The successful integration of the 1st year agricultural students into the university life. Methodological reference marks

The communication presents in detail a strategic plan of the management from the Agriculture Faculty to support the 1st year students who risk dropping out of university and who belong mainly to the disadvantaged groups. The educational approach was settled after the initial analysis of the causes that lead to the university dropout phenomenon. Some students come from families with low income; others live only with one parent or their parents work abroad (19.8%); some of them live in the countryside (45.3%); others come from disadvantaged social groups or they risk dropping out of university (86.6% in the academic year 2016/2017). In the academic year 2015-2016, 77 first year students were expelled, out of a total of 376, which represents 20.5 %. In the academic year 2016-2017, 321 first year students were enrolled, out of which 278 students are in a risky situation; 174 students belong to disadvantaged social and economic groups; the first year students of the graduation cycle do not have the necessary learning skills. In order to diminish the university dropout, the following efforts will be made: to find out the students’ individual particular features according to their age; to focus on the students about the teaching-learning-evaluating approaches; to initiate and to carry out social and emotional development programs; to counsel the students individually so that they overcome adapting issues which occur in the first year of the bachelor’s cycle; to acquire efficient learning techniques that could open for them new horizons to the lifelong education; partnerships among families, university, community and economic agents

Assessment of Lipophilicity Indices Derived from Retention Behavior of Antioxidant Compounds in RP-HPLC

Reverse phase high pressure liquid chromatography was employed in order to evaluate the lipophilicity of antioxidant compounds from different classes, such as phenolic acids, flavanones, flavanols, flavones, anthocyanins, stilbenes, xantonoids, and proanthocyanidins. The retention time of each compound was measured using five different HPLC columns: RP18 (LiChroCART, Purosphere RP-18e), C8 (Zorbax, Eclipse XDBC8), C16-Amide (Discovery RP-Amide C16), CN100 (Saulentechnik, Lichrosphere), and pentafluorophenyl (Phenomenex, Kinetex PFP), and the mobile phase consisted of methanol and water (0.1% formic acid) in different proportions. The measurements were conducted at two different column temperatures, room temperature (22 °C) and, in order to mimic the environment from the human body, 37 °C. Furthermore, principal component analysis (PCA) was used to obtain new lipophilicity indices and holistic lipophilicity charts. Additionally, highly representative depictions of the chromatographic behavior of the investigated compounds and stationary phases at different temperatures were obtained using two new chemometric approaches, namely two-way joining cluster analysis and sum of ranking differences

Evaluation of Mushrooms Based on FT-IR Fingerprint and Chemometrics

Edible mushrooms have been recognized as a highly nutritional food for a long time, thanks to their specific flavor and texture, as well as their therapeutic effects. This study proposes a new, simple approach based on FT-IR analysis, followed by statistical methods, in order to differentiate three wild mushroom species from Romanian spontaneous flora, namely, Armillaria mellea, Boletus edulis, and Cantharellus cibarius. The preliminary data treatment consisted of data set reduction with principal component analysis (PCA), which provided scores for the next methods. Linear discriminant analysis (LDA) managed to classify 100% of the three species, and the cross-validation step of the method returned 97.4% of correctly classified samples. Only one A. mellea sample overlapped on the B. edulis group. When kNN was used in the same manner as LDA, the overall percent of correctly classified samples from the training step was 86.21%, while for the holdout set, the percent rose to 94.74%. The lower values obtained for the training set were due to one C. cibarius sample, two B. edulis, and five A. mellea, which were placed to other species. In any case, for the holdout sample set, only one sample from B. edulis was misclassified. The fuzzy c-means clustering (FCM) analysis successfully classified the investigated mushroom samples according to their species, meaning that, in every partition, the predominant species had the biggest DOMs, while samples belonging to other species had lower DOMs