Carob liqueur is a traditional Mediterranean alcoholic beverage obtained via a wide range of production techniques contributing to the different organoleptic attributes of the final product. The aim of this research was to evaluate the stability of the chemical composition and biological capacities (antioxidant and enzyme inhibition) under in vitro simulated gastrointestinal digestion of liqueurs prepared by flavouring the fig spirit with carob pulp by maceration, distillation, percolation, or aqueous and hydro-alcoholic infusions. For this purpose, the phenolic and furanic compositions, the total phenolic (TPC) and flavonoid (TFC) contents, antioxidant capacity (AC), and enzyme inhibitory potential against acethylcholinesterase, tyrosinase, α-glucosidase and α-amylase enzymes were evaluated. The content of gallic acid decreased after gastrointestinal digestion, while TPC, TFC, and AC significantly increased after each digestion phase. Overall, no significantly different enzyme inhibitions (p < 0.05) were observed among digested liqueurs, with moderate inhibition against acethylcholinesterase and tyrosinase (enzymes related with neurodegenerative diseases), and potent and low inhibitory capacities for α-glucosidase and α-amylase, respectively (ideal conditions employed in antidiabetic therapy). The study indicates that hydro-alcoholic infusion and maceration were the most appropriate methods to obtain liqueurs with higher values of the aforementioned parameters and safe levels of toxic furanics.

Identifying and mapping irrigated areas is essential for a variety of applications such as agricultural planning and water resource management. Irrigated plots are mainly identified using supervised classification of multispectral images from satellite or manned aerial platforms. Recently, hyperspectral sensors on-board Unmanned Aerial Vehicles (UAV) have proven to be useful analytical tools in agriculture due to their high spectral resolution. However, few efforts have been made to identify which wavelengths could be applied to provide relevant information in specific scenarios. In this study, hyperspectral reflectance data from UAV were used to compare the performance of several wavelength selection methods based on Partial Least Square (PLS) regression with the purpose of discriminating two systems of irrigation commonly used in olive orchards. The tested PLS methods include filter methods (Loading Weights, Regression Coefficient and Variable Importance in Projection); Wrapper methods (Genetic Algorithm-PLS, Uninformative Variable Elimination-PLS, Backward Variable Elimination-PLS, Sub-window Permutation Analysis-PLS, Iterative Predictive Weighting-PLS, Regularized Elimination Procedure-PLS, Backward Interval-PLS, Forward Interval-PLS and Competitive Adaptive Reweighted Sampling-PLS); and an Embedded method (Sparse-PLS). In addition, two non-PLS based methods, Lasso and Boruta, were also used. Linear Discriminant Analysis and nonlinear K-Nearest Neighbors techniques were established for identification and assessment. The results indicate that wavelength selection methods, commonly used in other disciplines, provide utility in remote sensing for agronomical purposes, the identification of irrigation techniques being one such example. In addition to the aforementioned, these PLS and non-PLS based methods can play an important role in multivariate analysis, which can be used for subsequent model analysis. Of all the methods evaluated, Genetic Algorithm-PLS and Boruta eliminated nearly 90% of the original spectral wavelengths acquired from a hyperspectral sensor onboard a UAV while increasing the identification accuracy of the classification.