Virgin olive oil (VOO) is one of the key components of the Mediterranean diet owing to the presence of monounsaturated fatty acids and various bioactive compounds. These beneficial traits, which are usually associated with the cultivar genotype, are highlighting the demand of identifying characteristics of olive oil that will ensure its authenticity. In this work, the fatty acid (FA) composition of 199 VOO samples from Koroneiki, Megaritiki, Amfissis, and Manaki cultivars was determined and studied by chemometrics. Olive cultivar greatly influenced the FA composition, namely, oleic acid (from 75.36% for Amfissis to 65.81% for Megaritiki) and linoleic acid (from 13.35% for Manaki to 6.70% for Koroneiki). Spearman’s rho correlation coefficients revealed differences and similarities among the olive oil cultivars. The use of the forward stepwise algorithm identified the FAs arachidonic acid, gadoleic acid, linoleic acid, α-linolenic acid, palmitoleic acid, and palmitic acid as the most significant for the differentiation of samples. The application of linear and quadratic cross-validation discriminant analysis resulted in the correct classification of 100.00% and 99.37% of samples, respectively. The findings demonstrated the special characteristics of the VOO samples derived from the four cultivars and their successful botanical differentiation based on FA composition.

Increased soil salinity, and therefore accumulation of ions, is one of the major abiotic stresses of cultivated plants that negatively affect their growth and yield. Among Medicago species, only Medicago truncatula, which is a model plant, has been extensively studied, while research regarding salinity responses of two important forage legumes of Medicago sativa (M. sativa) and Medicago arborea (M. arborea) has been limited. In the present work, differences between M. arborea, M. sativa and their hybrid Alborea were studied regarding growth parameters and metabolomic responses. The entries were subjected to three different treatments: (1) no NaCl application (control plants), (2) continuous application of 100 mM NaCl (acute stress) and (3) gradual application of NaCl at concentrations of 50-75-150 mM by increasing NaCl concentration every 10 days. According to the results, M. arborea maintained steady growth in all three treatments and appeared to be more resistant to salinity. Furthermore, results clearly demonstrated that M. arborea presented a different metabolic profile from that of M. sativa and their hybrid. In general, it was found that under acute and gradual stress, M. sativa overexpressed saponins in the shoots while M. arborea overexpressed saponins in the roots, which is the part of the plant where most of the saponins are produced and overexpressed. Alborea did not perform well, as more metabolites were downregulated than upregulated when subjected to salinity stress. Finally, saponins and hydroxycinnamic acids were key players of increased salinity tolerance.

The aim of this study was to investigate the sensory aroma profiles of white wines of the indigenous Greek grape varieties Assyrtiko, Malagousia, Moschofilero, and Roditis. Twenty-three panelists evaluated 17 wines of the aforementioned varieties using the frequency of attribute citation method. Three indices were calculated to assess panel performance in terms of reproducibility. Correspondence analysis and cluster analysis were employed to investigate the sensory space of the wines. Samples of the Roditis variety were characterized mainly by Banana and Vanilla odors; Assyrtiko samples had Earthy, Mushroom, and Nutty odors, as well as Lemon and Honey for some of the samples. Malagousia wines were described as having Lemon, Grapefruit, and Citrus blossom character, and they shared some descriptors with Assyrtiko wines, such as Mushroom and Earthy, and some with Moschofilero samples, i.e., floral and citrus notes. All Moschofilero wines exhibited a floral odor profile: specifically, Rose, Jasmine, or more Citrus blossom-like. Moreover, some Moschofilero samples also revealed a Grapefruit, Lemon, and/or Earthy character, while others expressed Honey notes. In conclusion, despite common characteristics found within varieties, some samples of different varieties exhibited overlapping profiles, and in some cases, samples of the same variety were quite different from each other.

Global DNA methylation changes in response to recurrent drought stress were investigated in two common Greek Medicago sativa L. varieties (Lamia and Chaironia-Institute of Ιndustrial and Forage Crops). The water deficit was implemented in two phases. At the end of the first phase, which lasted for 60 days, the plants were cut at the height of 5 cm and were watered regularly for two months before being subjected to the second drought stress, which lasted for two weeks. Finally, the following groups of plants were formed: CC (controls both in phase I and phase II), CD2 (Controls in phase I, experiencing drought in phase II), and D1D2 (were subjected to drought in both phase I and phase II). At the end of phase II, samples were taken for global DNA methylation analysis with the Methylation Sensitive Amplification Polymorphism (MSAP) method, and all plants were harvested in order to measure the fresh and dry weight of roots and shoots. The variety Lamia responded better, especially the D1D2 group, compared to Chaironia in terms of root and shoot dry weight. Additionally, the shoots of Lamia had a constant water status for CD2 and D1D2 group of plants. According to DNA methylation analysis by the MSAP method, Lamia had lower total DNA methylation percentage after the second drought episode (D1D2) as compared to the plants CD2 that had experienced only one drought episode. On the other hand, the total DNA methylation percentage of Chaironia was almost the same in plants grown under recurrent drought stress conditions compared to control plants. In conclusion, the decrease of DNA methylation of Lamia stressed plants probably indicates the existence of an epigenetic mechanism that may render drought tolerance.

Despite Xinomavro (Vitis vinifera L.) being a well-known noble red grape variety of northern Greece, little is known about its ‘‘bouquet’’ typicity. Volatile compounds of Xinomavro wines produced using a common vinification protocol were analyzed by gas chromatography–mass spectrometry and sensory descriptive analysis was carried out with a trained panel. Wines were characterized by the presence of fatty acids, ethyl and acetate esters, and alcohols, with contributions from terpenes and a volatile phenol. The most active aroma compounds were determined to be 3-methylbutyl acetate, β-damascenone, ethyl esters of octanoic and hexanoic acids, and eugenol. Those compounds positively correlated with fruity and spicy odor descriptors, with the wines being mostly characterized by five typical aroma terms: strawberry, berry fruit, spices, tomato, and green bell pepper. Partial least squares regression (PLSR) analysis was used to visualize relationship between the orthonasal sensory attributes and the volatile aroma compounds with calculated OAVs > 1. Key aroma-active volatiles in the wines were identified using GC-MS/olfactometry, providing a list of 40 compounds, among which 13 presented a modified detection frequency > 70%. This study is the first of its kind and provided strong indications regarding the aroma compounds defining the sensory characteristics of Xinomavro wines.

Cotton constitutes a significant commercial crop and a widely traded commodity around the world. The accurate prediction of its yield quantity could lead to high economic benefits for farmers as well as for the rural national economy. In this research, we propose a multiple-input neural network model for the prediction of cotton’s production. The proposed model utilizes as inputs three different kinds of data (soil data, cultivation management data, and yield management data) which are treated and handled independently. The significant advantages of the selected architecture are that it is able to efficiently exploit mixed data, which usually requires being processed separately, reduces overfitting, and provides more flexibility and adaptivity for low computational cost compared to a classical fully-connected neural network. An empirical study was performed utilizing data from three consecutive years from cotton farms in Central Greece (Thessaly) in which the prediction performance of the proposed model was evaluated against that of traditional neural network-based and state-of-the-art models. The numerical experiments revealed the superiority of the proposed approach.