In this work, we studied the impact of encapsulated elderberry extracts as natural meat extenders to preserve both the quality and the oxidative stability of beef burgers. In particular, the comprehensive chemical changes of beef burgers treated with different antioxidants, namely, (a) a control without antioxidants, (b) 0.5 g/kg sodium erythorbate (ERY), (c) 2.5 g/kg encapsulated elderberry extract (EE 2.5), and (d) 5 g/kg encapsulated elderberry extract (EE 5), each one packaged under modified atmosphere (80% O₂ and 20% CO₂) for 13 days storage at 2 ± 1 °C, were deeply evaluated. Overall, EEs showed a wide array of antioxidant compounds, namely polyphenols like anthocyanins, flavonols, and phenolic acids. Multivariate statistics provided marked chemical differences between burgers manufactured with EEs and synthetic antioxidants (ERY) during 13-days storage in terms of both metabolomic profiles and typical lipid/protein oxidation markers (such as malondialdehyde and total carbonyls). Most of the differences could be attributed to some discriminant compounds, namely glutathione, 4-hydroxy-2-nonenal, hydroxy/peroxy-derivatives of fatty acids, carbonyl compounds (such as 5-nonen-2-one and 1,5-octadien-3-one), and cholesterol. Interestingly, significant correlations (p < 0.01) were observed between malondialdehyde, total carbonyls, and these discriminant metabolites. The combination of spectrophotometric approaches and a high-throughput untargeted metabolomics analysis outlined a strong modulation of both lipid and protein oxidations, likely promoted by the encapsulated meat extender (elderberry), thus confirming its ability to delay oxidative phenomena during the shelf-life of beef burgers.

In this work, untargeted metabolomics was used to shed light on the impact of different pasture-based diets on the chemical profile of Sarda sheep milk. The study considered 11 dairy sheep farms located in Sardinia, and milk samples were collected in 4 different periods, namely January, March, May, and July 2019, when all sheep had 58, 98, 138, and 178 days in milk, respectively. The animal diet composition was based on the intake of grazed herbage in natural pasture, hay, and concentrate. Overall, the combination of two comprehensive databases on food, namely the Milk Composition Database and Phenol-Explorer, allowed the putative identification of 406 metabolites, with a significant (p < 0.01) enrichment of several metabolite classes, namely amino acids and peptides, monosaccharides, fatty acids, phenylacetic acids, benzoic acids, cinnamic acids, and flavonoids. The multivariate statistical approach based on supervised orthogonal projections to latent structures (OPLS-DA) allowed us to predict the chemical profile of sheep milk samples as a function of the high vs no fresh herbage intake, while the prediction model was not significant when considering both hay and concentrate intake. Among the discriminant markers of the herbage intake, we found five phenolic metabolites (such as hippuric and coumaric acids), together with lutein and cresol (belonging to carotenoids and their metabolites). Additionally, a high discriminant power was outlined for lipid derivatives followed by sugars, amino acids, and peptides. Finally, a pathway analysis revealed that the herbage intake affected mainly five biochemical pathways in milk, namely galactose metabolism, phenylalanine metabolism, alpha-linolenic acid metabolism, linoleic acid metabolism, and aromatic amino acids involved in protein synthesis (namely tyrosine, phenylalanine, and tryptophan).

• Rowanberry pomace extract extends the storage time of meat by inhibiting oxidation. • Pomace extract is effective against the off-flavours caused by carbonyl compounds. • Untargeted metabolomics detects an effect of rowanberry ingredients during storage. • Conventional analysis and metabolomics detection can identify food deterioration.

Coffee consumption is expected to steadily rise in the next few years, with an increasing incidence of Coffea canephora on the market. To date, consumers are demanding high-quality and healthy beverages produced in an environmentally respectful manner. The study aimed to determine the optimal combination of acrylamide formation, sensory quality, and energy efficiency for blockchain-driven environmental accounting during the roasting process of C. canephora of different cups and market quality. Coffee was roasted in a professional 5 kg drum roaster at three speeds (fast, intermediate, and slow) and profiles, resulting in a medium roast degree. The quantification of acrylamide complied with the European legal benchmark across all roasting speeds, enabling a qualified panel to perform a sensory analysis of coffees in an espresso brew, including aroma and taste attributes. The chemical fingerprint of coffee was initially investigated through an untargeted metabolomics approach based on high-resolution mass spectrometry (UHPLC-Q-Orbitrap-HRMS). An ANOVA Multiblock Orthogonal Partial Least Squares analysis (AMOPLS) applied to metabolomics data enabled an accurate discrimination of coffee samples based on coffee market quality and roasting speed. Notably, their interaction was identified as a statistically significant discriminant factor (Residual Structure Ratio p-value = 0.01), with the highest contribution to the model (Relative Sum of Squares = 32.6%). The majority of metabolites detected through the VIP² approach belong to the lipid and lipid-like molecules chemical class, highlighting their pivotal role in defining the signature of C. canephora coffee. Regarding energy efficiency, the consumption recorded by the natural gas meter at the fast, intermediate, and slow speeds did not show significant differences. The roaster and gas valve employed may influence the efficacy of the “Energy Calculator” of the roasting program “Artisan” (v. 2.10.4), requiring an appropriate configuration. The optimized program resulted in a mean underestimation of real methane consumption by 0.207 kWh (SD 0.124), making it a promising tool for carbon emission calculation in coffee roasting. Moreover, further investigations will be performed to build a multi-omics approach by integrating the UHPLC-Q-Orbitrap-HRMS database with the volatilomic analysis performed by the GCxGC-MS technique to reveal the potential network between the chemical profile and the sensory characteristics of the samples.