Experimental m/z values were compared to these databases and conclusions drawn with respect to the composition of fermenting beans with tentative fermentation products suggested.The cocoa botanical and geographical origin and the primary processing steps applied by cocoa farmers at the beginning of the supply chain influence the chemical compositional traits of the cocoa beans. These features are carried along the supply chain as intrinsic markers up to the final products. These intrinsic markers could be used for tracking and tracing purposes. In this study, we examined the retention and loss of compositional signatures from cocoa beans to chocolates. Volatile, elemental and stable isotope signatures of cocoa beans of 10 different origins and 11 corresponding chocolates were determined by high sensitivity-proton transfer reaction-mass spectrometry (HS-PTR-MS), inductively coupled plasma-MS (ICP-MS) and isotope ratio-MS (IR-MS), respectively. The volatile fingerprints provided mostly information on the origin and primary processing traits of the raw cocoa beans in the chocolates. Volatile compounds that are relevant markers include acetic acid (m/z 61), benzene (m/z 79), pyridine (m/z 80), 2-phenylethanol (m/z 123), and maltol (m/z 127). On the other hand, the elemental and stable isotope characteristics are more indicative of the cocoa content and added ingredients.  https://www.selleckchem.com/products/rvx-208.html  Possible elemental markers for cocoa origin include Fe, Cr, and Cd. VOCs appear to be the most robust markers carried from cocoa beans to chocolates of the groups examined. This provides the potential for track and trace of cocoa beans from farm to chocolates.The physicochemical and oxidative stability of oil emulsion has been one of the major challenges in food industry. Factors influencing the emulsion stability have seemingly been exhaustedly elucidated, such as temperature, pH, salts, proteins, polysaccharides and digestive enzymes. Here we report the previously unrecognized influence of catalase on emulsion stability. Submicron oil-in-water fish oil emulsion was prepared by high speed homogenization in the presence of polysorbate 80. Influence of catalase on the emulsion's stability was investigated in comparison with its deactivated version and bovine serum albumin (BSA) by visual examination, turbidity and DLS measurement and TEM observation. Catalase demulsified the emulsion instantly in a concentration-responsive manner at concentrations higher than 0.8 μmol/L, resulting a decreased turbidity, oil flocculation and precipitation of the enzyme itself. Neither BSA nor the thermally inactivated CAT caused demulsification at the same speed, indicating that CAT's demulsification effect was attributed to its enzymatic activity rather than its general protein properties. The enlargement of oil-polysorbate droplets and precipitation of CAT were confirmed by both TEM and DLS. Furthermore, CAT's demulsification effect was found irrelevant of the lipid oxidation. This insight into catalase's influences on emulsion not only sheds lights on food processing and shelf-life, nutritional value and potential biological effects, but also presents an exciting challenge to elucidate the mechanism behind.Live sea cucumbers (Stichopus japonicus) were stored in a solution containing oxalic acid and tea polyphenols as natural metal ion chelators. The inhibitory effects of these chelators on the autolysis phenomenon and the underlying mechanism of action were investigated for the first time by using scanning electron microscopy, differential scanning calorimetry, low-field nuclear magnetic resonance and confocal laser scanning microscopy. External stimuli cause autolysis through the release of calcium ions (Ca2+) from cells into the extracellular connective tissue, initiating activity of the matrix metalloprotease (MMP) in the sea cucumber body wall (SCBW). MMP subsequently degrades the microfibrillar networks, that support the interconnecting collagen fibres and the interfibrillar proteoglycan bridges linking the collagen fibrils, to release the water restricted within the interspaces between collagen fibres and collagen fibrils, ultimately causing mucoid degeneration of SCBW. The natural metal ion chelators significantly inhibited the activation of MMP by chelating Ca2+, consequently effectively preventing the autolysis of SCBW.In the development of sensory and consumer science, data are often collected in several blocks responding to different aspects of consumer experience. Sometimes the task of organizing the data and explaining their relation is non-trivial, especially when considering structural (casual) relationship between data sets. In this sense, PLS path modelling (PLS-PM) has been found as a good tool to model such relations, but this approach faces some issues regarding the assumption of uni-dimensionality of consumers' data blocks. Sequential Orthogonalised PLS path modelling (SO-PLS-PM) has been proposed as an alternative approach to handle the multi-dimensionality and to explain the relations between the original data blocks without any preprocessing of the data. This study aims at comparing the efficacy of SO-PLS-PM and PLS-PM (together with splitting blocks into uni-dimensional sub-blocks) for handling multi-dimensionality. Data sets from two satiety perception studies (yoghurt, biscuit) have been used as illustrations. The main novelty of this paper lies in underlining and solving a major, but little studied problem, related to the assumption of one-dimensional blocks in PLS-PM. The findings from the comparisons indicated that the two approaches (PLS-PM and SO-PLS-PM) highlighted the same main trends for the less complex samples (yoghurt samples) liking was the essential driver of satiation perception and portion size selection; while satiation mainly predicted satiety perception. For the more complex data set - from a sensory perspective - (biscuit samples), the relations between data blocks in PLS-PM model was difficult to interpret, whereas they were well explained by SO-PLS-PM. This underlines the ability of SO-PLS-PM to model multi-dimensional data sets without requiring any preprocessing steps.