spectroscopic data by PLS-DA evidenced a rather high classification accuracy, NIR providing better predictions than MIR (as evaluated both in cross-validation and on an external test set). SIMCA confirmed these results and showed how the category models for the class Sabina can be rather sensitive and highly specific. Lastly, as samples from two harvesting years (2009 and 2010) were investigated, it was possible to evidence that the different production year can have a relevant effect on the spectroscopic fingerprint. Notwithstanding this, it was still possible to build models that are transferable from one year to another with good accuracy.
| Original language | English |
|---|---|
| Journal | Analytica Chimica Acta |
| Volume | 717 |
| Pages (from-to) | 39-51 |
| Number of pages | 13 |
| ISSN | 0003-2670 |
| DOIs | |
| Publication status | Published - 2012 |
| Externally published | Yes |
Keywords
- Chemometrics
- Extra virgin olive oil
- Food traceability
- Infrared spectroscopy
- Partial least squares discriminant analysis (PLS-DA)
- SIMCA