Classification of extra virgin olive oils according to the protected designation of origin, olive variety and geographical origin

López-Feria, Silvia; Cárdenas, Soledad; García‐Mesa, José A.; Valcárcel, Miguel

doi:10.1016/j.talanta.2007.12.033

Cited by 47 publications

(29 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the space defined by the first two PCs two distinctive groups of olive oils could be found. Members of the first one on the left side of the score plot are olive oil samples collected from the two coastal locations (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20). This group is well separated from the second one consisting of the samples collected on three island locations .…”

Section: Pca Onmentioning

confidence: 99%

“…17 There have been numerous studies over the years, attempting to establish a link between composition and geographical origin using a variety of instrumental analytical techniques, generally coupled with multivariate data analysis. [18][19][20][21][22][23] In particular, FTIR spectroscopy was shown to be an effective tool in the characterization, geographical classification [24][25][26] or authentication of vegetable oils. 27 Advancement of Fourier transform infrared spectroscopy with attenuated total reflectance sampling (ATR) enables both qualitative and quantitative analysis of different edible oils.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Chemometric Analysis of Croatian Extra Virgin Olive Oils from Central Dalmatia Region

et al. 2013

View full text Add to dashboard Cite

Abstract. Extra virgin olive oil samples were collected from different geographical areas of Central Dalmatia region in Croatia including locations both on the coast and islands. This set included 41 oils of cultivar Oblica, 1 per cultivars Coratina and Leccino, and 5 of mixed cultivars Lastovka and Oblica. Attenuated total reflectance (ATR) spectra of non-treated samples were recorded and principal component analysis (PCA) was carried out on a set of obtained spectra, as well as their first and second derivatives. The quality of PCA models was assessed using leave-one-out cross-validation and optimal number of principal components was determined. In the case of ATR spectra, the first principal component accounted for 42.92 % of the total variance among the samples and the optimal number of components was 6, whereas in the case of second derivatives the first principal component accounted for 95.76 % of the total variance and the optimal number of components was 3. Classification of olive oils on the basis of geographical origin was proposed and underlying spectral differences among the spectra were determined by investigating principal component loadings. These differences arise as a result of variations in fatty acid composition. It was found out that ATR in combination with PCA could easily distinguish between samples collected from the coastal area and those from the islands. Classification results were further confirmed by using spherical principal components procedure, projection pursuit and robust PCA, as well as hierarchical cluster analysis.(doi: 10.5562/cca2377)

show abstract

Section: Pca Onmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Chemometric Analysis of Croatian Extra Virgin Olive Oils from Central Dalmatia Region

et al. 2013

View full text Add to dashboard Cite

show abstract

“…However, some of these methods are quite expensive, non-green techniques, and require fulfilment of straight standardised procedures to ensure accuracy. Therefore, other approaches have been reported trying to overcome some of these drawbacks, namely gas-, liquid-chromatography and, more recently, mass-spectrometry based-methodologies (Bakhouche et al, 2013;Garcia et al, 2013;Garrido-Delgado et al, 2011;Karabagias et al, 2013;Lauri et al, 2013;Longobardi et al, 2012;López-Feria, Cárdenas, García-Mesa, & Valcárcel, 2008;Matos et al, 2007;Montealegre et al, 2010;Romero, & Brenes, 2012;Ruiz-Samblás et al, 2012). These are based on the identification and quantification of several chemical olive oil components that allow assessment of the quality, authenticity or adulteration of olive oils.…”

Section: Introductionmentioning

confidence: 99%

Single-cultivar extra virgin olive oil classification using a potentiometric electronic tongue

et al. 2014

View full text Add to dashboard Cite

a b s t r a c tLabel authentication of monovarietal extra virgin olive oils is of great importance. A novel approach based on a potentiometric electronic tongue is proposed to classify oils obtained from single olive cultivars (Portuguese cvs. Cobrançosa, Madural, Verdeal Transmontana; Spanish cvs. Arbequina, Hojiblanca, Picual). A meta-heuristic simulated annealing algorithm was applied to select the most informative sets of sensors to establish predictive linear discriminant models. Olive oils were correctly classified according to olive cultivar (sensitivities greater than 97%) and each Spanish olive oil was satisfactorily discriminated from the Portuguese ones with the exception of cv. Arbequina (sensitivities from 61% to 98%). Also, the discriminant ability was related to the polar compounds contents of olive oils and so, indirectly, with organoleptic properties like bitterness, astringency or pungency. Therefore the proposed E-tongue can be foreseen as a useful auxiliary tool for trained sensory panels for the classification of monovarietal extra virgin olive oils.

show abstract

“…The samples belonging to each set were selected from a Principal Component Analysis NIRS (near infrared spectroscopy) technology has been proven to be an efficient tool for the rapid determination of fatty acids in cereals and oleaginous crops such as soy (Roberts et al, 2006) maize (Weinstock et al, 2006) and olives (Maggio et al, 2009) and for the determination of designation of origin (López-Feria et al, 2008). It also allows for determining the oxidative index of edible oils (Le Dréau et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

Control of quality and silo storage of sunflower seeds using near infrared technology

et al. 2013

View full text Add to dashboard Cite

RESUMEN Control de calidad y de almacenamiento de semillas de girasol mediante tecnología de infrarrojo cercanoEn este trabajo se evalúa la espectroscopía de infrarrojo cercano para su uso en el control de calidad y almacenamiento de semillas de girasol. Los resultados indican que el método analítico empleado puede utilizarse como méto-do de determinación rápida de humedad, grasa y contenidos altos/bajos de ácido oleico. Los rangos de aplicación son comparables con los valores que se han determinado mediante métodos clásicos de análisis, encontrándose entre 4.6-21.4% la humedad, 38.4-49.6% la grasa y 60.0-93.1% de ácido oleico del total de los ácidos grasos. Además se ha utilizado un análisis discriminarte lineal por pasos determinando las longitudes de onda más adecuadas para la clasificación de semillas de girasol en los grupos alto/bajo oleico. El modelo generado permitió la clasificación de semillas de girasol en los grupos alto y bajo oleico con unos porcentajes de muestras correctamente clasificadas de un 90.5% en validación interna y de un 89.4% en validación cruzada. PALABRAS CLAVE: Ácido oleico -Control de calidad -Espectroscopía de infrarrojo cercano -Semillas de girasol. SUMMARY Control of quality and silo storage of sunflower seeds using near infrared technologyThis work assesses the application of near infrared spectroscopy technology for the quality control of sunflower seeds direct from farmers and from a storage silo. The results show that the analytical method employing near infrared spectroscopy can be used as a rapid and non-destructive tool for the determination of moisture, fat and high/low oleic acid contents in samples of sunflower seeds. The ranges obtained were comparable to those reported for classic chemical methods, and were between 4.6-21.4% for moisture; 38.4-49.6% for fat, and 60.0-93.1% for oleic acid expressed as percentage of total fatty acids. A stepwise discriminant analysis was performed to determine the most useful wavelengths for classifying sunflower seeds in terms of their (high/low) oleic acid composition. The discriminant model allows the classification of sunflower seeds with high or low oleic acid contents, with a prediction rate of 90.5% for internal validation and of 89.4% for cross-validation.

show abstract

Classification of extra virgin olive oils according to the protected designation of origin, olive variety and geographical origin

Cited by 47 publications

References 23 publications

Chemometric Analysis of Croatian Extra Virgin Olive Oils from Central Dalmatia Region

Chemometric Analysis of Croatian Extra Virgin Olive Oils from Central Dalmatia Region

Single-cultivar extra virgin olive oil classification using a potentiometric electronic tongue

Control of quality and silo storage of sunflower seeds using near infrared technology

Contact Info

Product

Resources

About