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BACKGROUND Whey protein‐epigallocatechin gallate (WP‐EGCG) covalent conjugates and non‐covalent nanocomplexes were prepared and compared using Fourier‐transform infrared spectra. The effect of pH (at 2.6, 6.2, 7.1, and 8.2) on the non‐covalent nanocomplexes' functional properties and the WP‐EGCG interactions were investigated by studying antioxidant activity, emulsification, fluorescence quenching, and molecular docking, respectively. RESULTS With the formation of non‐covalent and covalent complexes, the amide band decreased; the ‐OH peak disappeared; the antioxidant activity of WP‐EGCG non‐covalent complexes was 2.59‐ and 2.61‐times stronger than WP‐EGCG covalent conjugates for 1‐diphenyl‐2‐picryl‐hydrazyl (DPPH) and ferric reducing ability of plasma (FRAP), respectively (particle size: 137 versus 370 nm). The antioxidant activity (DPPH 27.48–44.32%, FRAP 0.47–0.63) was stronger at pH 6.2–7.1 than at pH 2.6 and pH 8.2 (DPPH 19.50% and 26.36%, FRAP 0.39 and 0.41). Emulsification was highest (emulsifying activity index 181 m2 g−1, emulsifying stability index 107%) at pH 7.1. The interaction between whey protein (WP) and EGCG was stronger under neutral and weakly acidic conditions: KSV (5.11–8.95 × 102 L mol−1) and Kq (5.11–8.95 × 1010 L mol s−1) at pH 6.2–7.1. Binding constants (pH 6.2 and pH 7.1) increased with increasing temperature. Molecular docking suggested that hydrophobic interactions played key roles at pH 6.2 and pH 7.1 (∆H > 0, ∆S > 0). Hydrogen bonding was the dominant force at pH 2.6 and pH 8.2 (∆H < 0, ∆S < 0). CONCLUSION Environmental pH impacted the binding forces of WP‐EGCG nanocomplexes. The interaction between WP and EGCG was stronger under neutral and weakly acidic conditions. Neutral and weakly acidic conditions are preferable for WP‐EGCG non‐covalent nanocomplex formation. © 2023 Society of Chemical Industry.

Prelude Floating Liquefied Natural Gas (FLNG) facility is moored with an internal turret allowing it to free weathervane (FW), i.e. by leaving the unit to rotate according to environmental loads. During the engineering phase, the FLNG FW heading is estimated by the heading analysis (i.e. physics-based approach), and results are then used as input for other studies. Therefore, a good estimation of the various environmental effects (waves, current and wind) and their contributions in terms of loads on the FLNG is critical to ensure a correct prediction of the FW heading. For the predominant contributions (wind and current), the force coefficients have been initially derived from wind tunnel tests during the engineering phase. However, Prelude FLNG being now installed on-site, measurements over recent years have shown slight discrepancies with the numerical predictions by the heading analysis. Preliminary investigations were carried out and were aimed to improve some parameters of the numerical model. Nevertheless, it appeared that even with these improvements, discrepancies between numerical predictions and measurements were not always resolved. These discrepancies may have several origins, such as inadequacy of the numerical model, variability of the metocean data, uncertainties in measurements, etc. In order to overcome the aforementioned uncertainties and unknowns, it has been decided to set-up a machine learning model (i.e. data-based approach). This machine learning model (RBF ANN - Radial Basis Function Artificial Neural Network) was trained with the recorded metocean data (input) and measured FLNG FW heading (output). Considering the amount of the measured data available (two years with a time step of 10 minutes), the necessity to optimize the model’s hyperparameters and the computer capability, a stepwise approach has been applied to ensure an accurate model can be built in a reasonable timeframe. Finally, the machine learning model calculation shows a significant improvement in the prediction capability when compared to the measured FLNG FW heading. The resulting surrogate model is hence used to predict the FW heading and to derive the associated prediction intervals, which define the range of error with certain probability (for instance 95%). This paper describes the machine learning model used, the methodology and challenges of the approach, and discusses the results. The main conclusions and lessons learnt are also shared.

Polyphenols complexes were extracted from Common millet (CM), Long-grain rice (LGR) and Huaihe wheat (HW), purification method, components and functional characteristics were studied. Higher phenol content (18.7 in CM, 12.9 in LGR and 22.3 mg g −1 in HW) was obtained by the high-speed shear, with high antioxidants (DPPH 89.2% in HW, ABTS 81.6% in LGR, FRAP 0.83 in HW) after purified. Thirteen typical polyphenol complexes were identified from 21 peaks by UHPLC-LTQ-Orbitrap-MS/MS. Gingerenone A was first detected in CM, HW and LGR; (+/−)-Gingerol was first detected in CM and HW. Morphology by TEM displayed a rough spherical structure with 200-500 nm. Peculiarly contained hydroxyl polyphenol (5-(3,4-Dihydroxyphenyl) valeric acid, Apigenin-8-C-xylosyl-6-C-glucoside), polyphenols with B-rings (Trans-cinnamic acid, Gingerenone A) and the highest contents (+/−)-Gingerol with hydroxyl and Sinapinic acid with B-rings, CM had a higher α-amylase inhibition rate (80.5%) than LGR and HW, which providing a foundation for the development of hypoglycaemic drugs.

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