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This study explores the potential of olive leaves, long integral to Mediterranean traditional medicine, as a rich source of valuable compounds. The challenge lies in their considerable water content, hindering these compounds' full valorization. Four drying methods (air-drying, oven-drying, freeze-drying and solar-drying) were investigated for their impact on nutrient and bioactive compound content in the leaves of four olive varieties ("Arbequina", "Koroneiki", "Menara" and "Picholine Marocaine") cultivated in Morocco. In their fresh state, "Picholine Marocaine" exhibited the highest protein levels (6.11%), "Arbequina" had the highest phenolic content (20.18 mg gallic acid equivalents/g fresh weight (FW)), and "Koroneiki" and "Menara" were highest in flavonoids (3.28 mg quercetin equivalents/g FW). Specific drying methods proved optimal for different varieties. Oven-drying at 60 °C and 70 °C effectively preserved protein, while phenolic content varied with drying conditions. Air-drying and freeze-drying demonstrated effectiveness for flavonoids. In addition, an analytical approach using high-performance liquid chromatography and diode array detection (HPLC-DAD) was applied to investigate the effects of the different drying methods on the bioactive fraction of the analyzed samples. The results showed qualitative and quantitative differences depending on both the variety and the drying method used. A total of 11 phenolic compounds were tentatively identified, with oleuropein being the most abundant in all the samples analyzed. The freeze-dried samples showed the highest content of oleuropein in the varieties "Arbequina" and "Picholine Marocaine" compared to the other methods analyzed. In contrast, "Koroneiki" and "Menara" had higher oleuropein content when air dried. Overall, the obtained results highlight the importance of tailored drying techniques for the preservation of nutrients and bioactive compounds in olive leaves. [ABSTRACT FROM AUTHOR]

The triacylglycerols composition of monovarietal Picholine Marocaine olive oils, produced in seven of the most productive areas in northern Morocco, has been established for the first time, starting a register of typicality and geographical identity of Moroccan olive oils. Two hundred seventy-nine olive samples were collected over two consecutive crop seasons (2011/2012 ( n = 140) and 2012/2013 ( n = 139)), the analysis of the triacylglycerol profiles of the obtained oils was performed using high performance liquid chromatography, the influence of the region of provenance was investigated, and chemometric data analysis (including principal components analysis, linear discriminant analysis, partial least squares-discriminant analysis, and soft independent modeling of class analogies) was used to differentiate the studied samples according to their geographical origin. Twenty-one triacylglycerols were characterized and the variability observed among the studied samples could be related to the production area. The combination of triacylglycerols composition with chemometrics provided a powerful tool to verify the geographical origin of north Moroccan olive oils. Practical applications: Moroccan legislations established Geographic Indication labels for adding value to typical products. However, the general procedure to control the geographical authenticity of these protected products remains based on the traceability of their production and there is a lack of appropriate and reliable tools for their geographical unambiguous authentication and control. In this work, geographical authentication models for classifying olive oil samples (based on triacylglycerols composition) have been developed and evaluated, achieving adequate classification and prediction rates for oils coming from the main north Moroccan olive growing areas. The results obtained herein, could represent a significant contribution to the control of the geographical authenticity of Moroccan olive oils. The triacylglycerols composition of monovarietal Picholine Marocaine olive oils, from seven of the most productive areas in northern Morocco, is established in this study, analyzing 279 olive samples (over two consecutive crop seasons) by high performance liquid chromatography and Chemometrics. [ABSTRACT FROM AUTHOR]

Herewith, the potential of an approach based on the combination of the determination of phenolic compounds and the use of chemometric treatments has been evaluated to establish, for the first time, promising models to authenticate the provenance of Moroccan monovarietal olive oils produced under different geographical origin indication systems. To achieve this purpose, 136 commercial extra virgin olive oil samples from three diverse production areas (Meknès territory; the Protected Geographical Indication Ouazzane; and the Protected Designation of Origin Tyout-Chiadma) were collected over two consecutive crop seasons (2012/2013 and 2013/2014). Their phenolic fraction composition was investigated by using high performance liquid chromatography coupled to mass spectrometry (HPLC-ESI-IT MS). The results showed that geographical provenance and harvest season had a marked influence on the content of identified phenolic compounds. Principal components analysis (PCA) and linear discriminant analysis (LDA) were applied to test the potential of the determined compounds as geographical discriminant features, achieving a noticeable discrimination among the three evaluated regions. The contribution of each analyte to the statistic model has been evaluated in depth. [ABSTRACT FROM AUTHOR]

High Performance Liquid Chromatography (HPLC) with diode array (DAD) and fluorescence (FLD) detection was used to acquire the fingerprints of the phenolic fraction of monovarietal extra-virgin olive oils (extra-VOOs) collected over three consecutive crop seasons (2011/2012-2013/2014). The chromatographic fingerprints of 140 extra-VOO samples processed from olive fruits of seven olive varieties, were recorded and statistically treated for varietal authentication purposes. First, DAD and FLD chromatographic-fingerprint datasets were separately processed and, subsequently, were joined using “Low-level” and “Mid-Level” data fusion methods. After the preliminary examination by principal component analysis (PCA), three supervised pattern recognition techniques, Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogies (SIMCA) and K-Nearest Neighbors (k-NN) were applied to the four chromatographic-fingerprinting matrices. The classification models built were very sensitive and selective, showing considerably good recognition and prediction abilities. The combination “chromatographic dataset + chemometric technique” allowing the most accurate classification for each monovarietal extra-VOO was highlighted. [ABSTRACT FROM AUTHOR]