This study aimed to define a consortium of lactic acid bacteria (LAB) that will bring added value to dried fresh cheese through specific probiotic properties and the synthesis of bioactive peptides (biopeptides). The designed LAB consortium consisted of three Lactobacillus strains: S-layer carrying Levilactobacillus brevis D6, exopolysaccharides producing Limosilactobacillus fermentum D12 and plantaricin expressing Lactiplantibacillus plantarum D13, and one Enterococcus strain, Enterococcus faecium ZGZA7-10. Chosen autochthonous LAB strains exhibited efficient adherence to the Caco-2 cell line and impacted faecal microbiota biodiversity. The cheese produced by the LAB consortium showed better physicochemical, textural and sensory properties than the cheese produced by a commercial starter culture. Liquid chromatography coupled with matrix-assisted laser desorption/ionization-time of flight tandem mass spectrometry (LC-MALDI-TOF/TOF) showed the presence of 18 specific biopeptides in dried fresh cheeses. Their identification and relative quantification was confirmed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) using multiple reaction monitoring (MRM). The results also showed that their synthesis resulted mainly from β-casein and also α-S1 casein degradation by proteolytic activities of the LAB consortium. The designed LAB consortium enhanced the functional value of the final product through impact on biopeptide concentrations and specific probiotic properties.

Bioactive peptides (BPs) are molecules of paramount importance with great potential for the development of functional foods, nutraceuticals or therapeutics for the prevention or treatment of various diseases. A functional BP-rich dairy product was produced by lyophilisation of bovine milk fermented by the autochthonous strains Lactococcus lactis subsp. lactis ZGBP5-51, Enterococcus faecium ZGBP5-52 and Enterococcus faecalis ZGBP5-53 isolated from the same artisanal fresh cheese. The efficiency of the proteolytic system of the implemented strains in the production of BPs was confirmed by a combined high-throughput mass spectrometry (MS)-based peptidome profiling and an in silico approach. First, peptides released by microbial fermentation were identified via a non-targeted peptide analysis (NTA) comprising reversed-phase nano-liquid chromatography (RP nano-LC) coupled with matrix-assisted laser desorption/ionisation-time-of-flight/time-of-flight (MALDI-TOF/TOF) MS, and then quantified by targeted peptide analysis (TA) involving RP ultrahigh-performance LC (RP-UHPLC) coupled with triple-quadrupole MS (QQQ-MS). A combined database and literature search revealed that 10 of the 25 peptides identified in this work have bioactive properties described in the literature. Finally, by combining the output of MS-based peptidome profiling with in silico bioactivity prediction tools, three peptides (⁷⁵QFLPYPYYAKPA⁸⁶, ⁴⁰VAPFPEVFGK⁴⁹, ¹¹⁷ARHPHPHLSF¹²⁶), whose bioactive properties have not been previously reported in the literature, were identified as potential BP candidates.

In the present study, ground ivy was harvested from different natural habitats in Croatia and subjected to screening analysis for nutritional and bioactive composition. To achieve maximum recovery of phenolic compounds, different extraction techniques were investigated—heat-assisted (HAE), microwave-assisted (MAE) and subcritical water (SWE) extraction. Prepared extracts were analysed by spectrophotometric methods, LC-MS/MS and HPLC-PAD methodologies. Results regarding nutritive analyses, conducted using standard AOAC methods, showed the abundance of samples in terms of insoluble dietary fibre, protein, calcium and potassium, while rutin, chlorogenic, cryptochlorogenic, caffeic and rosmarinic acid were the most dominant phenolic compounds. In addition, LC-MS/MS analysis revealed the presence of apigenin and luteolin in glycosylated form. Maximum recovery of target phenolic compounds was achieved with MAE, while SWE led to the formation of new antioxidants, which is commonly known as neoformation. Moreover, efficient prediction of phenolic composition of prepared extracts was achieved using NIR spectroscopy combined with ANN modelling.