Although it has been claimed that nanoparticles can be produced from gelatin, a naturally occurring polypeptide, the commercial conversion of animal collagen to gelatin results in a heterogeneous product with a wide molecular-weight range. This is probably responsible for the widely observed variation in the experimental conditions required for nanoparticle formation. In this study, 0.2% w/v aqueous B225 gelatin solutions were incubated under various conditions of time, temperature, pH and ethanol concentration and characterized by both size-exclusion high-performance liquid chromatography (HPLC) and dynamic light scattering. Gelatin was shown to be denatured when the temperature was increased to 37°C (approx.) and the rate of renaturation was optimized over the temperature range 7–20°Cat pH5.0, equivalent to the isoelectric point (IEP). The molecular-weight profile remained unchanged at 37°C (approx.) in the pH range 5–7. When the gelatin solutions were mixed with ethanol, higher-molecular-weight fractions (microgel, δ and ζ fractions, all with molecular weights > 700 kDa) precipitated at ethanol concentrations lower than those required to precipitate the lower molecular weight material (< 700 kDa), with maximum precipitation occurring close to the isoelectric point (pH 5.0). The molecular weight profile of gelatin in solution is evidently critically affected in a time-dependent manner by both pH and temperature. These two factors influence the noncovalent interactions responsible for the molecular structure of gelatin. The molecular weight profiles, in turn, affect the phase behaviour of gelatin in hydroalcoholic solutions. Systematically investigating the effect of time, temperature, pH and ethanol concentration on the molecular-weight-distribution profile of a gelatin solution enabled a robust method to be developed for the preparation of colloidal dispersions of non-aggregated gelatin nanoparticles 220–250 nm in diameter. This contrasts with the multiparticulate aggregates produced by earlier literature methods.

Endurance exercise induces IL-6 production from myocytes that is thought to impair intracellular defence mechanisms. Curcumin inhibits NF-κB and activator protein 1, responsible for cytokine transcription, in cell lines. The aim of this study was to investigate the effect of curcumin supplementation on the cytokine and stress responses following 2 h of cycling. Eleven male recreational athletes (35.5 ± 5.7 years; Wmax 275 ± 6 W; 87.2 ± 10.3 kg) consuming a low carbohydrate diet of 2.3 ± 0.2 g/kg/day underwent three double blind trials with curcumin supplementation, placebo supplementation, and no supplementation (control) to observe the response of serum interleukins (IL-6, IL1-RA, IL-10), cortisol, c-reactive protein (CRP), and subjective assessment of training stress. Exercise was set at 95% lactate threshold (54 ± 7% Wmax) to ensure that all athletes completed the trial protocol. The trial protocol elicted a rise in IL-6 and IL1-RA, but not IL-10. The supplementation regimen failed to produce statistically significant results when compared to placebo and control. IL-6 serum concentrations one hour following exercise were (Median (IQR): 2.0 (1.8-3.6) Curcumin; 4.8 (2.1-7.3) Placebo; 3.5 (1.9-7.7) Control). Differences between supplementation and placebo failed to reach statistical significance (p = 0.18) with the median test. Repeated measures ANOVA time-trial interaction was at p = 0.06 between curcumin supplementation and placebo. A positive correlation (p = 0.02) between absolute exercise intensity and 1 h post-exercise for IL-6 concentration was observed. Participants reported “better than usual” scores in the subjective assessment of psychological stress when supplementing with curcumin, indicating that they felt less stressed during training days (p = 0.04) compared to placebo even though there was no difference in RPE during any of the training days or trials. The limitations of the current regimen and trial involved a number of factors including sample size, mode of exercise, intensity of exercise, and dose of curcumin. Nevertheless these results provide insight for future studies with larger samples, and multiple curcumin dosages to investigate if different curcumin regimens can lead to statistically different interleukin levels when compared to a control and placebo.

The potential application of multivariate three-way data analysis techniques, namely parallel factor analysis (PARAFAC) and discriminant multi-way partial least squares regression (DN-PLSR), on three-dimensional excitation emission matrix (3D-EEM) fluorescent data were used to identify the uniqueness and authenticity of Maltese extra virgin olive oil (EVOO). A non-negativity constrained PARAFAC model revealed that a four-component model provided the most appropriate solution. Examination of the extracted components in mode 2 and 3 showed that these belonged to different fluorophores present in extra virgin olive oil. Application of linear discriminate analysis (LDA) and binary logistic regression analysis on the concentration of the four extracted fluorophores, showed that it is possible to discriminate Maltese EVOOs from non-Maltese EVOOs. The application of DN-PLSR provided superior means for discrimination of Maltese EVOOs. Further inspection of the extracted latent variables and their variable importance plots (VIPs) provided strong proof of the existence of four types of fluorophores present in EVOOs and their potential application for the discrimination of Maltese EVOOs.

The price of honey, as a highly consumed natural product, depends on its botanical source and its production environment, causing honey to be vulnerable to adulteration through mislabeling and inappropriate, fraudulent production. In this study, a fast and simple approach is proposed to tackle this issue through non-target one dimensional zg30 and noesypr1d ¹H NMR fingerprint analysis, in combination with multivariate data analysis. Results suggest that composition differences in sugars, amino acids, and carboxylic acid were sufficient to discriminate between the tested honey of Maltese origin and that of non-local origin. Indeed, all chemometric models based on noesypr1d analysis of the whole fraction honey showed better prediction in geographical discrimination. The possibility of discrimination was further investigated through analysis of the honey’s phenolic extract composition. The partial least squares models were deemed unsuccessful to discriminate, however, some of the linear discriminant analysis models achieved a prediction accuracy of 100%. Lastly, the best performing models of both the whole fraction and the phenolic extracts were tested on five samples of unknown geographic for market surveillance, which attained a high agreement within the models. Thus, suggesting the use of non-target ¹H NMR coupled with the multivariate-data analysis and machine learning as a potential alternative to the current time-consuming analytical methods.