Abstract

Virgin olive oil quality is the result of complex interactions between olive variety, environment and cultivar practice. Evaluation of its quality is based on chemical and sensory analyses (ECC Regulation) that are time-consuming, expensive and destructive of the sample. Spectroscopic techniques present significant advantages in terms of speed and cost of analysis per sample. Italian extra virgin olive oils from Lombardy, Tuscany and Calabria were analysed by conventional analytical and spectroscopic methods. The sample set was composed of 60 single-cultivar (Casaliva, Leccino and Frantoio) extra virgin olive oils (monovarietal extra virgin olive oils) and 59 extra virgin olive oils produced from a mixture of cultivars from each geographical area (industrial extra virgin olive oils). Free acid content, peroxide value and spectrophotometric indices (K₂₃₂, K₂₇₀ and ΔK) were measured. Olive oils were also analysed by near infrared (NIR) and mid-infrared (MIR) spectroscopy in transmission and attenuated total reflectance, respectively, to classify oils on the basis of their geographical origin. Principal component analysis was applied both to chemical and spectral data as an exploratory technique. Classification methods studied were linear discriminant analysis, partial least squares discriminant analysis and soft independent modelling of class analogy (SIMCA). Both FT-NIR and FT-IR allowed sample classification of oils on the basis of geographical origin. NIR spectroscopy was able to classify better the industrial extra virgin olive oils producing a correct classification of about 90% of the samples, while the MIR technique was able to classify both monovarietal and industrial olive oils, allowing a higher correct classification of samples (>95%). SIMCA applied to MIR spectra classified about 70% of samples correctly on the basis of geographical origin.

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