Why are clams steamed with wine in Mediterranean cuisine?

Wine is renowned for its rich content of polyphenols, including resveratrol (Res), known for their health promoting properties. Steamed clam with wine, a popular Mediterranean delicacy that highlights the role of wine as a key ingredient. However, despite these benefits, resveratrol's low bioavailability poses challenges. Could the process of steaming together with clam alter the digestive fate of resveratrol from wine? This study explores the potential of proteoglycan-based nanoparticles from freshwater clam (CFNPs) as a delivery vehicle for enhancing the stability and bioavailability of resveratrol, compared with wine and free Res' solution, aiming to elucidate mechanisms facilitating Res' absorption. The results demonstrated that CFNPs can effectively encapsulate Res with an efficiency over 70%, leading to a uniform particle size of 70.5±0.1 nm (PDI < 0.2). Resveratrol loaded in CFNPs (CFNPs-Res) exhibited an improved antioxidant stability under various conditions, retaining over 90% of antioxidant capacity after three-day storage at room temperature. The controlled-release profile of Res loaded in CFNPs fits both first and Higuchi order kinetics and was more desirable than that of wine and the free Res. Examined by the simulated gastrointestinal digestion, CFNPs-Res showed a significantly higher bioaccessibility and antioxidant retention compared to free Res and the wines. The discovery and use of food derived nanoparticles to carry micronutrients and antioxidants could lead to a shift in functional food design and nutritional advice, advocating much more attention on these entities over solely conventional molecules.

Machine learning-enabled high-throughput industry screening of edible oils.

Long-term consumption of mixed fraudulent edible oils increases the risk of developing of chronic diseases which has been a threat to the public health globally. The complicated global supply-chain is making the industry malpractices had often gone undetected. In order to restore the confidence of consumers, traceability (and accountability) of every level in the supply chain is vital. In this work, we shown that machine learning (ML) assisted windowed spectroscopy (e.g., visible-band, infra-red band) produces high-throughput, non-destructive, and label-free authentication of edible oils (e.g., olive oils, sunflower oils), offers the feasibility for rapid analysis of large-scale industrial screening. We report achieving high-level of discriminant (AUC > 0.96) in the large-scale (n ≈ 11,500) of adulteration in olive oils. Notably, high clustering fidelity of 'spectral fingerprints' achieved created opportunity for (hypothesis-free) self-sustaining large database compilation which was never possible without machine learning. (137 words).

Novel time-domain NMR-based traits for rapid, label-free Olive oils profiling.

Olive oil is one of the oldest and essential edible oils in the market. The classification of olive oils (e.g. extra virgin, virgin, refined) is often influenced by factors ranging from its complex inherent physiochemical properties (e.g. fatty acid profiles) to the undisclosed manufacturing processes. Therefore, olive oils have been the target of adulteration due to its profitable margin. In this work, we demonstrate that multi-parametric time-domain NMR relaxometry can be used to rapidly (in minutes) identify and classify olive oils in label-free and non-destructive manner. The subtle differences in molecular microenvironment of the olive oils induce substantial changes in the relaxation mechanism in the time-domain NMR regime. We demonstrated that the proposed NMR-relaxation based detection (AUC = 0.95) is far more sensitive and specific than the current gold-standards in the field i.e. near-infrared spectroscopy (AUC = 0.84) and Ultraviolet-visible spectroscopy (AUC = 0.73), respectively. We further show that, albeit the inherent complexity of olive plant natural phenotypic variations, the proposed NMR-relaxation based traits may be a viable mean (AUC = 0.71) in tracing the regions of origin for olive trees, in agreement with their geographical orientation.