Evaluation of Tunisian olive oils from different cultivars
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abstract
Olive oil is a highly appreciated food product due to sensory and healthy attributes, being quite prone to
frauds. So, physicochemical evaluation of olive oils is legally required. In this study, 43 olive oil samples
produced in Tunisia from different olive cultivars (groups: Sahli cv – 11 samples; Chetoui cv- 26 samples;
and, Other cvs – 4 samples including Leguim cv and Arbequina cv) were physicochemically evaluated
taking into account: (i) free acidity (FA), K232 and K270 extinction coefficients, ΔK, and, peroxide values
(PV), required for quality level classification (i.e., extra-virgin, virgin and lampante olive oil: EVOO, VOO
and LOO); and (ii) other useful data for olive oil overall characterization (total phenols (TP); antioxidant
capacity (DPPH); and, oxidative stability (OS)). Based on the levels found for FA, PV and extinction
coefficients (K232, K270 and ΔK), the 43 olive oils were classified as: EVOO (4 samples), VOO (5 samples)
and LOO (33 samples). It was verified that TP, DPPH and OS levels of EVOO, VOO and LOO samples
were statistically similar (P-value ≥ 0.2340 for one-way ANOVA). Also, the olive cultivar (i.e., Sahli cv,
Chetoui cv and other cvs) did not statistically influenced the mean levels of the FA, PV, TP, K232 and
DPPH evaluated (P-value > 0.05), and slightly influenced the mean levels of K270 and OS (P-value <
0.05). Finally, as expected an almost linear tendency could be established between DPPH and OS or
TP. The possibility of using the 8 physicochemical parameters for classifying olive oils according to
cultivar; as well as TP, DPPH and OS for differentiating among olive oil quality levels was assessed
through a fusion physicochemical data-chemometric tools approach and further evaluated using a
repeated K-fold cross-validation procedure. The results from the linear discriminant analysis (LDA)
coupled with a simulated annealing (SA) variable selection algorithm pointed out that the
physicochemical data evaluated did not possess the aimed discrimination potential (sensitivities around
80% for cross-validation). So, for these olive oils, the identification of potential putative markers for the
recognition of olive oil cultivar and quality level must be performed. Alternatively, the potential use of
other analytical procedures like electrochemical analysis should be evaluated considering the
satisfactory results already achieved by the research team [1].
Olive oil is a highly appreciated food product due to sensory and healthy attributes, being quite prone to
frauds. So, physicochemical evaluation of olive oils is legally required. In this study, 43 olive oil samples
produced in Tunisia from different olive cultivars (groups: Sahli cv – 11 samples; Chetoui cv- 26 samples;
and, Other cvs – 4 samples including Leguim cv and Arbequina cv) were physicochemically evaluated
taking into account: (i) free acidity (FA), K232 and K270 extinction coefficients, ΔK, and, peroxide values
(PV), required for quality level classification (i.e., extra-virgin, virgin and lampante olive oil: EVOO, VOO
and LOO); and (ii) other useful data for olive oil overall characterization (total phenols (TP); antioxidant
capacity (DPPH); and, oxidative stability (OS)). Based on the levels found for FA, PV and extinction
coefficients (K232, K270 and K), the 43 olive oils were classified as: EVOO (4 samples), VOO (5 samples)
and LOO (33 samples). It was verified that TP, DPPH and OS levels of EVOO, VOO and LOO samples
were statistically similar (P-value ≥ 0.2340 for one-way ANOVA). Also, the olive cultivar (i.e., Sahli cv,
Chetoui cv and other cvs) did not statistically influenced the mean levels of the FA, PV, TP, K232 and
DPPH evaluated (P-value > 0.05), and slightly influenced the mean levels of K270 and OS (P-value <
0.05). Finally, as expected an almost linear tendency could be established between DPPH and OS or
TP. The possibility of using the 8 physicochemical parameters for classifying olive oils according to
cultivar; as well as TP, DPPH and OS for differentiating among olive oil quality levels was assessed
through a fusion physicochemical data-chemometric tools approach and further evaluated using a
repeated K-fold cross-validation procedure. The results from the linear discriminant analysis (LDA)
coupled with a simulated annealing (SA) variable selection algorithm pointed out that the
physicochemical data evaluated did not possess the aimed discrimination potential (sensitivities around
80% for cross-validation). So, for these olive oils, the identification of potential putative markers for the
recognition of olive oil cultivar and quality level must be performed. Alternatively, the potential use of
other analytical procedures like electrochemical analysis should be evaluated considering the
satisfactory results already achieved by the research team [1].
This work was financially supported by Project POCI-01-0145-FEDER-006984 – Associate Laboratory LSRE-LCM
and by Project UID/QUI/00616/2013 – CQ-VR both funded by FEDER through COMPETE2020 - Programa
Operacional Competitividade e Internacionalização (POCI) – and by national funds through FCT - Fundação para
a Ciência e a Tecnologia. Strategic funding of UID/BIO/04469/2013 unit is also acknowledged. Nuno Rodrigues
thanks FCT, POPH-QREN and FSE for the Ph.D. Grant (SFRH/BD/104038/2014).
