A novel strategy by combining foam fractionation with high-speed countercurrent chromatography for the rapid and efficient isolation of antioxidants and cytostatics from Camellia oleifera cake

Exploring Camellia oleifera Abel seed cake as sustainable source of protein for food applications: A review

The demand for sustainable plant-based protein is rising due to concerns over the environmental impact of animal-based protein. One promising yet overlooked protein source is the seed cake generated from Camellia oleifera oil extraction (COSC), which contains 14-20 % crude protein. COSC protein (COSCP) exhibit excellent nutritional and functional properties making it a promising ingredient for innovative food products. However, its adoption remains limited. This review discusses COSCP extraction methods, functional properties, and food applications to promote its broader utilization. It also examined how oil extraction methods influence COSCP functional characteristics and explores modification techniques to enhance its functionality. COSCP has excellent functional properties, making it suitable for use as emulsifier, foaming, and gelling agents in food systems. However, cross-linking of COSCP with saponins and phenolics during seed processing compromise the protein yield, purity, and functionality and need to be addressed to fully unlock the potential of COSCP in food applications.

StackedEnC-AOP: prediction of antioxidant proteins using transform evolutionary and sequential features based multi-scale vector with stacked ensemble learning

BACKGROUND

Antioxidant proteins are involved in several biological processes and can protect DNA and cells from the damage of free radicals. These proteins regulate the body's oxidative stress and perform a significant role in many antioxidant-based drugs. The current invitro-based medications are costly, time-consuming, and unable to efficiently screen and identify the targeted motif of antioxidant proteins.

METHODS

In this model, we proposed an accurate prediction method to discriminate antioxidant proteins namely StackedEnC-AOP. The training sequences are formulation encoded via incorporating a discrete wavelet transform (DWT) into the evolutionary matrix to decompose the PSSM-based images via two levels of DWT to form a Pseudo position-specific scoring matrix (PsePSSM-DWT) based embedded vector. Additionally, the Evolutionary difference formula and composite physiochemical properties methods are also employed to collect the structural and sequential descriptors. Then the combined vector of sequential features, evolutionary descriptors, and physiochemical properties is produced to cover the flaws of individual encoding schemes. To reduce the computational cost of the combined features vector, the optimal features are chosen using Minimum redundancy and maximum relevance (mRMR). The optimal feature vector is trained using a stacking-based ensemble meta-model.

RESULTS

Our developed StackedEnC-AOP method reported a prediction accuracy of 98.40% and an AUC of 0.99 via training sequences. To evaluate model validation, the StackedEnC-AOP training model using an independent set achieved an accuracy of 96.92% and an AUC of 0.98.

CONCLUSION

Our proposed StackedEnC-AOP strategy performed significantly better than current computational models with a ~ 5% and ~ 3% improved accuracy via training and independent sets, respectively. The efficacy and consistency of our proposed StackedEnC-AOP make it a valuable tool for data scientists and can execute a key role in research academia and drug design.

Foam and fluid properties of purified saponins and non-purified water extracts from Camellia oleifera cake (by-product)

The physicochemical and foam properties of non-purified water extracts (WE) and purified tea saponins (TS) from Camellia oleifera cake (byproduct) were compared. WE showed different fluid properties at equal saponin concentrations (1.0 wt%) compared to TS. Particularly, it exhibited limited micelle size (average 434.1 nm), effective viscosity (0.15 Pa·s), and surface tension (43.9 mN/m) independently of pH. Moreover, the foam properties of WE were comparable to TS and better than sodium caseinate, especially foam stability. WE foam was more stable than TS foam under pH (3-7) and heating (40-80 °C). In the presence of NaCl, sucrose, and ethanol (5-20 wt%), WE and TS were effective and had similar foam behavior. Low concentrations of sucrose (<10 wt%)/ethanol (<20 wt%) significantly increased the foam capacity, while ethanol over 30 wt% was unfavorable. WE/TS foam contributes significantly to the desired physicochemical and sensory attributes (taste, texture, and appearance) of foods.